Predicting early mortality after CPR in the ICU: a multimodal analytical approach
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Tarih
2025
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
MediHealth Academy Yayıncılık
Erişim Hakkı
info:eu-repo/semantics/openAccess
Özet
Aims: 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
Aims: 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
Aims: 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
Açıklama
Anahtar Kelimeler
Intensive Care, Yoğun Bakım
Kaynak
Anatolian Current Medical Journal
Anatolian Current Medical Journal
Anatolian Current Medical Journal
WoS Q Değeri
Scopus Q Değeri
Cilt
7
Sayı
4
