Improving prediction of surgical site infection risk with multilevel modeling.
BACKGROUND
Surgical site infection (SSI) surveillance is a key factor in the elaboration of strategies to reduce SSI occurrence and in providing surgeons with appropriate data feedback (risk indicators, clinical prediction rule).


OBJECTIVE
To improve the predictive performance of an individual-based SSI risk model by considering a multilevel hierarchical structure.


METHODS
Data were collected anonymously by the French SSI active surveillance system in 2011. An SSI diagnosis was made by the surgical teams and infection control practitioners following standardized criteria. A random 20% sample comprising 151 hospitals, 502 wards and 62280 patients was used. Three-level (patient, ward, hospital) hierarchical logistic regression models were initially performed. Parameters were estimated using the simulation-based Markov Chain Monte Carlo procedure.


RESULTS
A total of 623 SSI were diagnosed (1%). The hospital level was discarded from the analysis as it did not contribute to variability of SSI occurrence (p  = 0.32). Established individual risk factors (patient history, surgical procedure and hospitalization characteristics) were identified. A significant heterogeneity in SSI occurrence between wards was found (median odds ratio [MOR] 3.59, 95% credibility interval [CI] 3.03 to 4.33) after adjusting for patient-level variables. The effects of the follow-up duration varied between wards (p<10-9), with an increased heterogeneity when follow-up was <15 days (MOR 6.92, 95% CI 5.31 to 9.07]). The final two-level model significantly improved the discriminative accuracy compared to the single level reference model (p<10-9), with an area under the ROC curve of 0.84.


CONCLUSIONS
This study sheds new light on the respective contribution of patient-, ward- and hospital-levels to SSI occurrence and demonstrates the significant impact of the ward level over and above risk factors present at patient level (i.e., independently from patient case-mix).
Coordination Centre for Nosocomial Infection Control, Western Regions, Rennes, France; Department of Public Health, Faculty of Medicine, Rennes, France.
Lauren
L
Saunders
Coordination Centre for Nosocomial Infection Control, Western Regions, Rennes, France.
Marion
M
Perennec-Olivier
Coordination Centre for Nosocomial Infection Control, Western Regions, Rennes, France.
Pascal
P
Jarno
Coordination Centre for Nosocomial Infection Control, Northern Regions, Paris, France.
François
F
L'Hériteau
Coordination Centre for Nosocomial Infection Control, South-Western Regions, Bordeaux, France.
Anne-Gaëlle
AG
Venier
Coordination Centre for Nosocomial Infection Control, Eastern Regions, Nancy, France.
Loïc
L
Simon
Coordination Centre for Nosocomial Infection Control, South-Eastern Regions, Lyon, France.
Marine
M
Giard
Institute for Public Health Surveillance, Saint Maurice, France.
Jean-Michel
JM
Thiolet
Department of Public Health, Faculty of Medicine, Rennes, France.
Jean-François
JF
Viel
D000368:Aged; D062665:Epidemiological Monitoring; D005260:Female; D006801:Humans; D016015:Logistic Models; D008297:Male; D008390:Markov Chains; D008875:Middle Aged; D008954:Models, Biological; D009010:Monte Carlo Method; D055361:Multilevel Analysis; D018570:Risk Assessment / Q000379:methods*; D012307:Risk Factors; D013530:Surgical Wound Infection / Q000453:epidemiology*