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Zilker S, Weinzierl S, Kraus M, Zschech P, Matzner M. A machine learning framework for interpretable predictions in patient pathways: The case of predicting ICU admission for patients with symptoms of sepsis. Health Care Manag Sci 2024:10.1007/s10729-024-09673-8. [PMID: 38771522 DOI: 10.1007/s10729-024-09673-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2023] [Accepted: 04/13/2024] [Indexed: 05/22/2024]
Abstract
Proactive analysis of patient pathways helps healthcare providers anticipate treatment-related risks, identify outcomes, and allocate resources. Machine learning (ML) can leverage a patient's complete health history to make informed decisions about future events. However, previous work has mostly relied on so-called black-box models, which are unintelligible to humans, making it difficult for clinicians to apply such models. Our work introduces PatWay-Net, an ML framework designed for interpretable predictions of admission to the intensive care unit (ICU) for patients with symptoms of sepsis. We propose a novel type of recurrent neural network and combine it with multi-layer perceptrons to process the patient pathways and produce predictive yet interpretable results. We demonstrate its utility through a comprehensive dashboard that visualizes patient health trajectories, predictive outcomes, and associated risks. Our evaluation includes both predictive performance - where PatWay-Net outperforms standard models such as decision trees, random forests, and gradient-boosted decision trees - and clinical utility, validated through structured interviews with clinicians. By providing improved predictive accuracy along with interpretable and actionable insights, PatWay-Net serves as a valuable tool for healthcare decision support in the critical case of patients with symptoms of sepsis.
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Affiliation(s)
- Sandra Zilker
- Technische Hochschule Nürnberg Georg Simon Ohm, Professorship for Business Analytics, Hohfederstraße 40, 90489, Nuremberg, Germany.
- Friedrich-Alexander-Universität Erlangen-Nürnberg, Chair of Digital Industrial Service Systems, Fürther Straße 248, 90429, Nuremberg, Germany.
| | - Sven Weinzierl
- Friedrich-Alexander-Universität Erlangen-Nürnberg, Chair of Digital Industrial Service Systems, Fürther Straße 248, 90429, Nuremberg, Germany
| | - Mathias Kraus
- University of Regensburg, Chair for Explainable AI in Business Value Creation, Bajuwarenstraße 4, 93053, Regensburg, Germany
| | - Patrick Zschech
- Leipzig University, Professorship for Intelligent Information Systems and Processes, Grimmaische Straße 12, 04109, Leipzig, Germany
| | - Martin Matzner
- Friedrich-Alexander-Universität Erlangen-Nürnberg, Chair of Digital Industrial Service Systems, Fürther Straße 248, 90429, Nuremberg, Germany
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Fritz BA, Pugazenthi S, Budelier TP, Tellor Pennington BR, King CR, Avidan MS, Abraham J. User-Centered Design of a Machine Learning Dashboard for Prediction of Postoperative Complications. Anesth Analg 2024; 138:804-813. [PMID: 37339083 PMCID: PMC10730770 DOI: 10.1213/ane.0000000000006577] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/22/2023]
Abstract
BACKGROUND Machine learning models can help anesthesiology clinicians assess patients and make clinical and operational decisions, but well-designed human-computer interfaces are necessary for machine learning model predictions to result in clinician actions that help patients. Therefore, the goal of this study was to apply a user-centered design framework to create a user interface for displaying machine learning model predictions of postoperative complications to anesthesiology clinicians. METHODS Twenty-five anesthesiology clinicians (attending anesthesiologists, resident physicians, and certified registered nurse anesthetists) participated in a 3-phase study that included (phase 1) semistructured focus group interviews and a card sorting activity to characterize user workflows and needs; (phase 2) simulated patient evaluation incorporating a low-fidelity static prototype display interface followed by a semistructured interview; and (phase 3) simulated patient evaluation with concurrent think-aloud incorporating a high-fidelity prototype display interface in the electronic health record. In each phase, data analysis included open coding of session transcripts and thematic analysis. RESULTS During the needs assessment phase (phase 1), participants voiced that (a) identifying preventable risk related to modifiable risk factors is more important than nonpreventable risk, (b) comprehensive patient evaluation follows a systematic approach that relies heavily on the electronic health record, and (c) an easy-to-use display interface should have a simple layout that uses color and graphs to minimize time and energy spent reading it. When performing simulations using the low-fidelity prototype (phase 2), participants reported that (a) the machine learning predictions helped them to evaluate patient risk, (b) additional information about how to act on the risk estimate would be useful, and (c) correctable problems related to textual content existed. When performing simulations using the high-fidelity prototype (phase 3), usability problems predominantly related to the presentation of information and functionality. Despite the usability problems, participants rated the system highly on the System Usability Scale (mean score, 82.5; standard deviation, 10.5). CONCLUSIONS Incorporating user needs and preferences into the design of a machine learning dashboard results in a display interface that clinicians rate as highly usable. Because the system demonstrates usability, evaluation of the effects of implementation on both process and clinical outcomes is warranted.
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Affiliation(s)
| | | | | | | | | | | | - Joanna Abraham
- From the Department of Anesthesiology
- Institute for Informatics, Washington University School of Medicine, St. Louis, Missouri
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3
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França ARM, Rocha E, Bastos LSL, Bozza FA, Kurtz P, Maccariello E, Lapa E Silva JR, Salluh JIF. Development and validation of a machine learning model to predict the use of renal replacement therapy in 14,374 patients with COVID-19. J Crit Care 2024; 80:154480. [PMID: 38016226 DOI: 10.1016/j.jcrc.2023.154480] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2023] [Revised: 11/11/2023] [Accepted: 11/15/2023] [Indexed: 11/30/2023]
Abstract
PURPOSE To develop a model to predict the use of renal replacement therapy (RRT) in COVID-19 patients. MATERIALS AND METHODS Retrospective analysis of multicenter cohort of intensive care unit (ICU) admissions of Brazil involving COVID-19 critically adult patients, requiring ventilatory support, admitted to 126 Brazilian ICUs, from February 2020 to December 2021 (development) and January to May 2022 (validation). No interventions were performed. RESULTS Eight machine learning models' classifications were evaluated. Models were developed using an 80/20 testing/train split ratio and cross-validation. Thirteen candidate predictors were selected using the Recursive Feature Elimination (RFE) algorithm. Discrimination and calibration were assessed. Temporal validation was performed using data from 2022. Of 14,374 COVID-19 patients with initial respiratory support, 1924 (13%) required RRT. RRT patients were older (65 [53-75] vs. 55 [42-68]), had more comorbidities (Charlson's Comorbidity Index 1.0 [0.00-2.00] vs 0.0 [0.00-1.00]), had higher severity (SAPS-3 median: 61 [51-74] vs 48 [41-58]), and had higher in-hospital mortality (71% vs 22%) compared to non-RRT. Risk factors for RRT, such as Creatinine, Glasgow Coma Scale, Urea, Invasive Mechanical Ventilation, Age, Chronic Kidney Disease, Platelets count, Vasopressors, Noninvasive Ventilation, Hypertension, Diabetes, modified frailty index (mFI) and Gender, were identified. The best discrimination and calibration were found in the Random Forest (AUC [95%CI]: 0.78 [0.75-0.81] and Brier's Score: 0.09 [95%CI: 0.08-0.10]). The final model (Random Forest) showed comparable performance in the temporal validation (AUC [95%CI]: 0.79 [0.75-0.84] and Brier's Score, 0.08 [95%CI: 0.08-0.1]). CONCLUSIONS An early ML model using easily available clinical and laboratory data accurately predicted the use of RRT in critically ill patients with COVID-19. Our study demonstrates that using ML techniques is feasible to provide early prediction of use of RRT in COVID-19 patients.
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Affiliation(s)
- Allan R M França
- Postgraduate Program of Internal Medicine, Federal University of Rio de Janeiro, (UFRJ), Rio de Janeiro, Brazil.
| | - Eduardo Rocha
- Postgraduate Program of Internal Medicine, Federal University of Rio de Janeiro, (UFRJ), Rio de Janeiro, Brazil; Postgraduate Program, D'Or Institute for Research and Education (IDOR), Rio de Janeiro, RJ, Brazil
| | - Leonardo S L Bastos
- Department of Industrial Engineering (DEI), Pontifical Catholic University of Rio de Janeiro (PUC-Rio), Rio de Janeiro, RJ, Brazil
| | - Fernando A Bozza
- Postgraduate Program, D'Or Institute for Research and Education (IDOR), Rio de Janeiro, RJ, Brazil; National Institute of Infectious Disease Evandro Chagas (INI), Oswaldo Cruz Foundation (FIOCRUZ), Rio de Janeiro, RJ, Brazil
| | - Pedro Kurtz
- Postgraduate Program, D'Or Institute for Research and Education (IDOR), Rio de Janeiro, RJ, Brazil; Hospital Copa Star, Rio de Janeiro, RJ, Brazil
| | - Elizabeth Maccariello
- Postgraduate Program, D'Or Institute for Research and Education (IDOR), Rio de Janeiro, RJ, Brazil
| | - José Roberto Lapa E Silva
- Postgraduate Program of Internal Medicine, Federal University of Rio de Janeiro, (UFRJ), Rio de Janeiro, Brazil
| | - Jorge I F Salluh
- Postgraduate Program of Internal Medicine, Federal University of Rio de Janeiro, (UFRJ), Rio de Janeiro, Brazil; Postgraduate Program, D'Or Institute for Research and Education (IDOR), Rio de Janeiro, RJ, Brazil
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Padte S, Samala Venkata V, Mehta P, Tawfeeq S, Kashyap R, Surani S. 21st century critical care medicine: An overview. World J Crit Care Med 2024; 13:90176. [PMID: 38633477 PMCID: PMC11019625 DOI: 10.5492/wjccm.v13.i1.90176] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/26/2023] [Revised: 12/28/2023] [Accepted: 01/24/2024] [Indexed: 03/05/2024] Open
Abstract
Critical care medicine in the 21st century has witnessed remarkable advancements that have significantly improved patient outcomes in intensive care units (ICUs). This abstract provides a concise summary of the latest developments in critical care, highlighting key areas of innovation. Recent advancements in critical care include Precision Medicine: Tailoring treatments based on individual patient characteristics, genomics, and biomarkers to enhance the effectiveness of therapies. The objective is to describe the recent advancements in Critical Care Medicine. Telemedicine: The integration of telehealth technologies for remote patient monitoring and consultation, facilitating timely interventions. Artificial intelligence (AI): AI-driven tools for early disease detection, predictive analytics, and treatment optimization, enhancing clinical decision-making. Organ Support: Advanced life support systems, such as Extracorporeal Membrane Oxygenation and Continuous Renal Replacement Therapy provide better organ support. Infection Control: Innovative infection control measures to combat emerging pathogens and reduce healthcare-associated infections. Ventilation Strategies: Precision ventilation modes and lung-protective strategies to minimize ventilator-induced lung injury. Sepsis Management: Early recognition and aggressive management of sepsis with tailored interventions. Patient-Centered Care: A shift towards patient-centered care focusing on psychological and emotional well-being in addition to medical needs. We conducted a thorough literature search on PubMed, EMBASE, and Scopus using our tailored strategy, incorporating keywords such as critical care, telemedicine, and sepsis management. A total of 125 articles meeting our criteria were included for qualitative synthesis. To ensure reliability, we focused only on articles published in the English language within the last two decades, excluding animal studies, in vitro/molecular studies, and non-original data like editorials, letters, protocols, and conference abstracts. These advancements reflect a dynamic landscape in critical care medicine, where technology, research, and patient-centered approaches converge to improve the quality of care and save lives in ICUs. The future of critical care promises even more innovative solutions to meet the evolving challenges of modern medicine.
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Affiliation(s)
- Smitesh Padte
- Department of Research, Global Remote Research Scholars Program, St. Paul, MN 55104, United States
| | | | - Priyal Mehta
- Department of Research, Global Remote Research Scholars Program, St. Paul, MN 55104, United States
| | - Sawsan Tawfeeq
- Department of Research, Global Remote Research Scholars Program, St. Paul, MN 55104, United States
| | - Rahul Kashyap
- Department of Research, Global Remote Research Scholars Program, St. Paul, MN 55104, United States
- Department of Research, WellSpan Health, York, PA 17403, United States
- Department of Pulmonary & Critical Care Medicine, Mayo Clinic, Rochester, MN 55905, United States
| | - Salim Surani
- Department of Pulmonary & Critical Care Medicine, Mayo Clinic, Rochester, MN 55905, United States
- Department of Medicine & Pharmacology, Texas A&M University, College Station, TX 77843, United States
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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] [What about the content of this article? (0)] [Affiliation(s)] [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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Liu L, Song W, Patil N, Sainlaire M, Jasuja R, Dykes PC. Predicting COVID-19 severity: Challenges in reproducibility and deployment of machine learning methods. Int J Med Inform 2023; 179:105210. [PMID: 37769368 DOI: 10.1016/j.ijmedinf.2023.105210] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2023] [Revised: 08/29/2023] [Accepted: 08/30/2023] [Indexed: 09/30/2023]
Abstract
The increasing use of electronic health records (EHR) based computable phenotypes in clinical research is providing new opportunities for development of data-driven medical applications. Adopted widely in the United States and globally, EHRs facilitate systematic collection of patients' longitudinal information, which serves as one of the important foundations for artificial intelligence applications in medicine. Harmonization of input variables and outcome definitions is critically important for wider clinical applicability of artificial intelligence (AI) methodologies. In this review, we focused on Coronavirus Disease 2019 (COVID-19) severity machine learning prediction models and explored the pipeline for standardizing future disease severity model development using EHR information. We identified 2,967 studies published between 01/01/2020 and 02/15/2022 and selected 135 independent studies that had built machine learning prediction models to predict severity related outcomes of COVID-19 patients based on EHR data for the final review. These 135 studies spanning across 27 counties covered a broad range of severity related prediction outcomes. We observed substantial inconsistency in COVID-19 severity phenotype definitions among models in these studies. Moreover, there was a gap between the outcome of these models and clinician-recognized clinical concepts. Accordingly, we recommend that robust clinical input metrics, with outcome definitions which eliminate ambiguity in interpretation, to reduce algorithmic bias, mitigate model brittleness and improve generalizability of a universal model for COVID-19 severity. This framework can potentially be extended to broader clinical application.
