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

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dc.contributor.authorMenteş, Oral
dc.contributor.authorÇelik, Deniz
dc.contributor.authorDoğanay, Güler Eraslan
dc.contributor.authorPehlivan, Merve Sarıyıldız
dc.contributor.authorCırık, Mustafa Özgür
dc.contributor.authorArı, Emrah
dc.contributor.authorArı, Maşide
dc.date.accessioned2026-01-24T11:56:48Z
dc.date.available2026-01-24T11:56:48Z
dc.date.issued2025
dc.departmentAlanya Alaaddin Keykubat Üniversitesi
dc.description.abstractAims: Mortality rates remain high among patients admitted to the intensive care unit (ICU) following successful return of spontaneous circulation (ROSC) after cardiopulmonary resuscitation (CPR). Identifying risk factors specific to this patient group may directly inform clinical decision-making processes. This study aimed to identify the clinical and laboratory parameters associated with mortality in post-CPR ICU patients and to compare machine learning models developed using these parameterswith traditional statistical analyses. Methods: This retrospective study included a total of 82 patients treated in a tertiary-level ICU between 2020 and 2023. The post-CPR group (n=41) consisted of patients admitted to the ICU following effective CPR and ROSC, while the control group (n=41) included randomly selected patients with similar clinical characteristics who had not undergone CPR. Demographic data, clinical scores (APACHE II, SOFA, NUTRIC), laboratory values, and survival outcomes were recorded. Mortality prediction models were developed using the Random Forest algorithm applied to class-balanced datasets generated with the ADASYN method. Results: The post-CPR group had significantly higher scores and biomarker levels, including APACHE II, SOFA, and CRP, whereas albumin and GFR levels were notably lower. Both ICU and hospital mortality rates were significantly elevated in this group (75.6% and 80.5%, respectively; p
dc.description.abstractAims: Mortality rates remain high among patients admitted to the intensive care unit (ICU) following successful return of spontaneous circulation (ROSC) after cardiopulmonary resuscitation (CPR). Identifying risk factors specific to this patient group may directly inform clinical decision-making processes. This study aimed to identify the clinical and laboratory parameters associated with mortality in post-CPR ICU patients and to compare machine learning models developed using these parameters with traditional statistical analyses. Methods: This retrospective study included a total of 82 patients treated in a tertiary-level ICU between 2020 and 2023. The post-CPR group (n=41) consisted of patients admitted to the ICU following effective CPR and ROSC, while the control group (n=41) included randomly selected patients with similar clinical characteristics who had not undergone CPR. Demographic data, clinical scores (APACHE II, SOFA, NUTRIC), laboratory values, and survival outcomes were recorded. Mortality prediction models were developed using the Random Forest algorithm applied to class-balanced datasets generated with the ADASYN method. Results: The post-CPR group had significantly higher scores and biomarker levels, including APACHE II, SOFA, and CRP, whereas albumin and GFR levels were notably lower. Both ICU and hospital mortality rates were significantly elevated in this group (75.6% and 80.5%, respectively; p
dc.identifier.doi10.38053/acmj.1704150
dc.identifier.endpage419
dc.identifier.issn2718-0115
dc.identifier.issue4
dc.identifier.startpage410
dc.identifier.urihttps://doi.org/10.38053/acmj.1704150
dc.identifier.urihttps://hdl.handle.net/20.500.12868/3300
dc.identifier.volume7
dc.language.isoen
dc.publisherMediHealth Academy Yayıncılık
dc.relation.ispartofAnatolian Current Medical Journal
dc.relation.ispartofAnatolian Current Medical Journal
dc.relation.publicationcategoryMakale - Ulusal Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_DergiPark_20260121
dc.subjectIntensive Care
dc.subjectYoğun Bakım
dc.titlePredicting early mortality after CPR in the ICU: a multimodal analytical approach
dc.title.alternativePredicting early mortality after CPR in the ICU: a multimodal analytical approach
dc.typeArticle

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