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Multicenter Study
. 2022 Jul;15(7):826-834.
doi: 10.1016/j.jiph.2022.06.008. Epub 2022 Jun 17.

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

Alyaa Elhazmi  1 , Awad Al-Omari  2 , Hend Sallam  3 , Hani N Mufti  4 , Ahmed A Rabie  5 , Mohammed Alshahrani  6 , Ahmed Mady  7 , Adnan Alghamdi  8 , Ali Altalaq  8 , Mohamed H Azzam  9 , Anees Sindi  10 , Ayman Kharaba  11 , Zohair A Al-Aseri  12 , Ghaleb A Almekhlafi  8 , Wail Tashkandi  13 , Saud A Alajmi  8 , Fahad Faqihi  14 , Abdulrahman Alharthy  14 , Jaffar A Al-Tawfiq  15 , Rami Ghazi Melibari  16 , Waleed Al-Hazzani  17 , Yaseen M Arabi  18
Affiliations
Multicenter Study

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

Alyaa Elhazmi et al. J Infect Public Health. 2022 Jul.

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.

Keywords: COVID-19; Decision tree; ICU; Predictors; SARS-Cov2.

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Conflict of interest statement

Declaration of Competing Interest None of the authors have a conflict of interest related to this work.

Figures

Fig. 1
Fig. 1
Kaplan Maier curve for COVID-19 cumulative incidence of mortality.
Fig. 2
Fig. 2
Decision tree (DT) algorithm for predictors of mortality.
Fig. 3
Fig. 3
Stepwise logistic regression of 28-day mortality.
See this image and copyright information in PMC

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