1
|
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.
Collapse
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.
| |
Collapse
|
2
|
Li R, Xu Z, Xu J, Pan X, Wu H, Huang X, Feng M. Predicting intubation for intensive care units patients: A deep learning approach to improve patient management. Int J Med Inform 2024; 186:105425. [PMID: 38554589 DOI: 10.1016/j.ijmedinf.2024.105425] [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: 10/24/2023] [Revised: 01/19/2024] [Accepted: 03/20/2024] [Indexed: 04/01/2024]
Abstract
OBJECTIVE For patients in the Intensive Care Unit (ICU), the timing of intubation has a significant association with patients' outcomes. However, accurate prediction of the timing of intubation remains an unsolved challenge due to the noisy, sparse, heterogeneous, and unbalanced nature of ICU data. In this study, our objective is to develop a workflow for pre-processing ICU data and to develop a customized deep learning model to predict the need for intubation. METHODS To improve the prediction accuracy, we transform the intubation prediction task into a time series classification task. We carefully design a sequence of data pre-processing steps to handle the multimodal noisy data. Firstly, we discretize the sequential data and address missing data using interpolation. Next, we employ a sampling strategy to address data imbalance and standardize the data to facilitate faster model convergence. Furthermore, we employ the feature selection technique and propose an ensemble model to combine features learned by different deep learning models. RESULTS The performance is evaluated on Medical Information Mart for Intensive Care (MIMIC)-III, an ICU dataset. Our proposed Deep Feature Fusion method achieves an area under the curve (AUC) of the receiver operating curve (ROC) of 0.8953, surpassing the performance of other deep learning and traditional machine learning models. CONCLUSION Our proposed Deep Feature Fusion method proves to be a viable approach for predicting intubation and outperforms other deep learning and classical machine learning models. The study confirms that high-frequency time-varying indicators, particularly Mean Blood Pressure (MeanBP) and peripheral oxygen saturation (SpO2), are significant risk factors for predicting intubation.
Collapse
Affiliation(s)
- Ruixi Li
- Harbin Institute of Technology Shenzhen, Shenzhen, China.
| | - Zenglin Xu
- Harbin Institute of Technology Shenzhen, Shenzhen, China; Peng Cheng Lab, Shenzhen, China.
| | - Jing Xu
- Harbin Institute of Technology Shenzhen, Shenzhen, China.
| | - Xinglin Pan
- Hong Kong Baptist University, Hong Kong, China.
| | - Hong Wu
- University of Electronic Science and Technology of China, Chengdu, China.
| | - Xiaobo Huang
- Sichuan Academy of Medical Sciences and Sichuan People's Hospital, Chengdu, China.
| | - Mengling Feng
- Saw Swee Hock School of Public Health and Institute of Data Science, National University of Singapore, Singapore.
| |
Collapse
|
3
|
Venturini M, Van Keilegom I, De Corte W, Vens C. Predicting time-to-intubation after critical care admission using machine learning and cured fraction information. Artif Intell Med 2024; 150:102817. [PMID: 38553157 DOI: 10.1016/j.artmed.2024.102817] [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: 06/02/2023] [Revised: 02/19/2024] [Accepted: 02/20/2024] [Indexed: 04/02/2024]
Abstract
Intubation for mechanical ventilation (MV) is one of the most common high-risk procedures performed in Intensive Care Units (ICUs). Early prediction of intubation may have a positive impact by providing timely alerts to clinicians and consequently avoiding high-risk late intubations. In this work, we propose a new machine learning method to predict the time to intubation during the first five days of ICU admission, based on the concept of cure survival models. Our approach combines classification and survival analysis, to effectively accommodate the fraction of patients not at risk of intubation, and provide a better estimate of time to intubation, for patients at risk. We tested our approach and compared it to other predictive models on a dataset collected from a secondary care hospital (AZ Groeninge, Kortrijk, Belgium) from 2015 to 2021, consisting of 3425 ICU stays. Furthermore, we utilised SHAP for feature importance analysis, extracting key insights into the relative significance of variables such as vital signs, blood gases, and patient characteristics in predicting intubation in ICU settings. The results corroborate that our approach improves the prediction of time to intubation in critically ill patients, by using routinely collected data within the first hours of admission in the ICU. Early warning of the need for intubation may be used to help clinicians predict the risk of intubation and rank patients according to their expected time to intubation.
Collapse
Affiliation(s)
- Michela Venturini
- KU Leuven, Campus KULAK-Department of Public Health and Primary Care, Etienne Sabbelaan 53, Kortrijk, 8500, Belgium; ITEC-imec and KU Leuven, Etienne Sabbelaan 51, Kortrijk, 8500, Belgium.
| | - Ingrid Van Keilegom
- Research Centre for Operations Research and Statistics, KU Leuven, Naamsestraat 69, Leuven, 3000, Belgium
| | - Wouter De Corte
- Department of Anesthesiology and Intensive Care Medicine, AZ Groeninge Hospital, President Kennedylaan 4, Kortrijk, 8500, Belgium
| | - Celine Vens
- KU Leuven, Campus KULAK-Department of Public Health and Primary Care, Etienne Sabbelaan 53, Kortrijk, 8500, Belgium; ITEC-imec and KU Leuven, Etienne Sabbelaan 51, Kortrijk, 8500, Belgium.
