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Yang M, Peng Z, van Pul C, Andriessen P, Dong K, Silvertand D, Li J, Liu C, Long X. Continuous prediction and clinical alarm management of late-onset sepsis in preterm infants using vital signs from a patient monitor. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2024; 255:108335. [PMID: 39047574 DOI: 10.1016/j.cmpb.2024.108335] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/11/2024] [Revised: 06/14/2024] [Accepted: 07/17/2024] [Indexed: 07/27/2024]
Abstract
BACKGROUND AND OBJECTIVE Continuous prediction of late-onset sepsis (LOS) could be helpful for improving clinical outcomes in neonatal intensive care units (NICU). This study aimed to develop an artificial intelligence (AI) model for assisting the bedside clinicians in successfully identifying infants at risk for LOS using non-invasive vital signs monitoring. METHODS In a retrospective study from the NICU of the Máxima Medical Center in Veldhoven, the Netherlands, a total of 492 preterm infants less than 32 weeks gestation were included between July 2016 and December 2018. Data on heart rate (HR), respiratory rate (RR), and oxygen saturation (SpO2) at 1 Hz were extracted from the patient monitor. We developed multiple AI models using 102 extracted features or raw time series to provide hourly LOS risk prediction. Shapley values were used to explain the model. For the best performing model, the effect of different vital signs and also the input type of signals on model performance was tested. To further assess the performance of applying the best performing model in a real-world clinical setting, we performed a simulation using four different alarm policies on continuous real-time predictions starting from three days after birth. RESULTS A total of 51 LOS patients and 68 controls were finally included according to the patient inclusion and exclusion criteria. When tested by seven-fold cross-validations, the mean (standard deviation) area under the receiver operating characteristic curve (AUC) six hours before CRASH was 0.875 (0.072) for the best performing model, compared to the other six models with AUC ranging from 0.782 (0.089) to 0.846 (0.083). The best performing model performed only slightly worse than the model learning from raw physiological waveforms (0.886 [0.068]), successfully detecting 96.1 % of LOS patients before CRASH. When setting the expected alarm window to 24 h and using a multi-threshold alarm policy, the sensitivity metric was 71.6 %, while the positive predictive value was 9.9 %, resulting in an average of 1.15 alarms per day per patient. CONCLUSIONS The proposed AI model, which learns from routinely collected vital signs, has the potential to assist clinicians in the early detection of LOS. Combined with interpretability and clinical alarm management, this model could be better translated into medical practice for future clinical implementation.
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Affiliation(s)
- Meicheng Yang
- State Key Laboratory of Digital Medical Engineering, School of Instrument Science and Engineering, Southeast University, Nanjing, China; Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, the Netherlands
| | - Zheng Peng
- Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, the Netherlands; Department of Clinical Physics, Máxima Medical Centre, Veldhoven, the Netherlands
| | - Carola van Pul
- Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, the Netherlands; Department of Clinical Physics, Máxima Medical Centre, Veldhoven, the Netherlands; Department of Applied Physics, Eindhoven University of Technology, Eindhoven, the Netherlands
| | - Peter Andriessen
- Department of Applied Physics, Eindhoven University of Technology, Eindhoven, the Netherlands; Department of Pediatrics, Máxima Medical Centre, Veldhoven, the Netherlands
| | - Kejun Dong
- Nell Hodgson Woodruff School of Nursing, Emory University, Atlanta, United States of America
| | - Demi Silvertand
- Department of Pediatrics, Máxima Medical Centre, Veldhoven, the Netherlands
| | - Jianqing Li
- State Key Laboratory of Digital Medical Engineering, School of Instrument Science and Engineering, Southeast University, Nanjing, China; School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing, China
| | - Chengyu Liu
- State Key Laboratory of Digital Medical Engineering, School of Instrument Science and Engineering, Southeast University, Nanjing, China
| | - Xi Long
- Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, the Netherlands.
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Zinzuwadia AN, Mineeva O, Li C, Farukhi Z, Giulianini F, Cade B, Chen L, Karlson E, Paynter N, Mora S, Demler O. Tailoring Risk Prediction Models to Local Populations. JAMA Cardiol 2024:2823894. [PMID: 39292486 DOI: 10.1001/jamacardio.2024.2912] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 09/19/2024]
Abstract
Importance Risk estimation is an integral part of cardiovascular care. Local recalibration of guideline-recommended models could address the limitations of existing tools. Objective To provide a machine learning (ML) approach to augment the performance of the American Heart Association's Predicting Risk of Cardiovascular Disease Events (AHA-PREVENT) equations when applied to a local population while preserving clinical interpretability. Design, Setting, and Participants This cohort study used a New England-based electronic health record cohort of patients without prior atherosclerotic cardiovascular disease (ASCVD) who had the data necessary to calculate the AHA-PREVENT 10-year risk of developing ASCVD in the event period (2007-2016). Patients with prior ASCVD events, death prior to 2007, or age 79 years or older in 2007 were subsequently excluded. The final study population of 95 326 patients was split into 3 nonoverlapping subsets for training, testing, and validation. The AHA-PREVENT model was adapted to this local population using the open-source ML model (MLM) Extreme Gradient Boosting model (XGBoost) with minimal predictor variables, including age, sex, and AHA-PREVENT. The MLM was monotonically constrained to preserve known associations between risk factors and ASCVD risk. Along with sex, race and ethnicity data from the electronic health record were collected to validate the performance of ASCVD risk prediction in subgroups. Data were analyzed from August 2021 to February 2024. Main Outcomes and Measures Consistent with the AHA-PREVENT model, ASCVD events were defined as the first occurrence of either nonfatal myocardial infarction, coronary artery disease, ischemic stroke, or cardiovascular death. Cardiovascular death was coded via government registries. Discrimination, calibration, and risk reclassification were assessed using the Harrell C index, a modified Hosmer-Lemeshow goodness-of-fit test and calibration curves, and reclassification tables, respectively. Results In the test set of 38 137 patients (mean [SD] age, 64.8 [6.9] years, 22 708 [59.5]% women and 15 429 [40.5%] men; 935 [2.5%] Asian, 2153 [5.6%] Black, 1414 [3.7%] Hispanic, 31 400 [82.3%] White, and 2235 [5.9%] other, including American Indian, multiple races, unspecified, and unrecorded, consolidated owing to small numbers), MLM-PREVENT had improved calibration (modified Hosmer-Lemeshow P > .05) compared to the AHA-PREVENT model across risk categories in the overall cohort (χ23 = 2.2; P = .53 vs χ23 > 16.3; P < .001) and sex subgroups (men: χ23 = 2.1; P = .55 vs χ23 > 16.3; P < .001; women: χ23 = 6.5; P = .09 vs. χ23 > 16.3; P < .001), while also surpassing a traditional recalibration approach. MLM-PREVENT maintained or improved AHA-PREVENT's calibration in Asian, Black, and White individuals. Both MLM-PREVENT and AHA-PREVENT performed equally well in discriminating risk (approximate ΔC index, ±0.01). Using a clinically significant 7.5% risk threshold, MLM-PREVENT reclassified a total of 11.5% of patients. We visualize the recalibration through MLM-PREVENT ASCVD risk charts that highlight preserved risk associations of the original AHA-PREVENT model. Conclusions and Relevance The interpretable ML approach presented in this article enhanced the accuracy of the AHA-PREVENT model when applied to a local population while still preserving the risk associations found by the original model. This method has the potential to recalibrate other established risk tools and is implementable in electronic health record systems for improved cardiovascular risk assessment.
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Affiliation(s)
| | | | - Chunying Li
- Brigham & Women's Hospital, Boston, Massachusetts
| | - Zareen Farukhi
- Brigham & Women's Hospital, Boston, Massachusetts
- Massachusetts General Hospital, Boston
| | | | - Brian Cade
- Brigham & Women's Hospital, Boston, Massachusetts
| | - Lin Chen
- Brigham & Women's Hospital, Boston, Massachusetts
| | | | - Nina Paynter
- Brigham & Women's Hospital, Boston, Massachusetts
| | - Samia Mora
- Brigham & Women's Hospital, Boston, Massachusetts
| | - Olga Demler
- Brigham & Women's Hospital, Boston, Massachusetts
- ETH Zurich, Zurich, Switzerland
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Kim YK, Seo WD, Lee SJ, Koo JH, Kim GC, Song HS, Lee M. Early Prediction of Cardiac Arrest in the Intensive Care Unit Using Explainable Machine Learning: Retrospective Study. J Med Internet Res 2024; 26:e62890. [PMID: 39288404 DOI: 10.2196/62890] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2024] [Revised: 07/30/2024] [Accepted: 08/21/2024] [Indexed: 09/19/2024] Open
Abstract
BACKGROUND Cardiac arrest (CA) is one of the leading causes of death among patients in the intensive care unit (ICU). Although many CA prediction models with high sensitivity have been developed to anticipate CA, their practical application has been challenging due to a lack of generalization and validation. Additionally, the heterogeneity among patients in different ICU subtypes has not been adequately addressed. OBJECTIVE This study aims to propose a clinically interpretable ensemble approach for the timely and accurate prediction of CA within 24 hours, regardless of patient heterogeneity, including variations across different populations and ICU subtypes. Additionally, we conducted patient-independent evaluations to emphasize the model's generalization performance and analyzed interpretable results that can be readily adopted by clinicians in real-time. METHODS Patients were retrospectively analyzed using data from the Medical Information Mart for Intensive Care-IV (MIMIC-IV) and the eICU-Collaborative Research Database (eICU-CRD). To address the problem of underperformance, we constructed our framework using feature sets based on vital signs, multiresolution statistical analysis, and the Gini index, with a 12-hour window to capture the unique characteristics of CA. We extracted 3 types of features from each database to compare the performance of CA prediction between high-risk patient groups from MIMIC-IV and patients without CA from eICU-CRD. After feature extraction, we developed a tabular network (TabNet) model using feature screening with cost-sensitive learning. To assess real-time CA prediction performance, we used 10-fold leave-one-patient-out cross-validation and a cross-data set method. We evaluated MIMIC-IV and eICU-CRD across different cohort populations and subtypes of ICU within each database. Finally, external validation using the eICU-CRD and MIMIC-IV databases was conducted to assess the model's generalization ability. The decision mask of the proposed method was used to capture the interpretability of the model. RESULTS The proposed method outperformed conventional approaches across different cohort populations in both MIMIC-IV and eICU-CRD. Additionally, it achieved higher accuracy than baseline models for various ICU subtypes within both databases. The interpretable prediction results can enhance clinicians' understanding of CA prediction by serving as a statistical comparison between non-CA and CA groups. Next, we tested the eICU-CRD and MIMIC-IV data sets using models trained on MIMIC-IV and eICU-CRD, respectively, to evaluate generalization ability. The results demonstrated superior performance compared with baseline models. CONCLUSIONS Our novel framework for learning unique features provides stable predictive power across different ICU environments. Most of the interpretable global information reveals statistical differences between CA and non-CA groups, demonstrating its utility as an indicator for clinical decisions. Consequently, the proposed CA prediction system is a clinically validated algorithm that enables clinicians to intervene early based on CA prediction information and can be applied to clinical trials in digital health.
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Affiliation(s)
- Yun Kwan Kim
- Technology Development, Seers Technology Co. Ltd., Pyeongtaek-si, Gyeonggi-do, Republic of Korea
- Department of Brain and Cognitive Engineering, Korea University, Seoul, Republic of Korea
| | - Won-Doo Seo
- Technology Development, Seers Technology Co. Ltd., Pyeongtaek-si, Gyeonggi-do, Republic of Korea
| | - Sun Jung Lee
- Technology Development, Seers Technology Co. Ltd., Pyeongtaek-si, Gyeonggi-do, Republic of Korea
| | - Ja Hyung Koo
- Technology Development, Seers Technology Co. Ltd., Pyeongtaek-si, Gyeonggi-do, Republic of Korea
| | - Gyung Chul Kim
- Technology Development, Seers Technology Co. Ltd., Pyeongtaek-si, Gyeonggi-do, Republic of Korea
| | - Hee Seok Song
- Technology Development, Seers Technology Co. Ltd., Pyeongtaek-si, Gyeonggi-do, Republic of Korea
| | - Minji Lee
- Department of Biomedical Software Engineering, The Catholic University of Korea, Bucheon-si, Gyeonggi-do, Republic of Korea
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Heumos L, Ehmele P, Treis T, Upmeier Zu Belzen J, Roellin E, May L, Namsaraeva A, Horlava N, Shitov VA, Zhang X, Zappia L, Knoll R, Lang NJ, Hetzel L, Virshup I, Sikkema L, Curion F, Eils R, Schiller HB, Hilgendorff A, Theis FJ. An open-source framework for end-to-end analysis of electronic health record data. Nat Med 2024:10.1038/s41591-024-03214-0. [PMID: 39266748 DOI: 10.1038/s41591-024-03214-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2023] [Accepted: 07/25/2024] [Indexed: 09/14/2024]
Abstract
With progressive digitalization of healthcare systems worldwide, large-scale collection of electronic health records (EHRs) has become commonplace. However, an extensible framework for comprehensive exploratory analysis that accounts for data heterogeneity is missing. Here we introduce ehrapy, a modular open-source Python framework designed for exploratory analysis of heterogeneous epidemiology and EHR data. ehrapy incorporates a series of analytical steps, from data extraction and quality control to the generation of low-dimensional representations. Complemented by rich statistical modules, ehrapy facilitates associating patients with disease states, differential comparison between patient clusters, survival analysis, trajectory inference, causal inference and more. Leveraging ontologies, ehrapy further enables data sharing and training EHR deep learning models, paving the way for foundational models in biomedical research. We demonstrate ehrapy's features in six distinct examples. We applied ehrapy to stratify patients affected by unspecified pneumonia into finer-grained phenotypes. Furthermore, we reveal biomarkers for significant differences in survival among these groups. Additionally, we quantify medication-class effects of pneumonia medications on length of stay. We further leveraged ehrapy to analyze cardiovascular risks across different data modalities. We reconstructed disease state trajectories in patients with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) based on imaging data. Finally, we conducted a case study to demonstrate how ehrapy can detect and mitigate biases in EHR data. ehrapy, thus, provides a framework that we envision will standardize analysis pipelines on EHR data and serve as a cornerstone for the community.
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Affiliation(s)
- Lukas Heumos
- Institute of Computational Biology, Helmholtz Munich, Munich, Germany
- Institute of Lung Health and Immunity and Comprehensive Pneumology Center with the CPC-M bioArchive; Helmholtz Zentrum Munich; member of the German Center for Lung Research (DZL), Munich, Germany
- TUM School of Life Sciences Weihenstephan, Technical University of Munich, Munich, Germany
| | - Philipp Ehmele
- Institute of Computational Biology, Helmholtz Munich, Munich, Germany
| | - Tim Treis
- Institute of Computational Biology, Helmholtz Munich, Munich, Germany
- TUM School of Life Sciences Weihenstephan, Technical University of Munich, Munich, Germany
| | | | - Eljas Roellin
- Institute of Computational Biology, Helmholtz Munich, Munich, Germany
- Department of Mathematics, School of Computation, Information and Technology, Technical University of Munich, Munich, Germany
| | - Lilly May
- Institute of Computational Biology, Helmholtz Munich, Munich, Germany
- Department of Mathematics, School of Computation, Information and Technology, Technical University of Munich, Munich, Germany
| | - Altana Namsaraeva
- Institute of Computational Biology, Helmholtz Munich, Munich, Germany
- Konrad Zuse School of Excellence in Learning and Intelligent Systems (ELIZA), Darmstadt, Germany
| | - Nastassya Horlava
- Institute of Computational Biology, Helmholtz Munich, Munich, Germany
- TUM School of Life Sciences Weihenstephan, Technical University of Munich, Munich, Germany
| | - Vladimir A Shitov
- Institute of Computational Biology, Helmholtz Munich, Munich, Germany
- TUM School of Life Sciences Weihenstephan, Technical University of Munich, Munich, Germany
| | - Xinyue Zhang
- Institute of Computational Biology, Helmholtz Munich, Munich, Germany
| | - Luke Zappia
- Institute of Computational Biology, Helmholtz Munich, Munich, Germany
- Department of Mathematics, School of Computation, Information and Technology, Technical University of Munich, Munich, Germany
| | - Rainer Knoll
- Systems Medicine, Deutsches Zentrum für Neurodegenerative Erkrankungen (DZNE), Bonn, Germany
| | - Niklas J Lang
- Institute of Lung Health and Immunity and Comprehensive Pneumology Center with the CPC-M bioArchive; Helmholtz Zentrum Munich; member of the German Center for Lung Research (DZL), Munich, Germany
| | - Leon Hetzel
- Institute of Computational Biology, Helmholtz Munich, Munich, Germany
- Department of Mathematics, School of Computation, Information and Technology, Technical University of Munich, Munich, Germany
| | - Isaac Virshup
- Institute of Computational Biology, Helmholtz Munich, Munich, Germany
| | - Lisa Sikkema
- Institute of Computational Biology, Helmholtz Munich, Munich, Germany
- TUM School of Life Sciences Weihenstephan, Technical University of Munich, Munich, Germany
| | - Fabiola Curion
- Institute of Computational Biology, Helmholtz Munich, Munich, Germany
- Department of Mathematics, School of Computation, Information and Technology, Technical University of Munich, Munich, Germany
| | - Roland Eils
- Health Data Science Unit, Heidelberg University and BioQuant, Heidelberg, Germany
- Center for Digital Health, Berlin Institute of Health (BIH) at Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Herbert B Schiller
- Institute of Lung Health and Immunity and Comprehensive Pneumology Center with the CPC-M bioArchive; Helmholtz Zentrum Munich; member of the German Center for Lung Research (DZL), Munich, Germany
- Research Unit, Precision Regenerative Medicine (PRM), Helmholtz Munich, Munich, Germany
| | - Anne Hilgendorff
- Institute of Lung Health and Immunity and Comprehensive Pneumology Center with the CPC-M bioArchive; Helmholtz Zentrum Munich; member of the German Center for Lung Research (DZL), Munich, Germany
- Center for Comprehensive Developmental Care (CDeCLMU) at the Social Pediatric Center, Dr. von Hauner Children's Hospital, LMU Hospital, Ludwig Maximilian University, Munich, Germany
| | - Fabian J Theis
- Institute of Computational Biology, Helmholtz Munich, Munich, Germany.
- TUM School of Life Sciences Weihenstephan, Technical University of Munich, Munich, Germany.
- Department of Mathematics, School of Computation, Information and Technology, Technical University of Munich, Munich, Germany.
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Guerreiro J, Garriga R, Lozano Bagén T, Sharma B, Karnik NS, Matić A. Transatlantic transferability and replicability of machine-learning algorithms to predict mental health crises. NPJ Digit Med 2024; 7:227. [PMID: 39251868 PMCID: PMC11384787 DOI: 10.1038/s41746-024-01203-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2023] [Accepted: 07/29/2024] [Indexed: 09/11/2024] Open
Abstract
Transferring and replicating predictive algorithms across healthcare systems constitutes a unique yet crucial challenge that needs to be addressed to enable the widespread adoption of machine learning in healthcare. In this study, we explored the impact of important differences across healthcare systems and the associated Electronic Health Records (EHRs) on machine-learning algorithms to predict mental health crises, up to 28 days in advance. We evaluated both the transferability and replicability of such machine learning models, and for this purpose, we trained six models using features and methods developed on EHR data from the Birmingham and Solihull Mental Health NHS Foundation Trust in the UK. These machine learning models were then used to predict the mental health crises of 2907 patients seen at the Rush University System for Health in the US between 2018 and 2020. The best one was trained on a combination of US-specific structured features and frequency features from anonymized patient notes and achieved an AUROC of 0.837. A model with comparable performance, originally trained using UK structured data, was transferred and then tuned using US data, achieving an AUROC of 0.826. Our findings establish the feasibility of transferring and replicating machine learning models to predict mental health crises across diverse hospital systems.
