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Background Postoperative ileus POI after colorectal surgery leads to increased morbidity, costs, and hospital stays. Identifying POI risk for early intervention is important for improving surgical outcomes especially given the increasing trend towards early discharge after surgery. The results were compared with atemporal models on a panel of benchmark metrics.
Results GRU-D exhibits robust transferability across different EHR systems and hospital sites, showing enhanced performance by integrating new measurements, even amid the extreme sparsity of real-world longitudinal data. On average, for labs, vitals, and assisted living status, Over the follow-up period with 4-hour intervals, Multi-source instance transfer witnessed the best performance, with a maximum of 2.
Whereas the impact of training data and transfer strategy is less crucial, underscoring the challenge of effectively leveraging transfer learning for rare outcomes. While atemporal Logit models show notably superior performance at certain pre-surgical points, their performance fluctuate significantly and generally underperform GRU-D in post-surgical hours. Further research on built-in explainability for meaningful intervention would be highly valuable for its integration into clinical practice.
Postoperative ileus POI poses a common challenge following colorectal surgery, contributing to heightened morbidity, increased costs, and prolonged hospital stays 1. These include a payment reform that emphasizes extended care episodes and Medicare penalties associated with readmissions within 30 days, an increasing preference for outpatient surgery to conserve limited hospital resources amid the COVID pandemic 3 , along with a contentious surge in interest for same-day or next-day discharge 4.
