Predicting early mortality after CPR in the ICU: a multimodal analytical approach

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Mortality rates were significantly elevated in post-CPR ICU patients, reaching 80.5% in hospitals following ROSC.
Using the Random Forest algorithm, high-risk factors such as APACHE II and SOFA scores were identified for predicting mortality.
This analysis included 82 patients in total, of which 41 had undergone successful CPR and ROSC, compared to a control group.
Findings indicate a need for improved predictive models in clinical settings to address high mortality rates following CPR.

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Retrospective analysis identifies mortality risk factors in ICU patients post-CPR, suggesting machine learning may enhance predictions.

Menteş et al. (2025) studied this question.

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