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Affiliation(s)
| | - Roger Garriga
- Koa Health, Barcelona, Spain
- Universitat Pompeu Fabra, Department of Information and Communication Technologies, Barcelona, Spain
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Yang M, Zhuang J, Hu W, Li J, Wang Y, Zhang Z, Liu C, Chen H. Enhancing Patient Selection in Sepsis Clinical Trials Design Through an AI Enrichment Strategy: Algorithm Development and Validation. J Med Internet Res 2024; 26:e54621. [PMID: 39231425 DOI: 10.2196/54621] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2023] [Revised: 04/22/2024] [Accepted: 07/21/2024] [Indexed: 09/06/2024] Open
Abstract
BACKGROUND Sepsis is a heterogeneous syndrome, and enrollment of more homogeneous patients is essential to improve the efficiency of clinical trials. Artificial intelligence (AI) has facilitated the identification of homogeneous subgroups, but how to estimate the uncertainty of the model outputs when applying AI to clinical decision-making remains unknown. OBJECTIVE We aimed to design an AI-based model for purposeful patient enrollment, ensuring that a patient with sepsis recruited into a trial would still be persistently ill by the time the proposed therapy could impact patient outcome. We also expected that the model could provide interpretable factors and estimate the uncertainty of the model outputs at a customized confidence level. METHODS In this retrospective study, 9135 patients with sepsis requiring vasopressor treatment within 24 hours after sepsis onset were enrolled from Beth Israel Deaconess Medical Center. This cohort was used for model development, and 10-fold cross-validation with 50 repeats was used for internal validation. In total, 3743 patients with sepsis from the eICU Collaborative Research Database were used as the external validation cohort. All included patients with sepsis were stratified based on disease progression trajectories: rapid death, recovery, and persistent ill. A total of 148 variables were selected for predicting the 3 trajectories. Four machine learning algorithms with 3 different setups were used. We estimated the uncertainty of the model outputs using conformal prediction (CP). The Shapley Additive Explanations method was used to explain the model. RESULTS The multiclass gradient boosting machine was identified as the best-performing model with good discrimination and calibration performance in both validation cohorts. The mean area under the receiver operating characteristic curve with SD was 0.906 (0.018) for rapid death, 0.843 (0.008) for recovery, and 0.807 (0.010) for persistent ill in the internal validation cohort. In the external validation cohort, the mean area under the receiver operating characteristic curve (SD) was 0.878 (0.003) for rapid death, 0.764 (0.008) for recovery, and 0.696 (0.007) for persistent ill. The maximum norepinephrine equivalence, total urine output, Acute Physiology Score III, mean systolic blood pressure, and the coefficient of variation of oxygen saturation contributed the most. Compared to the model without CP, using the model with CP at a mixed confidence approach reduced overall prediction errors by 27.6% (n=62) and 30.7% (n=412) in the internal and external validation cohorts, respectively, as well as enabled the identification of more potentially persistent ill patients. CONCLUSIONS The implementation of our model has the potential to reduce heterogeneity and enroll more homogeneous patients in sepsis clinical trials. The use of CP for estimating the uncertainty of the model outputs allows for a more comprehensive understanding of the model's reliability and assists in making informed decisions based on the predicted outcomes.
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Affiliation(s)
- Meicheng Yang
- State Key Laboratory of Digital Medical Engineering, School of Instrument Science and Engineering, Southeast University, Nanjing, China
| | - Jinqiang Zhuang
- Emergency Intensive Care Unit (EICU), The Affiliated Hospital of Yangzhou University, Yangzhou University, Yangzhou, China
- Key Laboratory of Big Data Analysis and Knowledge Services of Yangzhou City, Yangzhou University, Yangzhou, China
| | - Wenhan Hu
- Jiangsu Provincial Key Laboratory of Critical Care Medicine, Department of Critical Care Medicine, Zhongda Hospital, School of Medicine, Southeast University, Nanjing, China
| | - Jianqing Li
- State Key Laboratory of Digital Medical Engineering, School of Instrument Science and Engineering, Southeast University, Nanjing, China
- School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing, China
| | - Yu Wang
- Key Laboratory of Big Data Analysis and Knowledge Services of Yangzhou City, Yangzhou University, Yangzhou, China
| | - Zhongheng Zhang
- Department of Emergency Medicine, Key Laboratory of Precision Medicine in Diagnosis and Monitoring Research of Zhejiang Province, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Chengyu Liu
- State Key Laboratory of Digital Medical Engineering, School of Instrument Science and Engineering, Southeast University, Nanjing, China
| | - Hui Chen
- Jiangsu Provincial Key Laboratory of Critical Care Medicine, Department of Critical Care Medicine, Zhongda Hospital, School of Medicine, Southeast University, Nanjing, China
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Davies SJ, Sessler DI, Jian Z, Fleming NW, Mythen M, Maheshwari K, Veelo DP, Vlaar APJ, Settels J, Scheeren T, van der Ster BJP, Sander M, Cannesson M, Hatib F. Comparison of Differences in Cohort (Forward) and Case Control (Backward) Methodologic Approaches for Validation of the Hypotension Prediction Index. Anesthesiology 2024; 141:443-452. [PMID: 38557791 PMCID: PMC11323758 DOI: 10.1097/aln.0000000000004989] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2023] [Accepted: 03/18/2024] [Indexed: 04/04/2024]
Abstract
BACKGROUND The Hypotension Prediction Index (the index) software is a machine learning algorithm that detects physiologic changes that may lead to hypotension. The original validation used a case control (backward) analysis that has been suggested to be biased. This study therefore conducted a cohort (forward) analysis and compared this to the original validation technique. METHODS A retrospective analysis of data from previously reported studies was conducted. All data were analyzed identically with two different methodologies, and receiver operating characteristic curves were constructed. Both backward and forward analyses were performed to examine differences in area under the receiver operating characteristic curves for the Hypotension Prediction Index and other hemodynamic variables to predict a mean arterial pressure (MAP) less than 65 mmHg for at least 1 min 5, 10, and 15 min in advance. RESULTS The analysis included 2,022 patients, yielding 4,152,124 measurements taken at 20-s intervals. The area under the curve for the index predicting hypotension analyzed by backward and forward methodologies respectively was 0.957 (95% CI, 0.947 to 0.964) versus 0.923 (95% CI, 0.912 to 0.933) 5 min in advance, 0.933 (95% CI, 0.924 to 0.942) versus 0.923 (95% CI, 0.911 to 0.933) 10 min in advance, and 0.929 (95% CI, 0.918 to 0.938) versus 0.926 (95% CI, 0.914 to 0.937) 15 min in advance. No variable other than MAP had an area under the curve greater than 0.7. The areas under the curve using forward analysis for MAP predicting hypotension 5, 10, and 15 min in advance were 0.932 (95% CI, 0.920 to 0.940), 0.929 (95% CI, 0.918 to 0.938), and 0.932 (95% CI, 0.921 to 0.940), respectively. The R2 for the variation in the index due to MAP was 0.77. CONCLUSIONS Using an updated methodology, the study found that the utility of the Hypotension Prediction Index to predict future hypotensive events is high, with an area under the receiver operating characteristics curve similar to that of the original validation method. EDITOR’S PERSPECTIVE
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Affiliation(s)
- Simon J. Davies
- Department of Anaesthesia, Critical Care and Perioperative Medicine, York and Scarborough Teaching Hospitals National Health Service Foundation Trust, York, United Kingdom; and Centre for Health and Population Science, Hull York Medical School, York, United Kingdom
| | | | | | - Neal W. Fleming
- University of California–Davis School of Medicine, Sacramento, California
| | - Monty Mythen
- Edwards Lifesciences, Irvine, California; and University College London/University College London Hospital, National Institute of Health Research Biomedical Research Centre, London, United Kingdom
| | - Kamal Maheshwari
- Department of Outcomes Research, Cleveland Clinic, Cleveland, Ohio
| | - Denise P. Veelo
- Departments of Anaesthesia and Intensive Care, Amsterdam University Medical Center, Amsterdam, The Netherlands
| | - Alexander P. J. Vlaar
- Departments of Anaesthesia and Intensive Care, Amsterdam University Medical Center, Amsterdam, The Netherlands
| | | | - Thomas Scheeren
- Edwards Lifesciences, Irvine, California; and Department of Anesthesiology, University Medical Centre Groningen, Groningen, The Netherlands
| | - B. J. P. van der Ster
- Departments of Anaesthesia and Intensive Care, Amsterdam University Medical Center, Amsterdam, The Netherlands; and Erasmus Medical Center, Rotterdam, The Netherlands
| | - Michael Sander
- Department of Anaesthesiology, Intensive Care Medicine and Pain Medicine, University Hospital Giessen, Giessen, Germany
| | - Maxime Cannesson
- Department of Anesthesiology and Perioperative Medicine, UCLA, California
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Alkhalil M, Abbara A, Grangier C, Ekzayez A. AI in conflict zones: the potential to revitalise healthcare in Syria and beyond. BMJ Glob Health 2024; 9:e015755. [PMID: 39117373 PMCID: PMC11404241 DOI: 10.1136/bmjgh-2024-015755] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2024] [Accepted: 07/23/2024] [Indexed: 08/10/2024] Open
Affiliation(s)
- Munzer Alkhalil
- Research for Health System Strengthening in northern Syria (R4HSSS), Union for Medical and Relief Organizations, Gaziantep, Turkey
- LSE IDEAS Conflict and Civicness Research Group, The London School of Economics and Political Science, London, UK
| | - Aula Abbara
- Department of Infection, Imperial College London, London, UK
- Syria Public Health Network, London, UK
| | - Caroline Grangier
- ESSEC Business School, La Défense, France
- Antei Global, Paris, France
| | - Abdulkarim Ekzayez
- War Studies (Research for Health System Strengthening in northern Syria (R4HSSS), King's College London, London, UK
- Research & Development, Syria Development Centre, London, UK
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9
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Sun K, Roy A, Tobin JM. Artificial intelligence and machine learning: Definition of terms and current concepts in critical care research. J Crit Care 2024; 82:154792. [PMID: 38554543 DOI: 10.1016/j.jcrc.2024.154792] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2023] [Revised: 07/05/2023] [Accepted: 07/17/2023] [Indexed: 04/01/2024]
Abstract
With increasing computing power, artificial intelligence (AI) and machine learning (ML) have prospered, which facilitate the analysis of large datasets, especially those found in critical care. It is important to define these terminologies, to inform a standardized approach to critical care research. This manuscript hopes to clarify these terms with examples from medical literature. Three major components that are required for a successful ML implementation: (i) reliable dataset, (ii) ML algorithm, and (iii) unbiased model evaluation, are discussed. A reliable dataset can be structured or unstructured with limited noise, outliers, and missing values. ML, a subset of AI, is typically focused on supervised or unsupervised learning tasks in which the output is based on inputs and derived from iterative pattern recognition algorithms, while AI is the overall ability of a machine to "think" or mimic human behavior; and to analyze data free from human influence. Even with successful implementation, advanced AI and ML algorithms have faced challenges in adoption into practice, mainly due to their lack of interpretability, which hinders trust, buy-in, and engagement from clinicians. Consequently, traditional algorithms, such as linear and logistic regression, that may have reduced predictive power but are highly interpretable, continue to be widely used.
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Affiliation(s)
- Kai Sun
- Department of Management Science and Statistics, University of Texas at San Antonio, One UTSA Circle, San Antonio, TX 78249, USA; Department of Anesthesiology, University of Texas Health Sciences Center San Antonio, 7703 Floyd Curl Dr, San Antonio, TX 78229, USA.
| | - Arkajyoti Roy
- Department of Management Science and Statistics, University of Texas at San Antonio, One UTSA Circle, San Antonio, TX 78249, USA.
| | - Joshua M Tobin
- Department of Anesthesiology, University of Texas Health Sciences Center San Antonio, 7703 Floyd Curl Dr, San Antonio, TX 78229, USA.
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10
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Schmulevich D, Hynes AM, Murali S, Benjamin AJ, Cannon JW. Optimizing damage control resuscitation through early patient identification and real-time performance improvement. Transfusion 2024; 64:1551-1561. [PMID: 39075741 DOI: 10.1111/trf.17806] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2023] [Revised: 02/05/2024] [Accepted: 02/16/2024] [Indexed: 07/31/2024]
Affiliation(s)
- Daniela Schmulevich
- Division of Traumatology, Surgical Critical Care & Emergency Surgery, Department of Surgery, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Allyson M Hynes
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Philadelphia, Pennsylvania, USA
- Department of Emergency Medicine, University of New Mexico School of Medicine, Albuquerque, New Mexico, USA
| | - Shyam Murali
- Division of Traumatology, Surgical Critical Care & Emergency Surgery, Department of Surgery, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Andrew J Benjamin
- Trauma and Acute Care Surgery, Department of Surgery, The University of Chicago, Chicago, Illinois, USA
| | - Jeremy W Cannon
- Division of Traumatology, Surgical Critical Care & Emergency Surgery, Department of Surgery, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania, USA
- Department of Surgery, Uniformed Services University F. Edward Hébert School of Medicine, Bethesda, Maryland, USA
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11
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Areias AC, Janela D, Moulder RG, Molinos M, Bento V, Moreira C, Yanamadala V, Correia FD, Costa F. Applying AI to Safely and Effectively Scale Care to Address Chronic MSK Conditions. J Clin Med 2024; 13:4366. [PMID: 39124635 PMCID: PMC11312972 DOI: 10.3390/jcm13154366] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2024] [Revised: 07/15/2024] [Accepted: 07/23/2024] [Indexed: 08/12/2024] Open
Abstract
Background/Objectives: The rising prevalence of musculoskeletal (MSK) conditions has not been balanced by a sufficient increase in healthcare providers. Scalability challenges are being addressed through the use of artificial intelligence (AI) in some healthcare sectors, with this showing potential to also improve MSK care. Digital care programs (DCP) generate automatically collected data, thus making them ideal candidates for AI implementation into workflows, with the potential to unlock care scalability. In this study, we aimed to assess the impact of scaling care through AI in patient outcomes, engagement, satisfaction, and adverse events. Methods: Post hoc analysis of a prospective, pre-post cohort study assessing the impact on outcomes after a 2.3-fold increase in PT-to-patient ratio, supported by the implementation of a machine learning-based tool to assist physical therapists (PTs) in patient care management. The intervention group (IG) consisted of a DCP supported by an AI tool, while the comparison group (CG) consisted of the DCP alone. The primary outcome concerned the pain response rate (reaching a minimal clinically important change of 30%). Other outcomes included mental health, program engagement, satisfaction, and the adverse event rate. Results: Similar improvements in pain response were observed, regardless of the group (response rate: 64% vs. 63%; p = 0.399). Equivalent recoveries were also reported in mental health outcomes, specifically in anxiety (p = 0.928) and depression (p = 0.187). Higher completion rates were observed in the IG (79.9% (N = 19,252) vs. CG 70.1% (N = 8489); p < 0.001). Patient engagement remained consistent in both groups, as well as high satisfaction (IG: 8.76/10, SD 1.75 vs. CG: 8.60/10, SD 1.76; p = 0.021). Intervention-related adverse events were rare and even across groups (IG: 0.58% and CG 0.69%; p = 0.231). Conclusions: The study underscores the potential of scaling MSK care that is supported by AI without compromising patient outcomes, despite the increase in PT-to-patient ratios.
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Affiliation(s)
- Anabela C. Areias
- Sword Health, Inc., Draper, UT 84043, USA; (A.C.A.); (D.J.); (R.G.M.); (M.M.); (V.B.); (C.M.); (V.Y.); (F.D.C.)
| | - Dora Janela
- Sword Health, Inc., Draper, UT 84043, USA; (A.C.A.); (D.J.); (R.G.M.); (M.M.); (V.B.); (C.M.); (V.Y.); (F.D.C.)
| | - Robert G. Moulder
- Sword Health, Inc., Draper, UT 84043, USA; (A.C.A.); (D.J.); (R.G.M.); (M.M.); (V.B.); (C.M.); (V.Y.); (F.D.C.)
- Institute for Cognitive Science, University of Colorado Boulder, Boulder, CO 80309, USA
| | - Maria Molinos
- Sword Health, Inc., Draper, UT 84043, USA; (A.C.A.); (D.J.); (R.G.M.); (M.M.); (V.B.); (C.M.); (V.Y.); (F.D.C.)
| | - Virgílio Bento
- Sword Health, Inc., Draper, UT 84043, USA; (A.C.A.); (D.J.); (R.G.M.); (M.M.); (V.B.); (C.M.); (V.Y.); (F.D.C.)
| | - Carolina Moreira
- Sword Health, Inc., Draper, UT 84043, USA; (A.C.A.); (D.J.); (R.G.M.); (M.M.); (V.B.); (C.M.); (V.Y.); (F.D.C.)
- Instituto de Ciências Biomédicas Abel Salazar, 4050-313 Porto, Portugal
| | - Vijay Yanamadala
- Sword Health, Inc., Draper, UT 84043, USA; (A.C.A.); (D.J.); (R.G.M.); (M.M.); (V.B.); (C.M.); (V.Y.); (F.D.C.)
- Department of Surgery, Quinnipiac University Frank H. Netter School of Medicine, Hamden, CT 06473, USA
- Department of Neurosurgery, Hartford Healthcare Medical Group, Westport, CT 06103, USA
| | - Fernando Dias Correia
- Sword Health, Inc., Draper, UT 84043, USA; (A.C.A.); (D.J.); (R.G.M.); (M.M.); (V.B.); (C.M.); (V.Y.); (F.D.C.)
- Neurology Department, Centro Hospitalar e Universitário do Porto, 4099-001 Porto, Portugal
| | - Fabíola Costa
- Sword Health, Inc., Draper, UT 84043, USA; (A.C.A.); (D.J.); (R.G.M.); (M.M.); (V.B.); (C.M.); (V.Y.); (F.D.C.)
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12
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Rockenschaub P, Hilbert A, Kossen T, Elbers P, von Dincklage F, Madai VI, Frey D. The Impact of Multi-Institution Datasets on the Generalizability of Machine Learning Prediction Models in the ICU. Crit Care Med 2024:00003246-990000000-00357. [PMID: 38958568 DOI: 10.1097/ccm.0000000000006359] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/04/2024]
Abstract
OBJECTIVES To evaluate the transferability of deep learning (DL) models for the early detection of adverse events to previously unseen hospitals. DESIGN Retrospective observational cohort study utilizing harmonized intensive care data from four public datasets. SETTING ICUs across Europe and the United States. PATIENTS Adult patients admitted to the ICU for at least 6 hours who had good data quality. INTERVENTIONS None. MEASUREMENTS AND MAIN RESULTS Using carefully harmonized data from a total of 334,812 ICU stays, we systematically assessed the transferability of DL models for three common adverse events: death, acute kidney injury (AKI), and sepsis. We tested whether using more than one data source and/or algorithmically optimizing for generalizability during training improves model performance at new hospitals. We found that models achieved high area under the receiver operating characteristic (AUROC) for mortality (0.838-0.869), AKI (0.823-0.866), and sepsis (0.749-0.824) at the training hospital. As expected, AUROC dropped when models were applied at other hospitals, sometimes by as much as -0.200. Using more than one dataset for training mitigated the performance drop, with multicenter models performing roughly on par with the best single-center model. Dedicated methods promoting generalizability did not noticeably improve performance in our experiments. CONCLUSIONS Our results emphasize the importance of diverse training data for DL-based risk prediction. They suggest that as data from more hospitals become available for training, models may become increasingly generalizable. Even so, good performance at a new hospital still depended on the inclusion of compatible hospitals during training.
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Affiliation(s)
- Patrick Rockenschaub
- Charité Lab for Artificial Intelligence in Medicine (CLAIM), CharitéUniversitätsmedizin Berlin, Berlin, Germany
- QUEST Center for Responsible Research, Berlin Institute of Health at Charité Universitätsmedizin Berlin, Berlin, Germany
- Institute of Clinical Epidemiology, Public Health, Health Economics, Medical Statistics and Informatics, Medical University of Innsbruck, Innsbruck, Austria
| | - Adam Hilbert
- Charité Lab for Artificial Intelligence in Medicine (CLAIM), CharitéUniversitätsmedizin Berlin, Berlin, Germany
| | - Tabea Kossen
- Charité Lab for Artificial Intelligence in Medicine (CLAIM), CharitéUniversitätsmedizin Berlin, Berlin, Germany
| | - Paul Elbers
- Department of Intensive Care Medicine, Amsterdam UMC, Vrije Universiteit, Amsterdam, The Netherlands
| | - Falk von Dincklage
- Department of Anesthesia, Intensive Care, Emergency and Pain Medicine, Universitätsmedizin Greifswald, Greifswald, Germany
| | - Vince Istvan Madai
- QUEST Center for Responsible Research, Berlin Institute of Health at Charité Universitätsmedizin Berlin, Berlin, Germany
- Faculty of Computing, Engineering and the Built Environment, School of Computing and Digital Technology, Birmingham City University, Birmingham, United Kingdom
| | - Dietmar Frey
- Charité Lab for Artificial Intelligence in Medicine (CLAIM), CharitéUniversitätsmedizin Berlin, Berlin, Germany
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13
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Wang J, Wang B, Liu YY, Luo YL, Wu YY, Xiang L, Yang XM, Qu YL, Tian TR, Man Y. Recent Advances in Digital Technology in Implant Dentistry. J Dent Res 2024; 103:787-799. [PMID: 38822563 DOI: 10.1177/00220345241253794] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/03/2024] Open
Abstract
Digital technology has emerged as a transformative tool in dental implantation, profoundly enhancing accuracy and effectiveness across multiple facets, such as diagnosis, preoperative treatment planning, surgical procedures, and restoration delivery. The multiple integration of radiographic data and intraoral data, sometimes with facial scan data or electronic facebow through virtual planning software, enables comprehensive 3-dimensional visualization of the hard and soft tissue and the position of future restoration, resulting in heightened diagnostic precision. In virtual surgery design, the incorporation of both prosthetic arrangement and individual anatomical details enables the virtual execution of critical procedures (e.g., implant placement, extended applications, etc.) through analysis of cross-sectional images and the reconstruction of 3-dimensional surface models. After verification, the utilization of digital technology including templates, navigation, combined techniques, and implant robots achieved seamless transfer of the virtual treatment plan to the actual surgical sites, ultimately leading to enhanced surgical outcomes with highly improved accuracy. In restoration delivery, digital techniques for impression, shade matching, and prosthesis fabrication have advanced, enabling seamless digital data conversion and efficient communication among clinicians and technicians. Compared with clinical medicine, artificial intelligence (AI) technology in dental implantology primarily focuses on diagnosis and prediction. AI-supported preoperative planning and surgery remain in developmental phases, impeded by the complexity of clinical cases and ethical considerations, thereby constraining widespread adoption.
