Cross-national transportability and local adaptation of a parsimonious 30-day mortality prediction model for ICU patients with bloodstream infection: from MIMIC-IV to a Chinese tertiary hospital
Year: 2026
Type: article
Abstract: Timely risk stratification of ICU patients with bloodstream infection (BSI) is essential for guiding clinical decisions. Prediction models developed in Western populations may not generalize to other regions because healthcare systems, case mix, and clinical practice differ. We evaluated the cross-national transportability of a parsimonious ICU mortality model and tested local logistic recalibration in a Chinese external cohort. We developed a 30-day mortality model using MIMIC-IV ( n = 1,501) with Elastic Net regularization and multivariable logistic regression. The simplified model included five routinely available predictors: age, 24-hour Sequential Organ Failure Assessment (SOFA) score, Charlson Comorbidity Index (CCI), serum potassium, and fungal BSI. We externally validated the model in an independent cohort of 285 ICU patients with BSI from a tertiary teaching hospital in China. In internal validation, the simplified model achieved an area under the receiver operating characteristic curve (AUC) of 0.776, compared with 0.796 for the full model. In the Chinese cohort, discrimination remained acceptable (AUC 0.764), but calibration was poor, with systematic underestimation of 30-day mortality (calibration-in-the-large > 0). Local logistic recalibration improved calibration, bringing the calibration-in-the-large and slope closer to target values and reducing the Brier score to 0.182. The US-derived parsimonious model retained acceptable discrimination in this Chinese ICU cohort but showed important calibration drift. Local logistic recalibration may offer a practical, low-resource approach to support local adaptation. Because recalibration was estimated and evaluated in the same cohort, the improvement may be optimistic and requires validation in independent healthcare systems.
Language: English
Subfield: Epidemiology
Field: Medicine
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $2,990
OpenAlex ID: W7223477306
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