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Affiliation(s)
- Luwei Liu
- Department of Medicine, Brigham & Women's Hospital, Boston, MA, USA
| | - Wenyu Song
- Department of Medicine, Brigham & Women's Hospital, Boston, MA, USA; Harvard Medical School, Boston, MA, USA
| | - Namrata Patil
- Department of Surgery, Brigham & Women's Hospital, Boston, MA, USA; Harvard Medical School, Boston, MA, USA
| | | | - Ravi Jasuja
- Department of Medicine, Brigham & Women's Hospital, Boston, MA, USA; Harvard Medical School, Boston, MA, USA.
| | - Patricia C Dykes
- Department of Medicine, Brigham & Women's Hospital, Boston, MA, USA; Harvard Medical School, Boston, MA, USA
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Chimbunde E, Sigwadhi LN, Tamuzi JL, Okango EL, Daramola O, Ngah VD, Nyasulu PS. Machine learning algorithms for predicting determinants of COVID-19 mortality in South Africa. Front Artif Intell 2023; 6:1171256. [PMID: 37899965 PMCID: PMC10600470 DOI: 10.3389/frai.2023.1171256] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2023] [Accepted: 08/15/2023] [Indexed: 10/31/2023] Open
Abstract
Background COVID-19 has strained healthcare resources, necessitating efficient prognostication to triage patients effectively. This study quantified COVID-19 risk factors and predicted COVID-19 intensive care unit (ICU) mortality in South Africa based on machine learning algorithms. Methods Data for this study were obtained from 392 COVID-19 ICU patients enrolled between 26 March 2020 and 10 February 2021. We used an artificial neural network (ANN) and random forest (RF) to predict mortality among ICU patients and a semi-parametric logistic regression with nine covariates, including a grouping variable based on K-means clustering. Further evaluation of the algorithms was performed using sensitivity, accuracy, specificity, and Cohen's K statistics. Results From the semi-parametric logistic regression and ANN variable importance, age, gender, cluster, presence of severe symptoms, being on the ventilator, and comorbidities of asthma significantly contributed to ICU death. In particular, the odds of mortality were six times higher among asthmatic patients than non-asthmatic patients. In univariable and multivariate regression, advanced age, PF1 and 2, FiO2, severe symptoms, asthma, oxygen saturation, and cluster 4 were strongly predictive of mortality. The RF model revealed that intubation status, age, cluster, diabetes, and hypertension were the top five significant predictors of mortality. The ANN performed well with an accuracy of 71%, a precision of 83%, an F1 score of 100%, Matthew's correlation coefficient (MCC) score of 100%, and a recall of 88%. In addition, Cohen's k-value of 0.75 verified the most extreme discriminative power of the ANN. In comparison, the RF model provided a 76% recall, an 87% precision, and a 65% MCC. Conclusion Based on the findings, we can conclude that both ANN and RF can predict COVID-19 mortality in the ICU with accuracy. The proposed models accurately predict the prognosis of COVID-19 patients after diagnosis. The models can be used to prioritize COVID-19 patients with a high mortality risk in resource-constrained ICUs.
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Affiliation(s)
- Emmanuel Chimbunde
- Division of Epidemiology and Biostatistics, Department of Global Health, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, South Africa
| | - Lovemore N. Sigwadhi
- Division of Epidemiology and Biostatistics, Department of Global Health, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, South Africa
| | - Jacques L. Tamuzi
- Division of Epidemiology and Biostatistics, Department of Global Health, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, South Africa
| | | | - Olawande Daramola
- Department of Information Technology, Faculty of Informatics and Design, Cape Peninsula University of Technology, Cape Town, South Africa
| | - Veranyuy D. Ngah
- Division of Epidemiology and Biostatistics, Department of Global Health, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, South Africa
| | - Peter S. Nyasulu
- Division of Epidemiology and Biostatistics, Department of Global Health, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, South Africa
- Division of Epidemiology and Biostatistics, School of Public Health, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
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Rosenberger P, Korell L, Haeberle HA, Mirakaj V, Bernard A, Tang L, Körner A, Martus P, Koeppen M. Early vvECMO implantation may be associated with lower mortality in ARDS. Respir Res 2023; 24:230. [PMID: 37752522 PMCID: PMC10521539 DOI: 10.1186/s12931-023-02541-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2023] [Accepted: 09/15/2023] [Indexed: 09/28/2023] Open
Abstract
BACKGROUND Venovenous extracorporeal membrane oxygenation (vvECMO) is used to treat hypoxia in patients with severe acute respiratory distress syndrome (ARDS). Nevertheless, uncertainty exists regarding the optimal timing of initiation of vvECMO therapy. We aimed to investigate the association between number of days of invasive mechanical ventilation (IMV) prior to vvECMO implantation and mortality. METHODS In this retrospective observational study, we included patients treated at an academic intensive care unit with vvECMO for severe ARDS. The primary outcome was all-cause 28-day mortality. We conducted a multivariate logistic regression analysis to estimate the association between number of days of IMV prior to vvECMO implantation and mortality after adjustment for confounders. RESULTS Out of 274 patients who underwent ECMO for severe ARDS, 158 patients (median age: 58 years) with relevant data were included in the analysis. The mean duration of IMV prior to vvECMO was significantly shorter in survivors than in nonsurvivors [survivors median: 1; interquartile range: 1-3; non-survivors median 4; interquartile range: 1-5.75; p = 0.0001). Logistic regression showed an association between the duration of ventilation prior to vvECMO and patient mortality. The odds ratio for the all-cause 28-day mortality and in-hospital mortality was significantly reduced in patients who received vvECMO within the first 5 days of IMV. CONCLUSIONS Early vvECMO implantation may be associated with lower mortality in ARDS.
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Affiliation(s)
- Peter Rosenberger
- Department of Anesthesiology and Intensive Care Medicine, University Hospital Tübingen, Hoppe-Seyler-Straße 3, 72076, Tübingen, Germany.
| | - Lisa Korell
- Department of Anesthesiology and Intensive Care Medicine, University Hospital Tübingen, Hoppe-Seyler-Straße 3, 72076, Tübingen, Germany
| | - Helene A Haeberle
- Department of Anesthesiology and Intensive Care Medicine, University Hospital Tübingen, Hoppe-Seyler-Straße 3, 72076, Tübingen, Germany
| | - Valbona Mirakaj
- Department of Anesthesiology and Intensive Care Medicine, University Hospital Tübingen, Hoppe-Seyler-Straße 3, 72076, Tübingen, Germany
| | - Alice Bernard
- Department of Anesthesiology and Intensive Care Medicine, University Hospital Tübingen, Hoppe-Seyler-Straße 3, 72076, Tübingen, Germany
| | - Linyan Tang
- Department of Anesthesiology and Intensive Care Medicine, University Hospital Tübingen, Hoppe-Seyler-Straße 3, 72076, Tübingen, Germany
| | - Andreas Körner
- Department of Anesthesiology and Intensive Care Medicine, University Hospital Tübingen, Hoppe-Seyler-Straße 3, 72076, Tübingen, Germany
| | - Peter Martus
- Department of Anesthesiology and Intensive Care Medicine, University Hospital Tübingen, Hoppe-Seyler-Straße 3, 72076, Tübingen, Germany
- Institute for Clinical Epidemiology and Applied Biometry, Faculty of Medicine, University of Tübingen, Tübingen, Germany
- University Hospital, Tübingen, Germany
| | - Michael Koeppen
- Department of Anesthesiology and Intensive Care Medicine, University Hospital Tübingen, Hoppe-Seyler-Straße 3, 72076, Tübingen, Germany.
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Abstract
Although intensive care medicine (ICM) is a relatively young discipline, it has rapidly developed into a full-fledged and highly specialized specialty covering several fields of medicine. The COVID-19 pandemic led to a surge in intensive care unit demand and also bring unprecedented development opportunities for this area. Multiple new technologies such as artificial intelligence (AI) and machine learning (ML) were gradually being applied in this field. In this study, through an online survey, we have summarized the potential uses of ChatGPT/GPT-4 in ICM range from knowledge augmentation, device management, clinical decision-making support, early warning systems, and establishment of intensive care unit (ICU) database.
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Affiliation(s)
- Yanqiu Lu
- Department of Intensive Care Unit, The Second Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China
| | - Haiyang Wu
- Department of Graduate School, Tianjin Medical University, Tianjin, China
- Duke Molecular Physiology Institute, Duke University School of Medicine, Durham, NC, USA
| | - Shaoyan Qi
- Department of Intensive Care Unit, The Second Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China.
| | - Kunming Cheng
- Department of Intensive Care Unit, The Second Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China.
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Datta D, George Dalmida S, Martinez L, Newman D, Hashemi J, Khoshgoftaar TM, Shorten C, Sareli C, Eckardt P. Using machine learning to identify patient characteristics to predict mortality of in-patients with COVID-19 in south Florida. Front Digit Health 2023; 5:1193467. [PMID: 37588022 PMCID: PMC10426497 DOI: 10.3389/fdgth.2023.1193467] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2023] [Accepted: 07/12/2023] [Indexed: 08/18/2023] Open
Abstract
Introduction The SARS-CoV-2 (COVID-19) pandemic has created substantial health and economic burdens in the US and worldwide. As new variants continuously emerge, predicting critical clinical events in the context of relevant individual risks is a promising option for reducing the overall burden of COVID-19. This study aims to train an AI-driven decision support system that helps build a model to understand the most important features that predict the "mortality" of patients hospitalized with COVID-19. Methods We conducted a retrospective analysis of "5,371" patients hospitalized for COVID-19-related symptoms from the South Florida Memorial Health Care System between March 14th, 2020, and January 16th, 2021. A data set comprising patients' sociodemographic characteristics, pre-existing health information, and medication was analyzed. We trained Random Forest classifier to predict "mortality" for patients hospitalized with COVID-19. Results Based on the interpretability of the model, age emerged as the primary predictor of "mortality", followed by diarrhea, diabetes, hypertension, BMI, early stages of kidney disease, smoking status, sex, pneumonia, and race in descending order of importance. Notably, individuals aged over 65 years (referred to as "older adults"), males, Whites, Hispanics, and current smokers were identified as being at higher risk of death. Additionally, BMI, specifically in the overweight and obese categories, significantly predicted "mortality". These findings indicated that the model effectively learned from various categories, such as patients' sociodemographic characteristics, pre-hospital comorbidities, and medications, with a predominant focus on characterizing pre-hospital comorbidities. Consequently, the model demonstrated the ability to predict "mortality" with transparency and reliability. Conclusion AI can potentially provide healthcare workers with the ability to stratify patients and streamline optimal care solutions when time is of the essence and resources are limited. This work sets the platform for future work that forecasts patient responses to treatments at various levels of disease severity and assesses health disparities and patient conditions that promote improved health care in a broader context. This study contributed to one of the first predictive analyses applying AI/ML techniques to COVID-19 data using a vast sample from South Florida.