| |
Collapse
|
4
|
Fu W, Liu X, Guan L, Lin Z, He Z, Niu J, Huang Q, Liu Q, Chen R. Prognostic analysis of high-flow nasal cannula therapy and non-invasive ventilation in mild to moderate hypoxemia patients and construction of a machine learning model for 48-h intubation prediction-a retrospective analysis of the MIMIC database. Front Med (Lausanne) 2024; 11:1213169. [PMID: 38495114 PMCID: PMC10941954 DOI: 10.3389/fmed.2024.1213169] [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: 04/27/2023] [Accepted: 02/13/2024] [Indexed: 03/19/2024] Open
Abstract
Background This study aims to investigate the clinical outcome between high-flow nasal cannula (HFNC) and non-invasive ventilation (NIV) therapy in mild to moderate hypoxemic patients on the first ICU day and to develop a predictive model of 48-h intubation. Methods The study included adult patients from the MIMIC III and IV databases who first initiated HFNC or NIV therapy due to mild to moderate hypoxemia (100 < PaO2/FiO2 ≤ 300). The 48-h and 30-day intubation rates were compared using cross-sectional and survival analysis. Nine machine learning and six ensemble algorithms were deployed to construct the 48-h intubation predictive models, of which the optimal model was determined by its prediction accuracy. The top 10 risk and protective factors were identified using the Shapley interpretation algorithm. Result A total of 123,042 patients were screened, of which, 673 were from the MIMIC IV database for ventilation therapy comparison (HFNC n = 363, NIV n = 310) and 48-h intubation predictive model construction (training dataset n = 471, internal validation set n = 202) and 408 were from the MIMIC III database for external validation. The NIV group had a lower intubation rate (23.1% vs. 16.1%, p = 0.001), ICU 28-day mortality (18.5% vs. 11.6%, p = 0.014), and in-hospital mortality (19.6% vs. 11.9%, p = 0.007) compared to the HFNC group. Survival analysis showed that the total and 48-h intubation rates were not significantly different. The ensemble AdaBoost decision tree model (internal and external validation set AUROC 0.878, 0.726) had the best predictive accuracy performance. The model Shapley algorithm showed Sequential Organ Failure Assessment (SOFA), acute physiology scores (APSIII), the minimum and maximum lactate value as risk factors for early failure and age, the maximum PaCO2 and PH value, Glasgow Coma Scale (GCS), the minimum PaO2/FiO2 ratio, and PaO2 value as protective factors. Conclusion NIV was associated with lower intubation rate and ICU 28-day and in-hospital mortality. Further survival analysis reinforced that the effect of NIV on the intubation rate might partly be attributed to the other impact factors. The ensemble AdaBoost decision tree model may assist clinicians in making clinical decisions, and early organ function support to improve patients' SOFA, APSIII, GCS, PaCO2, PaO2, PH, PaO2/FiO2 ratio, and lactate values can reduce the early failure rate and improve patient prognosis.
Collapse
Affiliation(s)
- Wei Fu
- State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, the First Affiliated Hospital of Guangzhou Medical University, Guangzhou, Guangdong, China
| | - Xiaoqing Liu
- Department of Critical Care Medicine, the First Affiliated Hospital of Guangzhou Medical University, Guangzhou Institute of Respiratory Health, Guangzhou, China
| | - Lili Guan
- State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, the First Affiliated Hospital of Guangzhou Medical University, Guangzhou, Guangdong, China
| | - Zhimin Lin
- Department of Critical Care Medicine, the First Affiliated Hospital of Guangzhou Medical University, Guangzhou Institute of Respiratory Health, Guangzhou, China
| | - Zhenfeng He
- State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, the First Affiliated Hospital of Guangzhou Medical University, Guangzhou, Guangdong, China
| | - Jianyi Niu
- State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, the First Affiliated Hospital of Guangzhou Medical University, Guangzhou, Guangdong, China
| | - Qiaoyun Huang
- State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, the First Affiliated Hospital of Guangzhou Medical University, Guangzhou, Guangdong, China
| | - Qi Liu
- Emergency Intensive Care Department, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, Hena, China
| | - Rongchang Chen
- State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, the First Affiliated Hospital of Guangzhou Medical University, Guangzhou, Guangdong, China
| |
Collapse
|
5
|
Chanci D, Grunwell JR, Rafiei A, Moore R, Bishop NR, Rajapreyar P, Lima LM, Mai M, Kamaleswaran R. Development and Validation of a Model for Endotracheal Intubation and Mechanical Ventilation Prediction in PICU Patients. Pediatr Crit Care Med 2024; 25:212-221. [PMID: 37962125 PMCID: PMC10932861 DOI: 10.1097/pcc.0000000000003410] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/15/2023]
Abstract
OBJECTIVES To develop and externally validate an intubation prediction model for children admitted to a PICU using objective and routinely available data from the electronic medical records (EMRs). DESIGN Retrospective observational cohort study. SETTING Two PICUs within the same healthcare system: an academic, quaternary care center (36 beds) and a community, tertiary care center (56 beds). PATIENTS Children younger than 18 years old admitted to a PICU between 2010 and 2022. INTERVENTIONS None. MEASUREMENTS AND MAIN RESULTS Clinical data was extracted from the EMR. PICU stays with at least one mechanical ventilation event (≥ 24 hr) occurring within a window of 1-7 days after hospital admission were included in the study. Of 13,208 PICU stays in the derivation PICU cohort, 1,175 (8.90%) had an intubation event. In the validation cohort, there were 1,165 of 17,841 stays (6.53%) with an intubation event. We trained a Categorical Boosting (CatBoost) model using vital signs, laboratory tests, demographic data, medications, organ dysfunction scores, and other patient characteristics to predict the need of intubation and mechanical ventilation using a 24-hour window of data within their hospital stay. We compared the CatBoost model to an extreme gradient boost, random forest, and a logistic regression model. The area under the receiving operating characteristic curve for the derivation cohort and the validation cohort was 0.88 (95% CI, 0.88-0.89) and 0.92 (95% CI, 0.91-0.92), respectively. CONCLUSIONS We developed and externally validated an interpretable machine learning prediction model that improves on conventional clinical criteria to predict the need for intubation in children hospitalized in a PICU using information readily available in the EMR. Implementation of our model may help clinicians optimize the timing of endotracheal intubation and better allocate respiratory and nursing staff to care for mechanically ventilated children.
