Clinical Scores and Machine Learning at Matched Variable Domains in Acute Appendicitis: Performance of Recommended Rule-Out Thresholds in a CT-Verified Cohort

Background/Objectives: Clinical scores are recommended for excluding acute appendicitis without imaging, but their rule-out thresholds were derived in patients selected on suspicion of appendicitis. This study tested whether these thresholds retain their reported sensitivity in patients selected by CT performance, estimated the incremental diagnostic value of successive layers of clinical information, and, as an exploratory aim, compared machine learning models with the scores.

Methods

This single centre retrospective cohort comprised 761 emergency department patients undergoing abdominal CT for acute abdominal pain. The Alvarado score, the Appendicitis Inflammatory Response (AIR) score and the Adult Appendicitis Score (AAS) were reconstructed from data documented before CT. The reference standard was histopathology in operated patients and the index CT report with the clinical course otherwise. Models were fitted to five cumulative information layers by cross-validation, with nested algorithm selection and a temporal split.