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Affiliation(s)
- Debarshi Datta
- Christine E. Lynn College of Nursing, Florida Atlantic University, Boca Raton, FL, United States
| | - Safiya George Dalmida
- Christine E. Lynn College of Nursing, Florida Atlantic University, Boca Raton, FL, United States
| | - Laurie Martinez
- Christine E. Lynn College of Nursing, Florida Atlantic University, Boca Raton, FL, United States
| | - David Newman
- Christine E. Lynn College of Nursing, Florida Atlantic University, Boca Raton, FL, United States
| | - Javad Hashemi
- College of Engineering & Computer Science, Florida Atlantic University, Boca Raton, FL, United States
| | - Taghi M. Khoshgoftaar
- College of Engineering & Computer Science, Florida Atlantic University, Boca Raton, FL, United States
| | - Connor Shorten
- College of Engineering & Computer Science, Florida Atlantic University, Boca Raton, FL, United States
| | - Candice Sareli
- Memorial Healthcare System, Hollywood, FL, United States
| | - Paula Eckardt
- Memorial Healthcare System, Hollywood, FL, United States
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11
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Morís DI, de Moura J, Marcos PJ, Rey EM, Novo J, Ortega M. Comprehensive analysis of clinical data for COVID-19 outcome estimation with machine learning models. Biomed Signal Process Control 2023; 84:104818. [PMID: 36915863 PMCID: PMC9995330 DOI: 10.1016/j.bspc.2023.104818] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2022] [Revised: 11/22/2022] [Accepted: 03/05/2023] [Indexed: 03/11/2023]
Abstract
COVID-19 is a global threat for the healthcare systems due to the rapid spread of the pathogen that causes it. In such situation, the clinicians must take important decisions, in an environment where medical resources can be insufficient. In this task, the computer-aided diagnosis systems can be very useful not only in the task of supporting the clinical decisions but also to perform relevant analyses, allowing them to understand better the disease and the factors that can identify the high risk patients. For those purposes, in this work, we use several machine learning algorithms to estimate the outcome of COVID-19 patients given their clinical information. Particularly, we perform 2 different studies: the first one estimates whether the patient is at low or at high risk of death whereas the second estimates if the patient needs hospitalization or not. The results of the analyses of this work show the most relevant features for each studied scenario, as well as the classification performance of the considered machine learning models. In particular, the XGBoost algorithm is able to estimate the need for hospitalization of a patient with an AUC-ROC of 0 . 8415 ± 0 . 0217 while it can also estimate the risk of death with an AUC-ROC of 0 . 7992 ± 0 . 0104 . Results have demonstrated the great potential of the proposal to determine those patients that need a greater amount of medical resources for being at a higher risk. This provides the healthcare services with a tool to better manage their resources.
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Affiliation(s)
- Daniel I Morís
- Centro de Investigación CITIC, Universidade da Coruña, Campus de Elviña, s/n, 15071 A Coruña, Spain.,Grupo VARPA, Instituto de Investigación Biomédica de A Coruña (INIBIC), Universidade da Coruña, Xubias de Arriba, 84, 15006 A Coruña, Spain
| | - Joaquim de Moura
- Centro de Investigación CITIC, Universidade da Coruña, Campus de Elviña, s/n, 15071 A Coruña, Spain.,Grupo VARPA, Instituto de Investigación Biomédica de A Coruña (INIBIC), Universidade da Coruña, Xubias de Arriba, 84, 15006 A Coruña, Spain
| | - Pedro J Marcos
- Dirección Asistencial y Servicio de Neumología, Complejo Hospitalario Universitario de A Coruña (CHUAC), Instituto de Investigación Biomédica de A Coruña (INIBIC), Universidade da Coruña, Sergas, 15006 A Coruña, Spain
| | - Enrique Míguez Rey
- Grupo de Investigación en Virología Clínica, Sección de Enfermedades Infecciosas, Servicio de Medicina Interna, Instituto de Investigación Biomédica de A Coruña (INIBIC), Área Sanitaria A Coruña y CEE (ASCC), SERGAS, 15006 A Coruña, Spain
| | - Jorge Novo
- Centro de Investigación CITIC, Universidade da Coruña, Campus de Elviña, s/n, 15071 A Coruña, Spain.,Grupo VARPA, Instituto de Investigación Biomédica de A Coruña (INIBIC), Universidade da Coruña, Xubias de Arriba, 84, 15006 A Coruña, Spain
| | - Marcos Ortega
- Centro de Investigación CITIC, Universidade da Coruña, Campus de Elviña, s/n, 15071 A Coruña, Spain.,Grupo VARPA, Instituto de Investigación Biomédica de A Coruña (INIBIC), Universidade da Coruña, Xubias de Arriba, 84, 15006 A Coruña, Spain
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12
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Heller AR, Bartenschlager C, Brunner JO, Marckmann G. [German "Triage Act"-Regulation with fatal consequences]. Anaesthesiologie 2023:10.1007/s00101-023-01286-0. [PMID: 37233790 DOI: 10.1007/s00101-023-01286-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Accepted: 03/27/2023] [Indexed: 05/27/2023]
Abstract
With the coming into force of § 5c of the Infection Protection Act (IfSG), the so-called Triage Act, on 14 December 2022, a protracted discussion has come to a provisional conclusion, the result of which physicians and social associations but also lawyers and ethicists are equally dissatisfied. The explicit exclusion of the discontinuation of treatment that has already begun in favor of new patients with better chances of success (so-called tertiary or ex-post triage) prevents allocation decisions with the aim of enabling as many patients as possible to beneficially participate in medical care under crisis conditions. The result of the new regulation is de facto a first come first served allocation, which is associated with the highest mortality even among individuals with limitations or disabilities and was rejected by a large margin as unfair in a population survey. Mandating allocation decisions based on the likelihood of success but which are not permitted to be consistently implemented and prohibiting, for example the use of age and frailty as prioritization criteria, although both factors most strongly determine the short-term probability of survival according to evident data, shows the contradictory and dogmatic nature of the regulation. The only remaining possibility is the consistent termination of treatment that is no longer indicated or desired by the patient, regardless of the current resource situation; however, if a different decision is made in a crisis situation than in a situation without a lack of resources, this practice would not be justified and would be punishable. Accordingly, the highest efforts must be set on legally compliant documentation, especially in the stage of decompensated crisis care in a region. The goal of enabling as many patients as possible to beneficially participate in medical care under crisis conditions is in any case thwarted by the new German Triage Act.
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Affiliation(s)
- A R Heller
- Klinik für Anästhesiologie und Operative Intensivmedizin, Universitätsklinikum Augsburg, Stenglinstr. 2, 86156, Augsburg, Deutschland.
| | - C Bartenschlager
- Health Care Operations/Health Information Management, Wirtschaftswissenschaftliche und Medizinische Fakultät, Universität Augsburg, Augsburg, Deutschland
| | - J O Brunner
- Zentrum für Interdisziplinäre Gesundheitsforschung, Universität Augsburg, Augsburg, Deutschland
| | - G Marckmann
- Institut für Ethik, Geschichte und Theorie der Medizin, Ludwig-Maximilians-Universität München, München, Deutschland
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13
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Kołodziejczak MM, Sierakowska K, Tkachenko Y, Kowalski P. Artificial Intelligence in the Intensive Care Unit: Present and Future in the COVID-19 Era. J Pers Med 2023; 13:891. [PMID: 37373880 DOI: 10.3390/jpm13060891] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2023] [Revised: 05/18/2023] [Accepted: 05/22/2023] [Indexed: 06/29/2023] Open
Abstract
The development of artificial intelligence (AI) allows for the construction of technologies capable of implementing functions that represent the human mind, senses, and problem-solving skills, leading to automation, rapid data analysis, and acceleration of tasks. These solutions has been initially implemented in medical fields relying on image analysis; however, technological development and interdisciplinary collaboration allows for the introduction of AI-based enhancements to further medical specialties. During the COVID-19 pandemic, novel technologies established on big data analysis experienced a rapid expansion. Yet, despite the possibilities of advancements with these AI technologies, there are number of shortcomings that need to be resolved to assert the highest and the safest level of performance, especially in the setting of the intensive care unit (ICU). Within the ICU, numerous factors and data affect clinical decision making and work management that could be managed by AI-based technologies. Early detection of a patient's deterioration, identification of unknown prognostic parameters, or even improvement of work organization are a few of many areas where patients and medical personnel can benefit from solutions developed with AI.
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Affiliation(s)
- Michalina Marta Kołodziejczak
- Department of Anesthesiology and Intensive Care, Collegium Medicum Bydgoszcz, Nicolaus Copernicus University Torun, Antoni Jurasz University Hospital No.1, 85-094 Bydgoszcz, Poland
| | - Katarzyna Sierakowska
- Department of Anesthesiology and Intensive Care, Collegium Medicum Bydgoszcz, Nicolaus Copernicus University Torun, Antoni Jurasz University Hospital No.1, 85-094 Bydgoszcz, Poland
| | - Yurii Tkachenko
- Department of Anesthesiology and Intensive Care, Władysław Biegański Regional Specialized Hospital, 86-300 Grudziadz, Poland
| | - Piotr Kowalski
- Department of Anesthesiology and Intensive Care, Władysław Biegański Regional Specialized Hospital, 86-300 Grudziadz, Poland
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14
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Buttia C, Llanaj E, Raeisi-Dehkordi H, Kastrati L, Amiri M, Meçani R, Taneri PE, Ochoa SAG, Raguindin PF, Wehrli F, Khatami F, Espínola OP, Rojas LZ, de Mortanges AP, Macharia-Nimietz EF, Alijla F, Minder B, Leichtle AB, Lüthi N, Ehrhard S, Que YA, Fernandes LK, Hautz W, Muka T. Prognostic models in COVID-19 infection that predict severity: a systematic review. Eur J Epidemiol 2023; 38:355-372. [PMID: 36840867 PMCID: PMC9958330 DOI: 10.1007/s10654-023-00973-x] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2022] [Accepted: 01/28/2023] [Indexed: 02/26/2023]
Abstract
Current evidence on COVID-19 prognostic models is inconsistent and clinical applicability remains controversial. We performed a systematic review to summarize and critically appraise the available studies that have developed, assessed and/or validated prognostic models of COVID-19 predicting health outcomes. We searched six bibliographic databases to identify published articles that investigated univariable and multivariable prognostic models predicting adverse outcomes in adult COVID-19 patients, including intensive care unit (ICU) admission, intubation, high-flow nasal therapy (HFNT), extracorporeal membrane oxygenation (ECMO) and mortality. We identified and assessed 314 eligible articles from more than 40 countries, with 152 of these studies presenting mortality, 66 progression to severe or critical illness, 35 mortality and ICU admission combined, 17 ICU admission only, while the remaining 44 studies reported prediction models for mechanical ventilation (MV) or a combination of multiple outcomes. The sample size of included studies varied from 11 to 7,704,171 participants, with a mean age ranging from 18 to 93 years. There were 353 prognostic models investigated, with area under the curve (AUC) ranging from 0.44 to 0.99. A great proportion of studies (61.5%, 193 out of 314) performed internal or external validation or replication. In 312 (99.4%) studies, prognostic models were reported to be at high risk of bias due to uncertainties and challenges surrounding methodological rigor, sampling, handling of missing data, failure to deal with overfitting and heterogeneous definitions of COVID-19 and severity outcomes. While several clinical prognostic models for COVID-19 have been described in the literature, they are limited in generalizability and/or applicability due to deficiencies in addressing fundamental statistical and methodological concerns. Future large, multi-centric and well-designed prognostic prospective studies are needed to clarify remaining uncertainties.
