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Objectives <p>This study aimed to systematically evaluate whether glycemic variability (GV) could provide independent incremental prognostic value for in-hospital death among patients with community-acquired pneumonia (CAP), beyond conventional clinical variables including the SOFA score.</p> Methods <p>Data were retrieved from the Medical Information Mart for Intensive Care IV (MIMIC-IV) database, with a multicenter intensive care unit (ICU) database used as the external validation set. The coefficient of variation was employed to quantify GV. Feature selection was performed using the Boruta algorithm, and 9 machine learning (ML) models were constructed. The area under the receiver operating characteristic curve (AUC), Brier score, Deviance and other metrics were used to evaluate model performance, and SHapley Additive exPlanations (SHAP) analysis was conducted to reveal feature contributions. The predictive performance of the two models was further compared using the change in the area under the receiver operating characteristic curve (ΔAUC), Net Reclassification Index (NRI), Integrated Discrimination Improvement (IDI), and Decision Curve Analysis (DCA), to assess the incremental contribution of GV to model performance.</p> Results <p>A total of 5256 patients were included, of whom 1175 (22.36%) experienced in-hospital death. After Boruta feature selection, 15 key features were retained. In the internal validation set, among the 9 ML models, the LR model performed optimally, with the highest AUC of 0.7851 (95% confidence interval [CI]: 0.7587–0.8115), the lowest Brier score (0.138), and the lowest Deviance (0.861). SHAP analysis indicated that Sequential Organ Failure Assessment (SOFA) score, Charlson Comorbidity Index, age, respiratory rate (RR) and weight were the top 5 core predictive factors. The AUC of the LR model without GV was 0.7623, with a ΔAUC of -0.0002; both NRI and IDI were not applicable (NA). The calibration curves and DCA curves of the two models were similar.</p> Conclusion <p>In the model incorporating conventional clinical variables, GV did not yield independent incremental predictive value, highlighting the necessity of rigorous evaluation for additional biomarkers prior to clinical implementation.</p> Clinical trial number <p>Not applicable.</p>

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Glycemic variability does not provide incremental prognostic value for in-hospital death in community-acquired pneumonia patients: conventional clinical variables dominate

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年6月29日

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  • Jiaojiao Zhou,
  • Kaiyu Cai,
  • Yinggang Zhu,
  • Yanchun Gong,
  • Haiyan Ge,
  • Hua Sheng

摘要

Objectives

This study aimed to systematically evaluate whether glycemic variability (GV) could provide independent incremental prognostic value for in-hospital death among patients with community-acquired pneumonia (CAP), beyond conventional clinical variables including the SOFA score.

Methods

Data were retrieved from the Medical Information Mart for Intensive Care IV (MIMIC-IV) database, with a multicenter intensive care unit (ICU) database used as the external validation set. The coefficient of variation was employed to quantify GV. Feature selection was performed using the Boruta algorithm, and 9 machine learning (ML) models were constructed. The area under the receiver operating characteristic curve (AUC), Brier score, Deviance and other metrics were used to evaluate model performance, and SHapley Additive exPlanations (SHAP) analysis was conducted to reveal feature contributions. The predictive performance of the two models was further compared using the change in the area under the receiver operating characteristic curve (ΔAUC), Net Reclassification Index (NRI), Integrated Discrimination Improvement (IDI), and Decision Curve Analysis (DCA), to assess the incremental contribution of GV to model performance.

Results

A total of 5256 patients were included, of whom 1175 (22.36%) experienced in-hospital death. After Boruta feature selection, 15 key features were retained. In the internal validation set, among the 9 ML models, the LR model performed optimally, with the highest AUC of 0.7851 (95% confidence interval [CI]: 0.7587–0.8115), the lowest Brier score (0.138), and the lowest Deviance (0.861). SHAP analysis indicated that Sequential Organ Failure Assessment (SOFA) score, Charlson Comorbidity Index, age, respiratory rate (RR) and weight were the top 5 core predictive factors. The AUC of the LR model without GV was 0.7623, with a ΔAUC of -0.0002; both NRI and IDI were not applicable (NA). The calibration curves and DCA curves of the two models were similar.

Conclusion

In the model incorporating conventional clinical variables, GV did not yield independent incremental predictive value, highlighting the necessity of rigorous evaluation for additional biomarkers prior to clinical implementation.

Clinical trial number

Not applicable.