Innovative Method to Build Robust Prediction Models When Gold-Standard Outcomes Are Scarce

Abstract

Postsurgical hospital acquired infections (HAIs) are viewed as a quality benchmark in healthcare due to their association with morbidity, mortality and high cost. The prediction of HAIs allows for implementing prevention strategies at early stages to reduce postsurgical complications. In the United States, the National Surgical Quality Improvement Program (NSQIP) maintains a registry considered a “gold-standard” for HAIs outcome reporting, but it relies heavily on costly manual chart review and therefore only includes a small percent of surgery cases from each participating site. Most HAI prediction models rely on a wide range of weak risk factors, which are combined into models with many parameters and require larger sample sizes than available from NSQIP at a single health system. In this study, we propose an alternative approach to develop a robust prediction model, using the few NSQIP cases efficiently. Rather than training the HAIs prediction models directly on the small number of NSQIP patients, we leverage a simple detection model which detects HAIs after the fact on postoperative data and use this detection model to label a large non-NSQIP perioperative dataset on which prediction models are constructed. Detection models rely on strong signals requiring fewer samples to learn. We evaluate this approach in a single academic health system with 115,202 surgeries (10,354 in NSQIP). The prediction models were evaluated on the NSQIP “gold-standard” labels. While organ-space surgical site infection showed comparable performance, the proposed model demonstrated better performance for prediction of superficial surgical site infection, sepsis or septic shock, pneumonia, and urinary tract infection.