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Affiliation(s)
- J Wang
- State Key Laboratory of Oral Diseases and National Clinical Research Center for Oral Diseases, West China Hospital of Stomatology, Sichuan University, Chengdu, Sichuan, China
- Department of Oral Implantology, West China Hospital of Stomatology, Sichuan University, Chengdu, Sichuan, China
| | - B Wang
- Department of Stomatology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Sichuan, Henan
| | - Y Y Liu
- Department of Oral Implantology, The Affiliated Stomatological Hospital of Kunming Medical University, Kunming, Yunnan, Sichuan, China
| | - Y L Luo
- State Key Laboratory of Oral Diseases and National Clinical Research Center for Oral Diseases, West China Hospital of Stomatology, Sichuan University, Chengdu, Sichuan, China
- Department of Oral Implantology, West China Hospital of Stomatology, Sichuan University, Chengdu, Sichuan, China
| | - Y Y Wu
- State Key Laboratory of Oral Diseases and National Clinical Research Center for Oral Diseases, West China Hospital of Stomatology, Sichuan University, Chengdu, Sichuan, China
- Department of Oral Implantology, West China Hospital of Stomatology, Sichuan University, Chengdu, Sichuan, China
| | - L Xiang
- State Key Laboratory of Oral Diseases and National Clinical Research Center for Oral Diseases, West China Hospital of Stomatology, Sichuan University, Chengdu, Sichuan, China
- Department of Oral Implantology, West China Hospital of Stomatology, Sichuan University, Chengdu, Sichuan, China
| | - X M Yang
- State Key Laboratory of Oral Diseases and National Clinical Research Center for Oral Diseases, West China Hospital of Stomatology, Sichuan University, Chengdu, Sichuan, China
- Department of Oral Implantology, West China Hospital of Stomatology, Sichuan University, Chengdu, Sichuan, China
| | - Y L Qu
- State Key Laboratory of Oral Diseases and National Clinical Research Center for Oral Diseases, West China Hospital of Stomatology, Sichuan University, Chengdu, Sichuan, China
- Department of Prosthodontics, West China Hospital of Stomatology, Sichuan University, Chengdu, Sichuan, China
| | - T R Tian
- State Key Laboratory of Oral Diseases and National Clinical Research Center for Oral Diseases, West China Hospital of Stomatology, Sichuan University, Chengdu, Sichuan, China
- Department of Oral Implantology, West China Hospital of Stomatology, Sichuan University, Chengdu, Sichuan, China
| | - Y Man
- State Key Laboratory of Oral Diseases and National Clinical Research Center for Oral Diseases, West China Hospital of Stomatology, Sichuan University, Chengdu, Sichuan, China
- Department of Oral Implantology, West China Hospital of Stomatology, Sichuan University, Chengdu, Sichuan, China
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14
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Sikora A, Keats K, Murphy DJ, Devlin JW, Smith SE, Murray B, Buckley MS, Rowe S, Coppiano L, Kamaleswaran R. A common data model for the standardization of intensive care unit medication features. JAMIA Open 2024; 7:ooae033. [PMID: 38699649 PMCID: PMC11064096 DOI: 10.1093/jamiaopen/ooae033] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2023] [Revised: 02/12/2024] [Accepted: 04/09/2024] [Indexed: 05/05/2024] Open
Abstract
Objective Common data models provide a standard means of describing data for artificial intelligence (AI) applications, but this process has never been undertaken for medications used in the intensive care unit (ICU). We sought to develop a common data model (CDM) for ICU medications to standardize the medication features needed to support future ICU AI efforts. Materials and Methods A 9-member, multi-professional team of ICU clinicians and AI experts conducted a 5-round modified Delphi process employing conference calls, web-based communication, and electronic surveys to define the most important medication features for AI efforts. Candidate ICU medication features were generated through group discussion and then independently scored by each team member based on relevance to ICU clinical decision-making and feasibility for collection and coding. A key consideration was to ensure the final ontology both distinguished unique medications and met Findable, Accessible, Interoperable, and Reusable (FAIR) guiding principles. Results Using a list of 889 ICU medications, the team initially generated 106 different medication features, and 71 were ranked as being core features for the CDM. Through this process, 106 medication features were assigned to 2 key feature domains: drug product-related (n = 43) and clinical practice-related (n = 63). Each feature included a standardized definition and suggested response values housed in the electronic data library. This CDM for ICU medications is available online. Conclusion The CDM for ICU medications represents an important first step for the research community focused on exploring how AI can improve patient outcomes and will require ongoing engagement and refinement.
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Affiliation(s)
- Andrea Sikora
- Department of Clinical and Administrative Pharmacy, University of Georgia College of Pharmacy, Augusta, GA 30912, United States
| | - Kelli Keats
- Department of Pharmacy, Augusta University Medical Center, Augusta, GA 30912, United States
| | - David J Murphy
- Division of Pulmonary, Allergy, Critical Care and Sleep Medicine, Emory University, Atlanta, GA 30322, United States
| | - John W Devlin
- Northeastern University School of Pharmacy, Boston, MA 02115, United States
- Division of Pulmonary and Critical Care Medicine, Brigham and Women’s Hospital, Boston, MA 02115, United States
| | - Susan E Smith
- Department of Clinical and Administrative Pharmacy, University of Georgia College of Pharmacy, Athens, GA 30601, United States
| | - Brian Murray
- Department of Pharmacy, University of North Carolina Medical Center, Chapel Hill, NC 27514, United States
| | - Mitchell S Buckley
- Department of Pharmacy, Banner University Medical Center Phoenix, Phoenix, AZ 85032, United States
| | - Sandra Rowe
- Department of Pharmacy, Oregon Health and Science University, Portland, OR 97239, United States
| | - Lindsey Coppiano
- Department of Biomedical Informatics, Emory University School of Medicine, Atlanta, GA 30322, United States
- Department of Biomedical Engineering, Georgia Institute of Technology, Atlanta, GA 30322, United States
| | - Rishikesan Kamaleswaran
- Department of Biomedical Informatics, Emory University School of Medicine, Atlanta, GA 30322, United States
- Department of Biomedical Engineering, Georgia Institute of Technology, Atlanta, GA 30322, United States
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15
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Henry KE, Giannini HM. Early Warning Systems for Critical Illness Outside the Intensive Care Unit. Crit Care Clin 2024; 40:561-581. [PMID: 38796228 DOI: 10.1016/j.ccc.2024.03.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/28/2024]
Abstract
Early warning systems (EWSs) are designed and deployed to create a rapid assessment and response for patients with clinical deterioration outside the intensive care unit (ICU). These models incorporate patient-level data such as vital signs and laboratory values to detect or prevent adverse clinical events, such as vital signs and laboratories to allow detection and prevention of adverse clinical events such as cardiac arrest, intensive care transfer, or sepsis. The applicability, development, clinical utility, and general perception of EWS in clinical practice vary widely. Here, we review the field as it has grown from early vital sign-based scoring systems to contemporary multidimensional algorithms and predictive technologies for clinical decompensation outside the ICU.
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Affiliation(s)
- Katharine E Henry
- Department of Computer Science, Johns Hopkins University, Malone Hall, 3400 N Charles Street, Baltimore, MD 21218, USA
| | - Heather M Giannini
- Division of Pulmonary, Allergy and Critical Care, Hospital of the University of Pennsylvania, 5 West Gates Building, 5048, 3400 Spruce Street, Philadelphia, PA 19104, USA.
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16
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Lyu X, Fan B, Hüser M, Hartout P, Gumbsch T, Faltys M, Merz TM, Rätsch G, Borgwardt K. An empirical study on KDIGO-defined acute kidney injury prediction in the intensive care unit. Bioinformatics 2024; 40:i247-i256. [PMID: 38940165 PMCID: PMC11211814 DOI: 10.1093/bioinformatics/btae212] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/29/2024] Open
Abstract
MOTIVATION Acute kidney injury (AKI) is a syndrome that affects a large fraction of all critically ill patients, and early diagnosis to receive adequate treatment is as imperative as it is challenging to make early. Consequently, machine learning approaches have been developed to predict AKI ahead of time. However, the prevalence of AKI is often underestimated in state-of-the-art approaches, as they rely on an AKI event annotation solely based on creatinine, ignoring urine output. We construct and evaluate early warning systems for AKI in a multi-disciplinary ICU setting, using the complete KDIGO definition of AKI. We propose several variants of gradient-boosted decision tree (GBDT)-based models, including a novel time-stacking based approach. A state-of-the-art LSTM-based model previously proposed for AKI prediction is used as a comparison, which was not specifically evaluated in ICU settings yet. RESULTS We find that optimal performance is achieved by using GBDT with the time-based stacking technique (AUPRC = 65.7%, compared with the LSTM-based model's AUPRC = 62.6%), which is motivated by the high relevance of time since ICU admission for this task. Both models show mildly reduced performance in the limited training data setting, perform fairly across different subcohorts, and exhibit no issues in gender transfer. Following the official KDIGO definition substantially increases the number of annotated AKI events. In our study GBDTs outperform LSTM models for AKI prediction. Generally, we find that both model types are robust in a variety of challenging settings arising for ICU data. AVAILABILITY AND IMPLEMENTATION The code to reproduce the findings of our manuscript can be found at: https://github.com/ratschlab/AKI-EWS.
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Affiliation(s)
- Xinrui Lyu
- Department of Computer Science, ETH Zürich, Zürich, 8092, Switzerland
- NEXUS Personalized Health Technologies, ETH Zürich, Schlieren, 8952, Switzerland
- Swiss Institute for Bioinformatics, Lausanne, 1015, Switzerland
| | - Bowen Fan
- Swiss Institute for Bioinformatics, Lausanne, 1015, Switzerland
- Department of Biosystems Science and Engineering, ETH Zürich, Basel, 4056, Switzerland
| | - Matthias Hüser
- Department of Computer Science, ETH Zürich, Zürich, 8092, Switzerland
- Swiss Institute for Bioinformatics, Lausanne, 1015, Switzerland
| | - Philip Hartout
- Department of Biosystems Science and Engineering, ETH Zürich, Basel, 4056, Switzerland
- Department of Machine Learning and Systems Biology, Max Planck Institute of Biochemistry, Martinsried, 82152, Germany
| | - Thomas Gumbsch
- Swiss Institute for Bioinformatics, Lausanne, 1015, Switzerland
- Department of Biosystems Science and Engineering, ETH Zürich, Basel, 4056, Switzerland
| | - Martin Faltys
- Department of Intensive Care, Austin Hospital, Melbourne, Victoria, 3084, Australia
- Department of Intensive Care Medicine, University Hospital, University of Bern, Switzerland
| | - Tobias M Merz
- Cardiovascular Intensive Care Unit, Auckland City Hospital, Auckland, 1023, New Zealand
| | - Gunnar Rätsch
- Department of Computer Science, ETH Zürich, Zürich, 8092, Switzerland
- Swiss Institute for Bioinformatics, Lausanne, 1015, Switzerland
- Medical Informatics Unit, Zürich University Hospital, 8091, Switzerland
- AI Center at ETH Zürich, Zürich, 8092, Switzerland
- Department of Biology, ETH Zürich, Zürich, 8093, Switzerland
| | - Karsten Borgwardt
- Swiss Institute for Bioinformatics, Lausanne, 1015, Switzerland
- Department of Biosystems Science and Engineering, ETH Zürich, Basel, 4056, Switzerland
- Department of Machine Learning and Systems Biology, Max Planck Institute of Biochemistry, Martinsried, 82152, Germany
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17
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Bock C, Walter JE, Rieck B, Strebel I, Rumora K, Schaefer I, Zellweger MJ, Borgwardt K, Müller C. Enhancing the diagnosis of functionally relevant coronary artery disease with machine learning. Nat Commun 2024; 15:5034. [PMID: 38866791 PMCID: PMC11169272 DOI: 10.1038/s41467-024-49390-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2023] [Accepted: 05/31/2024] [Indexed: 06/14/2024] Open
Abstract
Functionally relevant coronary artery disease (fCAD) can result in premature death or nonfatal acute myocardial infarction. Its early detection is a fundamentally important task in medicine. Classical detection approaches suffer from limited diagnostic accuracy or expose patients to possibly harmful radiation. Here we show how machine learning (ML) can outperform cardiologists in predicting the presence of stress-induced fCAD in terms of area under the receiver operating characteristic (AUROC: 0.71 vs. 0.64, p = 4.0E-13). We present two ML approaches, the first using eight static clinical variables, whereas the second leverages electrocardiogram signals from exercise stress testing. At a target post-test probability for fCAD of <15%, ML facilitates a potential reduction of imaging procedures by 15-17% compared to the cardiologist's judgement. Predictive performance is validated on an internal temporal data split as well as externally. We also show that combining clinical judgement with conventional ML and deep learning using logistic regression results in a mean AUROC of 0.74.
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Affiliation(s)
- Christian Bock
- Department of Biosystems Science and Engineering, ETH Zürich, Basel, Switzerland
- Swiss Institute for Bioinformatics, Lausanne, Switzerland
| | - Joan Elias Walter
- Cardiovascular Research Institute Basel, University Hospital of Basel, University of Basel, Basel, Switzerland
- Department of Cardiology, University Hospital of Basel, University of Basel, Basel, Switzerland
- Department of Endocrinology, Diabetology and Clinical Nutrition, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Bastian Rieck
- Department of Biosystems Science and Engineering, ETH Zürich, Basel, Switzerland
- Swiss Institute for Bioinformatics, Lausanne, Switzerland
- Institute of AI for Health, Helmholtz Munich and Technical University of Munich, Munich, Germany
| | - Ivo Strebel
- Cardiovascular Research Institute Basel, University Hospital of Basel, University of Basel, Basel, Switzerland
- Department of Cardiology, University Hospital of Basel, University of Basel, Basel, Switzerland
| | - Klara Rumora
- Cardiovascular Research Institute Basel, University Hospital of Basel, University of Basel, Basel, Switzerland
- Department of Cardiology, University Hospital of Basel, University of Basel, Basel, Switzerland
| | - Ibrahim Schaefer
- Cardiovascular Research Institute Basel, University Hospital of Basel, University of Basel, Basel, Switzerland
- Department of Cardiology, University Hospital of Basel, University of Basel, Basel, Switzerland
| | - Michael J Zellweger
- Cardiovascular Research Institute Basel, University Hospital of Basel, University of Basel, Basel, Switzerland
- Department of Cardiology, University Hospital of Basel, University of Basel, Basel, Switzerland
| | - Karsten Borgwardt
- Department of Biosystems Science and Engineering, ETH Zürich, Basel, Switzerland.
- Swiss Institute for Bioinformatics, Lausanne, Switzerland.
- Department of Machine Learning and Systems Biology, Max Planck Institute of Biochemistry, Martinsried, Germany.
| | - Christian Müller
- Cardiovascular Research Institute Basel, University Hospital of Basel, University of Basel, Basel, Switzerland.
- Department of Cardiology, University Hospital of Basel, University of Basel, Basel, Switzerland.
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18
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Zhang G, Xie Q, Wang C, Xu J, Liu G, Su C. Intelligent alert system for predicting invasive mechanical ventilation needs via noninvasive parameters: employing an integrated machine learning method with integration of multicenter databases. Med Biol Eng Comput 2024:10.1007/s11517-024-03143-7. [PMID: 38861056 DOI: 10.1007/s11517-024-03143-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2023] [Accepted: 05/27/2024] [Indexed: 06/12/2024]
Abstract
The use of invasive mechanical ventilation (IMV) is crucial in rescuing patients with respiratory dysfunction. Accurately predicting the demand for IMV is vital for clinical decision-making. However, current techniques are invasive and challenging to implement in pre-hospital and emergency rescue settings. To address this issue, a real-time prediction method utilizing only non-invasive parameters was developed to forecast IMV demand in this study. The model introduced the concept of real-time warning and leveraged the advantages of machine learning and integrated methods, achieving an AUC value of 0.935 (95% CI 0.933-0.937). The AUC value for the multi-center validation using the AmsterdamUMCdb database was 0.727, surpassing the performance of traditional risk adjustment algorithms (OSI(oxygenation saturation index): 0.608, P/F(oxygenation index): 0.558). Feature weight analysis demonstrated that BMI, Gcsverbal, and age significantly contributed to the model's decision-making. These findings highlight the substantial potential of a machine learning real-time dynamic warning model that solely relies on non-invasive parameters to predict IMV demand. Such a model can provide technical support for predicting the need for IMV in pre-hospital and disaster scenarios.
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Affiliation(s)
- Guang Zhang
- Systems Engineering Institute, People's Liberation Army, Academy of Military Sciences, Tianjin, 300161, China
| | - Qingyan Xie
- School of Life Sciences, Tiangong University, Tianjin, 300387, China
| | - Chengyi Wang
- School of Life Sciences, Tiangong University, Tianjin, 300387, China
| | - Jiameng Xu
- School of Life Sciences, Tiangong University, Tianjin, 300387, China
| | - Guanjun Liu
- Systems Engineering Institute, People's Liberation Army, Academy of Military Sciences, Tianjin, 300161, China
| | - Chen Su
- Systems Engineering Institute, People's Liberation Army, Academy of Military Sciences, Tianjin, 300161, China.
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19
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Wang TJ, Huang CT, Wu CL, Chen CH, Wang MS, Chao WC, Huang YC, Pai KC. Predictive approach for liberation from acute dialysis in ICU patients using interpretable machine learning. Sci Rep 2024; 14:13142. [PMID: 38849453 PMCID: PMC11161460 DOI: 10.1038/s41598-024-63992-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2024] [Accepted: 06/04/2024] [Indexed: 06/09/2024] Open
Abstract
Renal recovery following dialysis-requiring acute kidney injury (AKI-D) is a vital clinical outcome in critical care, yet it remains an understudied area. This retrospective cohort study, conducted in a medical center in Taiwan from 2015 to 2020, enrolled patients with AKI-D during intensive care unit stays. We aimed to develop and temporally test models for predicting dialysis liberation before hospital discharge using machine learning algorithms and explore early predictors. The dataset comprised 90 routinely collected variables within the first three days of dialysis initiation. Out of 1,381 patients who received acute dialysis, 27.3% experienced renal recovery. The cohort was divided into the training group (N = 1135) and temporal testing group (N = 251). The models demonstrated good performance, with an area under the receiver operating characteristic curve of 0.85 (95% CI, 0.81-0.88) and an area under the precision-recall curve of 0.69 (95% CI, 0.62-0.76) for the XGBoost model. Key predictors included urine volume, Charlson comorbidity index, vital sign derivatives (trend of respiratory rate and SpO2), and lactate levels. We successfully developed early prediction models for renal recovery by integrating early changes in vital signs and inputs/outputs, which have the potential to aid clinical decision-making in the ICU.
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Affiliation(s)
- Tsai-Jung Wang
- Department of Critical Care Medicine, Taichung Veterans General Hospital, Taichung, Taiwan, ROC
- Devision of Nephrology, Department of Internal Medicine, Taichung Veterans General Hospital, Taichung, Taiwan, ROC
- Department of Nutrition, Chung Shan Medical University, Taichung, Taiwan, ROC
| | - Chun-Te Huang
- Devision of Nephrology, Department of Internal Medicine, Taichung Veterans General Hospital, Taichung, Taiwan, ROC
| | - Chieh-Liang Wu
- Department of Critical Care Medicine, Taichung Veterans General Hospital, Taichung, Taiwan, ROC
- Department of Post-Baccalaureate Medicine, College of Medicine, National Chung Hsing University, Taichung, Taiwan, ROC
| | - Cheng-Hsu Chen
- Devision of Nephrology, Department of Internal Medicine, Taichung Veterans General Hospital, Taichung, Taiwan, ROC
- Department of Post-Baccalaureate Medicine, College of Medicine, National Chung Hsing University, Taichung, Taiwan, ROC
| | - Min-Shian Wang
- Department of Critical Care Medicine, Taichung Veterans General Hospital, Taichung, Taiwan, ROC
| | - Wen-Cheng Chao
- Department of Critical Care Medicine, Taichung Veterans General Hospital, Taichung, Taiwan, ROC
- Department of Post-Baccalaureate Medicine, College of Medicine, National Chung Hsing University, Taichung, Taiwan, ROC
| | - Yi-Chia Huang
- Department of Nutrition, Chung Shan Medical University, Taichung, Taiwan, ROC
- Department of Nutrition, Chung Shan Medical University Hospital, Taichung, Taiwan, ROC
| | - Kai-Chih Pai
- College of Engineering, Tunghai University, No. 1727, Sec. 4, Taiwan Boulevard, Xitun District, Taichung City, 407224, Taiwan, ROC.
