Purpose: To create a simplified critical illness severity scoring system with high prediction accuracy for 30-day mortality using only commonly available variables.
Materials and methods: This is a retrospective cohort study of ICU admissions 2010-2015 in 306 ICUs in 117 Veterans Affairs (VA) hospitals. We randomly divided our cohort into a training dataset (75%) and a validation dataset (25%). We created a critical illness severity scoring system (CISSS) using age, comorbidities, heart rate, mean arterial blood pressure, temperature, respiratory rate, hematocrit, white blood cell count, creatinine, sodium, glucose, albumin, bilirubin, bicarbonate, use of invasive mechanical ventilation, and whether the admission was surgical or not. We validated the performance of CISSS to predict 30-day mortality internally.
Results: After excluding 31,743 re-admissions, we divided our sample (n = 534,001) into a training (n = 400,613) and a validation dataset (n = 133,388). In the training dataset, the area under the curve (AUC) of CISSS was 0.847(95%CI = 0.845-0.850). In the validation dataset, the AUC was 0.848 (95%CI = 0.844-0.852), the standardized mortality ratio (SMR) was 1.00 (95%CI = 0.98-1.02), and Brier's score for 30-day mortality was 0.058 (95%CI = 0.057-0.059). CISSS calibration was acceptable.
Conclusions: CISSS has very good performance and requires only commonly used variables that can be easily extracted by electronic health records.
Keywords: APACHE; Automated; Computerized; Illness severity score; Intensive care units; Mortality.
Published by Elsevier Inc.