Collapse
Affiliation(s)
- Daniela Chanci
- Department of Biomedical Informatics, Emory University, Atlanta, GA
| | - Jocelyn R Grunwell
- Department of Pediatrics, Emory University School of Medicine, Atlanta, GA
- Division of Critical Care Medicine, Children's Healthcare of Atlanta, Atlanta, GA
| | - Alireza Rafiei
- Department of Biomedical Informatics, Emory University, Atlanta, GA
| | - Ronald Moore
- Department of Biomedical Informatics, Emory University, Atlanta, GA
| | - Natalie R Bishop
- Department of Pediatrics, Emory University School of Medicine, Atlanta, GA
- Division of Critical Care Medicine, Children's Healthcare of Atlanta, Atlanta, GA
| | - Prakadeshwari Rajapreyar
- Department of Pediatrics, Emory University School of Medicine, Atlanta, GA
- Division of Critical Care Medicine, Children's Healthcare of Atlanta, Atlanta, GA
| | - Lisa M Lima
- Department of Pediatrics, Emory University School of Medicine, Atlanta, GA
- Division of Critical Care Medicine, Children's Healthcare of Atlanta, Atlanta, GA
| | - Mark Mai
- Department of Pediatrics, Emory University School of Medicine, Atlanta, GA
- Division of Critical Care Medicine, Children's Healthcare of Atlanta, Atlanta, GA
| | - Rishikesan Kamaleswaran
- Department of Biomedical Informatics, Emory University, Atlanta, GA
- Department of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA
| |
Collapse
|
6
|
Kloonen RMJS, Varisco G, de Kort E, Andriessen P, Niemarkt HJ, van Pul C. Predicting CPAP failure after less invasive surfactant administration (LISA) in preterm infants by machine learning model on vital parameter data: a pilot study. Physiol Meas 2023; 44:115005. [PMID: 37939392 DOI: 10.1088/1361-6579/ad0ab6] [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: 06/16/2023] [Accepted: 11/07/2023] [Indexed: 11/10/2023]
Abstract
Objective. Less invasive surfactant administration (LISA) has been introduced to preterm infants with respiratory distress syndrome on continuous positive airway pressure (CPAP) support in order to avoid intubation and mechanical ventilation. However, after this LISA procedure, a significant part of infants fails CPAP treatment (CPAP-F) and requires intubation in the first 72 h of life, which is associated with worse complication free survival chances. The aim of this study was to predict CPAP-F after LISA, based on machine learning (ML) analysis of high resolution vital parameter monitoring data surrounding the LISA procedure.Approach. Patients with a gestational age (GA) <32 weeks receiving LISA were included. Vital parameter data was obtained from a data warehouse. Physiological features (HR, RR, peripheral oxygen saturation (SpO2) and body temperature) were calculated in eight 0.5 h windows throughout a period 1.5 h before to 2.5 h after LISA. First, physiological data was analyzed to investigate differences between the CPAP-F and CPAP-Success (CPAP-S) groups. Next, the performance of two types of ML models (logistic regression: LR, support vector machine: SVM) for the prediction of CPAP-F were evaluated.Main results. Of 51 included patients, 18 (35%) had CPAP-F. Univariate analysis showed lower SpO2, temperature and heart rate variability (HRV) before and after the LISA procedure. The best performing ML model showed an area under the curve of 0.90 and 0.93 for LR and SVM respectively in the 0.5 h window directly after LISA, with GA, HRV, respiration rate and SpO2as most important features. Excluding GA decreased performance in both models.Significance. In this pilot study we were able to predict CPAP-F with a ML model of patient monitor signals, with best performance in the first 0.5 h after LISA. Using ML to predict CPAP-F based on vital signals gains insight in (possibly modifiable) factors that are associated with LISA failure and can help to guide personalized clinical decisions in early respiratory management.
Collapse
Affiliation(s)
- R M J S Kloonen
- Eindhoven University of Technology, PO Box 513, 5600 MB Eindhoven, The Netherlands
- Maxima Medical Centre Veldhoven, Department of Clinical Physics, Po Box 7777, 5600 MB, The Netherlands
| | - G Varisco
- Eindhoven University of Technology, PO Box 513, 5600 MB Eindhoven, The Netherlands
| | - E de Kort
- Maxima Medical Centre Veldhoven, Department of Pediatrics, Po Box 7777, 5600 MB, The Netherlands
| | - P Andriessen
- Maxima Medical Centre Veldhoven, Department of Pediatrics, Po Box 7777, 5600 MB, The Netherlands
| | - H J Niemarkt
- Eindhoven University of Technology, PO Box 513, 5600 MB Eindhoven, The Netherlands
- Maxima Medical Centre Veldhoven, Department of Pediatrics, Po Box 7777, 5600 MB, The Netherlands
| | - C van Pul
- Eindhoven University of Technology, PO Box 513, 5600 MB Eindhoven, The Netherlands
- Maxima Medical Centre Veldhoven, Department of Clinical Physics, Po Box 7777, 5600 MB, The Netherlands
| |
Collapse
|
7
|
Feng X, Wang D, Pan Q, Yan M, Liu X, Shen Y, Fang L, Cai G, Ning G. Reinforcement Learning Model for Managing Noninvasive Ventilation Switching Policy. IEEE J Biomed Health Inform 2023; 27:4120-4130. [PMID: 37159312 DOI: 10.1109/jbhi.2023.3274568] [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: 05/11/2023]
Abstract
Noninvasive ventilation (NIV) has been recognized as a first-line treatment for respiratory failure in patients with chronic obstructive pulmonary disease (COPD) and hypercapnia respiratory failure, which can reduce mortality and burden of intubation. However, during the long-term NIV process, failure to respond to NIV may cause overtreatment or delayed intubation, which is associated with increased mortality or costs. Optimal strategies for switching regime in the course of NIV treatment remain to be explored.For the goal of reducing 28-day mortality of the patients undergoing NIV, Double Dueling Deep Q Network (D3QN) of offline-reinforcement learning algorithm was adopted to develop an optimal regime model for making treatment decisions of discontinuing ventilation, continuing NIV, or intubation. The model was trained and tested using the data from Multi-Parameter Intelligent Monitoring in Intensive Care III (MIMIC-III) and evaluated by the practical strategies. Furthermore, the applicability of the model in majority disease subgroups (Catalogued by International Classification of Diseases, ICD) was investigated. Compared with physician's strategies, the proposed model achieved a higher expected return score (4.25 vs. 2.68) and its recommended treatments reduced the expected mortality from 27.82% to 25.44% in all NIV cases. In particular, for these patients finally received intubation in practice, if the model also supported the regime, it would warn of switching to intubation 13.36 hours earlier than clinicians (8.64 vs. 22 hours after the NIV treatment), granting a 21.7% reduction in estimated mortality. In addition, the model was applicable across various disease groups with distinguished achievement in dealing with respiratory disorders. The proposed model is promising to dynamically provide personalized optimal NIV switching regime for patients undergoing NIV with the potential of improving treatment outcomes.