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Affiliation(s)
- Chepkoech Buttia
- Institute of Social and Preventive Medicine, University of Bern, Bern, Switzerland
- Emergency Department, Inselspital, Bern University Hospital, University of Bern, Freiburgstrasse 16C, 3010 Bern, Switzerland
- Epistudia, Bern, Switzerland
| | - Erand Llanaj
- Department of Molecular Epidemiology, German Institute of Human Nutrition Potsdam-Rehbrücke, Nuthetal, Germany
- ELKH-DE Public Health Research Group of the Hungarian Academy of Sciences, Department of Public Health and Epidemiology, Faculty of Medicine, University of Debrecen, Debrecen, Hungary
- Epistudia, Bern, Switzerland
- German Center for Diabetes Research (DZD), München-Neuherberg, Germany
| | - Hamidreza Raeisi-Dehkordi
- Institute of Social and Preventive Medicine, University of Bern, Bern, Switzerland
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
| | - Lum Kastrati
- Institute of Social and Preventive Medicine, University of Bern, Bern, Switzerland
- Graduate School for Health Sciences, University of Bern, Bern, Switzerland
- Department of Diabetes, Endocrinology, Nutritional Medicine and Metabolism, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
| | - Mojgan Amiri
- Department of Epidemiology, Erasmus MC University Medical Center, Rotterdam, The Netherlands
| | - Renald Meçani
- Department of Pediatrics, “Mother Teresa” University Hospital Center, Tirana, University of Medicine, Tirana, Albania
- Division of Endocrinology and Diabetology, Department of Internal Medicine, Medical University of Graz, Graz, Austria
| | - Petek Eylul Taneri
- Institute of Social and Preventive Medicine, University of Bern, Bern, Switzerland
- HRB-Trials Methodology Research Network College of Medicine, Nursing and Health Sciences University of Galway, Galway, Ireland
| | | | - Peter Francis Raguindin
- Institute of Social and Preventive Medicine, University of Bern, Bern, Switzerland
- Swiss Paraplegic Research, Nottwil, Switzerland
- Faculty of Health Sciences, University of Lucerne, Lucerne, Switzerland
| | - Faina Wehrli
- Institute of Social and Preventive Medicine, University of Bern, Bern, Switzerland
| | - Farnaz Khatami
- Institute of Social and Preventive Medicine, University of Bern, Bern, Switzerland
- Graduate School for Health Sciences, University of Bern, Bern, Switzerland
- Department of Community Medicine, Tehran University of Medical Sciences, Tehran, Iran
| | - Octavio Pano Espínola
- Institute of Social and Preventive Medicine, University of Bern, Bern, Switzerland
- Department of Preventive Medicine and Public Health, University of Navarre, Pamplona, Spain
- Navarra Institute for Health Research, IdiSNA, Pamplona, Spain
| | - Lyda Z. Rojas
- Research Group and Development of Nursing Knowledge (GIDCEN-FCV), Research Center, Cardiovascular Foundation of Colombia, Floridablanca, Santander, Colombia
| | | | | | - Fadi Alijla
- Institute of Social and Preventive Medicine, University of Bern, Bern, Switzerland
- Graduate School for Health Sciences, University of Bern, Bern, Switzerland
| | - Beatrice Minder
- Public Health and Primary Care Library, University Library of Bern, University of Bern, Bern, Switzerland
| | - Alexander B. Leichtle
- University Institute of Clinical Chemistry, Inselspital, Bern University Hospital, and Center for Artificial Intelligence in Medicine (CAIM), University of Bern, Bern, Switzerland
| | - Nora Lüthi
- Emergency Department, Inselspital, Bern University Hospital, University of Bern, Freiburgstrasse 16C, 3010 Bern, Switzerland
| | - Simone Ehrhard
- Emergency Department, Inselspital, Bern University Hospital, University of Bern, Freiburgstrasse 16C, 3010 Bern, Switzerland
| | - Yok-Ai Que
- Department of Intensive Care Medicine, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
| | - Laurenz Kopp Fernandes
- Deutsches Herzzentrum Berlin (DHZB), Berlin, Germany
- Charité Universitätsmedizin Berlin, Berlin, Germany
| | - Wolf Hautz
- Emergency Department, Inselspital, Bern University Hospital, University of Bern, Freiburgstrasse 16C, 3010 Bern, Switzerland
| | - Taulant Muka
- Institute of Social and Preventive Medicine, University of Bern, Bern, Switzerland
- Epistudia, Bern, Switzerland
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15
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Chandel A, Leazer S, Alcover KC, Farley J, Berk J, Jayne C, Mcnutt R, Olsen M, Allard R, Yang J, Johnson C, Tripathi A, Rechtin M, Leon M, Williams M, Sheth P, Messer K, Chung KK, Collen J. Intensive Care and Organ Support Related Mortality in Patients With COVID-19: A Systematic Review and Meta-Analysis. Crit Care Explor 2023; 5:e0876. [PMID: 36890875 DOI: 10.1097/CCE.0000000000000876] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/06/2023] Open
Abstract
To perform a systematic review and meta-analysis to generate estimates of mortality in patients with COVID-19 that required hospitalization, ICU admission, and organ support. DATA SOURCES A systematic search of PubMed, Embase, and the Cochrane databases was conducted up to December 31, 2021. STUDY SELECTION Previously peer-reviewed observational studies that reported ICU, mechanical ventilation (MV), renal replacement therapy (RRT) or extracorporeal membrane oxygenation (ECMO)-related mortality among greater than or equal to 100 individual patients. DATA EXTRACTION Random-effects meta-analysis was used to generate pooled estimates of case fatality rates (CFRs) for in-hospital, ICU, MV, RRT, and ECMO-related mortality. ICU-related mortality was additionally analyzed by the study country of origin. Sensitivity analyses of CFR were assessed based on completeness of follow-up data, by year, and when only studies judged to be of high quality were included. DATA SYNTHESIS One hundred fifty-seven studies evaluating 948,309 patients were included. The CFR for in-hospital mortality, ICU mortality, MV, RRT, and ECMO were 25.9% (95% CI: 24.0-27.8%), 37.3% (95% CI: 34.6-40.1%), 51.6% (95% CI: 46.1-57.0%), 66.1% (95% CI: 59.7-72.2%), and 58.0% (95% CI: 46.9-68.9%), respectively. MV (52.7%, 95% CI: 47.5-58.0% vs 31.3%, 95% CI: 16.1-48.9%; p = 0.023) and RRT-related mortality (66.7%, 95% CI: 60.1-73.0% vs 50.3%, 95% CI: 42.4-58.2%; p = 0.003) decreased from 2020 to 2021. CONCLUSIONS We present updated estimates of CFR for patients hospitalized and requiring intensive care for the management of COVID-19. Although mortality remain high and varies considerably worldwide, we found the CFR in patients supported with MV significantly improved since 2020.
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16
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Lucas F, Sadigh S. Hematopathology of SARS-CoV-2 infection and COVID-19 disease. Surg Pathol Clin 2023; 16:197-211. [PMID: 37149356 PMCID: PMC9892324 DOI: 10.1016/j.path.2023.01.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
Coronavirus disease 2019 is caused by severe acute respiratory syndrome coronavirus 2 and is associated with pronounced hematopathologic findings. Peripheral blood features are heterogeneous and very often include neutrophilia, lymphopenia, myeloid left shift, abnormally segmented neutrophils, atypical lymphocytes/plasmacytoid lymphocytes, and atypical monocytes. Bone marrow biopsies and aspirates are often notable for histiocytosis and hemophagocytosis, whereas secondary lymphoid organs may exhibit lymphocyte depletion, pronounced plasmacytoid infiltrates, and hemophagocytosis. These changes are reflective of profound innate and adaptive immune dysregulation, and ongoing research efforts continue to identify clinically applicable biomarkers of disease severity and outcome.
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Affiliation(s)
- Fabienne Lucas
- Department of Pathology, Brigham and Women's Hospital, 75 Francis Street, Boston, MA 02115, USA
| | - Sam Sadigh
- Department of Pathology, Brigham and Women's Hospital, 75 Francis Street, Boston, MA 02115, USA.
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17
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Amiri P, Montazeri M, Ghasemian F, Asadi F, Niksaz S, Sarafzadeh F, Khajouei R. Prediction of mortality risk and duration of hospitalization of COVID-19 patients with chronic comorbidities based on machine learning algorithms. Digit Health 2023; 9:20552076231170493. [PMID: 37312960 PMCID: PMC10259141 DOI: 10.1177/20552076231170493] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2022] [Accepted: 03/31/2023] [Indexed: 06/15/2023] Open
Abstract
Background The severity of coronavirus (COVID-19) in patients with chronic comorbidities is much higher than in other patients, which can lead to their death. Machine learning (ML) algorithms as a potential solution for rapid and early clinical evaluation of the severity of the disease can help in allocating and prioritizing resources to reduce mortality. Objective The objective of this study was to predict the mortality risk and length of stay (LoS) of patients with COVID-19 and history of chronic comorbidities using ML algorithms. Methods This retrospective study was conducted by reviewing the medical records of COVID-19 patients with a history of chronic comorbidities from March 2020 to January 2021 in Afzalipour Hospital in Kerman, Iran. The outcome of patients, hospitalization was recorded as discharge or death. The filtering technique used to score the features and well-known ML algorithms were applied to predict the risk of mortality and LoS of patients. Ensemble Learning methods is also used. To evaluate the performance of the models, different measures including F1, precision, recall, and accuracy were calculated. The TRIPOD guideline assessed transparent reporting. Results This study was performed on 1291 patients, including 900 alive and 391 dead patients. Shortness of breath (53.6%), fever (30.1%), and cough (25.3%) were the three most common symptoms in patients. Diabetes mellitus(DM) (31.3%), hypertension (HTN) (27.3%), and ischemic heart disease (IHD) (14.2%) were the three most common chronic comorbidities of patients. Twenty-six important factors were extracted from each patient's record. Gradient boosting model with 84.15% accuracy was the best model for predicting mortality risk and multilayer perceptron (MLP) with rectified linear unit function (MSE = 38.96) was the best model for predicting the LoS. The most common chronic comorbidities among these patients were DM (31.3%), HTN (27.3%), and IHD (14.2%). The most important factors in predicting the risk of mortality were hyperlipidemia, diabetes, asthma, and cancer, and in predicting LoS was shortness of breath. Conclusion The results of this study showed that the use of ML algorithms can be a good tool to predict the risk of mortality and LoS of patients with COVID-19 and chronic comorbidities based on physiological conditions, symptoms, and demographic information of patients. The Gradient boosting and MLP algorithms can quickly identify patients at risk of death or long-term hospitalization and notify physicians to do appropriate interventions.
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Affiliation(s)
- Parastoo Amiri
- Student Research Committee, Kerman University of Medical Sciences, Kerman, Iran
| | - Mahdieh Montazeri
- Department of Health Information Sciences, Faculty of Management and Medical Information Sciences, Kerman University of Medical Sciences, Kerman, Iran
| | - Fahimeh Ghasemian
- Computer Engineering Department, Faculty of Engineering, Shahid Bahonar University of Kerman, Kerman, Iran
| | - Fatemeh Asadi
- Student Research Committee, School of Management and Medical Information, Kerman University of Medical Sciences, Kerman, Iran
| | - Saeed Niksaz
- Computer Engineering Department, Faculty of Engineering, Shahid Bahonar University of Kerman, Kerman, Iran
| | - Farhad Sarafzadeh
- Infectious and Internal Medicine Department, Afzalipour Hospital, Kerman University of Medical Sciences, Kerman, Iran
| | - Reza Khajouei
- Department of Health Information Sciences, Faculty of Management and Medical Information Sciences, Kerman University of Medical Sciences, Kerman, Iran
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18
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Haeberle HA, Calov S, Martus P, Serna-Higuita LM, Koeppen M, Goll A, Bernard A, Zarbock A, Meersch M, Weiss R, Mehrländer M, Marx G, Putensen C, Bakchoul T, Magunia H, Nieswandt B, Mirakaj V, Rosenberger P. Inhaled prostacyclin therapy in the acute respiratory distress syndrome: a randomized controlled multicenter trial. Respir Res 2023; 24:58. [PMID: 36805707 PMCID: PMC9938510 DOI: 10.1186/s12931-023-02346-0] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2022] [Accepted: 01/26/2023] [Indexed: 02/20/2023] Open
Abstract
BACKGROUND Acute respiratory distress syndrome (ARDS) results in significant hypoxia, and ARDS is the central pathology of COVID-19. Inhaled prostacyclin has been proposed as a therapy for ARDS, but data regarding its role in this syndrome are unavailable. Therefore, we investigated whether inhaled prostacyclin would affect the oxygenation and survival of patients suffering from ARDS. METHODS We performed a prospective randomized controlled single-blind multicenter trial across Germany. The trial was conducted from March 2019 with final follow-up on 12th of August 2021. Patients with moderate to severe ARDS were included and randomized to receive either inhaled prostacyclin (3 times/day for 5 days) or sodium chloride (Placebo). The primary outcome was the oxygenation index in the intervention and control groups on Day 5 of therapy. Secondary outcomes were mortality, secondary organ failure, disease severity and adverse events. RESULTS Of 707 patients approached 150 patients were randomized to receive inhaled prostacyclin (n = 73) or sodium chloride (n = 77). Data from 144 patients were analyzed. The baseline PaO2/FiO2 ratio did not differ between groups. The primary analysis of the study was negative, and prostacyclin improved oxygenation by 20 mmHg more than Placebo (p = 0.17). Secondary analysis showed that the oxygenation was significantly improved in patients with ARDS who were COVID-19-positive (34 mmHg, p = 0.04). Mortality did not differ between groups. Secondary organ failure and adverse events were similar in the intervention and control groups. CONCLUSIONS The primary result of our study was negative. Our data suggest that inhaled prostacyclin might be beneficial treatment in patients with COVID-19 induced ARDS. TRIAL REGISTRATION The study was approved by the Institutional Review Board of the Research Ethics Committee of the University of Tübingen (899/2018AMG1) and the corresponding ethical review boards of all participating centers. The trial was also approved by the Federal Institute for Drugs and Medical Devices (BfArM, EudraCT No. 2016003168-37) and registered at clinicaltrials.gov (NCT03111212) on April 6th 2017.
