Accuracy of hospital-based surveillance systems for surgical site infection after adult spine surgery: a Bayesian latent class analysis

Abstract

Background: Surgical site infections (SSIs) of the spine are morbid and costly complications. An accurate surveillance system is required to properly describe the disease burden and the impact of interventions that mitigate SSI risk. Unfortunately, uniform approaches to conducting SSI surveillance are lacking because of varying SSI case definitions, the lack of a perfect reference case definition and heterogeneous data sources.

Aim: To assess the accuracy of four independent data sources that capture SSIs after spine surgery, with estimation of a measurement-error-adjusted SSI incidence.

Methods: A Bayesian latent class model assessed the sensitivity/specificity of each data source to identify SSI and to estimate a measurement-error-adjusted incidence. The four data sources used were: the discharge abstract database (DAD), the National Surgical Quality Improvement Program (NSQIP) database, the Infection Prevention and Control Canada (IPAC) database, and the Spine Adverse Events Severity database.

Findings: A total of 904 patients underwent spine surgery in 2017. The most sensitive data source was DAD (0.799; 95% credible interval (CrI): 0.597-0.943); the least sensitive was NSQIP (0.497; 95% CrI: 0.308-0.694). The most specific data source was IPAC (0.997; 95% CrI: 0.993-1.000) and the least specific was DAD (0.969; 95% CrI: 0.956-0.981). The measurement-error-adjusted SSI incidence was 0.030 (95% CrI: 0.019-0.045). The crude incidence using the DAD overestimated the incidence, and the three other data sources underestimated it.

Conclusion: SSI surveillance in the spine surgery population is feasible using several data sources, provided that measurement error is considered.

Keywords: Accuracy; Bayesian; Latent class analysis; Spine surgery; Surgical site infection; Surveillance.