Collapse
|
8
|
Im JE, Park S, Kim YJ, Yoon SA, Lee JH. Predicting the need for intubation within 3 h in the neonatal intensive care unit using a multimodal deep neural network. Sci Rep 2023; 13:6213. [PMID: 37069174 PMCID: PMC10106895 DOI: 10.1038/s41598-023-33353-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2022] [Accepted: 04/12/2023] [Indexed: 04/19/2023] Open
Abstract
Respiratory distress is a common chief complaint in neonates admitted to the neonatal intensive care unit. Despite the increasing use of non-invasive ventilation in neonates with respiratory difficulty, some of them require advanced airway support. Delayed intubation is associated with increased morbidity, particularly in urgent unplanned cases. Early and accurate prediction of the need for intubation may provide more time for preparation and increase safety margins by avoiding the late intubation at high-risk infants. This study aimed to predict the need for intubation within 3 h in neonates initially managed with non-invasive ventilation for respiratory distress during the first 48 h of life using a multimodal deep neural network. We developed a multimodal deep neural network model to simultaneously analyze four time-series data collected at 1-h intervals and 19 variables including demographic, physiological and laboratory parameters. Evaluating the dataset of 128 neonates with respiratory distress who underwent non-invasive ventilation, our model achieved an area under the curve of 0.917, sensitivity of 85.2%, and specificity of 89.2%. These findings demonstrate promising results for the multimodal model in predicting neonatal intubation within 3 h.
Collapse
Affiliation(s)
- Jueng-Eun Im
- Biomedical Engineering, Chungbuk National University Hospital, Cheongju, Republic of Korea
| | - Seung Park
- Biomedical Engineering, Chungbuk National University Hospital, Cheongju, Republic of Korea
| | - Yoo-Jin Kim
- Department of Pediatrics, Chungbuk National University Hospital, Chungbuk National University College of Medicine, Chungdae-ro 1, Seowon-gu, Cheongju, 28644, Republic of Korea
| | - Shin Ae Yoon
- Department of Pediatrics, Chungbuk National University Hospital, Chungbuk National University College of Medicine, Chungdae-ro 1, Seowon-gu, Cheongju, 28644, Republic of Korea.
| | - Ji Hyuk Lee
- Department of Pediatrics, Chungbuk National University Hospital, Chungbuk National University College of Medicine, Chungdae-ro 1, Seowon-gu, Cheongju, 28644, Republic of Korea
| |
Collapse
|
9
|
Wełna M, Adamik B, Kübler A, Goździk W. The NUTRIC Score as a Tool to Predict Mortality and Increased Resource Utilization in Intensive Care Patients with Sepsis. Nutrients 2023; 15:nu15071648. [PMID: 37049489 PMCID: PMC10097365 DOI: 10.3390/nu15071648] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2023] [Revised: 03/24/2023] [Accepted: 03/26/2023] [Indexed: 03/31/2023] Open
Abstract
The Nutrition Risk in Critically Ill score (NUTRIC) is an important nutritional risk assessment instrument for patients in the intensive care unit (ICU). The purpose of this study was to evaluate the power of the score to predict mortality in patients treated for sepsis and to forecast increased resource utilization and nursing workload in the ICU. The NUTRIC score predicted mortality (AUC 0.833, p < 0.001) with the optimal cut-off value of 6 points. Among patients with a score ≥ 6 on ICU admission, the 28-day mortality was 61%, and 10% with a score < 6 (p < 0.001). In addition, a NUTRIC score of ≥6 was associated with a more intense use of ICU resources, as evidenced by a higher proportion of patients requiring vasopressor infusion (98 vs. 82%), mechanical ventilation (99 vs. 87%), renal replacement therapy (54 vs. 26%), steroids (68 vs. 31%), and blood products (60 vs. 43%); the nursing workload was also significantly higher in this group. In conclusion, the NUTRIC score obtained at admission to the ICU provided a good discriminative value for mortality and makes it possible to identify patients who will ultimately require intense use of ICU resources and an associated increase in the nursing workload during treatment.
Collapse
|
10
|
Zhang G, Luo L, Zhang L, Liu Z. Research Progress of Respiratory Disease and Idiopathic Pulmonary Fibrosis Based on Artificial Intelligence. Diagnostics (Basel) 2023; 13:diagnostics13030357. [PMID: 36766460 PMCID: PMC9914063 DOI: 10.3390/diagnostics13030357] [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: 12/22/2022] [Revised: 01/06/2023] [Accepted: 01/16/2023] [Indexed: 01/21/2023] Open
Abstract
Machine Learning (ML) is an algorithm based on big data, which learns patterns from the previously observed data through classifying, predicting, and optimizing to accomplish specific tasks. In recent years, there has been rapid development in the field of ML in medicine, including lung imaging analysis, intensive medical monitoring, mechanical ventilation, and there is need for intubation etiology prediction evaluation, pulmonary function evaluation and prediction, obstructive sleep apnea, such as biological information monitoring and so on. ML can have good performance and is a great potential tool, especially in the imaging diagnosis of interstitial lung disease. Idiopathic pulmonary fibrosis (IPF) is a major problem in the treatment of respiratory diseases, due to the abnormal proliferation of fibroblasts, leading to lung tissue destruction. The diagnosis mainly depends on the early detection of imaging and early treatment, which can effectively prolong the life of patients. If the computer can be used to assist the examination results related to the effects of fibrosis, a timely diagnosis of such diseases will be of great value to both doctors and patients. We also previously proposed a machine learning algorithm model that can play a good clinical guiding role in early imaging prediction of idiopathic pulmonary fibrosis. At present, AI and machine learning have great potential and ability to transform many aspects of respiratory medicine and are the focus and hotspot of research. AI needs to become an invisible, seamless, and impartial auxiliary tool to help patients and doctors make better decisions in an efficient, effective, and acceptable way. The purpose of this paper is to review the current application of machine learning in various aspects of respiratory diseases, with the hope to provide some help and guidance for clinicians when applying algorithm models.