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Affiliation(s)
- Helene A. Haeberle
- grid.411544.10000 0001 0196 8249Department of Anesthesiology and Intensive Care Medicine, Tübingen University Hospital, Hoppe-Seyler-Straße 3, 72076 Tübingen, Germany
| | - Stefanie Calov
- grid.411544.10000 0001 0196 8249Department of Anesthesiology and Intensive Care Medicine, Tübingen University Hospital, Hoppe-Seyler-Straße 3, 72076 Tübingen, Germany
| | - Peter Martus
- grid.10392.390000 0001 2190 1447Institute for Clinical Epidemiology and Applied Biometry, Faculty of Medicine, University of Tübingen, Tübingen, Germany
| | - Lina Maria Serna-Higuita
- grid.10392.390000 0001 2190 1447Institute for Clinical Epidemiology and Applied Biometry, Faculty of Medicine, University of Tübingen, Tübingen, Germany
| | - Michael Koeppen
- grid.411544.10000 0001 0196 8249Department of Anesthesiology and Intensive Care Medicine, Tübingen University Hospital, Hoppe-Seyler-Straße 3, 72076 Tübingen, Germany
| | - Almuth Goll
- grid.411544.10000 0001 0196 8249Department of Anesthesiology and Intensive Care Medicine, Tübingen University Hospital, Hoppe-Seyler-Straße 3, 72076 Tübingen, Germany
| | - Alice Bernard
- grid.411544.10000 0001 0196 8249Department of Anesthesiology and Intensive Care Medicine, Tübingen University Hospital, Hoppe-Seyler-Straße 3, 72076 Tübingen, Germany
| | - Alexander Zarbock
- grid.5949.10000 0001 2172 9288Department of Anesthesiology, Intensive Care and Pain Medicine, University of Münster, Münster, Germany
| | - Melanie Meersch
- grid.5949.10000 0001 2172 9288Department of Anesthesiology, Intensive Care and Pain Medicine, University of Münster, Münster, Germany
| | - Raphael Weiss
- grid.5949.10000 0001 2172 9288Department of Anesthesiology, Intensive Care and Pain Medicine, University of Münster, Münster, Germany
| | - Martin Mehrländer
- grid.411544.10000 0001 0196 8249Department of Anesthesiology and Intensive Care Medicine, Tübingen University Hospital, Hoppe-Seyler-Straße 3, 72076 Tübingen, Germany
| | - Gernot Marx
- grid.412301.50000 0000 8653 1507Department of Intensive Care Medicine, University Hospital RWTH Aachen, Aachen, Germany
| | - Christian Putensen
- grid.15090.3d0000 0000 8786 803XDepartment of Anesthesiology and Intensive Care Medicine, University Hospital Bonn, Bonn, Germany
| | - Tamam Bakchoul
- grid.411544.10000 0001 0196 8249Transfusion Medicine, Medical Faculty of Tuebingen, University Hospital of Tuebingen, Tübingen, Germany
| | - Harry Magunia
- grid.411544.10000 0001 0196 8249Department of Anesthesiology and Intensive Care Medicine, Tübingen University Hospital, Hoppe-Seyler-Straße 3, 72076 Tübingen, Germany
| | - Bernhard Nieswandt
- grid.411760.50000 0001 1378 7891Institute of Experimental Biomedicine I, University Hospital Würzburg, Würzburg, Germany
| | - Valbona Mirakaj
- grid.411544.10000 0001 0196 8249Department of Anesthesiology and Intensive Care Medicine, Tübingen University Hospital, Hoppe-Seyler-Straße 3, 72076 Tübingen, Germany
| | - Peter Rosenberger
- Department of Anesthesiology and Intensive Care Medicine, Tübingen University Hospital, Hoppe-Seyler-Straße 3, 72076, Tübingen, Germany.
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van de Sand L, Braß P, Gregorius J, Pattberg K, Engler A, Dittmer U, Taube C, Brock S, Berger MM, Brenner T, Witzke O, Krawczyk A. Upregulation of miRNA-200c during Disease Progression in COVID-19 Patients. J Clin Med 2022; 12. [PMID: 36615083 DOI: 10.3390/jcm12010283] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2022] [Revised: 12/20/2022] [Accepted: 12/27/2022] [Indexed: 12/31/2022] Open
Abstract
The COVID-19 pandemic has caused more than 6 million deaths worldwide since its first outbreak in December 2019 and continues to be a major health problem. Several studies have established that the infection by SARS-CoV-2 can be categorized in a viremic, acute and recovery or severe phase. Hyperinflammation during the acute pneumonia phase is a major cause of severe disease progression and death. Treatment of COVID-19 with directly acting antivirals is limited within a narrow window of time between first clinical symptoms and the hyperinflammatory response. Therefore, early initiation of treatment is crucial to assure optimal health care for patients. Molecular diagnostic biomarkers represent a potent tool to predict the course of disease and thus to assess the optimal treatment regimen and time point. Here, we investigated miRNA-200c as a potential marker for the prediction of the severity of COVID-19 to preventively initiate and personalize therapeutic interventions in the future. We found that miRNA-200c correlates with the severity of disease. With retrospective analysis, however, there is no correlation with prognosis at the time of hospitalization. Our study provides the basis for further evaluation of miRNA-200c as a predictive biomarker for the progress of COVID-19.
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20
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Rahmel T, Kraft F, Haberl H, Achtzehn U, Brandenburger T, Neb H, Jarczak D, Dietrich M, Magunia H, Zimmer F, Basten J, Landgraf C, Koch T, Zacharowski K, Weigand MA, Rosenberger P, Ullrich R, Meybohm P, Nierhaus A, Kindgen-Milles D, Timmesfeld N, Adamzik M. Intravenous IgM-enriched immunoglobulins in critical COVID-19: a multicentre propensity-weighted cohort study. Crit Care 2022; 26:204. [PMID: 35799196 PMCID: PMC9260992 DOI: 10.1186/s13054-022-04059-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2022] [Accepted: 06/13/2022] [Indexed: 12/15/2022] Open
Abstract
Background A profound inflammation-mediated lung injury with long-term acute respiratory distress and high mortality is one of the major complications of critical COVID-19. Immunoglobulin M (IgM)-enriched immunoglobulins seem especially capable of mitigating the inflicted inflammatory harm. However, the efficacy of intravenous IgM-enriched preparations in critically ill patients with COVID-19 is largely unclear. Methods In this retrospective multicentric cohort study, 316 patients with laboratory-confirmed critical COVID-19 were treated in ten German and Austrian ICUs between May 2020 and April 2021. The primary outcome was 30-day mortality. Analysis was performed by Cox regression models. Covariate adjustment was performed by propensity score weighting using machine learning-based SuperLearner to overcome the selection bias due to missing randomization. In addition, a subgroup analysis focusing on different treatment regimens and patient characteristics was performed. Results Of the 316 ICU patients, 146 received IgM-enriched immunoglobulins and 170 cases did not, which served as controls. There was no survival difference between the two groups in terms of mortality at 30 days in the overall cohort (HRadj: 0.83; 95% CI: 0.55 to 1.25; p = 0.374). An improved 30-day survival in patients without mechanical ventilation at the time of the immunoglobulin treatment did not reach statistical significance (HRadj: 0.23; 95% CI: 0.05 to 1.08; p = 0.063). Also, no statistically significant difference was observed in the subgroup when a daily dose of ≥ 15 g and a duration of ≥ 3 days of IgM-enriched immunoglobulins were applied (HRadj: 0.65; 95% CI: 0.41 to 1.03; p = 0.068). Conclusions Although we cannot prove a statistically reliable effect of intravenous IgM-enriched immunoglobulins, the confidence intervals may suggest a clinically relevant effect in certain subgroups. Here, an early administration (i.e. in critically ill but not yet mechanically ventilated COVID-19 patients) and a dose of ≥ 15 g for at least 3 days may confer beneficial effects without concerning safety issues. However, these findings need to be validated in upcoming randomized clinical trials. Trial registrationDRKS00025794, German Clinical Trials Register, https://www.drks.de. Registered 6 July 2021. Supplementary Information The online version contains supplementary material available at 10.1186/s13054-022-04059-0.
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21
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Lodato I, Iyer AV, To IZ, Lai ZY, Chan HSY, Leung WSW, Tang THC, Cheung VKL, Wu TC, Ng GWY. Prognostic Model of COVID-19 Severity and Survival among Hospitalized Patients Using Machine Learning Techniques. Diagnostics (Basel) 2022; 12:2728. [PMID: 36359571 PMCID: PMC9689804 DOI: 10.3390/diagnostics12112728] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2022] [Revised: 10/25/2022] [Accepted: 11/03/2022] [Indexed: 08/22/2023] Open
Abstract
We conducted a statistical study and developed a machine learning model to triage COVID-19 patients affected during the height of the COVID-19 pandemic in Hong Kong based on their medical records and test results (features) collected during their hospitalization. The correlation between the values of these features is studied against discharge status and disease severity as a preliminary step to identify those features with a more pronounced effect on the patient outcome. Once identified, they constitute the inputs of four machine learning models, Decision Tree, Random Forest, Gradient and RUSBoosting, which predict both the Mortality and Severity associated with the disease. We test the accuracy of the models when the number of input features is varied, demonstrating their stability; i.e., the models are already highly predictive when run over a core set of (6) features. We show that Random Forest and Gradient Boosting classifiers are highly accurate in predicting patients' Mortality (average accuracy ∼99%) as well as categorize patients (average accuracy ∼91%) into four distinct risk classes (Severity of COVID-19 infection). Our methodical and broad approach combines statistical insights with various machine learning models, which paves the way forward in the AI-assisted triage and prognosis of COVID-19 cases, which is potentially generalizable to other seasonal flus.
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Affiliation(s)
- Ivano Lodato
- Allos Limited, 1 Hok Cheung Street, Kowloon, Hong Kong, China
| | - Aditya Varna Iyer
- Allos Limited, 1 Hok Cheung Street, Kowloon, Hong Kong, China
- Department of Physics, University of Oxford, Oxford OX1 3PJ, UK
| | | | - Zhong-Yuan Lai
- Allos Limited, 1 Hok Cheung Street, Kowloon, Hong Kong, China
- Department of Physics, Fudan University, Shanghai 200433, China
| | - Helen Shuk-Ying Chan
- Division of Infectious Diseases, Department of Medicine, Queen Elizabeth Hospital, Hong Kong, China
| | - Winnie Suk-Wai Leung
- Division of Integrative Systems and Design, Hong Kong University of Science and Technology, Hong Kong, China
| | - Tommy Hing-Cheung Tang
- Division of Infectious Diseases, Department of Medicine, Queen Elizabeth Hospital, Hong Kong, China
| | - Victor Kai-Lam Cheung
- Multi-Disciplinary Simulation and Skills Centre, Queen Elizabeth Hospital, Hong Kong, China
| | - Tak-Chiu Wu
- Division of Infectious Diseases, Department of Medicine, Queen Elizabeth Hospital, Hong Kong, China
| | - George Wing-Yiu Ng
- Intensive Care Unit, Department of Medicine, Queen Elizabeth Hospital, Hong Kong, China
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22
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Loh HW, Ooi CP, Seoni S, Barua PD, Molinari F, Acharya UR. Application of explainable artificial intelligence for healthcare: A systematic review of the last decade (2011-2022). Comput Methods Programs Biomed 2022; 226:107161. [PMID: 36228495 DOI: 10.1016/j.cmpb.2022.107161] [Citation(s) in RCA: 79] [Impact Index Per Article: 39.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/29/2022] [Revised: 09/16/2022] [Accepted: 09/25/2022] [Indexed: 06/16/2023]
Abstract
BACKGROUND AND OBJECTIVES Artificial intelligence (AI) has branched out to various applications in healthcare, such as health services management, predictive medicine, clinical decision-making, and patient data and diagnostics. Although AI models have achieved human-like performance, their use is still limited because they are seen as a black box. This lack of trust remains the main reason for their low use in practice, especially in healthcare. Hence, explainable artificial intelligence (XAI) has been introduced as a technique that can provide confidence in the model's prediction by explaining how the prediction is derived, thereby encouraging the use of AI systems in healthcare. The primary goal of this review is to provide areas of healthcare that require more attention from the XAI research community. METHODS Multiple journal databases were thoroughly searched using PRISMA guidelines 2020. Studies that do not appear in Q1 journals, which are highly credible, were excluded. RESULTS In this review, we surveyed 99 Q1 articles covering the following XAI techniques: SHAP, LIME, GradCAM, LRP, Fuzzy classifier, EBM, CBR, rule-based systems, and others. CONCLUSION We discovered that detecting abnormalities in 1D biosignals and identifying key text in clinical notes are areas that require more attention from the XAI research community. We hope this is review will encourage the development of a holistic cloud system for a smart city.
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Affiliation(s)
- Hui Wen Loh
- School of Science and Technology, Singapore University of Social Sciences, Singapore
| | - Chui Ping Ooi
- School of Science and Technology, Singapore University of Social Sciences, Singapore
| | - Silvia Seoni
- Department of Electronics and Telecommunications, Biolab, Politecnico di Torino, Torino 10129, Italy
| | - Prabal Datta Barua
- Faculty of Engineering and Information Technology, University of Technology Sydney, Australia; School of Business (Information Systems), Faculty of Business, Education, Law & Arts, University of Southern Queensland, Australia
| | - Filippo Molinari
- Department of Electronics and Telecommunications, Biolab, Politecnico di Torino, Torino 10129, Italy
| | - U Rajendra Acharya
- School of Science and Technology, Singapore University of Social Sciences, Singapore; School of Business (Information Systems), Faculty of Business, Education, Law & Arts, University of Southern Queensland, Australia; School of Engineering, Ngee Ann Polytechnic, Singapore; Department of Bioinformatics and Medical Engineering, Asia University, Taiwan; Research Organization for Advanced Science and Technology (IROAST), Kumamoto University, Kumamoto, Japan.
