Formal Outcome Definition and Computational Phenotyping of Anastomotic Leak in Electronic Health Records and Perioperative Decision Systems

• Gabriel Sousa

  Departamento de Informática em Saúde, Universidade Federal de Ciências da Saúde de Porto Alegre, Rua Sarmento Leite 245, Centro Histórico, Porto Alegre, Brazil

  Author

• Matheus Oliveira

  Departamento de Engenharia Biomédica, Universidade Federal do ABC, Avenida dos Estados 5001, Bangú, Santo André, Brazil

  Author

• Felipe Nogueira

  Departamento de Tecnologia em Sistemas Biomédicos, Universidade Estadual de Ciências da Saúde de Alagoas, Rua Doutor Jorge de Lima 113, Trapiche da Barra, Maceió, Brazil

  Author

Outcome definition in perioperative data science is often treated as a preliminary coding task, yet for anastomotic leak it is the central methodological problem that governs label validity, error structure, transportability, and clinical interpretation. Anastomotic leak is not a single biochemical state, nor a single radiographic sign, nor a single billing event. It is a time-evolving postoperative failure process that may appear as localized contamination, systemic inflammatory deterioration, occult radiologic disruption, or operative reintervention, with each manifestation entering the record through different channels and at different delays. That heterogeneity makes binary classification deceptively simple and often unstable. This paper develops a technical framework for outcome definition and anastomotic leak phenotyping that begins from event semantics rather than algorithm choice. The discussion treats leak labels as functions of temporally indexed evidence streams, including diagnosis codes, procedures, laboratory trajectories, imaging triggers, antimicrobial escalation, and operative context. A phenotype is then defined as a probabilistic state over clinically coherent leak expressions, rather than a rigid mutually exclusive category. The paper formalizes composite outcome assembly, temporal anchoring, latent phenotype inference, uncertainty propagation, and subgroup-sensitive validation. It further argues that the mathematical object being predicted should be separated from the operational rule used to identify it, because the latter is only an imperfect measurement process. Under this view, robust AL modeling depends on constructing labels that are auditable, temporally consistent, and transportable across hospitals, while preserving clinically meaningful distinctions between contained, occult, severe, and management-defined leak states.

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