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20
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Hardenberg JHB. [Data-driven intensive care: a lack of comprehensive datasets]. Med Klin Intensivmed Notfmed 2024; 119:352-357. [PMID: 38668882 DOI: 10.1007/s00063-024-01141-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2024] [Accepted: 03/26/2024] [Indexed: 05/28/2024]
Abstract
Intensive care units provide a data-rich environment with the potential to generate datasets in the realm of big data, which could be utilized to train powerful machine learning (ML) models. However, the currently available datasets are too small and exhibit too little diversity due to their limitation to individual hospitals. This lack of extensive and varied datasets is a primary reason for the limited generalizability and resulting low clinical utility of current ML models. Often, these models are based on data from single centers and suffer from poor external validity. There is an urgent need for the development of large-scale, multicentric, and multinational datasets. Ensuring data protection and minimizing re-identification risks pose central challenges in this process. The "Amsterdam University Medical Center database (AmsterdamUMCdb)" and the "Salzburg Intensive Care database (SICdb)" demonstrate that open access datasets are possible in Europe while complying with the data protection regulations of the General Data Protection Regulation (GDPR). Another challenge in building intensive care datasets is the absence of semantic definitions in the source data and the heterogeneity of data formats. Establishing binding industry standards for the semantic definition is crucial to ensure seamless semantic interoperability between datasets.
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Affiliation(s)
- Jan-Hendrik B Hardenberg
- Medizinische Klinik mit Schwerpunkt Nephrologie und internistische Intensivmedizin, Charité - Universitätsmedizin Berlin, Augustenburger Platz 1, 13353, Berlin, Deutschland.
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21
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Kervezee L, Dashti HS, Pilz LK, Skarke C, Ruben MD. Using routinely collected clinical data for circadian medicine: A review of opportunities and challenges. PLOS DIGITAL HEALTH 2024; 3:e0000511. [PMID: 38781189 PMCID: PMC11115276 DOI: 10.1371/journal.pdig.0000511] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/25/2024]
Abstract
A wealth of data is available from electronic health records (EHR) that are collected as part of routine clinical care in hospitals worldwide. These rich, longitudinal data offer an attractive object of study for the field of circadian medicine, which aims to translate knowledge of circadian rhythms to improve patient health. This narrative review aims to discuss opportunities for EHR in studies of circadian medicine, highlight the methodological challenges, and provide recommendations for using these data to advance the field. In the existing literature, we find that data collected in real-world clinical settings have the potential to shed light on key questions in circadian medicine, including how 24-hour rhythms in clinical features are associated with-or even predictive of-health outcomes, whether the effect of medication or other clinical activities depend on time of day, and how circadian rhythms in physiology may influence clinical reference ranges or sampling protocols. However, optimal use of EHR to advance circadian medicine requires careful consideration of the limitations and sources of bias that are inherent to these data sources. In particular, time of day influences almost every interaction between a patient and the healthcare system, creating operational 24-hour patterns in the data that have little or nothing to do with biology. Addressing these challenges could help to expand the evidence base for the use of EHR in the field of circadian medicine.
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Affiliation(s)
- Laura Kervezee
- Group of Circadian Medicine, Department of Cell and Chemical Biology, Leiden University Medical Center, Leiden, the Netherlands
| | - Hassan S. Dashti
- Department of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, United States of America
| | - Luísa K. Pilz
- Department of Anesthesiology and Intensive Care Medicine CCM / CVK, Charité–Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt Universität zu Berlin, Berlin, Germany
- ECRC Experimental and Clinical Research Center, Charité–Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt Universität zu Berlin, Berlin, Germany
| | - Carsten Skarke
- Institute for Translational Medicine and Therapeutics (ITMAT), University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, United States of America
- Chronobiology and Sleep Institute (CSI), University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, United States of America
- Department of Medicine, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, United States of America
| | - Marc D. Ruben
- Divisions of Pulmonary and Sleep Medicine and Biomedical Informatics, Cincinnati Children’s Hospital Medical Center, Cincinnati, Ohio, United States of America
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22
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Staiger RD, Mehra T, Haile SR, Domenghino A, Kümmerli C, Abbassi F, Kozbur D, Dutkowski P, Puhan MA, Clavien PA. Experts vs. machine - comparison of machine learning to expert-informed prediction of outcome after major liver surgery. HPB (Oxford) 2024; 26:674-681. [PMID: 38423890 DOI: 10.1016/j.hpb.2024.02.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/31/2023] [Revised: 02/01/2024] [Accepted: 02/11/2024] [Indexed: 03/02/2024]
Abstract
BACKGROUND Machine learning (ML) has been successfully implemented for classification tasks (e.g., cancer diagnosis). ML performance for more challenging predictions is largely unexplored. This study's objective was to compare machine learning vs. expert-informed predictions for surgical outcome in patients undergoing major liver surgery. METHODS Single tertiary center data on preoperative parameters and postoperative complications for elective hepatic surgery patients were included (2008-2021). Expert-informed prediction models were established on 14 parameters identified by two expert liver surgeons to impact on postoperative outcome. ML models used all available preoperative patient variables (n = 62). Model performance was compared for predicting 3-month postoperative overall morbidity. Temporal validation and additional analysis in major liver resection patients were conducted. RESULTS 889 patients included. Expert-informed models showed low average bias (2-5 CCI points) with high over/underprediction. ML models performed similarly: average prediction 5-10 points higher than observed CCI values with high variability (95% CI -30 to 50). No performance improvement for major liver surgery patients. CONCLUSION No clinical relevance in the application of ML for predicting postoperative overall morbidity was found. Despite being a novel hype, ML has the potential for application in clinical practice. However, at this stage it does not replace established approaches of prediction modelling.
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Affiliation(s)
- Roxane D Staiger
- Department of Surgery & Transplantation, University Hospital Zurich, Zurich, Switzerland.
| | - Tarun Mehra
- Department of Medical Oncology and Hematology, University Hospital Zurich, Zurich, Switzerland
| | - Sarah R Haile
- Department of Epidemiology, Epidemiology, Biostatistics and Prevention Institute, University of Zurich, Zurich, Switzerland
| | - Anja Domenghino
- Department of Surgery & Transplantation, University Hospital Zurich, Zurich, Switzerland
| | | | - Fariba Abbassi
- Department of Surgery & Transplantation, University Hospital Zurich, Zurich, Switzerland
| | - Damian Kozbur
- Department of Economics, University of Zurich, Zurich, Switzerland
| | - Philipp Dutkowski
- Department of Surgery & Transplantation, University Hospital Zurich, Zurich, Switzerland
| | - Milo A Puhan
- Department of Epidemiology, Epidemiology, Biostatistics and Prevention Institute, University of Zurich, Zurich, Switzerland
| | - Pierre-Alain Clavien
- Department of Surgery & Transplantation, University Hospital Zurich, Zurich, Switzerland
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23
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Araújo R, Ramalhete L, Viegas A, Von Rekowski CP, Fonseca TAH, Calado CRC, Bento L. Simplifying Data Analysis in Biomedical Research: An Automated, User-Friendly Tool. Methods Protoc 2024; 7:36. [PMID: 38804330 PMCID: PMC11130801 DOI: 10.3390/mps7030036] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2024] [Revised: 04/20/2024] [Accepted: 04/22/2024] [Indexed: 05/29/2024] Open
Abstract
Robust data normalization and analysis are pivotal in biomedical research to ensure that observed differences in populations are directly attributable to the target variable, rather than disparities between control and study groups. ArsHive addresses this challenge using advanced algorithms to normalize populations (e.g., control and study groups) and perform statistical evaluations between demographic, clinical, and other variables within biomedical datasets, resulting in more balanced and unbiased analyses. The tool's functionality extends to comprehensive data reporting, which elucidates the effects of data processing, while maintaining dataset integrity. Additionally, ArsHive is complemented by A.D.A. (Autonomous Digital Assistant), which employs OpenAI's GPT-4 model to assist researchers with inquiries, enhancing the decision-making process. In this proof-of-concept study, we tested ArsHive on three different datasets derived from proprietary data, demonstrating its effectiveness in managing complex clinical and therapeutic information and highlighting its versatility for diverse research fields.
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Affiliation(s)
- Rúben Araújo
- NMS—NOVA Medical School, FCM—Faculdade de Ciências Médicas, Universidade NOVA de Lisboa, Campo Mártires da Pátria 130, 1169-056 Lisbon, Portugal
- CHRC—Comprehensive Health Research Centre, Universidade NOVA de Lisboa, 1150-082 Lisbon, Portugal
- ISEL—Instituto Superior de Engenharia de Lisboa, Instituto Politécnico de Lisboa, Rua Conselheiro Emídio Navarro 1, 1959-007 Lisbon, Portugal
| | - Luís Ramalhete
- NMS—NOVA Medical School, FCM—Faculdade de Ciências Médicas, Universidade NOVA de Lisboa, Campo Mártires da Pátria 130, 1169-056 Lisbon, Portugal
- Blood and Transplantation Center of Lisbon, IPST—Instituto Português do Sangue e da Transplantação, Alameda das Linhas de Torres 117, 1769-001 Lisbon, Portugal
- iNOVA4Health—Advancing Precision Medicine, RG11: Reno-Vascular Diseases Group, NMS—NOVA Medical School, FCM—Faculdade de Ciências Médicas, Universidade NOVA de Lisboa, 1169-056 Lisbon, Portugal
| | - Ana Viegas
- CHRC—Comprehensive Health Research Centre, Universidade NOVA de Lisboa, 1150-082 Lisbon, Portugal
- ESTeSL—Escola Superior de Tecnologia da Saúde de Lisboa, Instituto Politécnico de Lisboa, Avenida D. João II, Lote 4.69.01, 1990-096 Lisbon, Portugal
- Neurosciences Area, Clinical Neurophysiology Unit, ULSSJ—Unidade Local de Saúde São José, Rua José António Serrano, 1150-199 Lisbon, Portugal
| | - Cristiana P. Von Rekowski
- NMS—NOVA Medical School, FCM—Faculdade de Ciências Médicas, Universidade NOVA de Lisboa, Campo Mártires da Pátria 130, 1169-056 Lisbon, Portugal
- CHRC—Comprehensive Health Research Centre, Universidade NOVA de Lisboa, 1150-082 Lisbon, Portugal
- ISEL—Instituto Superior de Engenharia de Lisboa, Instituto Politécnico de Lisboa, Rua Conselheiro Emídio Navarro 1, 1959-007 Lisbon, Portugal
| | - Tiago A. H. Fonseca
- NMS—NOVA Medical School, FCM—Faculdade de Ciências Médicas, Universidade NOVA de Lisboa, Campo Mártires da Pátria 130, 1169-056 Lisbon, Portugal
- CHRC—Comprehensive Health Research Centre, Universidade NOVA de Lisboa, 1150-082 Lisbon, Portugal
- ISEL—Instituto Superior de Engenharia de Lisboa, Instituto Politécnico de Lisboa, Rua Conselheiro Emídio Navarro 1, 1959-007 Lisbon, Portugal
| | - Cecília R. C. Calado
- ISEL—Instituto Superior de Engenharia de Lisboa, Instituto Politécnico de Lisboa, Rua Conselheiro Emídio Navarro 1, 1959-007 Lisbon, Portugal
- Institute for Bioengineering and Biosciences (iBB), The Associate Laboratory Institute for Health and Bioeconomy–i4HB, Instituto Superior Técnico (IST), Universidade de Lisboa (UL), Av. Rovisco Pais, 1049-001 Lisboa, Portugal
| | - Luís Bento
- Intensive Care Department, ULSSJ—Unidade Local de Saúde São José, Rua José António Serrano, 1150-199 Lisbon, Portugal;
- Integrated Pathophysiological Mechanisms, CHRC—Comprehensive Health Research Centre, NMS—NOVA Medical School, FCM—Faculdade de Ciências Médicas, Universidade NOVA de Lisboa, Campo Mártires da Pátria 130, 1169-056 Lisbon, Portugal
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24
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Bernstorff M, Hansen L, Enevoldsen K, Damgaard J, Hæstrup F, Perfalk E, Danielsen AA, Østergaard SD. Development and validation of a machine learning model for prediction of type 2 diabetes in patients with mental illness. Acta Psychiatr Scand 2024. [PMID: 38575118 DOI: 10.1111/acps.13687] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/20/2023] [Revised: 03/08/2024] [Accepted: 03/28/2024] [Indexed: 04/06/2024]
Abstract
BACKGROUND Type 2 diabetes (T2D) is approximately twice as common among individuals with mental illness compared with the background population, but may be prevented by early intervention on lifestyle, diet, or pharmacologically. Such prevention relies on identification of those at elevated risk (prediction). The aim of this study was to develop and validate a machine learning model for prediction of T2D among patients with mental illness. METHODS The study was based on routine clinical data from electronic health records from the psychiatric services of the Central Denmark Region. A total of 74,880 patients with 1.59 million psychiatric service contacts were included in the analyses. We created 1343 potential predictors from 51 source variables, covering patient-level information on demographics, diagnoses, pharmacological treatment, and laboratory results. T2D was operationalised as HbA1c ≥48 mmol/mol, fasting plasma glucose ≥7.0 mmol/mol, oral glucose tolerance test ≥11.1 mmol/mol or random plasma glucose ≥11.1 mmol/mol. Two machine learning models (XGBoost and regularised logistic regression) were trained to predict T2D based on 85% of the included contacts. The predictive performance of the best performing model was tested on the remaining 15% of the contacts. RESULTS The XGBoost model detected patients at high risk 2.7 years before T2D, achieving an area under the receiver operating characteristic curve of 0.84. Of the 996 patients developing T2D in the test set, the model issued at least one positive prediction for 305 (31%). CONCLUSION A machine learning model can accurately predict development of T2D among patients with mental illness based on routine clinical data from electronic health records. A decision support system based on such a model may inform measures to prevent development of T2D in this high-risk population.
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Affiliation(s)
- Martin Bernstorff
- Department of Affective Disorders, Aarhus University Hospital - Psychiatry, Aarhus, Denmark
- Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
- Center for Humanities Computing, Aarhus University, Aarhus, Denmark
| | - Lasse Hansen
- Department of Affective Disorders, Aarhus University Hospital - Psychiatry, Aarhus, Denmark
- Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
- Center for Humanities Computing, Aarhus University, Aarhus, Denmark
| | - Kenneth Enevoldsen
- Department of Affective Disorders, Aarhus University Hospital - Psychiatry, Aarhus, Denmark
- Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
- Center for Humanities Computing, Aarhus University, Aarhus, Denmark
| | - Jakob Damgaard
- Department of Affective Disorders, Aarhus University Hospital - Psychiatry, Aarhus, Denmark
- Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
- Center for Humanities Computing, Aarhus University, Aarhus, Denmark
| | - Frida Hæstrup
- Department of Affective Disorders, Aarhus University Hospital - Psychiatry, Aarhus, Denmark
- Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
- Center for Humanities Computing, Aarhus University, Aarhus, Denmark
| | - Erik Perfalk
- Department of Affective Disorders, Aarhus University Hospital - Psychiatry, Aarhus, Denmark
- Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
| | - Andreas Aalkjær Danielsen
- Department of Affective Disorders, Aarhus University Hospital - Psychiatry, Aarhus, Denmark
- Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
| | - Søren Dinesen Østergaard
- Department of Affective Disorders, Aarhus University Hospital - Psychiatry, Aarhus, Denmark
- Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
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25
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Baumgart A, Beck G, Ghezel-Ahmadi D. [Artificial intelligence in intensive care medicine]. Med Klin Intensivmed Notfmed 2024; 119:189-198. [PMID: 38546864 DOI: 10.1007/s00063-024-01117-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2024] [Revised: 01/29/2024] [Accepted: 02/05/2024] [Indexed: 04/05/2024]
Abstract
The integration of artificial intelligence (AI) into intensive care medicine has made considerable progress in recent studies, particularly in the areas of predictive analytics, early detection of complications, and the development of decision support systems. The main challenges remain availability and quality of data, reduction of bias and the need for explainable results from algorithms and models. Methods to explain these systems are essential to increase trust, understanding, and ethical considerations among healthcare professionals and patients. Proper training of healthcare professionals in AI principles, terminology, ethical considerations, and practical application is crucial for the successful use of AI. Careful assessment of the impact of AI on patient autonomy and data protection is essential for its responsible use in intensive care medicine. A balance between ethical and practical considerations must be maintained to ensure patient-centered care while complying with data protection regulations. Synergistic collaboration between clinicians, AI engineers, and regulators is critical to realizing the full potential of AI in intensive care medicine and maximizing its positive impact on patient care. Future research and development efforts should focus on improving AI models for real-time predictions, increasing the accuracy and utility of AI-based closed-loop systems, and overcoming ethical, technical, and regulatory challenges, especially in generative AI systems.
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Affiliation(s)
- André Baumgart
- Zentrum für Präventivmedizin und Digitale Gesundheit, Medizinische Fakultät Mannheim der Universität Heidelberg, Theodor-Kutzer-Ufer 1-3, 68167, Mannheim, Deutschland.
| | - Grietje Beck
- Abteilung für Anästhesiologie, Intensivmedizin und Schmerzmedizin, Universitätsmedizin Mannheim gGmbH, Medizinische Fakultät Mannheim der Universität Heidelberg, Mannheim, Deutschland
| | - David Ghezel-Ahmadi
- Abteilung für Anästhesiologie, Intensivmedizin und Schmerzmedizin, Universitätsmedizin Mannheim gGmbH, Medizinische Fakultät Mannheim der Universität Heidelberg, Mannheim, Deutschland
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26
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Chromik J, Flint AR, Arnrich B. ARTEMIS: An alarm threshold and policy mining system for the intensive care unit. Int J Med Inform 2024; 184:105349. [PMID: 38301520 DOI: 10.1016/j.ijmedinf.2024.105349] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2023] [Revised: 01/11/2024] [Accepted: 01/24/2024] [Indexed: 02/03/2024]
Abstract
BACKGROUND Alarm fatigue is a major technology-induced hazard for patients and staff in intensive care units. Too many - mostly unnecessary - alarms cause desensitisation and lack of response in medical staff. Unsuitable alarm policies are one reason for alarm fatigue. But changing alarm policies is a delicate issue since it concerns patient safety. OBJECTIVE We present ARTEMIS, a novel, computer-aided clinical decision support system for policy makers that can help to considerably improve alarm policies using data from hospital information systems. METHODS Policy makers can use different policy components from ARTEMIS' internal library to assemble tailor-made alarm policies for their intensive care units. Alternatively, policy makers can provide even more highly customised policy components as Python functions using data the hospital information systems. This can even include machine learning models - for example for setting alarm thresholds. Finally, policy makers can evaluate their system of policies and compare the resulting alarm loads. RESULTS ARTEMIS reports and compares numbers of alarms caused by different alarm policies for an easily adaptable target population. ARTEMIS can compare policies side-by-side and provides grid comparisons and heat maps for parameter optimisation. For example, we found that the utility of alarm delays varies based on target population. Furthermore, policy makers can introduce virtual parameters that are not in the original data by providing a formula to compute them. Virtual parameters help measuring and alarming on the right metric, even if the patient monitors do not directly measure this metric. CONCLUSION ARTEMIS does not release the policy maker from assessing the policy from a medical standpoint. But as a knowledge discovery and clinical decision support system, it provides a strong quantitative foundation for medical decisions. At comparatively low cost of implementation, ARTEMIS can have a substantial impact on patients and staff alike - with organisational, economic, and clinical benefits for the implementing hospital.