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23
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Xu Y, Trivedi A, Becker N, Blazes M, Ferres JL, Lee A, Conrad Liles W, Bhatraju PK. Machine learning-based derivation and external validation of a tool to predict death and development of organ failure in hospitalized patients with COVID-19. Sci Rep 2022; 12:16913. [PMID: 36209335 PMCID: PMC9547892 DOI: 10.1038/s41598-022-20724-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2021] [Accepted: 09/19/2022] [Indexed: 12/29/2022] Open
Abstract
COVID-19 mortality risk stratification tools could improve care, inform accurate and rapid triage decisions, and guide family discussions regarding goals of care. A minority of COVID-19 prognostic tools have been tested in external cohorts. Our objective was to compare machine learning algorithms and develop a tool for predicting subsequent clinical outcomes in COVID-19. We conducted a retrospective cohort study that included hospitalized patients with COVID-19 from March 2020 to March 2021. Seven Hundred Twelve consecutive patients from University of Washington and 345 patients from Tongji Hospital in China were included. We applied three different machine learning algorithms to clinical and laboratory data collected within the initial 24 h of hospital admission to determine the risk of in-hospital mortality, transfer to the intensive care unit, shock requiring vasopressors, and receipt of renal replacement therapy. Mortality risk models were derived, internally validated in UW and externally validated in Tongji Hospital. The risk models for ICU transfer, shock and RRT were derived and internally validated in the UW dataset but were unable to be externally validated due to a lack of data on these outcomes. Among the UW dataset, 122 patients died (17%) during hospitalization and the mean days to hospital mortality was 15.7 +/- 21.5 (mean +/- SD). Elastic net logistic regression resulted in a C-statistic for in-hospital mortality of 0.72 (95% CI, 0.64 to 0.81) in the internal validation and 0.85 (95% CI, 0.81 to 0.89) in the external validation set. Age, platelet count, and white blood cell count were the most important predictors of mortality. In the sub-group of patients > 50 years of age, the mortality prediction model continued to perform with a C-statistic of 0.82 (95% CI:0.76,0.87). Prediction models also performed well for shock and RRT in the UW dataset but functioned with lower accuracy for ICU transfer. We trained, internally and externally validated a prediction model using data collected within 24 h of hospital admission to predict in-hospital mortality on average two weeks prior to death. We also developed models to predict RRT and shock with high accuracy. These models could be used to improve triage decisions, resource allocation, and support clinical trial enrichment.
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Affiliation(s)
- Yixi Xu
- grid.34477.330000000122986657School of Medicine, University of Washington, Seattle, WA USA ,grid.419815.00000 0001 2181 3404AI for Good Research, Microsoft, Seattle, USA
| | - Anusua Trivedi
- grid.34477.330000000122986657School of Medicine, University of Washington, Seattle, WA USA ,grid.419815.00000 0001 2181 3404AI for Good Research, Microsoft, Seattle, USA
| | - Nicholas Becker
- grid.34477.330000000122986657School of Medicine, University of Washington, Seattle, WA USA ,grid.419815.00000 0001 2181 3404AI for Good Research, Microsoft, Seattle, USA ,grid.34477.330000000122986657Computer Science and Engineering, University of Washington, Seattle, USA
| | - Marian Blazes
- grid.34477.330000000122986657School of Medicine, University of Washington, Seattle, WA USA
| | - Juan Lavista Ferres
- grid.34477.330000000122986657School of Medicine, University of Washington, Seattle, WA USA ,grid.419815.00000 0001 2181 3404AI for Good Research, Microsoft, Seattle, USA
| | - Aaron Lee
- grid.34477.330000000122986657School of Medicine, University of Washington, Seattle, WA USA
| | - W. Conrad Liles
- grid.34477.330000000122986657School of Medicine, University of Washington, Seattle, WA USA ,grid.34477.330000000122986657Department of Medicine and Sepsis Center of Research Excellence, University of Washington (SCORE-UW), Seattle, USA
| | - Pavan K. Bhatraju
- grid.34477.330000000122986657School of Medicine, University of Washington, Seattle, WA USA ,grid.34477.330000000122986657Pulmonary, Critical Care and Sleep Medicine, University of Washington Division of Pulmonary, Seattle, USA ,grid.34477.330000000122986657Department of Medicine and Sepsis Center of Research Excellence, University of Washington (SCORE-UW), Seattle, USA
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24
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Islam KR, Kumar J, Tan TL, Reaz MBI, Rahman T, Khandakar A, Abbas T, Hossain MSA, Zughaier SM, Chowdhury MEH. Prognostic Model of ICU Admission Risk in Patients with COVID-19 Infection Using Machine Learning. Diagnostics (Basel) 2022; 12:diagnostics12092144. [PMID: 36140545 PMCID: PMC9498213 DOI: 10.3390/diagnostics12092144] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2022] [Revised: 08/22/2022] [Accepted: 08/26/2022] [Indexed: 11/18/2022] Open
Abstract
With the onset of the COVID-19 pandemic, the number of critically sick patients in intensive care units (ICUs) has increased worldwide, putting a burden on ICUs. Early prediction of ICU requirement is crucial for efficient resource management and distribution. Early-prediction scoring systems for critically ill patients using mathematical models are available, but are not generalized for COVID-19 and Non-COVID patients. This study aims to develop a generalized and reliable prognostic model for ICU admission for both COVID-19 and non-COVID-19 patients using best feature combination from the patient data at admission. A retrospective cohort study was conducted on a dataset collected from the pulmonology department of Moscow City State Hospital between 20 April 2020 and 5 June 2020. The dataset contains ten clinical features for 231 patients, of whom 100 patients were transferred to ICU and 131 were stable (non-ICU) patients. There were 156 COVID positive patients and 75 non-COVID patients. Different feature selection techniques were investigated, and a stacking machine learning model was proposed and compared with eight different classification algorithms to detect risk of need for ICU admission for both COVID-19 and non-COVID patients combined and COVID patients alone. C-reactive protein (CRP), chest computed tomography (CT), lung tissue affected (%), age, admission to hospital, and fibrinogen parameters at hospital admission were found to be important features for ICU-requirement risk prediction. The best performance was produced by the stacking approach, with weighted precision, sensitivity, F1-score, specificity, and overall accuracy of 84.45%, 84.48%, 83.64%, 84.47%, and 84.48%, respectively, for both types of patients, and 85.34%, 85.35%, 85.11%, 85.34%, and 85.35%, respectively, for COVID-19 patients only. The proposed work can help doctors to improve management through early prediction of the risk of need for ICU admission of patients during the COVID-19 pandemic, as the model can be used for both types of patients.
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Affiliation(s)
- Khandaker Reajul Islam
- Department of Physiology, Faculty of Medicine, University Kebangsaan Malaysia, Kuala Lumpur 56000, Malaysia
| | - Jaya Kumar
- Department of Physiology, Faculty of Medicine, University Kebangsaan Malaysia, Kuala Lumpur 56000, Malaysia
- Correspondence: (J.K.); (M.E.H.C.)
| | - Toh Leong Tan
- Department of Emergency Medicine, Faculty of Medicine, Universiti Kebangsaan Malaysia, Kuala Lumpur 56000, Malaysia
| | - Mamun Bin Ibne Reaz
- Department of Electrical, Electronics and Systems Engineering, Universiti Kebangsaan Malaysia, Bangi 43600, Malaysia
| | - Tawsifur Rahman
- Department of Electrical Engineering, Qatar University, Doha P.O. Box 2713, Qatar
| | - Amith Khandakar
- Department of Electrical Engineering, Qatar University, Doha P.O. Box 2713, Qatar
| | - Tariq Abbas
- Urology Division, Surgery Department, Sidra Medicine, Doha P.O. Box 26999, Qatar
| | | | - Susu M. Zughaier
- Department of Basic Medical Sciences, College of Medicine, QU Health, Qatar University, Doha P.O. Box 2713, Qatar
| | - Muhammad E. H. Chowdhury
- Department of Electrical Engineering, Qatar University, Doha P.O. Box 2713, Qatar
- Correspondence: (J.K.); (M.E.H.C.)
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25
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Elhazmi A, Al-Omari A, Sallam H, Mufti HN, Rabie AA, Alshahrani M, Mady A, Alghamdi A, Altalaq A, Azzam MH, Sindi A, Kharaba A, Al-Aseri ZA, Almekhlafi GA, Tashkandi W, Alajmi SA, Faqihi F, Alharthy A, Al-Tawfiq JA, Melibari RG, Al-Hazzani W, Arabi YM. Machine learning decision tree algorithm role for predicting mortality in critically ill adult COVID-19 patients admitted to the ICU. J Infect Public Health 2022; 15:826-834. [PMID: 35759808 PMCID: PMC9212964 DOI: 10.1016/j.jiph.2022.06.008] [Citation(s) in RCA: 20] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2021] [Revised: 06/02/2022] [Accepted: 06/14/2022] [Indexed: 11/17/2022] Open
Abstract
Background Coronavirus disease-19 (COVID-19) is caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and is currently a major cause of intensive care unit (ICU) admissions globally. The role of machine learning in the ICU is evolving but currently limited to diagnostic and prognostic values. A decision tree (DT) algorithm is a simple and intuitive machine learning method that provides sequential nonlinear analysis of variables. It is simple and might be a valuable tool for bedside physicians during COVID-19 to predict ICU outcomes and help in critical decision-making like end-of-life decisions and bed allocation in the event of limited ICU bed capacities. Herein, we utilized a machine learning DT algorithm to describe the association of a predefined set of variables and 28-day ICU outcome in adult COVID-19 patients admitted to the ICU. We highlight the value of utilizing a machine learning DT algorithm in the ICU at the time of a COVID-19 pandemic. Methods This was a prospective and multicenter cohort study involving 14 hospitals in Saudi Arabia. We included critically ill COVID-19 patients admitted to the ICU between March 1, 2020, and October 31, 2020. The predictors of 28-day ICU mortality were identified using two predictive models: conventional logistic regression and DT analyses. Results There were 1468 critically ill COVID-19 patients included in the study. The 28-day ICU mortality was 540 (36.8 %), and the 90-day mortality was 600 (40.9 %). The DT algorithm identified five variables that were integrated into the algorithm to predict 28-day ICU outcomes: need for intubation, need for vasopressors, age, gender, and PaO2/FiO2 ratio. Conclusion DT is a simple tool that might be utilized in the ICU to identify critically ill COVID-19 patients who are at high risk of 28-day ICU mortality. However, further studies and external validation are still required.
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Affiliation(s)
- Alyaa Elhazmi
- Department of Critical Care, Dr. Sulaiman Al-Habib Medical Group, Riyadh, Saudi Arabia; College of Medicine, Alfaisal University, Riyadh, Saudi Arabia.
| | - Awad Al-Omari
- Research Center, Dr. Sulaiman Alhabib Medical Group, Riyadh, Saudi Arabia; College of Medicine, Alfaisal University, Riyadh, Saudi Arabia
| | - Hend Sallam
- Department of Adult Critical Care Medicine, King Faisal Specialist Hospital & Research Centre, Saudi Arabia
| | - Hani N Mufti
- Section of Cardiac Surgery, Department of Cardiac Sciences, King Faisal Cardiac Center, King Abdulaziz Medical City, MNGHA-WR, Jeddah, Saudi Arabia; College of Medicine, King Saud Bin Abdulaziz University for Health Sciences, Jeddah, Saudi Arabia. King Abdullah International Medical Research Center, Jeddah, Saudi Arabia Intensive Care Department, King Saud Medical City, Riyadh, Saudi Arabia
| | - Ahmed A Rabie
- Critical Care Department, King Saud Medical City, Riyadh, Saudi Arabia.
| | - Mohammed Alshahrani
- Emergency and Critical Care Department, King Fahad Hospital of The University, Imam Abdul Rahman ben Faisal University, Dammam, Saudi Arabia
| | - Ahmed Mady
- Critical Care Department, King Saud Medical City, Riyadh, Saudi Arabia; Department of Anesthesiology and Intensive Care, Tanta University Hospitals, Tanta, Egypt
| | - Adnan Alghamdi
- Prince Sultan Military Medical City, Military Medical Services, Ministry of Defence, Riyadh, Saudi Arabia
| | - Ali Altalaq
- Prince Sultan Military Medical City, Military Medical Services, Ministry of Defence, Riyadh, Saudi Arabia
| | - Mohamed H Azzam
- Intensive Care Department, King Abdullah Medical Complex, Jeddah, Saudi Arabia
| | - Anees Sindi
- Department of Anesthesia and Critical Care, Faculty of Medicine, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Ayman Kharaba
- Department of Critical Care, King Fahad Hospital, Al Medina Al Monawarah, Saudi Arabia
| | - Zohair A Al-Aseri
- Departments Of Emergency Medicine and Critical Care, College of Medicine, King Saud University, Riyadh, Saudi Arabia; College Of Medicine, Dar Al Uloom University, Riyadh, Saudi Arabia
| | - Ghaleb A Almekhlafi
- Prince Sultan Military Medical City, Military Medical Services, Ministry of Defence, Riyadh, Saudi Arabia
| | - Wail Tashkandi
- Department of Critical Care, Fakeeh Care Group, Jeddah, Saudi Arabia; Department of Surgery, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Saud A Alajmi
- Prince Sultan Military Medical City, Military Medical Services, Ministry of Defence, Riyadh, Saudi Arabia
| | - Fahad Faqihi
- Critical Care Department, King Saud Medical City, Riyadh, Saudi Arabia
| | | | - Jaffar A Al-Tawfiq
- Infectious Disease Unit, Specialty Internal Medicine, Johns Hopkins Aramco Healthcare, Dhahran, Saudi Arabia. Infectious Disease Division, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA; Infectious Disease Division, Department of Medicine, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Rami Ghazi Melibari
- Department of Critical Care, King Abdullah Medical City, Makah, Saudi Arabia
| | - Waleed Al-Hazzani
- Department of Medicine, McMaster University, Hamilton, Canada; Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, Canada
| | - Yaseen M Arabi
- College of Medicine, King Saud bin Abdulaziz University for Health Sciences, King Abdullah International Medical Research Center, Intensive Care Department, Ministry of National Guard Health Affairs, Riyadh, Saudi Arabia
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26
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Heubner L, Hattenhauer S, Güldner A, Petrick PL, Rößler M, Schmitt J, Schneider R, Held HC, Mehrholz J, Bodechtel U, Ragaller M, Koch T, Spieth PM. Characteristics and outcomes of sepsis patients with and without COVID-19. J Infect Public Health 2022; 15:670-676. [PMID: 35617831 PMCID: PMC9110019 DOI: 10.1016/j.jiph.2022.05.008] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2022] [Revised: 05/10/2022] [Accepted: 05/12/2022] [Indexed: 01/27/2023] Open
Abstract
BACKGROUND The aim of this study was to describe and compare clinical characteristics and outcomes in critically ill septic patients with and without COVID-19. METHODS From February 2020 to March 2021, patients from surgical and medical ICUs at the University Hospital Dresden were screened for sepsis. Patient characteristics and outcomes were assessed descriptively. Patient survival was analyzed using the Kaplan-Meier estimator. Associations between in-hospital mortality and risk factors were modeled using robust Poisson regression, which facilitates derivation of adjusted relative risks. RESULTS In 177 ICU patients treated for sepsis, COVID-19 was diagnosed and compared to 191 septic ICU patients without COVID-19. Age and sex did not differ significantly between sepsis patients with and without COVID-19, but SOFA score at ICU admission was significantly higher in septic COVID-19 patients. In-hospital mortality was significantly higher in COVID-19 patients with 59% compared to 29% in Non-COVID patients. Statistical analysis resulted in an adjusted relative risk for in-hospital mortality of 1.74 (95%-CI=1.35-2-24) in the presence of COVID-19 compared to other septic patients. Age, procalcitonin maximum value over 2 ng/ml, need for renal replacement therapy, need for invasive ventilation and septic shock were identified as additional risk factors for in-hospital mortality. CONCLUSION COVID-19 was identified as independent risk factor for higher in-hospital mortality in sepsis patients. The need for invasive ventilation and renal replacement therapy as well as the presence of septic shock and higher PCT should be considered to identify high-risk patients.
