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

Published online by Cambridge University Press:  02 August 2023

Jérémy Picard Open the ORCID record for Jérémy Picard [Opens in a new window] ,
Béatrice Nkoumazok ,
Isabelle Arnaud ,
Delphine Verjat-trannoy  and
Pascal Astagneau
Jérémy Picard*
Affiliation:
Service de maladies infectieuses et tropicales, CHRU Brest, Université de Bretagne Occidentale, Brest, France Sorbonne Université, INSERM, Institut Pierre Louis d’Epidémiologie et de Santé Publique, F75013 Paris, France Centre de prévention des infections associées aux soins (CPias), Paris, France
Béatrice Nkoumazok
Affiliation:
Centre de prévention des infections associées aux soins (CPias), Paris, France
Isabelle Arnaud
Affiliation:
Centre de prévention des infections associées aux soins (CPias), Paris, France
Delphine Verjat-trannoy
Affiliation:
Centre de prévention des infections associées aux soins (CPias), Paris, France
Pascal Astagneau
Affiliation:
Sorbonne Université, INSERM, Institut Pierre Louis d’Epidémiologie et de Santé Publique, F75013 Paris, France Centre de prévention des infections associées aux soins (CPias), Paris, France
*
Corresponding author: Jérémy Picard; Email: jeremy.picard@chu-brest.fr
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Abstract

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.

Information

Type
Original Article
Information
Infection Control & Hospital Epidemiology , Volume 45 , Issue 1 , January 2024 , pp. 27 - 34
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Copyright
© The Author(s), 2023. Published by Cambridge University Press on behalf of The Society for Healthcare Epidemiology of America

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