Validation of a novel method to identify healthcare-associated infections.

Despite its potential for use in large-scale analyses, previous attempts to utilise administrative data to identify healthcare-associated infections (HAI) have been shown to be unsuccessful. In this study, we validate the accuracy of a novel method of HAI identification based on antibiotic utilisation patterns derived from administrative data. We contemporaneously and independently identified HAIs using both chart review analysis and our method from four Japanese hospitals (N=584). The accuracy of our method was quantified using sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) relative to chart review analysis. We also analysed the inter-rater agreement between both identification methods using Cohen's kappa coefficient. Our method showed a sensitivity of 0.93 (95% CI: 0.87-0.96), specificity of 0.91 (0.89-0.94), PPV of 0.75 (0.68-0.81) and NPV of 0.98 (0.96-0.99). A kappa coefficient of 0.78 indicated a relatively high level of agreement between the two methods. Our results show that our method has sufficient validity for identification of HAIs in large groups of patients, though the relatively lower PPV may imply limited utilisation in the pinpointing of individual infections. Our method may have applications in large-scale HAI identification, risk-adjusted multicentre studies involving cost of illness, or even as the starting point of future cost-effectiveness analyses of HAI control measures.