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Affiliation(s)
- Lars Heubner
- Department of Anesthesiology and Critical Care Medicine, University Hospital Carl Gustav Carus and Carl Gustav Carus Faculty of Medicine, TU Dresden, Dresden, Germany
| | - Sara Hattenhauer
- Department of Anesthesiology and Critical Care Medicine, University Hospital Carl Gustav Carus and Carl Gustav Carus Faculty of Medicine, TU Dresden, Dresden, Germany
| | - Andreas Güldner
- Department of Anesthesiology and Critical Care Medicine, University Hospital Carl Gustav Carus and Carl Gustav Carus Faculty of Medicine, TU Dresden, Dresden, Germany
| | - Paul Leon Petrick
- Department of Anesthesiology and Critical Care Medicine, University Hospital Carl Gustav Carus and Carl Gustav Carus Faculty of Medicine, TU Dresden, Dresden, Germany
| | - Martin Rößler
- Center for Evidence-Based Healthcare (ZEGV), University Hospital Carl Gustav Carus and Carl Gustav Carus Faculty of Medicine, TU Dresden, Dresden, Germany
| | - Jochen Schmitt
- Center for Evidence-Based Healthcare (ZEGV), University Hospital Carl Gustav Carus and Carl Gustav Carus Faculty of Medicine, TU Dresden, Dresden, Germany
| | - Ralph Schneider
- Department of General, Thoracic and Vascular Surgery, University Hospital Carl Gustav Carus and Carl Gustav Carus Faculty of Medicine, TU Dresden, Dresden, Germany
| | - Hanns Christoph Held
- Department of Medicine I, University Hospital Carl Gustav Carus, University Hospital Carl Gustav Carus and Carl Gustav Carus Faculty of Medicine, TU Dresden, Dresden, Germany
| | - Jan Mehrholz
- Wissenschaftliches Institut, Klinik Bavaria Kreischa, Germany
| | - Ulf Bodechtel
- Department of Interdisciplinary Intensive Care Medicine and Intensive Rehabilitation, Klinik Bavaria Kreischa, Germany
| | - Maximilian Ragaller
- Department of Anesthesiology and Critical Care Medicine, University Hospital Carl Gustav Carus and Carl Gustav Carus Faculty of Medicine, TU Dresden, Dresden, Germany
| | - Thea Koch
- Department of Anesthesiology and Critical Care Medicine, University Hospital Carl Gustav Carus and Carl Gustav Carus Faculty of Medicine, TU Dresden, Dresden, Germany
| | - Peter Markus Spieth
- Department of Anesthesiology and Critical Care Medicine, University Hospital Carl Gustav Carus and Carl Gustav Carus Faculty of Medicine, TU Dresden, Dresden, Germany.
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Sarica A, Quattrone A, Quattrone A. Explainable machine learning with pairwise interactions for the classification of Parkinson's disease and SWEDD from clinical and imaging features. Brain Imaging Behav 2022; 16:2188-2198. [PMID: 35614327 PMCID: PMC9132761 DOI: 10.1007/s11682-022-00688-9] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/09/2022] [Indexed: 12/11/2022]
Abstract
Scans without evidence of dopaminergic deficit (SWEDD) refers to patients who mimics motor and non-motor symptoms of Parkinson's disease (PD) but showing integrity of dopaminergic system. For this reason, the differential diagnosis between SWEDD and PD patients is often not possible in absence of dopamine imaging. Machine Learning (ML) showed optimal performance in automatically distinguishing these two diseases from clinical and imaging data. However, the most common applied ML algorithms provide high accuracy at expense of findings intelligibility. In this work, a novel ML glass-box model, the Explainable Boosting Machine (EBM), based on Generalized Additive Models plus interactions (GA2Ms), was employed to obtain interpretability in classifying PD and SWEDD while still providing optimal performance. Dataset (168 healthy controls, HC; 396 PD; 58 SWEDD) was obtained from PPMI database and consisted of 178 among clinical and imaging features. Six binary EBM classifiers were trained on feature space with (SBR) and without (noSBR) dopaminergic striatal specific binding ratio: HC-PDSBR, HC-SWEDDSBR, PD-SWEDDSBR and HC-PDnoSBR, HC-SWEDDnoSBR, PD-SWEDDnoSBR. Excellent AUC-ROC (1) was reached in classifying HC from PD and SWEDD, both with and without SBR, and by PD-SWEDDSBR (0.986), while PD-SWEDDnoSBR showed lower AUC-ROC (0.882). Apart from optimal accuracies, EBM algorithm was able to provide global and local explanations, revealing that the presence of pairwise interactions between UPSIT Booklet #1 and Epworth Sleepiness Scale item 3 (ESS3), MDS-UPDRS-III pronation-supination movements right hand (NP3PRSPR) and MDS-UPDRS-III rigidity left upper limb (NP3RIGLU) could provide good performance in predicting PD and SWEDD also without imaging features.
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Affiliation(s)
- Alessia Sarica
- Neuroscience Research Center, Department of Medical and Surgical Sciences, Magna Graecia University, viale Europa, 88100, Catanzaro, Germaneto, Italy.
| | - Andrea Quattrone
- Institute of Neurology, Department of Medical and Surgical Sciences, Magna Graecia University, 88100, Catanzaro, Italy
| | - Aldo Quattrone
- Neuroscience Research Center, Department of Medical and Surgical Sciences, Magna Graecia University, viale Europa, 88100, Catanzaro, Germaneto, Italy.,Neuroimaging Research Unit, Institute of Molecular Bioimaging and Physiology, National Research Council, 88100, Catanzaro, Italy
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Mariani S, De Piero ME, Ravaux JM, Saelmans A, Kawczynski MJ, van Bussel BCT, Di Mauro M, Willers A, Swol J, Kowalewski M, Li T, Delnoij TSR, van der Horst ICC, Maessen J, Lorusso R. Temporary mechanical circulatory support for COVID‐19 patients: A systematic review of literature. Artif Organs 2022; 46:1249-1267. [PMID: 35490367 PMCID: PMC9325561 DOI: 10.1111/aor.14261] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2021] [Revised: 11/10/2021] [Accepted: 04/06/2022] [Indexed: 01/08/2023]
Abstract
Objective Myocardial damage occurs in up to 25% of coronavirus disease 2019 (COVID‐19) cases. While veno‐venous extracorporeal life support (V‐V ECLS) is used as respiratory support, mechanical circulatory support (MCS) may be required for severe cardiac dysfunction. This systematic review summarizes the available literature regarding MCS use rates, disease drivers for MCS initiation, and MCS outcomes in COVID‐19 patients. Methods PubMed/EMBASE were searched until October 14, 2021. Articles including adults receiving ECLS for COVID‐19 were included. The primary outcome was the rate of MCS use. Secondary outcomes included mortality at follow‐up, ECLS conversion rate, intubation‐to‐cannulation time, time on ECLS, cardiac diseases, use of inotropes, and vasopressors. Results Twenty‐eight observational studies (comprising both ECLS‐only populations and ECLS patients as part of larger populations) included 4218 COVID‐19 patients (females: 28.8%; median age: 54.3 years, 95%CI: 50.7–57.8) of whom 2774 (65.8%) required ECLS with the majority (92.7%) on V‐V ECLS, 4.7% on veno‐arterial ECLS and/or Impella, and 2.6% on other ECLS. Acute heart failure, cardiogenic shock, and cardiac arrest were reported in 7.8%, 9.7%, and 6.6% of patients, respectively. Vasopressors were used in 37.2%. Overall, 3.1% of patients required an ECLS change from V‐V ECLS to MCS for heart failure, myocarditis, or myocardial infarction. The median ECLS duration was 15.9 days (95%CI: 13.9–16.3), with an overall survival of 54.6% and 28.1% in V‐V ECLS and MCS patients. One study reported 61.1% survival with oxy‐right ventricular assist device. Conclusion MCS use for cardiocirculatory compromise has been reported in 7.3% of COVID‐19 patients requiring ECLS, which is a lower percentage compared to the incidence of any severe cardiocirculatory complication. Based on the poor survival rates, further investigations are warranted to establish the most appropriated indications and timing for MCS in COVID‐19.
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Affiliation(s)
- Silvia Mariani
- Cardio‐Thoracic Surgery Department, Heart and Vascular Centre Maastricht University Medical Centre (MUMC) Maastricht The Netherlands
- Cardiovascular Research Institute Maastricht (CARIM) Maastricht The Netherlands
| | - Maria Elena De Piero
- Cardio‐Thoracic Surgery Department, Heart and Vascular Centre Maastricht University Medical Centre (MUMC) Maastricht The Netherlands
- Cardiovascular Research Institute Maastricht (CARIM) Maastricht The Netherlands
| | - Justine M. Ravaux
- Cardio‐Thoracic Surgery Department, Heart and Vascular Centre Maastricht University Medical Centre (MUMC) Maastricht The Netherlands
- Cardiovascular Research Institute Maastricht (CARIM) Maastricht The Netherlands
| | - Alexander Saelmans
- Cardio‐Thoracic Surgery Department, Heart and Vascular Centre Maastricht University Medical Centre (MUMC) Maastricht The Netherlands
| | - Michal J. Kawczynski
- Cardio‐Thoracic Surgery Department, Heart and Vascular Centre Maastricht University Medical Centre (MUMC) Maastricht The Netherlands
- Cardiovascular Research Institute Maastricht (CARIM) Maastricht The Netherlands
| | - Bas C. T. van Bussel
- Department of Intensive Care Medicine Maastricht University Medical Centre (MUMC) Maastricht The Netherlands
- Care And Public Health Research Institute (CAPHRI) Maastricht University Maastricht The Netherlands
| | - Michele Di Mauro
- Cardio‐Thoracic Surgery Department, Heart and Vascular Centre Maastricht University Medical Centre (MUMC) Maastricht The Netherlands
- Cardiovascular Research Institute Maastricht (CARIM) Maastricht The Netherlands
| | - Anne Willers
- Cardio‐Thoracic Surgery Department, Heart and Vascular Centre Maastricht University Medical Centre (MUMC) Maastricht The Netherlands
- Cardiovascular Research Institute Maastricht (CARIM) Maastricht The Netherlands
| | - Justyna Swol
- Department of Pneumology, Allergology and Sleep Medicine Paracelsus Medical University Nuremberg Germany
| | - Mariusz Kowalewski
- Clinical Department of Cardiac Surgery Central Clinical Hospital of the Ministry of Interior and Administration, Centre of Postgraduate Medical Education Warsaw Poland
| | - Tong Li
- Department of Cardiothoracic, Transplantation and Vascular Surgery Hannover Medical School Hannover Germany
| | - Thijs S. R. Delnoij
- Department of Intensive Care Medicine Maastricht University Medical Centre (MUMC) Maastricht The Netherlands
- Department of Cardiology, Heart and Vascular Centre Maastricht University Medical Centre (MUMC) Maastricht The Netherlands
| | - Iwan C. C. van der Horst
- Cardiovascular Research Institute Maastricht (CARIM) Maastricht The Netherlands
- Department of Intensive Care Medicine Maastricht University Medical Centre (MUMC) Maastricht The Netherlands
| | - Jos Maessen
- Cardio‐Thoracic Surgery Department, Heart and Vascular Centre Maastricht University Medical Centre (MUMC) Maastricht The Netherlands
- Cardiovascular Research Institute Maastricht (CARIM) Maastricht The Netherlands
| | - Roberto Lorusso
- Cardio‐Thoracic Surgery Department, Heart and Vascular Centre Maastricht University Medical Centre (MUMC) Maastricht The Netherlands
- Cardiovascular Research Institute Maastricht (CARIM) Maastricht The Netherlands
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Outcome-Prädiktoren von COVID-19-Patienten auf ITS durch maschinelles Lernen. Anasthesiol Intensivmed Notfallmed Schmerzther 2022; 57:239-40. [DOI: 10.1055/a-1788-3227] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
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Alle S, Kanakan A, Siddiqui S, Garg A, Karthikeyan A, Mehta P, Mishra N, Chattopadhyay P, Devi P, Waghdhare S, Tyagi A, Tarai B, Hazarik PP, Das P, Budhiraja S, Nangia V, Dewan A, Sethuraman R, Subramanian C, Srivastava M, Chakravarthi A, Jacob J, Namagiri M, Konala V, Dash D, Sethi T, Jha S, Agrawal A, Pandey R, Vinod PK, Priyakumar UD. COVID-19 Risk Stratification and Mortality Prediction in Hospitalized Indian Patients: Harnessing clinical data for public health benefits. PLoS One 2022; 17:e0264785. [PMID: 35298502 PMCID: PMC8929610 DOI: 10.1371/journal.pone.0264785] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2021] [Accepted: 02/16/2022] [Indexed: 12/15/2022] Open
Abstract
The variability of clinical course and prognosis of COVID-19 highlights the necessity of patient sub-group risk stratification based on clinical data. In this study, clinical data from a cohort of Indian COVID-19 hospitalized patients is used to develop risk stratification and mortality prediction models. We analyzed a set of 70 clinical parameters including physiological and hematological for developing machine learning models to identify biomarkers. We also compared the Indian and Wuhan cohort, and analyzed the role of steroids. A bootstrap averaged ensemble of Bayesian networks was also learned to construct an explainable model for discovering actionable influences on mortality and days to outcome. We discovered blood parameters, diabetes, co-morbidity and SpO2 levels as important risk stratification features, whereas mortality prediction is dependent only on blood parameters. XGboost and logistic regression model yielded the best performance on risk stratification and mortality prediction, respectively (AUC score 0.83, AUC score 0.92). Blood coagulation parameters (ferritin, D-Dimer and INR), immune and inflammation parameters IL6, LDH and Neutrophil (%) are common features for both risk and mortality prediction. Compared with Wuhan patients, Indian patients with extreme blood parameters indicated higher survival rate. Analyses of medications suggest that a higher proportion of survivors and mild patients who were administered steroids had extreme neutrophil and lymphocyte percentages. The ensemble averaged Bayesian network structure revealed serum ferritin to be the most important predictor for mortality and Vitamin D to influence severity independent of days to outcome. The findings are important for effective triage during strains on healthcare infrastructure.
