Validation of SAPS3 admission score and its customization for use in Korean intensive care unit patients: A prospective multicentre study

Background and objective To externally validate the simplified acute physiology score 3 (SAPS3) and to customize it for use in Korean intensive care unit (ICU) patients. Methods This is a prospective multicentre cohort study involving 22 ICUs from 15 centres throughout Korea. The study population comprised patients who were consecutively admitted to participating ICUs from 1 July 2010 to 31 January 2011. Results A total of 4617 patients were enrolled. ICU mortality was 14.3%, and hospital mortality was 20.6%. The patients were randomly assigned into one of two cohorts: a development (n = 2309) or validation (n = 2308) cohort. In the development cohort, the general SAPS3 had good discrimination (area under the receiver operating characteristics curve = 0.829), but poor calibration (Hosmer-Lemeshow goodness-of-fit test H = 123.06, P < 0.001, C = 118.45, P < 0.001). The Australasia SAPS3 did not improve calibration (H = 73.53, P < 0.001, C = 70.52, P < 0.001). Customization was achieved by altering the logit of the original SAPS3 equation. The new equation for Korean ICU patients was validated in the validation cohort, and demonstrated both good discrimination (area under the receiver operating characteristics curve = 0.835) and good calibration (H = 4.61, P = 0.799, C = 5.67, P = 0.684). Conclusions General and regional Australasia SAPS3 admission scores showed poor calibration for use in Korean ICU patients, but the prognostic power of the SAPS3 was significantly improved by customization. Prediction models should be customized before being used to predict mortality in different regions of the world. We investigated to validate the simplified acute physiology score 3 (SAPS3) and to customize it in Korean ICUs. General and Australasia SAPS3 showed poor calibration, but the prognostic power was improved by customization. Prediction models should be customized before being used to predict mortality in different regions.