Second opinion machine learning for fast-track pathway assignment in hip and knee replacement surgery: the use of patient-reported outcome measures

• Published in: BMC medical informatics and decision making 2024-07, Vol.24 (Suppl 4), p.203-17, Article 203
• Main Authors: Campagner, Andrea, Milella, Frida, Banfi, Giuseppe, Cabitza, Federico
• Format: Article
• Language: English
• Subjects: Aged; Algorithms; Arthroplasty (hip); Arthroplasty (knee); Arthroplasty, Replacement, Hip; Arthroplasty, Replacement, Knee; Clinical decision making; Controllability; Controllable AI; Convalescence; Critical Pathways; Datasets; Decision making; Decision trees; Effectiveness; Electronic health records; Explainable artificial intelligence; Fast track; Female; Health aspects; Hip; Hospitals; Humans; Knee; Learning algorithms; Machine Learning; Male; Medical decision making; Medical machine learning; Methods; Middle Aged; Orthopedics; Parameter sensitivity; Patient Reported Outcome Measures; Patients; Performance enhancement; Prediction models; Predictions; Regularization methods; Rehabilitation; Second opinion; Surgery
• ISSN: 1472-6947; 1472-6947
• DOI: 10.1186/s12911-024-02602-3

The frequency of hip and knee arthroplasty surgeries has been rising steadily in recent decades. This trend is attributed to an aging population, leading to increased demands on healthcare systems. Fast Track (FT) surgical protocols, perioperative procedures designed to expedite patient recovery and early mobilization, have demonstrated efficacy in reducing hospital stays, convalescence periods, and associated costs. However, the criteria for selecting patients for FT procedures have not fully capitalized on the available patient data, including patient-reported outcome measures (PROMs). Our study focused on developing machine learning (ML) models to support decision making in assigning patients to FT procedures, utilizing data from patients' self-reported health status. These models are specifically designed to predict the potential health status improvement in patients initially selected for FT. Our approach focused on techniques inspired by the concept of controllable AI. This includes eXplainable AI (XAI), which aims to make the model's recommendations comprehensible to clinicians, and cautious prediction, a method used to alert clinicians about potential control losses, thereby enhancing the models' trustworthiness and reliability. Our models were trained and tested using a dataset comprising 899 records from individual patients admitted to the FT program at IRCCS Ospedale Galeazzi-Sant'Ambrogio. After training and selecting hyper-parameters, the models were assessed using a separate internal test set. The interpretable models demonstrated performance on par or even better than the most effective 'black-box' model (Random Forest). These models achieved sensitivity, specificity, and positive predictive value (PPV) exceeding 70%, with an area under the curve (AUC) greater than 80%. The cautious prediction models exhibited enhanced performance while maintaining satisfactory coverage (over 50%). Further, when externally validated on a separate cohort from the same hospital-comprising patients from a subsequent time period-the models showed no pragmatically notable decline in performance. Our results demonstrate the effectiveness of utilizing PROMs as basis to develop ML models for planning assignments to FT procedures. Notably, the application of controllable AI techniques, particularly those based on XAI and cautious prediction, emerges as a promising approach. These techniques provide reliable and interpretable support, essential for informed decision-m