Phenotype Atlas Construction and Border-Zone Outcome Definition for Anastomotic Leak in Longitudinal Surgical Electronic Health Records

• Rizky Pratama

  Universitas Trunojoyo Madura, Faculty of Engineering, Department of Informatics Engineering, Jalan Raya Telang, Kamal, Bangkalan 69162, East Java, Indonesia

  Author

• Dimas Saputra

  Universitas Siliwangi, Faculty of Engineering, Department of Computer Science, Jalan Siliwangi No. 24, Kahuripan, Tawang, Tasikmalaya 46115, West Java, Indonesia

  Author

Background work in postoperative informatics has expanded access to richly structured surgical records, yet the difficulty of defining clinically important complications has not diminished as datasets have grown. Anastomotic leak remains one of the clearest examples of this problem because it is recognized through evolving combinations of local findings, physiologic deterioration, procedural response, imaging practice, and delayed documentation rather than through a single stable observation. As a result, a computational leak label often compresses a much richer clinical process into a binary endpoint that hides ambiguity, severity variation, and institutional differences in recognition pathways. This paper develops a technical framework for outcome definition and anastomotic leak phenotyping based on the idea of a phenotype atlas. Instead of beginning with a fixed positive class and later subdividing it, the framework starts from a map of clinically meaningful postoperative states, including contained leak, progressive leak, catastrophic leak, unresolved leak-like courses, and non-leak mimics that occupy adjacent diagnostic territory. Outcome definition is then treated as the problem of deciding where the event boundary should be placed within that atlas for a particular purpose. The discussion emphasizes temporal narrative reconstruction, border-zone cases, annotation discipline, uncertainty retention, and cross-setting interpretation. It argues that the most consequential methodological question is not whether a dataset contains enough variables to train a predictive model, but whether the states represented by those variables have been organized in a way that preserves clinically meaningful distinctions. A phenotype-atlas approach offers a route to more transparent labels, more interpretable model behavior, and more realistic handling of difficult postoperative trajectories.

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