Machine learning decision tree algorithm role for predicting mortality in critically ill adult COVID-19 patients admitted to the ICU

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 valu...

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Veröffentlicht in:Journal of infection and public health Jg. 15; H. 7; S. 826 - 834
Hauptverfasser: Elhazmi, Alyaa, Al-Omari, Awad, Sallam, Hend, Mufti, Hani N., Rabie, Ahmed A., Alshahrani, Mohammed, Mady, Ahmed, Alghamdi, Adnan, Altalaq, Ali, Azzam, Mohamed H., Sindi, Anees, Kharaba, Ayman, Al-Aseri, Zohair A., Almekhlafi, Ghaleb A., Tashkandi, Wail, Alajmi, Saud A., Faqihi, Fahad, Alharthy, Abdulrahman, Al-Tawfiq, Jaffar A., Melibari, Rami Ghazi, Al-Hazzani, Waleed, Arabi, Yaseen M.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: England Elsevier Ltd 01.07.2022
The Author(s). Published by Elsevier Ltd on behalf of King Saud Bin Abdulaziz University for Health Sciences
Elsevier
Schlagworte:
Adult
Algorithms
Cohort Studies
COVID-19
Critical Illness
Decision tree
Decision Trees
Humans
ICU
Intensive Care Units
Machine Learning
Original
Pandemics
Predictors
Prospective Studies
Retrospective Studies
SARS-CoV-2
SARS-Cov2
COVID-19
SARS-Cov2
ICU
Decision tree
Predictors
ISSN:1876-0341, 1876-035X, 1876-035X
Online-Zugang:Volltext
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Zusammenfassung: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. 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. 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. 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.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
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ORCID: 0000-0001-5735-6241
ISSN:1876-0341
1876-035X
1876-035X
DOI:10.1016/j.jiph.2022.06.008