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Affiliation(s)
- Shanmukh Alle
- Center for Computational Natural Sciences and Bioinformatics, International Institute of Information Technology, Hyderabad, Telangana, India
| | - Akshay Kanakan
- INtegrative GENomics of HOst-PathogEn (INGEN-HOPE) laboratory, CSIR-Institute of Genomics and Integrative Biology (CSIR-IGIB), Delhi, India
| | - Samreen Siddiqui
- Max Super Speciality Hospital (A Unit of Devki Devi Foundation), Max Healthcare, Delhi, India
| | - Akshit Garg
- Center for Computational Natural Sciences and Bioinformatics, International Institute of Information Technology, Hyderabad, Telangana, India
| | - Akshaya Karthikeyan
- Center for Computational Natural Sciences and Bioinformatics, International Institute of Information Technology, Hyderabad, Telangana, India
| | - Priyanka Mehta
- INtegrative GENomics of HOst-PathogEn (INGEN-HOPE) laboratory, CSIR-Institute of Genomics and Integrative Biology (CSIR-IGIB), Delhi, India
| | - Neha Mishra
- INtegrative GENomics of HOst-PathogEn (INGEN-HOPE) laboratory, CSIR-Institute of Genomics and Integrative Biology (CSIR-IGIB), Delhi, India
| | - Partha Chattopadhyay
- INtegrative GENomics of HOst-PathogEn (INGEN-HOPE) laboratory, CSIR-Institute of Genomics and Integrative Biology (CSIR-IGIB), Delhi, India
- Intel Technology India Private Limited, Bangalore, Karnataka, India
| | - Priti Devi
- INtegrative GENomics of HOst-PathogEn (INGEN-HOPE) laboratory, CSIR-Institute of Genomics and Integrative Biology (CSIR-IGIB), Delhi, India
- Intel Technology India Private Limited, Bangalore, Karnataka, India
| | - Swati Waghdhare
- Max Super Speciality Hospital (A Unit of Devki Devi Foundation), Max Healthcare, Delhi, India
| | - Akansha Tyagi
- Max Super Speciality Hospital (A Unit of Devki Devi Foundation), Max Healthcare, Delhi, India
| | - Bansidhar Tarai
- Max Super Speciality Hospital (A Unit of Devki Devi Foundation), Max Healthcare, Delhi, India
| | - Pranjal Pratim Hazarik
- Max Super Speciality Hospital (A Unit of Devki Devi Foundation), Max Healthcare, Delhi, India
| | - Poonam Das
- Max Super Speciality Hospital (A Unit of Devki Devi Foundation), Max Healthcare, Delhi, India
| | - Sandeep Budhiraja
- Max Super Speciality Hospital (A Unit of Devki Devi Foundation), Max Healthcare, Delhi, India
| | - Vivek Nangia
- Max Super Speciality Hospital (A Unit of Devki Devi Foundation), Max Healthcare, Delhi, India
| | - Arun Dewan
- Max Super Speciality Hospital (A Unit of Devki Devi Foundation), Max Healthcare, Delhi, India
| | | | - C. Subramanian
- Indraprastha Institute of Information Technology Delhi, New Delhi, India
| | - Mashrin Srivastava
- Indraprastha Institute of Information Technology Delhi, New Delhi, India
| | | | - Johnny Jacob
- Indraprastha Institute of Information Technology Delhi, New Delhi, India
| | - Madhuri Namagiri
- Indraprastha Institute of Information Technology Delhi, New Delhi, India
| | - Varma Konala
- Indraprastha Institute of Information Technology Delhi, New Delhi, India
| | - Debasish Dash
- INtegrative GENomics of HOst-PathogEn (INGEN-HOPE) laboratory, CSIR-Institute of Genomics and Integrative Biology (CSIR-IGIB), Delhi, India
| | - Tavpritesh Sethi
- Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, Uttar Pradesh, India
| | - Sujeet Jha
- Max Super Speciality Hospital (A Unit of Devki Devi Foundation), Max Healthcare, Delhi, India
- * E-mail: (SJ); (AA); (RP); (PKV); (UDP)
| | - Anurag Agrawal
- INtegrative GENomics of HOst-PathogEn (INGEN-HOPE) laboratory, CSIR-Institute of Genomics and Integrative Biology (CSIR-IGIB), Delhi, India
- * E-mail: (SJ); (AA); (RP); (PKV); (UDP)
| | - Rajesh Pandey
- INtegrative GENomics of HOst-PathogEn (INGEN-HOPE) laboratory, CSIR-Institute of Genomics and Integrative Biology (CSIR-IGIB), Delhi, India
- * E-mail: (SJ); (AA); (RP); (PKV); (UDP)
| | - P. K. Vinod
- Center for Computational Natural Sciences and Bioinformatics, International Institute of Information Technology, Hyderabad, Telangana, India
- * E-mail: (SJ); (AA); (RP); (PKV); (UDP)
| | - U. Deva Priyakumar
- Center for Computational Natural Sciences and Bioinformatics, International Institute of Information Technology, Hyderabad, Telangana, India
- * E-mail: (SJ); (AA); (RP); (PKV); (UDP)
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Nwanosike EM, Conway BR, Merchant HA, Hasan SS. Potential applications and performance of machine learning techniques and algorithms in clinical practice: A systematic review. Int J Med Inform 2021; 159:104679. [PMID: 34990939 DOI: 10.1016/j.ijmedinf.2021.104679] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2021] [Revised: 12/08/2021] [Accepted: 12/27/2021] [Indexed: 12/11/2022]
Abstract
PURPOSE The advent of clinically adapted machine learning algorithms can solve numerous problems ranging from disease diagnosis and prognosis to therapy recommendations. This systematic review examines the performance of machine learning (ML) algorithms and evaluates the progress made to date towards their implementation in clinical practice. METHODS Systematic searching of databases (PubMed, MEDLINE, Scopus, Google Scholar, Cochrane Library and WHO Covid-19 database) to identify original articles published between January 2011 and October 2021. Studies reporting ML techniques in clinical practice involving humans and ML algorithms with a performance metric were considered. RESULTS Of 873 unique articles identified, 36 studies were eligible for inclusion. The XGBoost (extreme gradient boosting) algorithm showed the highest potential for clinical applications (n = 7 studies); this was followed jointly by random forest algorithm, logistic regression, and the support vector machine, respectively (n = 5 studies). Prediction of outcomes (n = 33), in particular Inflammatory diseases (n = 7) received the most attention followed by cancer and neuropsychiatric disorders (n = 5 for each) and Covid-19 (n = 4). Thirty-three out of the thirty-six included studies passed more than 50% of the selected quality assessment criteria in the TRIPOD checklist. In contrast, none of the studies could achieve an ideal overall bias rating of 'low' based on the PROBAST checklist. In contrast, only three studies showed evidence of the deployment of ML algorithm(s) in clinical practice. CONCLUSIONS ML is potentially a reliable tool for clinical decision support. Although advocated widely in clinical practice, work is still in progress to validate clinically adapted ML algorithms. Improving quality standards, transparency, and interpretability of ML models will further lower the barriers to acceptability.
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Affiliation(s)
- Ezekwesiri Michael Nwanosike
- Department of Pharmacy, School of Applied Sciences, University of Huddersfield, Queensgate Huddersfield HD1 3DH, West Yorkshire, United Kingdom
| | - Barbara R Conway
- Department of Pharmacy, School of Applied Sciences, University of Huddersfield, Queensgate Huddersfield HD1 3DH, West Yorkshire, United Kingdom
| | - Hamid A Merchant
- Department of Pharmacy, School of Applied Sciences, University of Huddersfield, Queensgate Huddersfield HD1 3DH, West Yorkshire, United Kingdom
| | - Syed Shahzad Hasan
- Department of Pharmacy, School of Applied Sciences, University of Huddersfield, Queensgate Huddersfield HD1 3DH, West Yorkshire, United Kingdom; School of Biomedical Sciences & Pharmacy, University of Newcastle, Callaghan, Australia.
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Xu Y, Trivedi A, Becker N, Blazes M, Ferres J, Lee A, Liles W, Bhatraju P. Machine Learning-based Derivation and External Validation of a Tool to Predict Death and Development of Organ Failure in Hospitalized Patients with COVID-19. Res Sq 2021:rs.3.rs-1009310. [PMID: 34816256 PMCID: PMC8609901 DOI: 10.21203/rs.3.rs-1009310/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
Abstract
BackgroundCOVID-19 mortality risk stratification tools could improve care, inform accurate and rapid triage decisions, and guide family discussions regarding goals of care. A minority of COVID-19 prognostic tools have been tested in external cohorts. Our objective was to compare machine learning algorithms and develop a tool for predicting subsequent clinical outcomes in COVID-19. MethodsWe conducted a retrospective cohort study that included hospitalized patients with COVID-19 from March 2020 to March 2021. 712 consecutive patients from University of Washington (UW) and 345 patients from Tongji Hospital in China were included. We applied three different machine learning algorithms to clinical and laboratory data collected within the initial 24 hours of hospital admission to determine the risk of in-hospital mortality, transfer to the intensive care unit (ICU), shock requiring vasopressors, and receipt of renal replacement therapy (RRT). Mortality risk models were derived, internally validated in UW and externally validated in Tongji Hospital. The risk models for ICU transfer, shock and RRT were derived and internally validated in the UW dataset. ResultsAmong the UW dataset, 122 patients died (17%) during hospitalization and the mean days to hospital mortality was 15.7 +/- 21.5 (mean +/- SD). Elastic net logistic regression resulted in a C-statistic for in-hospital mortality of 0.72 (95% CI, 0.64 to 0.81) in the internal validation and 0.85 (95% CI, 0.81 to 0.89) in the external validation set. Age, platelet count, and white blood cell count were the most important predictors of mortality. In the sub-group of patients > 50 years of age, the mortality prediction model continued to perform with a C-statistic of 0.82 (95% CI:0.76,0.87). Mortality prediction models also performed well for shock and RRT in the UW dataset but functioned with lower accuracy for ICU transfer. ConclusionsWe trained, internally and externally validated a prediction model using data collected within 24 hours of hospital admission to predict in-hospital mortality on average two weeks prior to death. We also developed models to predict RRT and shock with high accuracy. These models could be used to improve triage decisions, resource allocation, and support clinical trial enrichment.
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