Collapse
Affiliation(s)
- Gerui Zhang
- Department of Critical Care Unit, The First Affiliated Hospital of Dalian Medical University, 222, Zhongshan Road, Dalian 116011, China
| | - Lin Luo
- Department of Critical Care Unit, The Second Hospital of Dalian Medical University, 467 Zhongshan Road, Shahekou District, Dalian 116023, China
| | - Limin Zhang
- Department of Respiratory, The First Affiliated Hospital of Dalian Medical University, 222, Zhongshan Road, Dalian 116011, China
| | - Zhuo Liu
- Department of Respiratory, The First Affiliated Hospital of Dalian Medical University, 222, Zhongshan Road, Dalian 116011, China
- Correspondence:
| |
Collapse
|
11
|
Wang H, Wang C, Xu J, Yuan J, Liu G, Zhang G. Invasive mechanical ventilation probability estimation using machine learning methods based on non-invasive parameters. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2022.104193] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
|
12
|
Eizadi-Mood N, Heshmat R, Meamar R, Motamedi N. The Relative Risk of Toxico-Clinical Parameters with respect to Poisoning Severity and Outcomes in Patients with Acute Poisoning. Adv Biomed Res 2022; 11:107. [PMID: 36660757 PMCID: PMC9843600 DOI: 10.4103/abr.abr_290_21] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2021] [Revised: 12/29/2021] [Accepted: 01/01/2022] [Indexed: 01/21/2023] Open
Abstract
Background Complications or death risk factors is necessary for better monitoring and treatment. The aim of this study was to define the relative risk of toxico-clinical parameters with regard to poisoning severity and outcomes in patients with acute poisoning. Materials and Methods This cross-sectional study entailed of patients with acute poisoning admitted to the poisoning emergency center of khorshid hospital, Isfahan, Iran from December 2018 until March 2019. Patients (n = 300) were categorized into four groups (minor, moderate, severe, and fatal poisoning) based on severity. Multivariate logistic regression analysis was employed to calculate the odds ratio (OR) as the estimate of the relative risk of the different variables for the poisoning severity and outcomes prediction. Results In the minor group, opioids/opiates, alcohols, and benzodiazepines (14.7%) were the most prevalent poisoning, multidrug (23.3%) was in the moderate and severe groups and finally, pesticides poisoning (23%) was most common in the fatal group. The predictive factors for poisoning severity were pre-hospital antidote administration [OR, (95%CI); P value) [7.08 (1.77-28.34); 0.006]; loss of consciousness [4.38 (1.84-10.42), 0.001]; abnormal ECG [4.56 (1.65-12.56); 0.003]; and time interval of poisoning to admission in the hospital [1.15 (1.02-1.28); 0.01). Patients without complications was observed in 49.7% of subjects. Patients with the loss of consciousness [66.06 (2.41-180.07); 0.01); underlying disease [3.65 (1.09-12.24); 0.03]; abnormal respiration [1.14 (1.02-1.27); 0.02); have had a greater risk of complications and death. Conclusion Important factors for poisoning severity and/or outcome were loss of consciousness, pre-hospital antidote administration, abnormal ECG or respiration, underlying disease, and delay to presentation to hospital.
Collapse
Affiliation(s)
- Nastaran Eizadi-Mood
- Department of Clinical Toxicology, School of Medicine, Isfahan Clinical Toxicology Research Center, Isfahan University of Medical Sciences, Isfahan, Iran
| | - Rasol Heshmat
- Department of Clinical Toxicology, School of Medicine, Isfahan University of Medical Sciences, Isfahan, Iran
| | - Rokhsareh Meamar
- Isfahan Clinical Toxicology Research Center, Isfahan University of Medical Sciences, Isfahan, Iran,Address for correspondence: Dr. Rokhsareh Meamar, Isfahan Clinical Toxicology Research Center, Khorshid Hospital, Ostandari Street, Hasht Behest Avenue, Postal Code: 81458-31451, Isfahan, Iran. E-mail:
| | - Narges Motamedi
- Department of Preventive and Community Medicine, Isfahan University of Medical Sciences, Isfahan, Iran
| |
Collapse
|
13
|
Luckscheiter A, Zink W, Lohs T, Eisenberger J, Thiel M, Viergutz T. Machine learning for the prediction of preclinical airway management in injured patients: a registry-based trial. Clin Exp Emerg Med 2022; 9:304-313. [PMID: 36418016 PMCID: PMC9834832 DOI: 10.15441/ceem.22.335] [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] [Received: 06/16/2022] [Accepted: 10/16/2022] [Indexed: 11/27/2022] Open
Abstract
OBJECTIVE The aim of this study was to determine the feasibility of using machine learning to establish the need for preclinical airway management for injured patients based on a standardized emergency dataset. METHODS A registry-based, retrospective analysis was conducted of adult trauma patients who were treated by physician-staffed emergency medical services in southwestern Germany between 2018 and 2020. The primary outcome was to assess the feasibility of using the random forest (RF) and Naive Bayes (NB) machine learning algorithms to predict the need for preclinical airway management. The secondary outcome was to use a principal component analysis to determine the attributes that can be used and advanced for future model development. RESULTS In total, 25,556 adults with multiple injuries were identified, including 1,451 patients (5.7%) who required airway management. Key attributes were auscultation, injury pattern, oxygen therapy, thoracic drainage, noninvasive ventilation, catecholamines, pelvic sling, colloid infusion, initial vital signs, preemergency status, and shock index. The area under the receiver operating characteristics curve was between 0.96 (RF; 95% confidence interval [CI], 0.96-0.97) and 0.93 (NB; 95% CI, 0.92-0.93; P<0.01). For the prediction of airway management, RF yielded a higher precision-recall area than NB (0.83 [95% CI, 0.8-0.85] vs. 0.66 [95% CI, 0.61-0.72], respectively; P<0.01). CONCLUSION To predict the need for preclinical airway management in injured patients, attributes that are commonly recorded in standardized datasets can be used with machine learning. In future models, the RF algorithm could be used because it has robust prediction accuracy.
