External validation of the computer aided risk scoring system in predicting in-hospital mortality following emergency medical admissions

Background: Clinical prediction models have the potential to improve the quality of care and enhance patient safety outcomes. A Computer-aided Risk Scoring system (CARSS) was previously developed to predict in-hospital mortality following emergency admissions based on routinely collected blood tests and vitals. We aimed to externally validate the CARSS model. Methods: In this retrospective external validation study, we considered all adult (>= 18 years) emergency medical admissions discharged between 11/11/2020 and 11/11/2022 from The Rotherham Foundation Trust (TRFT), UK. We assessed the predictive performance of the CARSS model based on its discriminative (c-statistic) and calibration characteristics (calibration slope and calibration plots). Results: Out of 32,774 admissions, 20,422 (62.3 %) admissions were included. The TRFT sample had similar demographic characteristics to the development sample but had higher mortality (6.1 % versus 5.7 %). The CARSS model demonstrated good discrimination (c-statistic 0.87 [95 % CI 0.86-0.88]) and good calibration to the TRFT dataset (slope = 1.03 [95 % CI 0.98-1.08] intercept = 0 [95 % CI - 0.06-0.07]) after re-calibrating for differences in baseline mortality (intercept = 0.96 [95 % CI 0.90-1.03] before re-calibration). Conclusion: In summary, the CARSS model is externally validated after correcting the baseline risk of death between development and validation datasets. External validation of the CARSS model showed that it underpredicted in-hospital mortality. Re-calibration of this model showed adequate performance in the TRFT dataset.