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Affiliation(s)
- Jonas Chromik
- Hasso Plattner Institute, Rudolf-Breitscheid-Straße 187, Potsdam, 14482, Brandenburg, Germany.
| | - Anne Rike Flint
- Institute of Medical Informatics at Charité - Universitätsmedizin Berlin, Charitéplatz 1, Berlin, 10117, Berlin, Germany
| | - Bert Arnrich
- Hasso Plattner Institute, Rudolf-Breitscheid-Straße 187, Potsdam, 14482, Brandenburg, Germany
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27
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Keats K, Deng S, Chen X, Zhang T, Devlin JW, Murphy DJ, Smith SE, Murray B, Kamaleswaran R, Sikora A. Unsupervised machine learning analysis to identify patterns of ICU medication use for fluid overload prediction. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.03.21.24304663. [PMID: 38562806 PMCID: PMC10984037 DOI: 10.1101/2024.03.21.24304663] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/04/2024]
Abstract
INTRODUCTION Intravenous (IV) medications are a fundamental cause of fluid overload (FO) in the intensive care unit (ICU); however, the association between IV medication use (including volume), administration timing, and FO occurrence remains unclear. METHODS This retrospective cohort study included consecutive adults admitted to an ICU ≥72 hours with available fluid balance data. FO was defined as a positive fluid balance ≥7% of admission body weight within 72 hours of ICU admission. After reviewing medication administration record (MAR) data in three-hour periods, IV medication exposure was categorized into clusters using principal component analysis (PCA) and Restricted Boltzmann Machine (RBM). Medication regimens of patients with and without FO were compared within clusters to assess for temporal clusters associated with FO using the Wilcoxon rank sum test. Exploratory analyses of the medication cluster most associated with FO for medications frequently appearing and used in the first 24 hours was conducted. RESULTS FO occurred in 127/927 (13.7%) of the patients enrolled. Patients received a median (IQR) of 31 (13-65) discrete IV medication administrations over the 72-hour period. Across all 47,803 IV medication administrations, ten unique IV medication clusters were identified with 121-130 medications in each cluster. Among the ten clusters, cluster 7 had the greatest association with FO; the mean number of cluster 7 medications received was significantly greater in patients in the FO cohort compared to patients who did not experience FO (25.6 vs.10.9. p<0.0001). 51 of the 127 medications in cluster 7 (40.2%) appeared in > 5 separate 3-hour periods during the 72-hour study window. The most common cluster 7 medications included continuous infusions, antibiotics, and sedatives/analgesics. Addition of cluster 7 medications to a prediction model with APACHE II score and receipt of diuretics improved the ability for the model to predict fluid overload (AUROC 5.65, p =0.0004). CONCLUSIONS Using ML approaches, a unique IV medication cluster was strongly associated with FO. Incorporation of this cluster improved the ability to predict development of fluid overload in ICU patients compared with traditional prediction models. This method may be further developed into real-time clinical applications to improve early detection of adverse outcomes.
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Affiliation(s)
- Kelli Keats
- Augusta University Medical Center, Department of Pharmacy, Augusta, GA
| | - Shiyuan Deng
- University of Georgia Franklin College of Arts and Sciences, Department of Statistics, Athens, GA, USA
| | - Xianyan Chen
- University of Georgia Franklin College of Arts and Sciences, Department of Statistics, Athens, GA, USA
| | - Tianyi Zhang
- University of Georgia Franklin College of Arts and Sciences, Department of Statistics, Athens, GA, USA
| | - John W Devlin
- Northeastern University School of Pharmacy, Boston, MA
- Brigham and Women's Hospital, Division of Pulmonary and Critical Care Medicine, Boston, MA
| | - David J Murphy
- Emory University, Division of Pulmonary, Allergy, Critical Care and Sleep Medicine, Atlanta, GA, USA
| | - Susan E Smith
- University of Georgia College of Pharmacy, Department of Clinical and Administrative Pharmacy, Athens, GA, USA
| | - Brian Murray
- University of Colorado Skaggs School of Pharmacy, Aurora, CO, USA
| | - Rishikesan Kamaleswaran
- Department of Biomedical Informatics, Emory University School of Medicine, Atlanta, GA, USA
- Department of Biomedical Engineering, Georgia Institute of Technology, Atlanta, GA, USA
| | - Andrea Sikora
- 1120 15th Street, HM-118 Augusta, GA 30912
- University of Georgia College of Pharmacy, Department of Clinical and Administrative Pharmacy, Augusta, GA, USA
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Schweingruber N, Bremer J, Wiehe A, Mader MMD, Mayer C, Woo MS, Kluge S, Grensemann J, Quandt F, Gempt J, Fischer M, Thomalla G, Gerloff C, Sauvigny J, Czorlich P. Early prediction of ventricular peritoneal shunt dependency in aneurysmal subarachnoid haemorrhage patients by recurrent neural network-based machine learning using routine intensive care unit data. J Clin Monit Comput 2024:10.1007/s10877-024-01151-4. [PMID: 38512361 DOI: 10.1007/s10877-024-01151-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2023] [Accepted: 03/08/2024] [Indexed: 03/23/2024]
Abstract
Aneurysmal subarachnoid haemorrhage (aSAH) can lead to complications such as acute hydrocephalic congestion. Treatment of this acute condition often includes establishing an external ventricular drainage (EVD). However, chronic hydrocephalus develops in some patients, who then require placement of a permanent ventriculoperitoneal (VP) shunt. The aim of this study was to employ recurrent neural network (RNN)-based machine learning techniques to identify patients who require VP shunt placement at an early stage. This retrospective single-centre study included all patients who were diagnosed with aSAH and treated in the intensive care unit (ICU) between November 2010 and May 2020 (n = 602). More than 120 parameters were analysed, including routine neurocritical care data, vital signs and blood gas analyses. Various machine learning techniques, including RNNs and gradient boosting machines, were evaluated for their ability to predict VP shunt dependency. VP-shunt dependency could be predicted using an RNN after just one day of ICU stay, with an AUC-ROC of 0.77 (CI: 0.75-0.79). The accuracy of the prediction improved after four days of observation (Day 4: AUC-ROC 0.81, CI: 0.79-0.84). At that point, the accuracy of the prediction was 76% (CI: 75.98-83.09%), with a sensitivity of 85% (CI: 83-88%) and a specificity of 74% (CI: 71-78%). RNN-based machine learning has the potential to predict VP shunt dependency on Day 4 after ictus in aSAH patients using routine data collected in the ICU. The use of machine learning may allow early identification of patients with specific therapeutic needs and accelerate the execution of required procedures.
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Affiliation(s)
- Nils Schweingruber
- Department of Neurology, University Medical Center Hamburg-Eppendorf, 20246, Hamburg, Germany
| | - Jan Bremer
- Department of Neurology, University Medical Center Hamburg-Eppendorf, 20246, Hamburg, Germany
| | - Anton Wiehe
- Department of Neurology, University Medical Center Hamburg-Eppendorf, 20246, Hamburg, Germany
- Department of Informatics, University of Hamburg, 22527, Hamburg, Germany
| | - Marius Marc-Daniel Mader
- Department of Neurosurgery, University Medical Center Hamburg-Eppendorf, Martinistr. 52, 20246, Hamburg, Germany
- Institute for Stem Cell Biology and Regenerative Medicine, Stanford University School of Medicine, Stanford, CA, 94305, USA
| | - Christina Mayer
- Department of Neurology, University Medical Center Hamburg-Eppendorf, 20246, Hamburg, Germany
- Institute of Neuroimmunology and Multiple Sclerosis (INIMS), Center for Molecular Neurobiology Hamburg (ZMNH), University Medical Center Hamburg-Eppendorf, 20246, Hamburg, Germany
| | - Marcel Seungsu Woo
- Department of Neurology, University Medical Center Hamburg-Eppendorf, 20246, Hamburg, Germany
- Institute of Neuroimmunology and Multiple Sclerosis (INIMS), Center for Molecular Neurobiology Hamburg (ZMNH), University Medical Center Hamburg-Eppendorf, 20246, Hamburg, Germany
| | - Stefan Kluge
- Department of Intensive Care Medicine, University Medical Center Hamburg-Eppendorf, 20246, Hamburg, Germany
| | - Jörn Grensemann
- Department of Intensive Care Medicine, University Medical Center Hamburg-Eppendorf, 20246, Hamburg, Germany
| | - Fanny Quandt
- Department of Neurology, University Medical Center Hamburg-Eppendorf, 20246, Hamburg, Germany
| | - Jens Gempt
- Department of Neurosurgery, University Medical Center Hamburg-Eppendorf, Martinistr. 52, 20246, Hamburg, Germany
| | - Marlene Fischer
- Department of Intensive Care Medicine, University Medical Center Hamburg-Eppendorf, 20246, Hamburg, Germany
| | - Götz Thomalla
- Department of Neurology, University Medical Center Hamburg-Eppendorf, 20246, Hamburg, Germany
| | - Christian Gerloff
- Department of Neurology, University Medical Center Hamburg-Eppendorf, 20246, Hamburg, Germany
| | - Jennifer Sauvigny
- Department of Neurosurgery, University Medical Center Hamburg-Eppendorf, Martinistr. 52, 20246, Hamburg, Germany
| | - Patrick Czorlich
- Department of Neurosurgery, University Medical Center Hamburg-Eppendorf, Martinistr. 52, 20246, Hamburg, Germany.
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Cho KJ, Kim KH, Choi J, Yoo D, Kim J. External Validation of Deep Learning-Based Cardiac Arrest Risk Management System for Predicting In-Hospital Cardiac Arrest in Patients Admitted to General Wards Based on Rapid Response System Operating and Nonoperating Periods: A Single-Center Study. Crit Care Med 2024; 52:e110-e120. [PMID: 38381018 PMCID: PMC10876170 DOI: 10.1097/ccm.0000000000006137] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/22/2024]
Abstract
OBJECTIVES The limitations of current early warning scores have prompted the development of deep learning-based systems, such as deep learning-based cardiac arrest risk management systems (DeepCARS). Unfortunately, in South Korea, only two institutions operate 24-hour Rapid Response System (RRS), whereas most hospitals have part-time or no RRS coverage at all. This study validated the predictive performance of DeepCARS during RRS operation and nonoperation periods and explored its potential beyond RRS operating hours. DESIGN Retrospective cohort study. SETTING In this 1-year retrospective study conducted at Yonsei University Health System Severance Hospital in South Korea, DeepCARS was compared with conventional early warning systems for predicting in-hospital cardiac arrest (IHCA). The study focused on adult patients admitted to the general ward, with the primary outcome being IHCA-prediction performance within 24 hours of the alarm. PATIENTS We analyzed the data records of adult patients admitted to a general ward from September 1, 2019, to August 31, 2020. INTERVENTIONS None. MEASUREMENTS AND MAIN RESULTS Performance evaluation was conducted separately for the operational and nonoperational periods of the RRS, using the area under the receiver operating characteristic curve (AUROC) as the metric. DeepCARS demonstrated a superior AUROC as compared with the Modified Early Warning Score (MEWS) and the National Early Warning Score (NEWS), both during RRS operating and nonoperating hours. Although the MEWS and NEWS exhibited varying performance across the two periods, DeepCARS showed consistent performance. CONCLUSIONS The accuracy and efficiency for predicting IHCA of DeepCARS were superior to that of conventional methods, regardless of whether the RRS was in operation. These findings emphasize that DeepCARS is an effective screening tool suitable for hospitals with full-time RRS, part-time RRS, and even those without any RRS.
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Affiliation(s)
- Kyung-Jae Cho
- Department of Research and Development, VUNO, Seoul, Republic of Korea
| | - Kwan Hyung Kim
- Department of Anesthesiology and Pain Medicine, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Jaewoo Choi
- Department of Research and Development, VUNO, Seoul, Republic of Korea
| | - Dongjoon Yoo
- Department of Research and Development, VUNO, Seoul, Republic of Korea
- Department of Critical Care Medicine and Emergency Medicine, Inha University Hospital, Incheon, Republic of Korea
| | - Jeongmin Kim
- Department of Anesthesiology and Pain Medicine, Yonsei University College of Medicine, Seoul, Republic of Korea
- Anesthesia and Pain Research Institute, Yonsei University College of Medicine, Seoul, Republic of Korea
- Institute for Innovation in Digital Healthcare, Yonsei University, Seoul, Republic of Korea
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van Doorn WPTM, Helmich F, van Dam PMEL, Jacobs LHJ, Stassen PM, Bekers O, Meex SJR. Explainable Machine Learning Models for Rapid Risk Stratification in the Emergency Department: A Multicenter Study. J Appl Lab Med 2024; 9:212-222. [PMID: 38102476 DOI: 10.1093/jalm/jfad094] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2023] [Accepted: 08/30/2023] [Indexed: 12/17/2023]
Abstract
BACKGROUND Risk stratification of patients presenting to the emergency department (ED) is important for appropriate triage. Diagnostic laboratory tests are an essential part of the workup and risk stratification of these patients. Using machine learning, the prognostic power and clinical value of these tests can be amplified greatly. In this study, we applied machine learning to develop an accurate and explainable clinical decision support tool model that predicts the likelihood of 31-day mortality in ED patients (the RISKINDEX). This tool was developed and evaluated in four Dutch hospitals. METHODS Machine learning models included patient characteristics and available laboratory data collected within the first 2 h after ED presentation, and were trained using 5 years of data from consecutive ED patients from the Maastricht University Medical Center (Maastricht), Meander Medical Center (Amersfoort), and Zuyderland Medical Center (Sittard and Heerlen). A sixth year of data was used to evaluate the models using area under the receiver-operating-characteristic curve (AUROC) and calibration curves. The Shapley additive explanations (SHAP) algorithm was used to obtain explainable machine learning models. RESULTS The present study included 266 327 patients with 7.1 million laboratory results available. Models show high diagnostic performance with AUROCs of 0.94, 0.98, 0.88, and 0.90 for Maastricht, Amersfoort, Sittard and Heerlen, respectively. The SHAP algorithm was utilized to visualize patient characteristics and laboratory data patterns that underlie individual RISKINDEX predictions. CONCLUSIONS Our clinical decision support tool has excellent diagnostic performance in predicting 31-day mortality in ED patients. Follow-up studies will assess whether implementation of these algorithms can improve clinically relevant end points.
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Affiliation(s)
- William P T M van Doorn
- Central Diagnostic Laboratory, Department of Clinical Chemistry, Maastricht University Medical Center, Maastricht, the Netherlands
- CARIM School for Cardiovascular Diseases, Maastricht University, Maastricht, the Netherlands
| | - Floris Helmich
- Department of Clinical Chemistry & Hematology, Zuyderland Medical Center, Heerlen, the Netherlands
| | - Paul M E L van Dam
- Department of Internal Medicine, Division of General Internal Medicine, Section Acute Medicine, Maastricht University Medical Center, Maastricht University, Maastricht, the Netherlands
| | - Leo H J Jacobs
- Laboratory of Clinical Chemistry, Meander Medical Center, Amersfoort, the Netherlands
| | - Patricia M Stassen
- Department of Internal Medicine, Division of General Internal Medicine, Section Acute Medicine, Maastricht University Medical Center, Maastricht University, Maastricht, the Netherlands
- CAPHRI School for Care and Public Health Research Institute, Maastricht University, Maastricht, the Netherlands
| | - Otto Bekers
- Central Diagnostic Laboratory, Department of Clinical Chemistry, Maastricht University Medical Center, Maastricht, the Netherlands
- CARIM School for Cardiovascular Diseases, Maastricht University, Maastricht, the Netherlands
| | - Steven J R Meex
- Central Diagnostic Laboratory, Department of Clinical Chemistry, Maastricht University Medical Center, Maastricht, the Netherlands
- CARIM School for Cardiovascular Diseases, Maastricht University, Maastricht, the Netherlands
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Abdullah HR, Lim DYZ, Ke Y, Salim NNM, Lan X, Dong Y, Feng M. The SingHealth Perioperative and Anesthesia Subject Area Registry (PASAR), a large-scale perioperative data mart and registry. Korean J Anesthesiol 2024; 77:58-65. [PMID: 37935575 PMCID: PMC10834714 DOI: 10.4097/kja.23580] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2023] [Revised: 09/28/2023] [Accepted: 11/07/2023] [Indexed: 11/09/2023] Open
Abstract
BACKGROUND To enhance perioperative outcomes, a perioperative registry that integrates high-quality real-world data throughout the perioperative period is essential. Singapore General Hospital established the Perioperative and Anesthesia Subject Area Registry (PASAR) to unify data from the preoperative, intraoperative, and postoperative stages. This study presents the methodology employed to create this database. METHODS Since 2016, data from surgical patients have been collected from the hospital electronic medical record systems, de-identified, and stored securely in compliance with privacy and data protection laws. As a representative sample, data from initiation in 2016 to December 2022 were collected. RESULTS As of December 2022, PASAR data comprise 26 tables, encompassing 153,312 patient admissions and 168,977 operation sessions. For this period, the median age of the patients was 60.0 years, sex distribution was balanced, and the majority were Chinese. Hypertension and cardiovascular comorbidities were also prevalent. Information including operation type and time, intensive care unit (ICU) length of stay, and 30-day and 1-year mortality rates were collected. Emergency surgeries resulted in longer ICU stays, but shorter operation times than elective surgeries. CONCLUSIONS The PASAR provides a comprehensive and automated approach to gathering high-quality perioperative patient data.
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Affiliation(s)
- Hairil Rizal Abdullah
- Department of Anesthesiology, Singapore General Hospital, Singapore
- Duke-NUS Medical School, Singapore
| | - Daniel Yan Zheng Lim
- Duke-NUS Medical School, Singapore
- Department of Gastroenterology, Singapore General Hospital, Singapore
| | - Yuhe Ke
- Department of Anesthesiology, Singapore General Hospital, Singapore
| | | | - Xiang Lan
- Saw Swee Hock School of Public Health and Institute of Data Science, National University of Singapore, Singapore
| | - Yizhi Dong
- Saw Swee Hock School of Public Health and Institute of Data Science, National University of Singapore, Singapore
| | - Mengling Feng
- Saw Swee Hock School of Public Health and Institute of Data Science, National University of Singapore, Singapore
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Boussen S, Benard-Tertrais M, Ogéa M, Malet A, Simeone P, Antonini F, Bruder N, Velly L. Heart rate complexity helps mortality prediction in the intensive care unit: A pilot study using artificial intelligence. Comput Biol Med 2024; 169:107934. [PMID: 38183707 DOI: 10.1016/j.compbiomed.2024.107934] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2023] [Revised: 12/10/2023] [Accepted: 01/01/2024] [Indexed: 01/08/2024]
Abstract
BACKGROUND In intensive care units (ICUs), accurate mortality prediction is crucial for effective patient management and resource allocation. The Simplified Acute Physiology Score II (SAPS-2), though commonly used, relies heavily on comprehensive clinical data and blood samples. This study sought to develop an artificial intelligence (AI) model utilizing key hemodynamic parameters to predict ICU mortality within the first 24 h and assess its performance relative to SAPS-2. METHODS We conducted an analysis of select hemodynamic parameters and the structure of heart rate curves to identify potential predictors of ICU mortality. A machine-learning model was subsequently trained and validated on distinct patient cohorts. The AI algorithm's performance was then compared to the SAPS-2, focusing on classification accuracy, calibration, and generalizability. MEASUREMENTS AND MAIN RESULTS The study included 1298 ICU admissions from March 27th, 2015, to March 27th, 2017. An additional cohort from 2022 to 2023 comprised 590 patients, resulting in a total dataset of 1888 patients. The observed mortality rate stood at 24.0%. Key determinants of mortality were the Glasgow Coma Scale score, heart rate complexity, patient age, duration of diastolic blood pressure below 50 mmHg, heart rate variability, and specific mean and systolic blood pressure thresholds. The AI model, informed by these determinants, exhibited a performance profile in predicting mortality that was comparable, if not superior, to the SAPS-2. CONCLUSIONS The AI model, which integrates heart rate and blood pressure curve analyses with basic clinical parameters, provides a methodological approach to predict in-hospital mortality in ICU patients. This model offers an alternative to existing tools that depend on extensive clinical data and laboratory inputs. Its potential integration into ICU monitoring systems may facilitate more streamlined mortality prediction processes.
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Affiliation(s)
- Salah Boussen
- Intensive Care and Anesthesiology Department, La Timone Teaching Hospital, Aix-Marseille Université Assistance Publique Hôpitaux de Marseille, Marseille, France; Laboratoire de Biomécanique Appliquée-Université Gustave-Eiffel, Aix-Marseille Université, UMR T24, 51 boulevard Pierre Dramard, 13015, Marseille, France.
| | - Manuela Benard-Tertrais
- Intensive Care and Anesthesiology Department, La Timone Teaching Hospital, Aix-Marseille Université Assistance Publique Hôpitaux de Marseille, Marseille, France
| | - Mathilde Ogéa
- Intensive Care and Anesthesiology Department, La Timone Teaching Hospital, Aix-Marseille Université Assistance Publique Hôpitaux de Marseille, Marseille, France
| | - Arthur Malet
- Intensive Care and Anesthesiology Department, La Timone Teaching Hospital, Aix-Marseille Université Assistance Publique Hôpitaux de Marseille, Marseille, France
| | - Pierre Simeone
- Intensive Care and Anesthesiology Department, La Timone Teaching Hospital, Aix-Marseille Université Assistance Publique Hôpitaux de Marseille, Marseille, France; Aix Marseille University, CNRS, Inst Neurosci Timone, UMR7289, Marseille, France
| | - François Antonini
- Intensive Care and Anesthesiology Department, Hôpital Nord Teaching Hospital, Aix-Marseille Université Assistance Publique Hôpitaux de Marseille, Marseille, France
| | - Nicolas Bruder
- Intensive Care and Anesthesiology Department, La Timone Teaching Hospital, Aix-Marseille Université Assistance Publique Hôpitaux de Marseille, Marseille, France
| | - Lionel Velly
- Intensive Care and Anesthesiology Department, La Timone Teaching Hospital, Aix-Marseille Université Assistance Publique Hôpitaux de Marseille, Marseille, France; Aix Marseille University, CNRS, Inst Neurosci Timone, UMR7289, Marseille, France
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Zwerwer LR, Luz CF, Soudis D, Giudice N, Nijsten MWN, Glasner C, Renes MH, Sinha B. Identifying the need for infection-related consultations in intensive care patients using machine learning models. Sci Rep 2024; 14:2317. [PMID: 38282072 PMCID: PMC10822855 DOI: 10.1038/s41598-024-52741-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2023] [Accepted: 01/23/2024] [Indexed: 01/30/2024] Open
Abstract
Infection-related consultations on intensive care units (ICU) have a positive impact on quality of care and clinical outcome. However, timing of these consultations is essential and to date they are typically event-triggered and reactive. Here, we investigate a proactive approach to identify patients in need for infection-related consultations by machine learning models using routine electronic health records. Data was retrieved from a mixed ICU at a large academic tertiary care hospital including 9684 admissions. Infection-related consultations were predicted using logistic regression, random forest, gradient boosting machines, and long short-term memory neural networks (LSTM). Overall, 7.8% of admitted patients received an infection-related consultation. Time-sensitive modelling approaches performed better than static approaches. Using LSTM resulted in the prediction of infection-related consultations in the next clinical shift (up to eight hours in advance) with an area under the receiver operating curve (AUROC) of 0.921 and an area under the precision recall curve (AUPRC) of 0.541. The successful prediction of infection-related consultations for ICU patients was done without the use of classical triggers, such as (interim) microbiology reports. Predicting this key event can potentially streamline ICU and consultant workflows and improve care as well as outcome for critically ill patients with (suspected) infections.