Collapse
Affiliation(s)
- André Luckscheiter
- Department of Anesthesiology, Intensive Care and Emergency Medicine, Ludwigshafen Municipal Hospital, Ludwigshafen, Germany,Correspondence to: André Luckscheiter Department of Anesthesiology, Intensive Care and Emergency Medicine, Ludwigshafen Municipal Hospital, Bremserstrasse 79, Ludwigshafen 67063, Germany E-mail:
| | - Wolfgang Zink
- Department of Anesthesiology, Intensive Care and Emergency Medicine, Ludwigshafen Municipal Hospital, Ludwigshafen, Germany
| | - Torsten Lohs
- Center for Quality Management in Emergency Medical Services Baden-Wuerttemberg (SQR-BW), Stuttgart, Germany
| | - Johanna Eisenberger
- Center for Quality Management in Emergency Medical Services Baden-Wuerttemberg (SQR-BW), Stuttgart, Germany
| | - Manfred Thiel
- Department of Anesthesiology and Intensive Care Medicine, University Medical Center Mannheim, Mannheim, Germany
| | - Tim Viergutz
- Clinic for Anesthesia, Intensive Care and Pain Therapy, BG Trauma Center Tuebingen, Tuebingen, Germany
| |
Collapse
|
14
|
Xie T, Xin Q, Zhang X, Tong Y, Ren H, Liu C, Zhang J. Construction and validation of a nomogram for predicting survival in elderly patients with cardiac surgery. Front Public Health 2022; 10:972797. [PMID: 36339155 PMCID: PMC9626768 DOI: 10.3389/fpubh.2022.972797] [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] [Received: 06/19/2022] [Accepted: 09/30/2022] [Indexed: 01/25/2023] Open
Abstract
Background In recent years, the number of elderly patients undergoing cardiac surgery has rapidly increased and is associated with poor outcomes. However, there is still a lack of adequate models for predicting the risk of death after cardiac surgery in elderly patients. This study sought to identify independent risk factors for 1-year all-cause mortality in elderly patients after cardiac surgery and to develop a predictive model. Methods A total of 3,752 elderly patients with cardiac surgery were enrolled from the Medical Information Mart for Intensive Care III (MIMIC-III) dataset and randomly divided into training and validation sets. The primary outcome was the all-cause mortality at 1 year. The Least absolute shrinkage and selection operator (LASSO) regression was used to decrease data dimensionality and select features. Multivariate logistic regression was used to establish the prediction model. The concordance index (C-index), receiver operating characteristic curve (ROC), and decision curve analysis (DCA) were used to measure the predictive performance of the nomogram. Results Our results demonstrated that age, sex, Sequential Organ Failure Assessment (SOFA), respiratory rate (RR), creatinine, glucose, and RBC transfusion (red blood cell) were independent factors for elderly patient mortality after cardiac surgery. The C-index of the training and validation sets was 0.744 (95%CI: 0.707-0.781) and 0.751 (95%CI: 0.709-0.794), respectively. The area under the curve (AUC) and decision curve analysis (DCA) results substantiated that the nomogram yielded an excellent performance predicting the 1-year all-cause mortality after cardiac surgery. Conclusions We developed a novel nomogram model for predicting the 1-year all-cause mortality for elderly patients after cardiac surgery, which could be an effective and useful clinical tool for clinicians for tailored therapy and prognosis prediction.
Collapse
Affiliation(s)
- Tonghui Xie
- Department of Hepatobiliary Surgery, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
| | - Qi Xin
- Department of Hepatobiliary Surgery, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
| | - Xing Zhang
- Department of Hepatobiliary Surgery, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
| | - Yingmu Tong
- Department of Hepatobiliary Surgery, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
| | - Hong Ren
- Department of Thoracic Surgery, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China,*Correspondence: Hong Ren
| | - Chang Liu
- Department of Hepatobiliary Surgery, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China,Chang Liu
| | - Jingyao Zhang
- Department of Hepatobiliary Surgery, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China,Department of Surgical ICU (SICU), The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China,Jingyao Zhang
| |
Collapse
|
15
|
Luo MH, Huang DL, Luo JC, Su Y, Li JK, Tu GW, Luo Z. Data science in the intensive care unit. World J Crit Care Med 2022; 11:311-316. [PMID: 36160936 PMCID: PMC9483002 DOI: 10.5492/wjccm.v11.i5.311] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/11/2022] [Revised: 05/03/2022] [Accepted: 07/17/2022] [Indexed: 02/05/2023] Open
Abstract
In this editorial, we comment on the current development and deployment of data science in intensive care units (ICUs). Data in ICUs can be classified into qualitative and quantitative data with different technologies needed to translate and interpret them. Data science, in the form of artificial intelligence (AI), should find the right interaction between physicians, data and algorithm. For individual patients and physicians, sepsis and mechanical ventilation have been two important aspects where AI has been extensively studied. However, major risks of bias, lack of generalizability and poor clinical values remain. AI deployment in the ICUs should be emphasized more to facilitate AI development. For ICU management, AI has a huge potential in transforming resource allocation. The coronavirus disease 2019 pandemic has given opportunities to establish such systems which should be investigated further. Ethical concerns must be addressed when designing such AI.
