Comorbidities directly extracted from the hospital database for adjusting SSI risk in the new national semiautomated surveillance system in France: The SPICMI network.

OBJECTIVE: To evaluate the performance of a comorbidity-based risk-adjustment model for surgical-site infection (SSI) reporting and benchmarking using a panel of variables extracted from the hospital discharge database (HDD), including comorbidities, compared to other models that use variables from different data sources. METHODS: The French national surveillance program for SSI (SPICMI) has collected data from voluntary hospitals in the first 6 months of 2020 and 2021, for 16 selected surgery procedures, using a semiautomated algorithm for detection. Four risk-adjustment models were selected with logistic regression analysis, combining the different patterns of variables: National Nosocomial Infections Surveillance System (NNIS) risk-index components, individual operative data, and 6 individual comorbidities according to International Classification of Disease, Tenth Revision (ICD-10) diagnosis: obesity, diabetes, malnutrition, hypertension, cancer, or immunosuppression. Areas under the curve (AUCs) were calculated and compared. RESULTS: Overall, 294 SSI were detected among 11,975 procedures included. All 6 comorbidities were related to SSI in the univariate analysis. The AUC of the selected model including comorbidities (0.675; 95% confidence interval [CI], 0.642-0.707), was significantly higher than the AUC of the model without comorbidities (0.641; 95% CI, 0.609-0.672; P = .016) or the AUC using the NNIS-index components (0.598; 95% CI, 0.564-0.630; P < .001). The HDD-based model AUC (0.659; 95% CI, 0.625-0.692) did not differ significantly from the selected model without comorbidities (P = .23). CONCLUSION: Including HDD-based comorbidities as patient case-mix variables instead of NNIS risk index factors could be an effective approach for risk-adjustment of automated SSI surveillance more widely accessible to hospitals.