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Affiliation(s)
- Leslie R Zwerwer
- Department of Health Sciences, University Medical Center Groningen, University of Groningen, Hanzeplein 1, 9713 GZ, Groningen, The Netherlands.
- Center for Information Technology, University of Groningen, Nettelbosje 1, 9747 AJ, Groningen, The Netherlands.
| | - Christian F Luz
- Department of Medical Microbiology and Infection Prevention, University Medical Center Groningen, University of Groningen, Hanzeplein 1, 9713 GZ, Groningen, The Netherlands
| | - Dimitrios Soudis
- Center for Information Technology, University of Groningen, Nettelbosje 1, 9747 AJ, Groningen, The Netherlands
| | - Nicoletta Giudice
- Center for Information Technology, University of Groningen, Nettelbosje 1, 9747 AJ, Groningen, The Netherlands
| | - Maarten W N Nijsten
- Department of Critical Care, University Medical Center Groningen, University of Groningen, Hanzeplein 1, 9713 GZ, Groningen, The Netherlands
| | - Corinna Glasner
- Department of Medical Microbiology and Infection Prevention, University Medical Center Groningen, University of Groningen, Hanzeplein 1, 9713 GZ, Groningen, The Netherlands
| | - Maurits H Renes
- Department of Critical Care, University Medical Center Groningen, University of Groningen, Hanzeplein 1, 9713 GZ, Groningen, The Netherlands
| | - Bhanu Sinha
- Department of Medical Microbiology and Infection Prevention, University Medical Center Groningen, University of Groningen, Hanzeplein 1, 9713 GZ, Groningen, The Netherlands
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Chang YT, Prompsy P, Kimeswenger S, Tsai YC, Ignatova D, Pavlova O, Iselin C, French LE, Levesque MP, Kuonen F, Bobrowicz M, Brunner PM, Pascolo S, Hoetzenecker W, Guenova E. MHC-I upregulation safeguards neoplastic T cells in the skin against NK cell-mediated eradication in mycosis fungoides. Nat Commun 2024; 15:752. [PMID: 38272918 PMCID: PMC10810852 DOI: 10.1038/s41467-024-45083-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2023] [Accepted: 01/15/2024] [Indexed: 01/27/2024] Open
Abstract
Cancer-associated immune dysfunction is a major challenge for effective therapies. The emergence of antibodies targeting tumor cell-surface antigens led to advancements in the treatment of hematopoietic malignancies, particularly blood cancers. Yet their impact is constrained against tumors of hematopoietic origin manifesting in the skin. In this study, we employ a clonality-supervised deep learning methodology to dissect key pathological features implicated in mycosis fungoides, the most common cutaneous T-cell lymphoma. Our investigations unveil the prominence of the IL-32β-major histocompatibility complex (MHC)-I axis as a critical determinant in tumor T-cell immune evasion within the skin microenvironment. In patients' skin, we find MHC-I to detrimentally impact the functionality of natural killer (NK) cells, diminishing antibody-dependent cellular cytotoxicity and promoting resistance of tumor skin T-cells to cell-surface targeting therapies. Through murine experiments in female mice, we demonstrate that disruption of the MHC-I interaction with NK cell inhibitory Ly49 receptors restores NK cell anti-tumor activity and targeted T-cell lymphoma elimination in vivo. These findings underscore the significance of attenuating the MHC-I-dependent immunosuppressive networks within skin tumors. Overall, our study introduces a strategy to reinvigorate NK cell-mediated anti-tumor responses to overcome treatment resistance to existing cell-surface targeted therapies for skin lymphoma.
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Affiliation(s)
- Yun-Tsan Chang
- Department of Dermatology, Lausanne University Hospital (CHUV) and Faculty of Biology and Medicine, University of Lausanne, Lausanne, Switzerland
| | - Pacôme Prompsy
- Department of Dermatology, Lausanne University Hospital (CHUV) and Faculty of Biology and Medicine, University of Lausanne, Lausanne, Switzerland
| | - Susanne Kimeswenger
- Department of Dermatology and Venerology, Medical Faculty, Johannes Kepler University, Linz, Austria
| | - Yi-Chien Tsai
- Department of Dermatology, Lausanne University Hospital (CHUV) and Faculty of Biology and Medicine, University of Lausanne, Lausanne, Switzerland
| | - Desislava Ignatova
- Department of Dermatology, University Hospital of Zurich and Faculty of Medicine, University of Zurich, Zurich, Switzerland
| | - Olesya Pavlova
- Department of Dermatology, Lausanne University Hospital (CHUV) and Faculty of Biology and Medicine, University of Lausanne, Lausanne, Switzerland
| | - Christoph Iselin
- Department of Dermatology, Lausanne University Hospital (CHUV) and Faculty of Biology and Medicine, University of Lausanne, Lausanne, Switzerland
| | - Lars E French
- Department of Dermatology and Allergology, Ludwig-Maximilians-University of Munich, Munich, Germany
- Dr. Phillip Frost Department of Dermatology and Cutaneous Surgery, University of Miami Miller School of Medicine, Miami, FL, USA
| | - Mitchell P Levesque
- Department of Dermatology, University Hospital of Zurich and Faculty of Medicine, University of Zurich, Zurich, Switzerland
| | - François Kuonen
- Department of Dermatology, Lausanne University Hospital (CHUV) and Faculty of Biology and Medicine, University of Lausanne, Lausanne, Switzerland
| | | | - Patrick M Brunner
- Department of Dermatology, Icahn School of Medicine at Mount Sinai, New York, USA
| | - Steve Pascolo
- Department of Dermatology, University Hospital of Zurich and Faculty of Medicine, University of Zurich, Zurich, Switzerland
| | - Wolfram Hoetzenecker
- Department of Dermatology and Venerology, Medical Faculty, Johannes Kepler University, Linz, Austria.
| | - Emmanuella Guenova
- Department of Dermatology, Lausanne University Hospital (CHUV) and Faculty of Biology and Medicine, University of Lausanne, Lausanne, Switzerland.
- Department of Dermatology, University Hospital of Zurich and Faculty of Medicine, University of Zurich, Zurich, Switzerland.
- Department of Dermatology, Hospital 12 de Octubre, Medical School, University Complutense, Madrid, Spain.
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Su L, Liu S, Long Y, Chen C, Chen K, Chen M, Chen Y, Cheng Y, Cui Y, Ding Q, Ding R, Duan M, Gao T, Gu X, He H, He J, Hu B, Hu C, Huang R, Huang X, Jiang H, Jiang J, Lan Y, Li J, Li L, Li L, Li W, Li Y, Lin J, Luo X, Lyu F, Mao Z, Miao H, Shang X, Shang X, Shang Y, Shen Y, Shi Y, Sun Q, Sun W, Tang Z, Wang B, Wang H, Wang H, Wang L, Wang L, Wang S, Wang Z, Wang Z, Wei D, Wu J, Wu Q, Xing X, Yang J, Yang X, Yu J, Yu W, Yu Y, Yuan H, Zhai Q, Zhang H, Zhang L, Zhang M, Zhang Z, Zhao C, Zheng R, Zhong L, Zhou F, Zhu W. Chinese experts' consensus on the application of intensive care big data. Front Med (Lausanne) 2024; 10:1174429. [PMID: 38264049 PMCID: PMC10804886 DOI: 10.3389/fmed.2023.1174429] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2023] [Accepted: 11/09/2023] [Indexed: 01/25/2024] Open
Abstract
The development of intensive care medicine is inseparable from the diversified monitoring data. Intensive care medicine has been closely integrated with data since its birth. Critical care research requires an integrative approach that embraces the complexity of critical illness and the computational technology and algorithms that can make it possible. Considering the need of standardization of application of big data in intensive care, Intensive Care Medicine Branch of China Health Information and Health Care Big Data Society, Standard Committee has convened expert group, secretary group and the external audit expert group to formulate Chinese Experts' Consensus on the Application of Intensive Care Big Data (2022). This consensus makes 29 recommendations on the following five parts: Concept of intensive care big data, Important scientific issues, Standards and principles of database, Methodology in solving big data problems, Clinical application and safety consideration of intensive care big data. The consensus group believes this consensus is the starting step of application big data in the field of intensive care. More explorations and big data based retrospective research should be carried out in order to enhance safety and reliability of big data based models of critical care field.
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Affiliation(s)
- Longxiang Su
- Department of Critical Care Medicine, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, China
| | - Shengjun Liu
- Department of Critical Care Medicine, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, China
| | - Yun Long
- Department of Critical Care Medicine, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, China
| | - Chaodong Chen
- Department of Surgical Intensive Critical Unit, Beijing Chao-yang Hospital, Capital Medical University, Beijing, China
| | - Kai Chen
- Department of Critical Care Medicine, Fujian Provincial Key Laboratory of Critical Care Medicine, Shengli Clinical Medical College of Fujian Medical University, Fujian Provincial Hospital, Fujian Provincial Center for Critical Care Medicine, Fuzhou, Fujian, China
| | - Ming Chen
- Department of Critical Care Medicine, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, Nanjing, Jiangsu, China
| | - Yaolong Chen
- Evidence-based Medicine Center, School of Basic Medical Sciences, Lanzhou University, Lanzhou, China
| | - Yisong Cheng
- Department of Critical Care Medicine, West China Hospital of Sichuan University, Chengdu, China
| | - Yating Cui
- Department of Critical Care Medicine, The First Medical Center, Chinese PLA General Hospital, Beijing, China
| | - Qi Ding
- Department of Surgical Intensive Critical Unit, Beijing Chao-yang Hospital, Capital Medical University, Beijing, China
| | - Renyu Ding
- Department of Intensive Care Unit, The First Hospital of China Medical University, Shenyang, Liaoning, China
| | - Meili Duan
- Department of Critical Care Medicine, Beijing Friendship Hospital, Capital Medical University, Beijing, China
| | - Tao Gao
- Department of Critical Care Medicine, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, Nanjing, Jiangsu, China
| | - Xiaohua Gu
- Department of Critical Care Medicine, Northern Jiangsu People’s Hospital; Clinical Medical College, Yangzhou University, Yangzhou, China
| | - Hongli He
- Intensive Care Unit, Sichuan Academy of Medical Sciences & Sichuan Provincial People’s Hospital, School of Medicine of University of Electronic Science and Technology, Chengdu, China
| | - Jiawei He
- Department of Critical Care Medicine, Beijing Friendship Hospital, Capital Medical University, Beijing, China
| | - Bo Hu
- Department of Critical Care Medicine, Zhongnan Hospital of Wuhan University, Wuhan, Hubei, China
| | - Chang Hu
- Department of Critical Care Medicine, Zhongnan Hospital of Wuhan University, Wuhan, Hubei, China
| | - Rui Huang
- Department of Critical Care Medicine, The Second Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang, China
| | - Xiaobo Huang
- Intensive Care Unit, Sichuan Academy of Medical Sciences & Sichuan Provincial People’s Hospital, School of Medicine of University of Electronic Science and Technology, Chengdu, China
| | - Huizhen Jiang
- Department of Information Center, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, China
| | - Jing Jiang
- Department of Critical Care Medicine, Chongqing General Hospital, Chongqing, China
| | - Yunping Lan
- Intensive Care Unit, Sichuan Academy of Medical Sciences & Sichuan Provincial People’s Hospital, School of Medicine of University of Electronic Science and Technology, Chengdu, China
| | - Jun Li
- Department of Critical Care Medicine, Fujian Provincial Key Laboratory of Critical Care Medicine, Shengli Clinical Medical College of Fujian Medical University, Fujian Provincial Hospital, Fujian Provincial Center for Critical Care Medicine, Fuzhou, Fujian, China
| | - Linfeng Li
- Medical Data Research Institute, Chongqing Medical University, Chongqing, China
| | - Lu Li
- Department of Critical Care Medicine, Zhongnan Hospital of Wuhan University, Wuhan, Hubei, China
| | - Wenxiong Li
- Department of Surgical Intensive Critical Unit, Beijing Chao-yang Hospital, Capital Medical University, Beijing, China
| | - Yongzai Li
- Information Network Center, QiLu Hospital, ShanDong University, Jinan, China
| | - Jin Lin
- Department of Critical Care Medicine, Beijing Friendship Hospital, Capital Medical University, Beijing, China
| | - Xufei Luo
- Evidence-based Medicine Center, School of Basic Medical Sciences, Lanzhou University, Lanzhou, China
| | - Feng Lyu
- Department of Computer Science and Engineering, Central South University, Changsha, China
| | - Zhi Mao
- Department of Critical Care Medicine, The First Medical Center, Chinese PLA General Hospital, Beijing, China
| | - He Miao
- Department of Intensive Care Unit, The First Hospital of China Medical University, Shenyang, Liaoning, China
| | - Xiaopu Shang
- Department of Information Management, Beijing Jiaotong University, Beijing, China
| | - Xiuling Shang
- Department of Critical Care Medicine, Fujian Provincial Key Laboratory of Critical Care Medicine, Shengli Clinical Medical College of Fujian Medical University, Fujian Provincial Hospital, Fujian Provincial Center for Critical Care Medicine, Fuzhou, Fujian, China
| | - You Shang
- Department of Critical Care Medicine, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Yuwen Shen
- Intensive Care Unit of Cardiovascular Surgery Department, Qilu Hospital of Shandong University, Jinan, China
| | - Yinghuan Shi
- National Institute of Healthcare Data Science, Nanjing University, Nanjing, China
| | - Qihang Sun
- British Chinese Society of Health Informatics, Beijing, China
| | - Weijun Sun
- Faculty of Automation, Guangdong University of Technology, Guangzhou, China
| | - Zhiyun Tang
- Department of Intensive Care Unit, Zhejiang Provincial People’s Hospital, Affiliated People’s Hospital, Emergency and Intensive Care Unit Center, Hangzhou Medical College, Hangzhou, Zhejiang, China
| | - Bo Wang
- Department of Critical Care Medicine, West China Hospital of Sichuan University, Chengdu, China
| | - Haijun Wang
- Department of Intensive Care Unit, National Cancer Center/National Clinical Research Center, Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Hongliang Wang
- Department of Critical Care Medicine, The Second Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang, China
| | - Li Wang
- Department of Epidemiology and Biostatistics, Institute of Basic Medical Sciences Chinese Academy of Medical Sciences; School of Basic Medicine Peking Union Medical College, Beijing, China
| | - Luhao Wang
- Department of Critical Care Medicine, Sun Yat-Sen University First Affiliated Hospital, Guangzhou, China
| | - Sicong Wang
- Department of Critical Care Medicine, The Second Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang, China
| | - Zhanwen Wang
- Intensive Care Unit, XiangYa Hospital, Central South University, Changsha, China
- National Clinical Research Center for Geriatric Disorders, Xiang Ya Hospital, Central South University, Changsha, China
- Hunan Provincial Clinical Research Center for Critical Care Medicine, Xiang Ya Hospital, Central South University, Changsha, China
| | - Zhong Wang
- Department of Intensive Care Unit, The First Hospital of China Medical University, Shenyang, Liaoning, China
| | - Dong Wei
- National Institute of Healthcare Data Science, Nanjing University, Nanjing, China
| | - Jianfeng Wu
- Intensive Care Unit, XiangYa Hospital, Central South University, Changsha, China
| | - Qin Wu
- Department of Critical Care Medicine, West China Hospital of Sichuan University, Chengdu, China
| | - Xuezhong Xing
- Department of Epidemiology and Biostatistics, Institute of Basic Medical Sciences Chinese Academy of Medical Sciences; School of Basic Medicine Peking Union Medical College, Beijing, China
| | - Jin Yang
- Department of Critical Care Medicine, Chongqing General Hospital, Chongqing, China
| | - Xianghong Yang
- Department of Intensive Care Unit, National Cancer Center/National Clinical Research Center, Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Jiangquan Yu
- Department of Critical Care Medicine, Northern Jiangsu People’s Hospital; Clinical Medical College, Yangzhou University, Yangzhou, China
| | - Wenkui Yu
- Department of Critical Care Medicine, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, Nanjing, Jiangsu, China
| | - Yuan Yu
- Intensive Care Unit of Cardiovascular Surgery Department, Qilu Hospital of Shandong University, Jinan, China
| | - Hao Yuan
- Department of Critical Care Medicine, Sun Yat-Sen University First Affiliated Hospital, Guangzhou, China
| | - Qian Zhai
- National Institute of Healthcare Data Science, Nanjing University, Nanjing, China
| | - Hao Zhang
- Department of Intensive Care Unit, National Cancer Center/National Clinical Research Center, Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Lina Zhang
- Intensive Care Unit, XiangYa Hospital, Central South University, Changsha, China
- National Clinical Research Center for Geriatric Disorders, Xiang Ya Hospital, Central South University, Changsha, China
- Hunan Provincial Clinical Research Center for Critical Care Medicine, Xiang Ya Hospital, Central South University, Changsha, China
| | - Meng Zhang
- Department of Critical Care Medicine, Chongqing General Hospital, Chongqing, China
| | - Zhongheng Zhang
- Department of Emergency Medicine, Key Laboratory of Precision Medicine in Diagnosis and Monitoring Research of Zhejiang Province, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Chunguang Zhao
- Intensive Care Unit, XiangYa Hospital, Central South University, Changsha, China
- National Clinical Research Center for Geriatric Disorders, Xiang Ya Hospital, Central South University, Changsha, China
- Hunan Provincial Clinical Research Center for Critical Care Medicine, Xiang Ya Hospital, Central South University, Changsha, China
| | - Ruiqiang Zheng
- Department of Critical Care Medicine, Northern Jiangsu People’s Hospital; Clinical Medical College, Yangzhou University, Yangzhou, China
| | - Lei Zhong
- Department of Intensive Care Unit, National Cancer Center/National Clinical Research Center, Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Feihu Zhou
- Department of Critical Care Medicine, The First Medical Center, Chinese PLA General Hospital, Beijing, China
| | - Weiguo Zhu
- Department of General Medicine, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, China
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Pey V, Doumard E, Komorowski M, Rouget A, Delmas C, Vardon-Bounes F, Poette M, Ratineau V, Dray C, Ader I, Minville V. A locally optimised machine learning approach to early prognostication of long-term neurological outcomes after out-of-hospital cardiac arrest. Digit Health 2024; 10:20552076241234746. [PMID: 38628633 PMCID: PMC11020739 DOI: 10.1177/20552076241234746] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 02/07/2024] [Indexed: 04/19/2024] Open
Abstract
Background Out-of-hospital cardiac arrest (OHCA) represents a major burden for society and health care, with an average incidence in adults of 67 to 170 cases per 100,000 person-years in Europe and in-hospital survival rates of less than 10%. Patients and practitioners would benefit from a prognostication tool for long-term good neurological outcomes. Objective We aim to develop a machine learning (ML) pipeline on a local database to classify patients according to their neurological outcomes and identify prognostic features. Methods We collected clinical and biological data consecutively from 595 patients who presented OHCA and were routed to a single regional cardiac arrest centre in the south of France. We applied recursive feature elimination and ML analyses to identify the main features associated with a good neurological outcome, defined as a Cerebral Performance Category score less than or equal to 2 at six months post-OHCA. Results We identified 12 variables 24 h after admission, capable of predicting a six-month good neurological outcome. The best model (extreme gradient boosting) achieved an AUC of 0.96 and an accuracy of 0.92 in the test cohort. Conclusion We demonstrated that it is possible to build accurate, locally optimised prediction and prognostication scores using datasets of limited size and breadth. We proposed and shared a generic machine-learning pipeline which allows external teams to replicate the approach locally.