Collapse
Affiliation(s)
- Ming-Hao Luo
- Shanghai Medical College, Fudan University, Shanghai 200032, China
| | - Dan-Lei Huang
- Shanghai Medical College, Fudan University, Shanghai 200032, China
| | - Jing-Chao Luo
- Department of Critical Care Medicine, Zhongshan Hospital, Fudan University, Shanghai 200032, China
| | - Ying Su
- Department of Critical Care Medicine, Zhongshan Hospital, Fudan University, Shanghai 200032, China
| | - Jia-Kun Li
- Department of Critical Care Medicine, Zhongshan Hospital, Fudan University, Shanghai 200032, China
| | - Guo-Wei Tu
- Department of Critical Care Medicine, Zhongshan Hospital, Fudan University, Shanghai 200032, China
| | - Zhe Luo
- Department of Critical Care Medicine, Zhongshan Hospital, Fudan University, Shanghai 200032, China
| |
Collapse
|
16
|
Makhnevich A, Gandomi A, Wu Y, Qiu M, Jafari D, Rolston D, Tsegaye A, Hajizadeh N. A Novel Method to Improve the Identification of Time of Intubation for Retrospective EHR Data Analysis During a Time of Resource Strain, the COVID-19 Pandemic. Am J Med Qual 2022; 37:327-334. [PMID: 35285459 PMCID: PMC9241560 DOI: 10.1097/jmq.0000000000000048] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
Abstract
Accurate determinations of the time of intubation (TOI) are critical for retrospective electronic health record (EHR) data analyses. In a retrospective study, the authors developed and validated an improved query (Ti) to identify TOI across numerous settings in a large health system, using EHR data, during the COVID-19 pandemic. Further, they evaluated the affect of Ti on peri-intubation patient parameters compared to a previous method-ventilator parameters (Tv). Ti identified an earlier TOI for 84.8% (n = 1666) of cases with a mean (SD) of 3.5 hours (15.5), resulting in alternate values for: partial pressure of arterial oxygen (PaO 2 ) in 18.4% of patients (mean 43.95 mmHg [54.24]); PaO 2 /fractional inspired oxygen (FiO 2 ) in 17.8% of patients (mean 48.29 [69.81]), and oxygen saturation/FiO 2 in 62.7% (mean 16.75 [34.14]), using the absolute difference in mean values within the first 4 hours of intubation. Differences in PaO 2 /FiO 2 using Ti versus Tv resulted in the reclassification of 7.3% of patients into different acute respiratory distress syndrome (ARDS) severity categories.
Collapse
Affiliation(s)
- Alexander Makhnevich
- Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Manhasset, NY
- Center for Health Innovations and Outcomes Research, Feinstein Institute for Medical Research, Manhasset, NY
| | - Amir Gandomi
- Center for Health Innovations and Outcomes Research, Feinstein Institute for Medical Research, Manhasset, NY
- Frank G. Zarb School of Business, Hofstra University, Hempstead, NY
| | - Yiduo Wu
- AiD Technologies, Stony Brook, NY
| | - Michael Qiu
- Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Manhasset, NY
- Center for Health Innovations and Outcomes Research, Feinstein Institute for Medical Research, Manhasset, NY
| | - Daniel Jafari
- Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Manhasset, NY
| | - Daniel Rolston
- Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Manhasset, NY
| | - Adey Tsegaye
- Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Manhasset, NY
| | - Negin Hajizadeh
- Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Manhasset, NY
- Center for Health Innovations and Outcomes Research, Feinstein Institute for Medical Research, Manhasset, NY
| |
Collapse
|
17
|
Zhang K, Karanth S, Patel B, Murphy R, Jiang X. A multi-task Gaussian process self-attention neural network for real-time prediction of the need for mechanical ventilators in COVID-19 patients. J Biomed Inform 2022; 130:104079. [PMID: 35489596 PMCID: PMC9044651 DOI: 10.1016/j.jbi.2022.104079] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2021] [Revised: 04/06/2022] [Accepted: 04/18/2022] [Indexed: 02/04/2023]
Abstract
OBJECTIVE The Coronavirus Disease 2019 (COVID-19) pandemic has overwhelmed the capacity of healthcare resources and posed a challenge for worldwide hospitals. The ability to distinguish potentially deteriorating patients from the rest helps facilitate reasonable allocation of medical resources, such as ventilators, hospital beds, and human resources. The real-time accurate prediction of a patient's risk scores could also help physicians to provide earlier respiratory support for the patient and reduce the risk of mortality. METHODS We propose a robust real-time prediction model for the in-hospital COVID-19 patients' probability of requiring mechanical ventilation (MV). The end-to-end neural network model incorporates the Multi-task Gaussian Process to handle the irregular sampling rate in observational data together with a self-attention neural network for the prediction task. RESULTS We evaluate our model on a large database with 9,532 nationwide in-hospital patients with COVID-19. The model demonstrates significant robustness and consistency improvements compared to conventional machine learning models. The proposed prediction model also shows performance improvements in terms of area under the receiver operating characteristic curve (AUROC) and area under the precision-recall curve (AUPRC) compared to various deep learning models, especially at early times after a patient's hospital admission. CONCLUSION The availability of large and real-time clinical data calls for new methods to make the best use of them for real-time patient risk prediction. It is not ideal for simplifying the data for traditional methods or for making unrealistic assumptions that deviate from observation's true dynamics. We demonstrate a pilot effort to harmonize cross-sectional and longitudinal information for mechanical ventilation needing prediction.
Collapse
Affiliation(s)
- Kai Zhang
- School of Biomedical Informatics, University of Texas Health Science Center at Houston, Houston, TX 77030, USA.
