A predictive algorithm for perioperative complications and readmission after ankle arthrodesis

• Published in: European journal of orthopaedic surgery & traumatology 2024-04, Vol.34 (3), p.1373-1379
• Main Authors: Shah, Akash A., Devana, Sai K., Lee, Changhee, SooHoo, Nelson F.
• Format: Article
• Language: English
• Subjects: Adult; Algorithms; Ankle; Ankle - surgery; Ankle Joint - surgery; Arthrodesis - adverse effects; Arthroplasty, Replacement, Ankle - adverse effects; Fractures; Fractures, Bone - surgery; Humans; Medicine; Medicine & Public Health; Original Article; Patient Readmission; Postoperative Complications - epidemiology; Postoperative Complications - etiology; Postoperative Complications - surgery; Retrospective Studies; Surgical Orthopedics; Traumatic Surgery; Treatment Outcome
• ISSN: 1432-1068; 1633-8065; 1432-1068
• DOI: 10.1007/s00590-023-03805-6

Purpose Ankle arthrodesis is a mainstay of surgical management for ankle arthritis. Accurately risk-stratifying patients who undergo ankle arthrodesis would be of great utility. There is a paucity of accurate prediction models that can be used to pre-operatively risk-stratify patients for ankle arthrodesis. We aim to develop a predictive model for major perioperative complication or readmission after ankle arthrodesis. Methods This is a retrospective cohort study of adult patients who underwent ankle arthrodesis at any non-federal California hospital between 2015 and 2017. The primary outcome is readmission within 30 days or major perioperative complication. We build logistic regression and ML models spanning different classes of modeling approaches, assessing discrimination and calibration. We also rank the contribution of the included variables to model performance for prediction of adverse outcomes. Results A total of 1084 patients met inclusion criteria for this study. There were 131 patients with major complication or readmission (12.1%). The XGBoost algorithm demonstrates the highest discrimination with an area under the receiver operating characteristic curve of 0.707 and is well-calibrated. The features most important for prediction of adverse outcomes for the XGBoost model include: diabetes, peripheral vascular disease, teaching hospital status, morbid obesity, history of musculoskeletal infection, history of hip fracture, renal failure, implant complication, history of major fracture. Conclusion We report a well-calibrated algorithm for prediction of major perioperative complications and 30-day readmission after ankle arthrodesis. This tool may help accurately risk-stratify patients and decrease likelihood of major complications.