Evaluation of machine learning models for personalized prediction of benefit from temporary mechanical circulatory support after out-of-hospital cardiac arrest
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Oxford University Press
Abstract
The role of temporary mechanical circulatory support (tMCS) after out-of-hospital cardiac arrest (OHCA) remains controversial.
This study evaluates machine learning (ML) models for predicting mortality and neurological outcomes, highlighting
their potential as a tool to guide early tMCS decision-making.
This retrospective study analysed five years of data from 564 adult non-traumatic OHCA patients treated at Marburg
University Hospital. Four ML models (ANN, SVM, RF, XGBoost) were trained to predict in-hospital mortality and neurological
outcome based on demographic, clinical, and treatment-related variables. Feature selection and SHAP analysis were
used to optimize performance and identify patients potentially benefiting from tMCS. Overall, 144 patients (31.2%) out of
461 patients who fulfilled the inclusion criteria received tMCS: 39 left-ventricular microaxial flow pump, 76 venoarterial
extracorporeal membrane oxygenation (VA-ECMO), and 29 biventricular support (ECMELLA). In 69 patients (14.9%)
VA-ECMO implantation was performed as part of extracorporeal cardiopulmonary resuscitation. The survival rate of
the tMCS group was 34.7% (50/144) compared to 52.7% (167/317) in the non-tMCS group. The highest predictive power
for survival probability (with/without tMCS) could be achieved by XGBoost and RF when applied to the non-tMCS group.
Machine learning identified 2.5% of non-tMCS patients likely to survive if treated with tMCS. In 23 (RF model) and 31
(XGBoost model) patients, the probability of survival increased by at least 5% with tMCS compared to their predicted outcome
without tMCS. RF slightly outperformed XGBoost [area under the receiver operating characteristic curve (AUC) 0.85
vs. AUC 0.82].
XGBoost and RF models accurately predict mortality and tMCS benefit in OHCA patients, supporting ML-based personalized
therapy.
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Except where otherwised noted, this item's license is described as Attribution 4.0 International