Improving prediction of surgical site infection risk with multilevel modeling

Publié le 2 Mai 2014
Mis à jour le 5 juillet 2019

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). AIM: to improve the predictive performance of an individual-based SSI risk model by considering a multilevel hierarchical structure. PATIENTS AND 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 inférieur à 10-9), with an increased heterogeneity when follow-up was inférieur à 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 inférieur à 10-9), with an area under the ROC curve of 0.84. CONCLUSION: 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). (R.A.)

Auteur : Saunders L, Perennec Olivier M, Jarno P, L'heriteau F, Venier AG, Simon L, Giard M, Thiolet JM, Viel JF, RAISIN group
PloS one, 2014, vol. 9, n°. 5, p. e95295