Analysis of risk factors and predictive value of a nomogram model for sepsis in patients with diabetic foot.

BACKGROUND

Sepsis is a severe complication in hospitalized patients with diabetic foot (DF), often associated with high morbidity and mortality. Despite its clinical significance, limited tools exist for early risk prediction.

AIM

To identify key risk factors and evaluate the predictive value of a nomogram model for sepsis in this population.

METHODS

This retrospective study included 216 patients with DF admitted from January 2022 to June 2024. Patients were classified into sepsis ( = 31) and non-sepsis ( = 185) groups. Baseline characteristics, clinical parameters, and laboratory data were analyzed. Independent risk factors were identified through multivariable logistic regression, and a nomogram model was developed and validated. The model's performance was assessed by its discrimination (AUC), calibration (Hosmer-Lemeshow test, calibration plots), and clinical utility [decision curve analysis (DCA)].

RESULTS

The multivariable analysis identified six independent predictors of sepsis: Diabetes duration, DF Texas grade, white blood cell count, glycated hemoglobin, C-reactive protein, and albumin. A nomogram integrating these factors achieved excellent diagnostic performance, with an AUC of 0.908 (95%CI: 0.865-0.956) and robust internal validation (AUC: 0.906). Calibration results showed strong agreement between predicted and observed probabilities (Hosmer-Lemeshow = 0.926). DCA demonstrated superior net benefit compared to extreme intervention scenarios, highlighting its clinical utility.

CONCLUSION

The nomogram prediction model, based on six key risk factors, demonstrates strong predictive value, calibration, and clinical utility for sepsis in patients with DF. This tool offers a practical approach for early risk stratification, enabling timely interventions and improved clinical management in this high-risk population.