Behavioral and Engagement Predictors of Arthroplasty Clinic No-Shows: A Calibrated Machine Learning Analysis.

Rashed Alananzeh, Kameel Khabaz, Timothy Liu, Nicole J Newman-Hung, Alexandra I Stavrakis, Lauren E Wessel

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Article in Arthroplasty today, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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6 authors.

Rashed AlananzehDepartment of Orthopaedic Surgery, University of California, Los Angeles, Los Angeles, CA, USA.

Kameel KhabazDepartment of Orthopaedic Surgery, University of California, Los Angeles, Los Angeles, CA, USA.

Timothy LiuDepartment of Orthopaedic Surgery, University of California, Los Angeles, Los Angeles, CA, USA.

Nicole J Newman-HungDepartment of Orthopaedic Surgery, University of California, Los Angeles, Los Angeles, CA, USA.

Alexandra I StavrakisDepartment of Orthopaedic Surgery, University of California, Los Angeles, Los Angeles, CA, USA.

Lauren E WesselDepartment of Orthopaedic Surgery, University of California, Los Angeles, Los Angeles, CA, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Missed clinic visits in arthroplasty care can impair preoperative optimization, delay surgery, and disrupt follow-up. Drivers of arthroplasty-specific no-shows remain incompletely characterized. Methods: We extracted 94,723 scheduled arthroplasty appointments at a single tertiary academic center (March 2013-May 2025), retaining each patient's most recent encounter (N = 17,614) for primary analyses. Demographic, socioeconomic, clinical, scheduling, and prior-attendance variables were drawn from the electronic health record. Multivariable logistic regression and isotonic-calibrated machine learning models estimated no-show risk. Discrimination, calibration, and classification were assessed at multiple operating thresholds, and feature contributions were examined via Shapley Additive exPlanations. Results: The no-show rate was 6.9%. Prior attendance behavior was the strongest predictor: each 10-percentage-point increase in historical no-show proportion conferred ∼26% higher odds (adjusted odds ratio [aOR]: 1.26 per 10 pp; full-range aOR: 9.86; 95% confidence interval: 6.16-15.76; Conclusions: Arthroplasty clinic no-shows reflects engagement patterns, administrative processes, and patient context. Calibrated, interpretable machine-learning models capturing ∼60% of no-shows within the highest-risk 20% of encounters support prospective evaluation for targeted, supportive outreach with equity safeguards.

Indexed as

Appointment adherenceArthroplastyClinic nonattendanceMachine learningNo-showSocial determinants of health

Identifiers

PMID42598448

DOI10.1016/j.artd.2026.102096

PMCPMC13471054

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