Faculty, Staff and Student Publications

Language

English

Publication Date

8-4-2025

Journal

Communications Medicine

DOI

10.1038/s43856-025-01053-9

PMID

40760048

PMCID

PMC12322181

PubMedCentral® Posted Date

8-4-2025

PubMedCentral® Full Text Version

Post-print

Abstract

Background: Ileus, a postoperative complication after colorectal surgery, increases morbidity, costs, and hospital stays. Assessing risk of ileus is crucial, especially with the trend towards early discharge. Prior studies assessed risk of ileus with regression models, the role of deep learning remains unexplored.

Methods: We evaluated the Gated Recurrent Unit with Decay (GRU-D) for real-time ileus risk assessment in 7349 colorectal surgeries across three Mayo Clinic sites with two Electronic Health Record (EHR) systems. The results were compared with atemporal models on a panel of benchmark metrics.

Results: Here we show that despite extreme data sparsity (e.g., 72.2% of labs, 26.9% of vitals lack measurements within 24 h post-surgery), GRU-D demonstrates improved performance by integrating new measurements and exhibits robust transferability. In brute-force transfer, AUROC decreases by no more than 5%, while multi-source instance transfer yields up to a 2.6% improvement in AUROC and an 86% narrower confidence interval. Although atemporal models perform better at certain pre-surgical time points, their performance fluctuates considerably and generally falls short of GRU-D in post-surgical hours.

Conclusions: GRU-D's dynamic risk assessment capability is crucial in scenarios where clinical follow-up is essential, warranting further research on built-in explainability for clinical integration.

Keywords

Health services, Prognosis

Published Open-Access

yes

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