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

Aug 2, 2026

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Cardiac arrest represents a major global public health challenge, and survival after intensive care depends on multiple interacting determinants. A retrospective analysis of 161 ICU medical records evaluated post-arrest mortality predictors using a random forest classifier and conditional mortality odds from logistic regression models with variable interactions. The study identified the most influential mortality-related parameters, including procalcitonin, age, high-sensitivity C-reactive protein, serum albumin, and potassium. Nutritional status-associated measures may improve risk stratification, especially when procalcitonin exceeds 0.17 ng/ml.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages22

Reading Time55 min

AccessFree to download

Background

Material/Methods

Results

Conclusions

Introduction

DATABASE ANALYSIS e-ISSN 1643-3750 © Med Sci Monit, 2024; 30: e944408 DOI: 10. 12659/MSM.944408 Received: 2024.03.07 Accepted: 2024.06.07 Available online: 2024.07.12 Published: 2024.08.10 Authors’ Contribution: Study Design A Data Collection B Statistical Analysis C Data Interpretation D Manuscript Preparation E Literature Search F Funds Collection G ABCDEF 1 ABDEF 2-4 DEG 5 E 5 E 6 E 1 E 7 E 3 BDE 6 Corresponding Author: Financial support: Conflict of interest: Machine Learning and Clinical Predictors of Mortality in Cardiac Arrest Patients: A Comprehensive Analysis Łukasz Lewandowski Michał Czapla Izabella Uchmanowicz Grzegorz Kubielas Stanisław Zieliński Małgorzata Krzystek-Korpacka Catherine Ross Raúl Juárez-Vela Marzena Zielińska 1 Department of Medical Biochemistry, Wrocław Medical University, Wrocław, Poland 2 Department of Emergency Medical Service, Wrocław Medical University, Wrocław, Poland 3 Group of Research in Care (GRUPAC), University of La Rioja, Logrono, Spain 4 Institute of Heart Diseases, Wrocław University Hospital, Wrocław, Poland 5 Department of Nursing and Obstetrics, Faculty of Health Sciences, Wrocław Medical University, Wrocław, Poland 6 Department and Clinic of Anaesthesiology and Intensive Therapy, Faculty of Medicine, Wrocław Medical University, Wrocław, Poland 7 The Centre for Cardiovascular Health, School of Health and Social Care, Edinburgh Napier University, Edinburgh, United Kingdom Michał Czapla, [e-mail: michal.czapla@umw.edu.pl](e-mail: michal.czapla@umw.edu.pl) This research was funded by the Ministry of Science and Higher Education of Poland under the statutory grant of the Wrocław Medical University (SUBZ.E250.24.042) None declared 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/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 inpatients 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. Keywords: Death, Sudden, Cardiac • Machine Learning • Malnutrition • Mortality • Return of Spontaneous Circulation Abbreviations: AIC – Akaike Information Criterion; ALS – advanced life support; BLS – basic life support; BMI – body mass index; CA – cardiac arrest; hsCR

FAQs

How was mortality prediction modeled in cardiac arrest patients?

A random forest classifier was used to assess mortality-related parameters, and conditional mortality odds were evaluated with logistic regression models that included interactions between variables.

Which factors were most influential for mortality prediction?

The most influential parameters were procalcitonin, age, high-sensitivity C-reactive protein, albumin, and potassium, ranked by descending importance.

How do nutritional status indicators relate to mortality risk?

Nutritional status-associated measures such as serum albumin concentration, BMI, and NRS-2002 may help predict mortality, with decision-tree results supporting usefulness especially when procalcitonin is greater than 0.17 ng/ml.

Aug 2, 2026

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