Development and internal validation of the Simplified Mortality Score for the Intensive Care Unit (SMS‐ICU). Issue 3 (6th December 2017) - Granholm, A.; Perner, A.; Krag, M.; Hjortrup, P. B.; Haase, N.; Holst, L. B.; Marker, S.; Collet, M. O.; Jensen, A. K. G.; Møller, M. H.
Abstract : Background: Intensive care unit (ICU) mortality prediction scores deteriorate over time, and their complexity decreases clinical applicability and commonly causes problems with missing data. We aimed to develop and internally validate a new and simple score that predicts 90‐day mortality in adults upon acute admission to the ICU: the Simplified Mortality Score for the Intensive Care Unit (SMS‐ICU). Methods: We used data from an international cohort of 2139 patients acutely admitted to the ICU and 1947 ICU patients with severe sepsis/septic shock from 2009 to 2016. We performed multiple imputations for missing data and used binary logistic regression analysis with variable selection by backward elimination, followed by conversion to a simple point‐based score. We assessed the apparent performance and validated the score internally using bootstrapping to present optimism‐corrected performance estimates. Results: The SMS‐ICU comprises seven variables available in 99.5% of the patients: two numeric variables: age and lowest systolic blood pressure, and five dichotomous variables: haematologic malignancy/metastatic cancer, acute surgical admission and use of vasopressors/inotropes, respiratory support and renal replacement therapy. Discrimination (area under the receiver operating characteristic curve) was 0.72 (95% CI: 0.71–0.74), overall performance (Nagelkerke's R 2 ) was 0.19 and calibration (intercept and slope) was 0.00 and 0.99, respectively. Optimism‐corrected performance was similar to apparent performance. Conclusions: The SMS‐ICU predicted 90‐day mortality with reasonable and stable performance. If performance remains adequate after external validation, the SMS‐ICU could prove a valuable tool for ICU clinicians and researchers because of its simplicity and expected very low number of missing values.