Key result
Novel eight-variable nomogram outperforms conventional scores for predicting post-cardiac arrest ICU mortality with ~0.80 AUROC.
Why the study?
Cardiac arrest is associated with extremely high mortality and long-term disability, necessitating tools to identify risk factors and support prognostic assessment and risk stratification for ICU mortality.
Does an 8-variable predictive nomogram improve the prediction of ICU mortality compared to conventional scoring systems in patients with cardiac arrest?
Observational (n=5,834)
Yes
Does an 8-variable predictive nomogram improve the prediction of ICU mortality compared to conventional scoring systems in patients with cardiac arrest?
A novel 8-variable predictive nomogram demonstrated superior discriminative performance for predicting ICU mortality in cardiac arrest patients compared to conventional scoring systems like SAPS II and SOFA.
May enhance ICU mortality prediction over standard scores in cardiac arrest; leaves open prospective validation before clinical adoption.
Cardiac arrest (CA) is a critical clinical event associated with extremely high mortality and long-term disability. This study aimed to identify risk factors associated with ICU mortality and develop a practical nomogram for risk prediction to support prognostic assessment and risk stratification among patients with CA. A retrospective analysis was conducted using two large public critical care databases. The MIMIC-IV database was used as the training cohort for model development, while the eICU-CRD database served as the external validation cohort. Variables were initially screened using least absolute shrinkage and selection operator (LASSO) regression, and a predictive nomogram was subsequently established using binary logistic regression analysis. The discriminative performance of the model was evaluated using the receiver operating characteristic (ROC) curve. Calibration was assessed using calibration curves and the Hosmer–Lemeshow goodness-of-fit test. Clinical applicability and net benefit were further analyzed using decision curve analysis (DCA). A total of 3,029 patients from the MIMIC-IV database and 2,805 patients from the eICU-CRD database were enrolled in this study. After variable selection by LASSO regression, eight predictors were included in the nomogram. The model was compared with conventional ICU scoring systems, including SAPS II, SOFA, and CCI. In the training cohort, the AUROC values were 0.802 for the nomogram, 0.725 for SAPS II, 0.709 for SOFA, and 0.509 for CCI. In the external validation cohort, the corresponding AUROC values were 0.733, 0.596, 0.701, and 0.517, respectively. The Hosmer–Lemeshow test yielded P -values of 0.976 in the training set and 0.905 in the validation set, indicating favorable goodness-of-fit and satisfactory calibration. Moreover, DCA demonstrated that the nomogram provided greater net benefit compared with traditional scoring systems. The established nomogram for predicting ICU mortality in patients with CA demonstrated acceptable predictive performance and calibration. As a quantitative risk stratification tool, it may provide clinicians with useful individualized prognostic information for this vulnerable patient population.
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Wei et al. (2026) conducted an observational in Cardiac arrest (n=5,834). Predictive nomogram vs. Conventional ICU scoring systems (SAPS II, SOFA, CCI) was evaluated on ICU mortality prediction (AUROC) (AUROC 0.802). An eight-variable nomogram predicted ICU mortality in cardiac arrest patients with an AUROC of 0.802 in the training cohort and 0.733 in validation, outperforming conventional scoring systems.
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