External validation of the CALLY index and development of a laboratory-based model for 28-day mortality in sepsis: a...

Early identification of high-risk sepsis patients remains challenging. The C-reactive protein-Albumin-Lymphocyte (CALLY) index, which integrates inflammation, nutritional status, and immune function, may hold prognostic value in this setting.

We retrospectively analyzed sepsis patients from two Chinese hospitals (discovery cohort: n = 695; validation cohort: n = 515). Three machine learning algorithms were applied to identify key predictors. A logistic regression model was constructed, internally validated, and externally tested. Model performance was compared with APACHE II and SOFA scores using DeLong's test.

The non-survivors had markedly lower CALLY values (median 0.2 vs. 0.8, P < 0.001). CALLY, lactate dehydrogenase (LDH), total proteins (TP), and platelet count (PLT) were consistently selected as top predictors across all three machine learning methods. The final model demonstrated good discrimination in the development cohort (AUC = 0.877; 95% CI: 0.847-0.908), with a significantly higher AUC than APACHE II (P < 0.001) and SOFA (P = 0.001). Calibration was satisfactory (Hosmer-Lemeshow P = 0.638), and decision curve analysis indicated clinical net benefit. In external validation, the model retained predictive ability (AUC = 0.920), and CALLY remained significantly lower in non-survivors (P < 0.001).

The CALLY index is an acceptable, readily available biomarker strongly associated with 28-day mortality in sepsis. A prediction model incorporating CALLY, LDH, TP, and PLT offers performance comparable or superior to conventional severity scores and may support early risk stratification in clinical practice, provided that calibration-in-the-large is updated when the model is applied to settings with substantially different baseline mortality.