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Affiliation(s)
- Vincent Pey
- RESTORE Research Center, University Toulouse 3-Paul Sabatier, INSERM, CNRS, EFS, ENVT, Toulouse, France
- Department of Anaesthesiology and Critical Care, University Hospital of Toulouse, University Toulouse 3-Paul Sabatier, Toulouse, France
| | - Emmanuel Doumard
- RESTORE Research Center, University Toulouse 3-Paul Sabatier, INSERM, CNRS, EFS, ENVT, Toulouse, France
| | - Matthieu Komorowski
- Division of Anaesthetics, Pain Medicine, and Intensive Care, Department of Surgery and Cancer, Faculty of Medicine, Imperial College London, London, UK
| | - Antoine Rouget
- Department of Anaesthesiology and Critical Care, University Hospital of Toulouse, University Toulouse 3-Paul Sabatier, Toulouse, France
| | - Clément Delmas
- Department of Cardiology, University Hospital of Rangueil, Toulouse, France
| | - Fanny Vardon-Bounes
- Department of Anaesthesiology and Critical Care, University Hospital of Toulouse, University Toulouse 3-Paul Sabatier, Toulouse, France
| | - Michaël Poette
- Department of Anaesthesiology and Critical Care, University Hospital of Toulouse, University Toulouse 3-Paul Sabatier, Toulouse, France
| | - Valentin Ratineau
- Department of Anaesthesiology and Critical Care, University Hospital of Toulouse, University Toulouse 3-Paul Sabatier, Toulouse, France
| | - Cédric Dray
- RESTORE Research Center, University Toulouse 3-Paul Sabatier, INSERM, CNRS, EFS, ENVT, Toulouse, France
| | - Isabelle Ader
- RESTORE Research Center, University Toulouse 3-Paul Sabatier, INSERM, CNRS, EFS, ENVT, Toulouse, France
| | - Vincent Minville
- RESTORE Research Center, University Toulouse 3-Paul Sabatier, INSERM, CNRS, EFS, ENVT, Toulouse, France
- Department of Anaesthesiology and Critical Care, University Hospital of Toulouse, University Toulouse 3-Paul Sabatier, Toulouse, France
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Myatra SN, Jagiasi BG, Singh NP, Divatia JV. Role of artificial intelligence in haemodynamic monitoring. Indian J Anaesth 2024; 68:93-99. [PMID: 38406336 PMCID: PMC10893816 DOI: 10.4103/ija.ija_1260_23] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/25/2023] [Revised: 12/28/2023] [Accepted: 01/08/2024] [Indexed: 02/27/2024] Open
Abstract
This narrative review explores the evolving role of artificial intelligence (AI) in haemodynamic monitoring, emphasising its potential to revolutionise patient care. The historical reliance on invasive procedures for haemodynamic assessments is contrasted with the emerging non-invasive AI-driven approaches that address limitations and risks associated with traditional methods. Developing the hypotension prediction index and introducing CircEWSTM and CircEWS-lite TM showcase AI's effectiveness in predicting and managing circulatory failure. The crucial aspects include the balance between AI and healthcare professionals, ethical considerations, and the need for regulatory frameworks. The use of AI in haemodynamic monitoring will keep growing with ongoing research, better technology, and teamwork. As we navigate these advancements, it is crucial to balance AI's power and healthcare professionals' essential role. Clinicians must continue to use their clinical acumen to ensure that patient outliers or system problems do not compromise the treatment of the condition and patient safety.
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Affiliation(s)
- Sheila N. Myatra
- Department of Anesthesiology, Critical Care and Pain, Tata Memorial Hospital, Homi Bhabha National Institute, Mumbai, Maharashtra, India
| | - Bharat G. Jagiasi
- Director of Critical Care Department, Kokilaben Dhirubhai Ambani Hospital, Navi Mumbai, Maharashtra, India
| | - Neeraj P. Singh
- Department of Anesthesiology, Critical Care and Pain, Tata Memorial Hospital, Homi Bhabha National Institute, Mumbai, Maharashtra, India
| | - Jigeeshu V. Divatia
- Department of Anesthesiology, Critical Care and Pain, Tata Memorial Hospital, Homi Bhabha National Institute, Mumbai, Maharashtra, India
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38
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O'Reilly D, McGrath J, Martin-Loeches I. Optimizing artificial intelligence in sepsis management: Opportunities in the present and looking closely to the future. JOURNAL OF INTENSIVE MEDICINE 2024; 4:34-45. [PMID: 38263963 PMCID: PMC10800769 DOI: 10.1016/j.jointm.2023.10.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 04/22/2023] [Revised: 09/29/2023] [Accepted: 10/01/2023] [Indexed: 01/25/2024]
Abstract
Sepsis remains a major challenge internationally for healthcare systems. Its incidence is rising due to poor public awareness and delays in its recognition and subsequent management. In sepsis, mortality increases with every hour left untreated. Artificial intelligence (AI) is transforming worldwide healthcare delivery at present. This review has outlined how AI can augment strategies to address this global disease burden. AI and machine learning (ML) algorithms can analyze vast quantities of increasingly complex clinical datasets from electronic medical records to assist clinicians in diagnosing and treating sepsis earlier than traditional methods. Our review highlights how these models can predict the risk of sepsis and organ failure even before it occurs. This gives providers additional time to plan and execute treatment plans, thereby avoiding increasing complications associated with delayed diagnosis of sepsis. The potential for cost savings with AI implementation is also discussed, including improving workflow efficiencies, reducing administrative costs, and improving healthcare outcomes. Despite these advantages, clinicians have been slow to adopt AI into clinical practice. Some of the limitations posed by AI solutions include the lack of diverse data sets for model building so that they are widely applicable for routine clinical use. Furthermore, the subsequent algorithms are often based on complex mathematics leading to clinician hesitancy to embrace such technologies. Finally, we highlight the need for robust political and regulatory frameworks in this area to achieve the trust and approval of clinicians and patients to implement this transformational technology.
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Affiliation(s)
- Darragh O'Reilly
- Department of Intensive Care Medicine, Multidisciplinary Intensive Care Research Organization (MICRO), St James’ Hospital, Dublin, Ireland
| | - Jennifer McGrath
- Department of Intensive Care Medicine, Multidisciplinary Intensive Care Research Organization (MICRO), St James’ Hospital, Dublin, Ireland
| | - Ignacio Martin-Loeches
- Department of Intensive Care Medicine, Multidisciplinary Intensive Care Research Organization (MICRO), St James’ Hospital, Dublin, Ireland
- Department of Respiratory Intensive care, Hospital Clinic, Universitat de Barcelona, IDIBAPS, CIBERES, Barcelona, Spain
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Li C, Liu X, Shen P, Sun Y, Zhou T, Chen W, Chen Q, Lin H, Tang X, Gao P. Improving cardiovascular risk prediction through machine learning modelling of irregularly repeated electronic health records. EUROPEAN HEART JOURNAL. DIGITAL HEALTH 2024; 5:30-40. [PMID: 38264696 PMCID: PMC10802828 DOI: 10.1093/ehjdh/ztad058] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/25/2023] [Revised: 08/03/2023] [Accepted: 09/19/2023] [Indexed: 01/25/2024]
Abstract
Aims Existing electronic health records (EHRs) often consist of abundant but irregular longitudinal measurements of risk factors. In this study, we aim to leverage such data to improve the risk prediction of atherosclerotic cardiovascular disease (ASCVD) by applying machine learning (ML) algorithms, which can allow automatic screening of the population. Methods and results A total of 215 744 Chinese adults aged between 40 and 79 without a history of cardiovascular disease were included (6081 cases) from an EHR-based longitudinal cohort study. To allow interpretability of the model, the predictors of demographic characteristics, medication treatment, and repeatedly measured records of lipids, glycaemia, obesity, blood pressure, and renal function were used. The primary outcome was ASCVD, defined as non-fatal acute myocardial infarction, coronary heart disease death, or fatal and non-fatal stroke. The eXtreme Gradient boosting (XGBoost) algorithm and Least Absolute Shrinkage and Selection Operator (LASSO) regression models were derived to predict the 5-year ASCVD risk. In the validation set, compared with the refitted Chinese guideline-recommended Cox model (i.e. the China-PAR), the XGBoost model had a significantly higher C-statistic of 0.792, (the differences in the C-statistics: 0.011, 0.006-0.017, P < 0.001), with similar results reported for LASSO regression (the differences in the C-statistics: 0.008, 0.005-0.011, P < 0.001). The XGBoost model demonstrated the best calibration performance (men: Dx = 0.598, P = 0.75; women: Dx = 1.867, P = 0.08). Moreover, the risk distribution of the ML algorithms differed from that of the conventional model. The net reclassification improvement rates of XGBoost and LASSO over the Cox model were 3.9% (1.4-6.4%) and 2.8% (0.7-4.9%), respectively. Conclusion Machine learning algorithms with irregular, repeated real-world data could improve cardiovascular risk prediction. They demonstrated significantly better performance for reclassification to identify the high-risk population correctly.
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Affiliation(s)
- Chaiquan Li
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University Health Science Center, No. 38 Xueyuan Road, Haidian District, 100191 Beijing, China
| | - Xiaofei Liu
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University Health Science Center, No. 38 Xueyuan Road, Haidian District, 100191 Beijing, China
| | - Peng Shen
- Yinzhou District Center for Disease Control and Prevention, No. 1221 Xueshi Road, Yinzhou District, 315199 Ningbo, China
| | - Yexiang Sun
- Yinzhou District Center for Disease Control and Prevention, No. 1221 Xueshi Road, Yinzhou District, 315199 Ningbo, China
| | - Tianjing Zhou
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University Health Science Center, No. 38 Xueyuan Road, Haidian District, 100191 Beijing, China
| | - Weiye Chen
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University Health Science Center, No. 38 Xueyuan Road, Haidian District, 100191 Beijing, China
| | - Qi Chen
- Yinzhou District Center for Disease Control and Prevention, No. 1221 Xueshi Road, Yinzhou District, 315199 Ningbo, China
| | - Hongbo Lin
- Yinzhou District Center for Disease Control and Prevention, No. 1221 Xueshi Road, Yinzhou District, 315199 Ningbo, China
| | - Xun Tang
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University Health Science Center, No. 38 Xueyuan Road, Haidian District, 100191 Beijing, China
- Key Laboratory of Epidemiology of Major Diseases, Peking University, Ministry of Education, No. 38 Xueyuan Road, Haidian District, 100191 Beijing, China
| | - Pei Gao
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University Health Science Center, No. 38 Xueyuan Road, Haidian District, 100191 Beijing, China
- Key Laboratory of Epidemiology of Major Diseases, Peking University, Ministry of Education, No. 38 Xueyuan Road, Haidian District, 100191 Beijing, China
- Center for Real-world Evidence Evaluation, Peking University Clinical Research Institute, No. 38 Xueyuan Road, Haidian District, 100191 Beijing, China
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Ma L, Zhang C, Gao J, Jiao X, Yu Z, Zhu Y, Wang T, Ma X, Wang Y, Tang W, Zhao X, Ruan W, Wang T. Mortality prediction with adaptive feature importance recalibration for peritoneal dialysis patients. PATTERNS (NEW YORK, N.Y.) 2023; 4:100892. [PMID: 38106617 PMCID: PMC10724364 DOI: 10.1016/j.patter.2023.100892] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/25/2023] [Revised: 08/18/2023] [Accepted: 11/10/2023] [Indexed: 12/19/2023]
Abstract
The study aims to develop AICare, an interpretable mortality prediction model, using electronic medical records (EMR) from follow-up visits for end-stage renal disease (ESRD) patients. AICare includes a multichannel feature extraction module and an adaptive feature importance recalibration module. It integrates dynamic records and static features to perform personalized health context representation learning. The dataset encompasses 13,091 visits and demographic data of 656 peritoneal dialysis (PD) patients spanning 12 years. An additional public dataset of 4,789 visits from 1,363 hemodialysis (HD) patients is also considered. AICare outperforms traditional deep learning models in mortality prediction while retaining interpretability. It uncovers mortality-feature relationships and variations in feature importance and provides reference values. An AI-doctor interaction system is developed for visualizing patients' health trajectories and risk indicators.
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Affiliation(s)
| | | | - Junyi Gao
- Centre for Medical Informatics, University of Edinburgh, Edinburgh, UK
- Health Data Research UK, London, UK
| | | | | | | | | | - Xinyu Ma
- Peking University, Beijing, China
| | | | - Wen Tang
- Department of Nephrology, Peking University Third Hospital, Beijing, China
| | - Xinju Zhao
- Department of Nephrology, Peking University People’s Hospital, Beijing, China
| | - Wenjie Ruan
- Department of Computer Science, University of Exeter, Exeter, UK
| | - Tao Wang
- Department of Nephrology, Peking University Third Hospital, Beijing, China
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Pozzi G, van den Hoven J. Physicians' Professional Role in Clinical Care: AI as a Change Agent. THE AMERICAN JOURNAL OF BIOETHICS : AJOB 2023; 23:57-59. [PMID: 38010672 DOI: 10.1080/15265161.2023.2272924] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/29/2023]
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42
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Ryan CT, Zeng Z, Chatterjee S, Wall MJ, Moon MR, Coselli JS, Rosengart TK, Li M, Ghanta RK. Machine learning for dynamic and early prediction of acute kidney injury after cardiac surgery. J Thorac Cardiovasc Surg 2023; 166:e551-e564. [PMID: 36347651 PMCID: PMC10071138 DOI: 10.1016/j.jtcvs.2022.09.045] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/09/2022] [Revised: 08/29/2022] [Accepted: 09/10/2022] [Indexed: 11/05/2022]
Abstract
OBJECTIVE Acute kidney injury after cardiac surgery increases morbidity and mortality. Diagnosis relies on oliguria or increased serum creatinine, which develop 48 to 72 hours after injury. We hypothesized machine learning incorporating preoperative, operative, and intensive care unit data could dynamically predict acute kidney injury before conventional identification. METHODS Cardiac surgery patients at a tertiary hospital (2008-2019) were identified using electronic medical records in the Medical Information Mart for Intensive Care IV database. Preoperative and intraoperative parameters included demographics, Charlson Comorbidity subcategories, and operative details. Intensive care unit data included hemodynamics, medications, fluid intake/output, and laboratory results. Kidney Disease: Improving Global Outcomes creatinine criteria were used for acute kidney injury diagnosis. An ensemble machine learning model was trained for hourly predictions of future acute kidney injury within 48 hours. Performance was evaluated by area under the receiver operating characteristic curve and balanced accuracy. RESULTS Within the cohort (n = 4267), there were approximately 7 million data points. Median baseline creatinine was 1.0 g/dL (interquartile range, 0.8-1.2), with 17% (735/4267) of patients having chronic kidney disease. Postoperative stage 1 acute kidney injury occurred in 50% (2129/4267), stage 2 occurred in 8% (324/4267), and stage 3 occurred in 4% (183/4267). For hourly prediction of any acute kidney injury over the next 48 hours, area under the receiver operating characteristic curve was 0.82, and balanced accuracy was 75%. For hourly prediction of stage 2 or greater acute kidney injury over the next 48 hours, area under the receiver operating characteristic curve was 0.95 and balanced accuracy was 86%. The model predicted acute kidney injury before clinical detection in 89% of cases. CONCLUSIONS Ensemble machine learning models using electronic medical records data can dynamically predict acute kidney injury risk after cardiac surgery. Continuous postoperative risk assessment could facilitate interventions to limit or prevent renal injury.
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Affiliation(s)
- Christopher T Ryan
- Michael E. DeBakey Department of Surgery, Baylor College of Medicine, Houston, Tex
| | - Zijian Zeng
- Department of Statistics, Rice University, Houston, Tex
| | - Subhasis Chatterjee
- Michael E. DeBakey Department of Surgery, Baylor College of Medicine, Houston, Tex; Department of Cardiovascular Surgery, Texas Heart Institute, Houston, Tex
| | - Matthew J Wall
- Michael E. DeBakey Department of Surgery, Baylor College of Medicine, Houston, Tex
| | - Marc R Moon
- Michael E. DeBakey Department of Surgery, Baylor College of Medicine, Houston, Tex; Department of Cardiovascular Surgery, Texas Heart Institute, Houston, Tex
| | - Joseph S Coselli
- Michael E. DeBakey Department of Surgery, Baylor College of Medicine, Houston, Tex; Department of Cardiovascular Surgery, Texas Heart Institute, Houston, Tex
| | - Todd K Rosengart
- Michael E. DeBakey Department of Surgery, Baylor College of Medicine, Houston, Tex; Department of Cardiovascular Surgery, Texas Heart Institute, Houston, Tex
| | - Meng Li
- Department of Statistics, Rice University, Houston, Tex
| | - Ravi K Ghanta
- Michael E. DeBakey Department of Surgery, Baylor College of Medicine, Houston, Tex.
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Sadegh-Zadeh SA, Sakha H, Movahedi S, Fasihi Harandi A, Ghaffari S, Javanshir E, Ali SA, Hooshanginezhad Z, Hajizadeh R. Advancing prognostic precision in pulmonary embolism: A clinical and laboratory-based artificial intelligence approach for enhanced early mortality risk stratification. Comput Biol Med 2023; 167:107696. [PMID: 37979394 DOI: 10.1016/j.compbiomed.2023.107696] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2023] [Revised: 10/25/2023] [Accepted: 11/07/2023] [Indexed: 11/20/2023]
Abstract
BACKGROUND Acute pulmonary embolism (PE) is a critical medical emergency that necessitates prompt identification and intervention. Accurate prognostication of early mortality is vital for recognizing patients at elevated risk for unfavourable outcomes and administering suitable therapy. Machine learning (ML) algorithms hold promise for enhancing the precision of early mortality prediction in PE patients. OBJECTIVE To devise an ML algorithm for early mortality prediction in PE patients by employing clinical and laboratory variables. METHODS This study utilized diverse oversampling techniques to improve the performance of various machine learning models including ANN, SVM, DT, RF, and AdaBoost for early mortality prediction. Appropriate oversampling methods were chosen for each model based on algorithm characteristics and dataset properties. Predictor variables included four lab tests, eight physiological time series indicators, and two general descriptors. Evaluation used metrics like accuracy, F1_score, precision, recall, Area Under the Curve (AUC) and Receiver Operating Characteristic (ROC) curves, providing a comprehensive view of models' predictive abilities. RESULTS The findings indicated that the RF model with random oversampling exhibited superior performance among the five models assessed, achieving elevated accuracy and precision alongside high recall for predicting the death class. The oversampling approaches effectively equalized the sample distribution among the classes and enhanced the models' performance. CONCLUSIONS The suggested ML technique can efficiently prognosticate mortality in patients afflicted with acute PE. The RF model with random oversampling can aid healthcare professionals in making well-informed decisions regarding the treatment of patients with acute PE. The study underscores the significance of oversampling methods in managing imbalanced data and emphasizes the potential of ML algorithms in refining early mortality prediction for PE patients.
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Affiliation(s)
- Seyed-Ali Sadegh-Zadeh
- Department of Computing, School of Digital, Technologies and Arts, Staffordshire University, Stoke-on-Trent, England, United Kingdom
| | - Hanie Sakha
- Department of Computing, School of Digital, Technologies and Arts, Staffordshire University, Stoke-on-Trent, England, United Kingdom
| | | | | | - Samad Ghaffari
- Cardiovascular Research Centre, Tabriz University of Medical Sciences, Tabriz, Iran
| | - Elnaz Javanshir
- Cardiovascular Research Centre, Tabriz University of Medical Sciences, Tabriz, Iran
| | - Syed Ahsan Ali
- Health Education England West Midlands, Birmingham, England, United Kingdom
| | - Zahra Hooshanginezhad
- Department of Cardiovascular Medicine, Shiraz University of Medical Sciences, Shiraz, Iran
| | - Reza Hajizadeh
- Department of Cardiology, Urmia University of Medical Sciences, Urmia, Iran.
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Popat A, Yadav S, Patel SK, Baddevolu S, Adusumilli S, Rao Dasari N, Sundarasetty M, Anand S, Sankar J, Jagtap YG. Artificial Intelligence in the Early Prediction of Cardiogenic Shock in Acute Heart Failure or Myocardial Infarction Patients: A Systematic Review and Meta-Analysis. Cureus 2023; 15:e50395. [PMID: 38213372 PMCID: PMC10783597 DOI: 10.7759/cureus.50395] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 12/12/2023] [Indexed: 01/13/2024] Open
Abstract
Cardiogenic shock (CS) may have a negative impact on mortality in patients with heart failure (HF) or acute myocardial infarction (AMI). Early prediction of CS can result in improved survival. Artificial intelligence (AI) through machine learning (ML) models have shown promise in predictive medicine. Here, we conduct a systematic review and meta-analysis to assess the effectiveness of these models in the early prediction of CS. A thorough search of the PubMed, Web of Science, Cochrane, and Scopus databases was conducted from the time of inception until November 2, 2023, to find relevant studies. Our outcomes were area under the curve (AUC), the sensitivity and specificity of the ML model, the accuracy of the ML model, and the predictor variables that had the most impact in predicting CS. Comprehensive Meta-Analysis (CMA) Version 3.0 was used to conduct the meta-analysis. Six studies were considered in our study. The pooled mean AUC was 0.808 (95% confidence interval: 0.727, 0.890). The AUC in the included studies ranged from 0.77 to 0.91. ML models performed well, with accuracy ranging from 0.88 to 0.93 and sensitivity and specificity of 58%-78% and 88%-93%, respectively. Age, blood pressure, heart rate, oxygen saturation, and blood glucose were the most significant variables required by ML models to acquire their outputs. In conclusion, AI has the potential for early prediction of CS, which may lead to a decrease in the high mortality rate associated with it. Future studies are needed to confirm the results.
