2426. Performance of Statistical Process Control Charts for Detecting Clinically-Significant Increases in Clostridium difficile Infection Rates

Abstract Background Clostridium difficile infections (CDIs) are the most common type of healthcare-associated infection in the United States, with an estimated annual incidence of 500,000 cases and excess healthcare costs of $5 billion per year. The prevalence and severity of CDIs have been increasing in recent years, making it of vital importance to detect outbreaks sufficiently early to minimize negative health outcomes. Statistical process control (SPC) methods have proven to be a versatile tool in healthcare, enabling near real-time monitoring of adverse events rates and thereby improving patients’ health. The aim of this study was to investigate the performance of SPC in detecting clinically significant increases in CDI rates. Methods We retrospectively analyzed monthly CDI rates at 6 community hospitals in the Duke Infection Control Outreach Network from 2009–2017. Detected CDIs were stratified by surveillance system (LabID or traditional), infection source, recurrence type, and diagnostic test (nucleic acid amplification or enzyme-linked immunosorbent assay). Recurrent and community-associated CDIs were excluded from all analyses. Several variations of Shewhart and exponentially-weighted moving average (EWMA) u-charts were applied to each hospital (Figure 1), including using different baseline types (global, fixed, or rolling) and baseline data streams (hospital or network-wide). To help assess chart performance, epidemiologists determined the clinical relevance (yes/no) of 167 statistical signals generated using earlier iterations of these charts. Performance was quantified via sensitivity, specificity, and accuracy. Results EWMA u-charts with global network-wide baselines performed the best (Figure 2), detecting the largest number of clinically relevant signals (56% sensitivity) with high specificity (96%). Charts utilizing network-wide baselines were generally more accurate than those using local hospital data for that purpose (accuracy of 46–72% vs. 43–45%). Similarly, charts with fixed baselines performed better than those with rolling ones (accuracy of 43–62% vs. 43–47%). Conclusion SPC charts are easily applicable to CDI surveillance; however, their parameters would need to be optimized to maximize utility. Disclosures All authors: No reported disclosures.