Explainable Machine Learning Models for Classifying Preoperative Anxiety in Patients With Femoral Neck Fractures: A Retrospective Cohort Study.

Abstract

Aim: To develop and internally validate explainable machine learning models for concurrent classification of preoperative anxiety in patients with femoral neck fractures.

Design: Single-centre retrospective observational cohort study.

Methods: Clinical, perioperative and psychosocial data from 271 surgical admissions (270 patients) during 2024 were analysed. Preoperative anxiety was classified during the preoperative bedside assessment phase using a prespecified score greater than 8 on the anxiety subscale of the Hospital Anxiety and Depression Scale. Three tree-based ensemble models were compared using fivefold, patient-grouped nested cross-validation with fold-specific preprocessing, feature selection, resampling, tuning and separate probability calibration.

Results: Preoperative anxiety according to this prespecified definition was identified in 79 admissions. The anxiety group was older, had lower psychological resilience and perceived social support, and had greater surgical fear and frailty. The three algorithms showed similar internal discrimination, and paired comparisons did not support superiority of one model. Psychological resilience, perceived social support and surgical fear were selected in every validation fold for all models. Frailty index was selected in three of five folds for each model.

Conclusion: The models identified a stable psychosocial feature profile for concurrent classification but require external and prospective validation before clinical use.

Implications for nursing practice: These findings may inform future research on structured nursing assessment of preoperative anxiety. The models should not replace validated screening or clinical judgement and require prospective evaluation before routine use.

Impact: Psychological resilience, perceived social support and surgical fear were the most stable selected features.

Reporting method: The Strengthening the Reporting of Observational Studies in Epidemiology and TRIPOD+AI reporting guidelines were followed.

No patient or public contribution: None, because this study retrospectively analysed existing de-identified records.