What question did this study set out to answer?

To develop a rapid and personalized prognostic model for mortality in patients with suspected hospital-acquired bloodstream infections using dynamic laboratory indicators.

April 13, 2026Open Access

A rapidly personalized in-hospital bloodstream infection prediction model: a multicenter retrospective study

Key Points

To develop a rapid and personalized prognostic model for mortality in patients with suspected hospital-acquired bloodstream infections using dynamic laboratory indicators.
Conducted a multicenter retrospective study using data from MIMIC-IV and AMU2.
Extracted demographic data, Charlson comorbidity index, and serial laboratory values.
Applied maximum variation rate to quantify dynamic changes in laboratory parameters.
Employed propensity score matching and inverse probability weighting to reduce confounding effects.
Developed an extreme gradient boosting model for predicting 28-day mortality, validated with ROC curves.
Model achieved an AUC of 0.74 in the internal validation cohort AMU2 and 0.71 in MIMIC-IV.
Elevated creatinine and CRP levels, along with reduced hemoglobin and platelet counts, were linked to increased mortality risk.
Six maximum variation rate indicators were identified from 24,606 patients, enhancing prediction accuracy.

Abstract

Hospital-acquired bloodstream infections (BSIs) are associated with high morbidity and mortality. Early identification of high mortality risk patients is critical for improving outcomes. Existing prediction models, often based on single-center static indicators, lack generalizability and fail to capture dynamic physiological changes. This study aimed to develop a rapid and personalized prognostic model for mortality in patients with suspected BSIs using dynamic laboratory indicators from multicenter data. A multicenter retrospective study was conducted using data from the medical information mart for intensive care IV (MIMIC-IV) database and the second hospital of Anhui medical (AMU2). Adults with suspected hospital-acquired BSIs were included. Demographic data, the Charlson comorbidity index, and serial laboratory values were extracted. The maximum variation rate (MVR) was used to quantify dynamic changes in laboratory parameters. Propensity score matching (PSM) and inverse probability weighting (IPW) were applied to reduce confounding. An extreme gradient boosting (XGBoost) model was developed to predict 28-day mortality, interpreted using shapely additive explanations (SHAP) values, and validated via time-dependent receiver operating characteristic (ROC) curves. Six MVR indicators were selected from among 24,606 patients: bilirubin, creatinine, C-reactive protein (CRP), hemoglobin, international normalized ratio and platelet count. The model achieved an area under the curve (AUC) of 0.74 in the internal validation cohort AMU2 and 0.71 in the external validation cohort MIMIC-IV. SHAP analysis revealed that elevated creatinine and CRP levels, in addition to reduced hemoglobin, platelet and white blood cell counts (WBC), were associated with an increased mortality risk. A dynamic model based on routinely available laboratory trends effectively predicts mortality risk in patients with suspected hospital-acquired BSIs. Monitoring temporal changes in laboratory values offers superior predictive value over static measurements, supporting early clinical intervention and prognostic assessment. Not applicable.

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