Machine Learning and Clinical Predictors of Mortality in Cardiac Arrest Patients: A Comprehensive Analysis

Lewandowski, Łukasz; Czapla, Michał; Uchmanowicz, Izabella; Kubielas, Grzegorz; Zieliński, Stanisław; Krzystek-Korpacka, Małgorzata; Ross, Catherine; Juárez-Vela, Raúl; Zielińska, Marzena

Abstract

BACKGROUND: Cardiac arrest (CA) is a global public health challenge. This study explored the predictors of mortality and their interactions utilizing machine learning algorithms and their related mortality odds among patients following CA.

MATERIAL AND METHODS: The study retrospectively investigated 161 medical records of CA patients admitted to the Intensive Care Unit (ICU). The random forest classifier algorithm was used to assess the parameters of mortality. The best classification trees were chosen from a set of 100 trees proposed by the algorithm. Conditional mortality odds were investigated with the use of logistic regression models featuring interactions between variables.

RESULTS: In the logistic regression model, male sex was associated with 5.68-fold higher mortality odds. The mortality odds among the asystole/pulseless electrical activity (PEA) patients were modulated by body mass index (BMI) and among ventricular fibrillation/pulseless ventricular tachycardia (VF/pVT) patients were by serum albumin concentration (decrease by 2.85-fold with 1 g/dl increase). Procalcitonin (PCT) concentration, age, high-sensitivity C-reactive protein (hsCRP), albumin, and potassium were the most influential parameters for mortality prediction with the use of the random forest classifier. Nutritional status-associated parameters (serum albumin concentration, BMI, and Nutritional Risk Score 2002 [NRS-2002]) may be useful in predicting mortality in patients with CA, especially in patients with PCT >0.17 ng/ml, as showed by the decision tree chosen from the random forest classifier based on goodness of fit (AUC score).

CONCLUSIONS: Mortality in patients following CA is modulated by many co-existing factors. The conclusions refer to sets of conditions rather than universal truths. For individual factors, the 5 most important classifiers of mortality (in descending order of importance) were PCT, age, hsCRP, albumin, and potassium.

Citation

Lewandowski, Ł., Czapla, M., Uchmanowicz, I., Kubielas, G., Zieliński, S., Krzystek-Korpacka, M., Ross, C., Juárez-Vela, R., & Zielińska, M. (2024). Machine Learning and Clinical Predictors of Mortality in Cardiac Arrest Patients: A Comprehensive Analysis. Medical Science Monitor, 30, Article e944408. https://doi.org/10.12659/msm.944408

• Journal Article Type: Article
• Acceptance Date: Jun 7, 2024
• Online Publication Date: Jul 12, 2024
• Publication Date: Aug 10, 2024
• Deposit Date: Sep 2, 2024
• Publicly Available Date: Sep 2, 2024
• Journal: Medical Science Monitor
• Electronic ISSN: 1643-3750
• Peer Reviewed: Peer Reviewed
• Volume: 30
• Article Number: e944408
• DOI: https://doi.org/10.12659/msm.944408
• Keywords: Death, Sudden, Cardiac, machine learning, malnutrition, Mortality, return of spontaneous circulation