为了评估基于共病风险调整模型的手术部位感染（SSI）报告和基准模型的表现，使用从医院出院数据库（HDD）提取的变量面板，包括共病，与使用不同数据源的其他模型相比。法国国家SSI监测计划（SPICMI）在2020年和2021年的前6个月从自愿医院收集了数据，针对16种选定的手术程序，使用半自动算法进行检测。选择了四种风险调整模型，使用逻辑回归分析结合不同模式的变量：国家医院内感染监测系统（NNIS）风险指数组成部分，个体手术数据以及根据国际疾病分类第十版（ICD-10）诊断确定的6种个体共病：肥胖，糖尿病，营养不良，高血压，癌症或免疫抑制。计算并比较了曲线下面积（AUC）。 总体而言，包括11,975个手术中检测到了294个SSI。单变量分析显示，这6种共病与SSI有关。所选模型包括共病的AUC（0.675; 95%置信区间[CI]，0.642-0.707）显著高于不包括共病的模型的AUC（0.641; 95%CI，0.609-0.672; P = .016）或使用NNIS风险指数组成部分的AUC（0.598; 95%CI，0.564-0.630; P < .001）。基于HDD的模型AUC（0.659; 95%CI，0.625-0.692）与不包括共病的所选模型相比没有显著差异（P = .23）。 将基于HDD的共病作为病例混杂变量来替代NNIS风险指数因素，可能是一种更广泛可供医院使用的自动化SSI监测风险调整的有效方法。

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.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.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).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.