Hierarchical Outcome Construction and Latent Phenotype Inference for Anastomotic Leak in Perioperative Electronic Health Record Systems

Abstract

Postoperative adverse event modeling in gastrointestinal surgery increasingly relies on electronic health record data, yet the most difficult methodological task is often not model fitting but the formal specification of the event itself. Anastomotic leak is especially challenging because it does not appear in the record as a single stable datum. Instead, it emerges through delayed and heterogeneous manifestations that may include radiographic suspicion, inflammatory deterioration, procedural source control, antimicrobial escalation, operative revision, or diagnostic coding entered after the underlying process has already evolved. A clinically coherent computational system therefore requires more than a binary endpoint. It requires an explicit outcome construction framework and a phenotype representation that can distinguish between alternative expressions of the same underlying failure process. This paper develops a technical account of outcome definition and AL phenotyping using a hierarchical event model grounded in perioperative longitudinal data. The central argument is that the target of prediction should be treated as a latent postoperative state, while observed leak labels should be treated as operational reconstructions assembled from partially informative evidence streams. Building on that distinction, the paper formalizes event anchoring, temporal eligibility, hierarchical evidence fusion, graph-based phenotype representation, dynamic state estimation, uncertainty propagation, and cross-site transport. It further shows why binary AL labels often merge biologically different trajectories and why phenotype-aware modeling is better suited to surveillance, calibration, and implementation. The resulting framework supports interpretable risk estimation while preserving clinically relevant distinctions between early catastrophic failure, contained radiographic leak, management-defined leak, and ambiguous postoperative inflammatory states.