| | - Siddharth Karanth
- Department of Internal Medicine, McGovern Medical School of The University of Texas Health Science Center at Houston, Houston, TX 77030, USA
| | - Bela Patel
- Department of Internal Medicine, McGovern Medical School of The University of Texas Health Science Center at Houston, Houston, TX 77030, USA
| | - Robert Murphy
- School of Biomedical Informatics, University of Texas Health Science Center at Houston, Houston, TX 77030, USA
| | - Xiaoqian Jiang
- School of Biomedical Informatics, University of Texas Health Science Center at Houston, Houston, TX 77030, USA
| |
Collapse
|
18
|
Wang H, Fan T, Yang B, Lin Q, Li W, Yang M. Development and Internal Validation of Supervised Machine Learning Algorithms for Predicting the Risk of Surgical Site Infection Following Minimally Invasive Transforaminal Lumbar Interbody Fusion. Front Med (Lausanne) 2022; 8:771608. [PMID: 34988091 PMCID: PMC8720930 DOI: 10.3389/fmed.2021.771608] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2021] [Accepted: 11/30/2021] [Indexed: 12/20/2022] Open
Abstract
Purpose: Machine Learning (ML) is rapidly growing in capability and is increasingly applied to model outcomes and complications in medicine. Surgical site infections (SSI) are a common post-operative complication in spinal surgery. This study aimed to develop and validate supervised ML algorithms for predicting the risk of SSI following minimally invasive transforaminal lumbar interbody fusion (MIS-TLIF). Methods: This single-central retrospective study included a total of 705 cases between May 2012 and October 2019. Data of patients who underwent MIS-TLIF was extracted by the electronic medical record system. The patient's clinical characteristics, surgery-related parameters, and routine laboratory tests were collected. Stepwise logistic regression analyses were used to screen and identify potential predictors for SSI. Then, these factors were imported into six ML algorithms, including k-Nearest Neighbor (KNN), Decision Tree (DT), Support Vector Machine (SVM), Random Forest (RF), Multi-Layer Perceptron (MLP), and Naïve Bayes (NB), to develop a prediction model for predicting the risk of SSI following MIS-TLIF under Quadrant channel. During the training process, 10-fold cross-validation was used for validation. Indices like the area under the receiver operating characteristic (AUC), sensitivity, specificity, and accuracy (ACC) were reported to test the performance of ML models. Results: Among the 705 patients, SSI occurred in 33 patients (4.68%). The stepwise logistic regression analyses showed that pre-operative glycated hemoglobin A1c (HbA1c), estimated blood loss (EBL), pre-operative albumin, body mass index (BMI), and age were potential predictors of SSI. In predicting SSI, six ML models posted an average AUC of 0.60–0.80 and an ACC of 0.80–0.95, with the NB model standing out, registering an average AUC and an ACC of 0.78 and 0.90. Then, the feature importance of the NB model was reported. Conclusions: ML algorithms are impressive tools in clinical decision-making, which can achieve satisfactory prediction of SSI with the NB model performing the best. The NB model may help access the risk of SSI following MIS-TLIF and facilitate clinical decision-making. However, future external validation is needed.
Collapse
Affiliation(s)
- Haosheng Wang
- Department of Orthopedics, Taizhou Central Hospital (Affiliated Hospital to Taizhou College), Taizhou, China.,Department of Orthopedics, Baoji City Hospital of Traditional Chinese Medicine, Baoji, China
| | - Tingting Fan
- Department of Endocrinology, Baoji City Hospital of Traditional Chinese Medicine, Baoji, China
| | - Bo Yang
- Department of Orthopedics, Baoji City Hospital of Traditional Chinese Medicine, Baoji, China
| | - Qiang Lin
- Department of Orthopedics, Baoji City Hospital of Traditional Chinese Medicine, Baoji, China
| | - Wenle Li
- Department of Orthopedics, Xianyang Central Hospital, Xianyang, China.,Clinical Medical Research Center, Xianyang Central Hospital, Xianyang, China
| | - Mingyu Yang
- Department of Orthopedics, Taizhou Central Hospital (Affiliated Hospital to Taizhou College), Taizhou, China
| |
Collapse
|
19
|
A Novel Survival Analysis Approach to Predict the Need for Intubation in Intensive Care Units. Artif Intell Med 2022. [DOI: 10.1007/978-3-031-09342-5_35] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
|
20
|
Feng X, Pan S, Yan M, Shen Y, Liu X, Cai G, Ning G. Dynamic prediction of late noninvasive ventilation failure in intensive care unit using a time adaptive machine model. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2021; 208:106290. [PMID: 34298473 DOI: 10.1016/j.cmpb.2021.106290] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/13/2021] [Accepted: 07/09/2021] [Indexed: 06/13/2023]
Abstract
BACKGROUND Noninvasive ventilation (NIV) failure is strongly associated with poor prognosis. Nowadays, plenty of mature studies have been proposed to predict early NIV failure (within 48 hours of NIV), however, the prediction for late NIV failure (after 48 hours of NIV) lacks sufficient research. Late NIV failure delays intubation resulting in the increasing mortality of the patients. Therefore, it is of great significance to expeditiously predict the late NIV failure. In order to dynamically predict late NIV failure, we proposed a Time Updated Light Gradient Boosting Machine (TULightGBM) model. MATERIAL AND METHODS In this work, 5653 patients undergoing NIV over 48 hours were extracted from the database of Medical Information Mart for Intensive Care Ⅲ (MIMIC-Ⅲ) for model construction. The TULightGBM model consists of a series of sub-models which learn clinical information from updating data within 48 hours of NIV and integrates the outputs of the sub-models by the dynamic attention mechanism to predict late NIV failure. The performance of the proposed TULightGBM model was assessed by comparison with common models of logistic regression (LR), random forest (RF), LightGBM, eXtreme gradient boosting (XGBoost), artificial neural network (ANN), and long short-term memory (LSTM). RESULTS The TULightGBM model yielded prediction results at 8, 16, 24, 36, and 48 hours after the start of the NIV with dynamic AUC values of 0.8323, 0.8435, 0.8576, 0.8886, and 0.9123, respectively. Furthermore, the sensitivity, specificity, and accuracy of the TULightGBM model were 0.8207, 0.8164, and 0.8184, respectively. The proposed model achieved superior performance over other tested models. CONCLUSIONS The TULightGBM model is able to dynamically predict the late NIV failure with high accuracy and offer potential decision support for clinical practice.
Collapse
Affiliation(s)
- Xue Feng
- Department of Biomedical Engineering, Zhejiang University, 38 Zheda Road, Hangzhou 310027, China.
| | - Su Pan
- Department of Biomedical Engineering, Zhejiang University, 38 Zheda Road, Hangzhou 310027, China
| | - Molei Yan
- Department of Intensive Care Unit, Zhejiang Hospital, 12 Lingyin Road, Hangzhou 310013, China
| | - Yanfei Shen
- Department of Intensive Care Unit, Zhejiang Hospital, 12 Lingyin Road, Hangzhou 310013, China
| | - Xiaoqing Liu
- Deepwise AI LAB, 8 Haidian Road, Beijng 100089, China
| | - Guolong Cai
- Department of Intensive Care Unit, Zhejiang Hospital, 12 Lingyin Road, Hangzhou 310013, China.
| | - Gangmin Ning
- Department of Biomedical Engineering, Zhejiang University, 38 Zheda Road, Hangzhou 310027, China.
| |
Collapse
|