EVALUATION OF PROGNOSTIC RISK MODELS BASED ON AGE AND COMORBIDITY IN SEPTIC PATIENTS: INSIGHTS FROM MACHINE LEARNING...

Background: Age and comorbidity significantly impact the prognosis of septic patients and inform treatment decisions. To provide clinicians with effective tools for identifying high-risk patients, this study assesses the predictive value of the age-adjusted Charlson Comorbidity Index (ACCI) and its simplified version, the quick ACCI (qACCI), for mortality in septic patients. Methods: This retrospective study included septic patients from four Chinese medical centers. The internal validation cohort comprised patients from Xinhua Hospital, Ruijin Hospital, and Huashan Hospital, while participants from Renji Hospital served as the external validation cohort. Machine learning models identified ACCI's feature importance. Restricted cubic spline regression and subgroup analysis assess the correlation between ACCI and mortality risk. The qACCI, derived from the ACCI components, was also evaluated for predictive reliability. Results: A total of 3,287 septic patients were included: 2,974 in the internal cohort (mean age 67.96 years; 37.5% male) and 313 in the external cohort (mean age 67.90 years; 48.2% male). Machine learning models identified ACCI as a key predictor of in-hospital mortality. A linear correlation was confirmed between ACCI and risks of in-hospital, 30-day, and ICU mortality. Sensitivity analysis revealed consistent results across subgroups, demonstrating significantly higher mortality risks in the moderate- (hazard ratio [HR] 2.18, 95% CI 1.77-2.70) and high-ACCI (HR 3.72, 95% CI 2.99-4.65) groups compared to the low-ACCI group (HR 1, reference). The ACCI achieved an AUC of 0.788 for in-hospital mortality, outperforming the SOFA in gastrointestinal (0.831 vs. 0.794) and central nervous system infections (0.803 vs. 0.739). The qACCI showed moderate predictive performance in both the internal (AUC, 0.734) and external (AUC, 0.758) cohorts. Conclusions: As composite indicators of age and comorbidity, ACCI and qACCI provide valuable and reliable tools for clinicians to identify high-risk patients early.