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Affiliation(s)
- Apurva Popat
- Internal Medicine, Marshfield Clinic Health System, Marshfield, USA
| | - Sweta Yadav
- Internal Medicine, Gujarat Medical Education & Research Society (GMERS) Medical College, Ahmedabad, IND
| | - Sagar K Patel
- Internal Medicine, Gujarat Adani Institute of Medical Sciences, Bhuj, IND
| | | | | | - Nikitha Rao Dasari
- College of Medicine, Kamineni Academy of Medical Sciences and Research Centre, Hyderabad, IND
| | - Manoj Sundarasetty
- Radiodiagnosis, Bhaskar Medical College and General Hospital, Hyderabad, IND
| | - Sunethra Anand
- Internal Medicine, Chengalpattu Medical College and Hospital, Chennai, IND
| | - Jawahar Sankar
- Internal Medicine, Chengalpattu Medical College and Hospital, Chennai, IND
| | - Yugandha G Jagtap
- Paediatrics, General Medicine, Mahatma Gandhi Mission (MGM) Medical School, Mumbai, IND
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Zhang Y, Du S, Hu T, Xu S, Lu H, Xu C, Li J, Zhu X. Establishment of a model for predicting preterm birth based on the machine learning algorithm. BMC Pregnancy Childbirth 2023; 23:779. [PMID: 37950186 PMCID: PMC10636958 DOI: 10.1186/s12884-023-06058-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2023] [Accepted: 10/09/2023] [Indexed: 11/12/2023] Open
Abstract
BACKGROUND The purpose of this study was to construct a preterm birth prediction model based on electronic health records and to provide a reference for preterm birth prediction in the future. METHODS This was a cross-sectional design. The risk factors for the outcomes of preterm birth were assessed by multifactor logistic regression analysis. In this study, a logical regression model, decision tree, Naive Bayes, support vector machine, and AdaBoost are used to construct the prediction model. Accuracy, recall, precision, F1 value, and receiver operating characteristic curve, were used to evaluate the prediction performance of the model, and the clinical application of the model was verified. RESULTS A total of 5411 participants were included and were used for model construction. AdaBoost model has the best prediction ability among the five models. The accuracy of the model for the prediction of "non-preterm birth" was the highest, reaching 100%, and that of "preterm birth" was 72.73%. CONCLUSIONS By constructing a preterm birth prediction model based on electronic health records, we believe that machine algorithms have great potential for preterm birth identification. However, more relevant studies are needed before its application in the clinic.
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Affiliation(s)
- Yao Zhang
- School of Nursing, Wenzhou Medical University, Wenzhou, Zhejiang, China
| | - Sisi Du
- School of Nursing, Wenzhou Medical University, Wenzhou, Zhejiang, China
| | - Tingting Hu
- School of Nursing, Wenzhou Medical University, Wenzhou, Zhejiang, China
- People's Hospital of Deyang City, Deyang, Sichuan, China
| | - Shichao Xu
- The Second Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, China
| | - Hongmei Lu
- The Second Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, China
| | - Chunyan Xu
- School of Nursing, Wenzhou Medical University, Wenzhou, Zhejiang, China
- Hangzhou Hospital of Traditional Chinese Medicine, Hangzhou, Zhejiang, China
| | - Jufang Li
- School of Nursing, Wenzhou Medical University, Wenzhou, Zhejiang, China.
- Wenzhou Manna Medical Technology Ltd, Wenzhou, Zhejiang, China.
| | - Xiaoling Zhu
- School of Nursing, Wenzhou Medical University, Wenzhou, Zhejiang, China.
- Wenzhou Manna Medical Technology Ltd, Wenzhou, Zhejiang, China.
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Sikora A, Zhang T, Murphy DJ, Smith SE, Murray B, Kamaleswaran R, Chen X, Buckley MS, Rowe S, Devlin JW. Machine learning vs. traditional regression analysis for fluid overload prediction in the ICU. Sci Rep 2023; 13:19654. [PMID: 37949982 PMCID: PMC10638304 DOI: 10.1038/s41598-023-46735-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2023] [Accepted: 11/04/2023] [Indexed: 11/12/2023] Open
Abstract
Fluid overload, while common in the ICU and associated with serious sequelae, is hard to predict and may be influenced by ICU medication use. Machine learning (ML) approaches may offer advantages over traditional regression techniques to predict it. We compared the ability of traditional regression techniques and different ML-based modeling approaches to identify clinically meaningful fluid overload predictors. This was a retrospective, observational cohort study of adult patients admitted to an ICU ≥ 72 h between 10/1/2015 and 10/31/2020 with available fluid balance data. Models to predict fluid overload (a positive fluid balance ≥ 10% of the admission body weight) in the 48-72 h after ICU admission were created. Potential patient and medication fluid overload predictor variables (n = 28) were collected at either baseline or 24 h after ICU admission. The optimal traditional logistic regression model was created using backward selection. Supervised, classification-based ML models were trained and optimized, including a meta-modeling approach. Area under the receiver operating characteristic (AUROC), positive predictive value (PPV), and negative predictive value (NPV) were compared between the traditional and ML fluid prediction models. A total of 49 of the 391 (12.5%) patients developed fluid overload. Among the ML models, the XGBoost model had the highest performance (AUROC 0.78, PPV 0.27, NPV 0.94) for fluid overload prediction. The XGBoost model performed similarly to the final traditional logistic regression model (AUROC 0.70; PPV 0.20, NPV 0.94). Feature importance analysis revealed severity of illness scores and medication-related data were the most important predictors of fluid overload. In the context of our study, ML and traditional models appear to perform similarly to predict fluid overload in the ICU. Baseline severity of illness and ICU medication regimen complexity are important predictors of fluid overload.
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Affiliation(s)
- Andrea Sikora
- Department of Clinical and Administrative Pharmacy, University of Georgia College of Pharmacy, 1120 15th Street, HM-118, Augusta, GA, 30912, USA
| | - Tianyi Zhang
- Department of Statistics, University of Georgia Franklin College of Arts and Sciences, Athens, GA, USA
| | - David J Murphy
- Division of Pulmonary, Allergy, Critical Care and Sleep Medicine, Emory University, Atlanta, GA, USA
| | - Susan E Smith
- Department of Clinical and Administrative Pharmacy, University of Georgia College of Pharmacy, 1120 15th Street, HM-118, Augusta, GA, 30912, USA
| | - Brian Murray
- Department of Pharmacy, University of North Carolina Medical Center, Chapel Hill, NC, USA
| | - Rishikesan Kamaleswaran
- Department of Biomedical Informatics, Emory University School of Medicine, Atlanta, GA, USA
- Department of Biomedical Engineering, Georgia Institute of Technology, Atlanta, GA, USA
| | - Xianyan Chen
- Department of Statistics, University of Georgia Franklin College of Arts and Sciences, Athens, GA, USA
| | | | - Sandra Rowe
- Department of Pharmacy, Oregon Health and Science University, Portland, OR, USA
| | - John W Devlin
- Northeastern University School of Pharmacy, Boston, MA, USA.
- Division of Pulmonary and Critical Care Medicine, Brigham and Women's Hospital, Boston, MA, USA.
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Gong JL, Yu J, Wang TL, He XS, Tang YH, Zhu XF. Application of extended criteria donor grafts in liver transplantation for acute-on-chronic liver failure: A retrospective cohort study. World J Gastroenterol 2023; 29:5630-5640. [PMID: 38077155 PMCID: PMC10701327 DOI: 10.3748/wjg.v29.i41.5630] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/23/2023] [Revised: 09/24/2023] [Accepted: 10/23/2023] [Indexed: 11/07/2023] Open
Abstract
BACKGROUND There is no consensus on the usage of extended criteria donor (ECD) grafts in liver transplantation (LT) for acute-on-chronic liver failure (ACLF) patients. AIM To summarize the experience of using ECD livers in ACLF-LT. METHODS A retrospective cohort study was conducted, enrolling patients who underwent LT at the First Affiliated Hospital of Sun Yat-Sen University from January 2015 to November 2021. The patients were divided into ECD and non-ECD groups for analysis. RESULTS A total of 145 recipients were enrolled in this study, of which ECD and non-ECD recipients accounted for 53.8% and 46.2%, respectively. Donation after cardiac death (DCD) recipients accounted for the minority compared with donation after brain death (DBD) recipients (16.6% vs 83.4%). Neither overall survival nor graft survival significantly differed between ECD and non-ECD and DCD and DBD recipients. ECD grafts were associated with a significantly higher incidence of early allograft dysfunction (EAD) than non-ECD grafts (67.9% vs 41.8%, P = 0.002). Postoperative outcomes between DCD and DBD recipients were comparable (P > 0.05). ECD graft (P = 0.009), anhepatic phase (P = 0.034) and recipient gamma glutamyltransferase (P = 0.016) were independent risk factors for EAD. Recipient preoperative number of extrahepatic organ failures > 2 (P = 0.015) and intraoperative blood loss (P = 0.000) were independent predictors of poor post-LT survival. CONCLUSION Although related to a higher risk of EAD, ECD grafts can be safely used in ACLF-LT. The main factors affecting post-LT survival in ACLF patients are their own severe preoperative disease and intraoperative blood loss.
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Affiliation(s)
- Jin-Long Gong
- Department of Hepatobiliary Surgery, Hunan Provincial People's Hospital, The First Affiliated Hospital of Hunan Normal University, Changsha 410005, Hunan Province, China
- Organ Transplant Center, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou 510080, Guangdong Province, China
| | - Jia Yu
- Organ Transplant Center, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou 510080, Guangdong Province, China
- Department of Gastroenterology Surgery, The First Affiliated Hospital of University of South China, Hengyang 421005, Hunan Province, China
| | - Tie-Long Wang
- Organ Transplant Center, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou 510080, Guangdong Province, China
| | - Xiao-Shun He
- Organ Transplant Center, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou 510080, Guangdong Province, China
| | - Yun-Hua Tang
- Organ Transplant Center, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou 510080, Guangdong Province, China
| | - Xiao-Feng Zhu
- Organ Transplant Center, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou 510080, Guangdong Province, China
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Mushtaq AH, Shafqat A, Salah HT, Hashmi SK, Muhsen IN. Machine learning applications and challenges in graft-versus-host disease: a scoping review. Curr Opin Oncol 2023; 35:594-600. [PMID: 37820094 DOI: 10.1097/cco.0000000000000996] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/13/2023]
Abstract
PURPOSE OF REVIEW This review delves into the potential of artificial intelligence (AI), particularly machine learning (ML), in enhancing graft-versus-host disease (GVHD) risk assessment, diagnosis, and personalized treatment. RECENT FINDINGS Recent studies have demonstrated the superiority of ML algorithms over traditional multivariate statistical models in donor selection for allogeneic hematopoietic stem cell transplantation. ML has recently enabled dynamic risk assessment by modeling time-series data, an upgrade from the static, "snapshot" assessment of patients that conventional statistical models and older ML algorithms offer. Regarding diagnosis, a deep learning model, a subset of ML, can accurately identify skin segments affected with chronic GVHD with satisfactory results. ML methods such as Q-learning and deep reinforcement learning have been utilized to develop adaptive treatment strategies (ATS) for the personalized prevention and treatment of acute and chronic GVHD. SUMMARY To capitalize on these promising advancements, there is a need for large-scale, multicenter collaborations to develop generalizable ML models. Furthermore, addressing pertinent issues such as the implementation of stringent ethical guidelines is crucial before the widespread introduction of AI into GVHD care.
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Affiliation(s)
- Ali Hassan Mushtaq
- Department of Internal Medicine, Cleveland Clinic Foundation, Cleveland, Ohio, USA
| | - Areez Shafqat
- College of Medicine, Alfaisal University, Riyadh, Saudi Arabia
| | - Haneen T Salah
- Department of Pathology and Genomic Medicine, Houston Methodist Hospital, Houston, Texas
| | - Shahrukh K Hashmi
- Division of Hematology, Department of Medicine, Mayo Clinic, Rochester, Minnesota, USA
- Department of Medicine, Sheikh Shakbout Medical City
- Medical Affairs, Khalifa University, Abu Dhabi, United Arab Emirates
| | - Ibrahim N Muhsen
- Section of Hematology and Oncology, Department of Medicine, Baylor College of Medicine, Houston, Texas, USA
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Kim Y, Kim H, Choi J, Cho K, Yoo D, Lee Y, Park SJ, Jeong MH, Jeong SH, Park KH, Byun SY, Kim T, Ahn SH, Cho WH, Lee N. Early prediction of need for invasive mechanical ventilation in the neonatal intensive care unit using artificial intelligence and electronic health records: a clinical study. BMC Pediatr 2023; 23:525. [PMID: 37872515 PMCID: PMC10591351 DOI: 10.1186/s12887-023-04350-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/16/2023] [Accepted: 10/06/2023] [Indexed: 10/25/2023] Open
Abstract
BACKGROUND Respiratory support is crucial for newborns with underdeveloped lung. The clinical outcomes of patients depend on the clinician's ability to recognize the status underlying the presented symptoms and signs. With the increasing number of high-risk infants, artificial intelligence (AI) should be considered as a tool for personalized neonatal care. Continuous monitoring of vital signs is essential in cardiorespiratory care. In this study, we developed deep learning (DL) prediction models for rapid and accurate detection of mechanical ventilation requirements in neonates using electronic health records (EHR). METHODS We utilized data from the neonatal intensive care unit in a single center, collected between March 3, 2012, and March 4, 2022, including 1,394 patient records used for model development, consisting of 505 and 889 patients with and without invasive mechanical ventilation (IMV) support, respectively. The proposed model architecture includes feature embedding using feature-wise fully connected (FC) layers, followed by three bidirectional long short-term memory (LSTM) layers. RESULTS A mean gestational age (GA) was 36.61 ± 3.25 weeks, and the mean birth weight was 2,734.01 ± 784.98 g. The IMV group had lower GA, birth weight, and longer hospitalization duration than the non-IMV group (P < 0.05). Our proposed model, tested on a dataset from March 4, 2019, to March 4, 2022. The mean AUROC of our proposed model for IMV support prediction performance demonstrated 0.861 (95%CI, 0.853-0.869). It is superior to conventional approaches, such as newborn early warning score systems (NEWS), Random Forest, and eXtreme gradient boosting (XGBoost) with 0.611 (95%CI, 0.600-0.622), 0.837 (95%CI, 0.828-0.845), and 0.0.831 (95%CI, 0.821-0.845), respectively. The highest AUPRC value is shown in the proposed model at 0.327 (95%CI, 0.308-0.347). The proposed model performed more accurate predictions as gestational age decreased. Additionally, the model exhibited the lowest alarm rate while maintaining the same sensitivity level. CONCLUSION Deep learning approaches can help accurately standardize the prediction of invasive mechanical ventilation for neonatal patients and facilitate advanced neonatal care. The results of predictive, recall, and alarm performances of the proposed model outperformed the other models.
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Affiliation(s)
- Younga Kim
- Department of Pediatrics, Pusan National University School of Medicine, 20, Geumo-Ro, Mulgeum-Eup, Yangsan, 50612, Republic of Korea
| | | | | | | | | | | | - Su Jeong Park
- Department of Pediatrics, Pusan National University School of Medicine, 20, Geumo-Ro, Mulgeum-Eup, Yangsan, 50612, Republic of Korea
| | - Mun Hui Jeong
- Department of Pediatrics, Pusan National University School of Medicine, 20, Geumo-Ro, Mulgeum-Eup, Yangsan, 50612, Republic of Korea
| | - Seong Hee Jeong
- Department of Pediatrics, Pusan National University School of Medicine, 20, Geumo-Ro, Mulgeum-Eup, Yangsan, 50612, Republic of Korea
| | - Kyung Hee Park
- Department of Pediatrics, Pusan National University School of Medicine, 20, Geumo-Ro, Mulgeum-Eup, Yangsan, 50612, Republic of Korea
| | - Shin-Yun Byun
- Department of Pediatrics, Pusan National University School of Medicine, 20, Geumo-Ro, Mulgeum-Eup, Yangsan, 50612, Republic of Korea
| | - Taehwa Kim
- Division of Pulmonology, Allergy and Critical Care Medicine, Department of Internal Medicine, Pusan National University School of Medicine, and Research Institute for Convergence of Biomedical Science and Technology, Pusan National University Yangsan Hospital, Yangsan, Korea
| | - Sung-Ho Ahn
- Department of Neurology, Division of Biostatistics, Research Institute for Convergence of Biomedical Science and Technology, Pusan National University Yangsan Hospital, Pusan National University School of Medicine, Busan, Korea
| | - Woo Hyun Cho
- Division of Pulmonology, Allergy and Critical Care Medicine, Department of Internal Medicine, Pusan National University School of Medicine, and Research Institute for Convergence of Biomedical Science and Technology, Pusan National University Yangsan Hospital, Yangsan, Korea
| | - Narae Lee
- Department of Pediatrics, Pusan National University School of Medicine, 20, Geumo-Ro, Mulgeum-Eup, Yangsan, 50612, Republic of Korea.
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Zhao J, Dong Y, Bai H, Bai F, Yan X, Duan J, Wan R, Xu J, Fei K, Wang J, Wang Z. Multi-omics indicators of long-term survival benefits after immune checkpoint inhibitor therapy. CELL REPORTS METHODS 2023; 3:100596. [PMID: 37738982 PMCID: PMC10626191 DOI: 10.1016/j.crmeth.2023.100596] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/18/2023] [Revised: 05/08/2023] [Accepted: 08/30/2023] [Indexed: 09/24/2023]
Abstract
Molecular indicators of long-term survival (LTS) in response to immune-checkpoint inhibitor (ICI) treatment have the potential to provide both mechanistic and therapeutic insights. In this study, we construct predictive models of LTS following ICI therapy based on data from 158 clinical trials involving 21,023 patients of 25 cancer types with available 1-year overall survival (OS) rates. We present evidence for the use of 1-year OS rate as a surrogate for LTS. Based on these and corresponding TCGA multi-omics data, total neoantigen, metabolism score, CD8+ T cell, and MHC_score were identified as predictive biomarkers. These were integrated into a Gaussian process regression model that estimates "long-term survival predictive score of immunotherapy" (iLSPS). We found that iLSPS outperformed the predictive capabilities of individual biomarkers and successfully predicted LTS of patient groups with melanoma and lung cancer. Our study explores the feasibility of modeling LTS based on multi-omics indicators and machine-learning methods.
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Affiliation(s)
- Jie Zhao
- CAMS Key Laboratory of Translational Research on Lung Cancer, State Key Laboratory of Molecular Oncology, Department of Medical Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100021, China
| | - Yiting Dong
- CAMS Key Laboratory of Translational Research on Lung Cancer, State Key Laboratory of Molecular Oncology, Department of Medical Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100021, China
| | - Hua Bai
- CAMS Key Laboratory of Translational Research on Lung Cancer, State Key Laboratory of Molecular Oncology, Department of Medical Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100021, China
| | - Fan Bai
- Biomedical Pioneering Innovation Center (BIOPIC), School of Life Sciences, Peking University, Beijing 100021, China
| | - Xiaoyan Yan
- Clinical Research Institute, Peking University, Beijing 100021, China
| | - Jianchun Duan
- CAMS Key Laboratory of Translational Research on Lung Cancer, State Key Laboratory of Molecular Oncology, Department of Medical Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100021, China
| | - Rui Wan
- CAMS Key Laboratory of Translational Research on Lung Cancer, State Key Laboratory of Molecular Oncology, Department of Medical Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100021, China
| | - Jiachen Xu
- CAMS Key Laboratory of Translational Research on Lung Cancer, State Key Laboratory of Molecular Oncology, Department of Medical Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100021, China
| | - Kailun Fei
- CAMS Key Laboratory of Translational Research on Lung Cancer, State Key Laboratory of Molecular Oncology, Department of Medical Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100021, China
| | - Jie Wang
- CAMS Key Laboratory of Translational Research on Lung Cancer, State Key Laboratory of Molecular Oncology, Department of Medical Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100021, China.
| | - Zhijie Wang
- CAMS Key Laboratory of Translational Research on Lung Cancer, State Key Laboratory of Molecular Oncology, Department of Medical Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100021, China.
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