Christian Klemt1, Wayne Brian Cohen-Levy1, Matthew Gerald Robinson1, Jillian C Burns1, Kyle Alpaugh1, Ingwon Yeo1, Young-Min Kwon2. 1. Bioengineering Laboratory, Department of Orthopaedic Surgery, Massachusetts General Hospital, Harvard Medical School, 55 Fruit Street, Boston, MA, 02114, USA. 2. Bioengineering Laboratory, Department of Orthopaedic Surgery, Massachusetts General Hospital, Harvard Medical School, 55 Fruit Street, Boston, MA, 02114, USA. ymkwon@mgh.harvard.edu.
Abstract
INTRODUCTION: Revision total hip arthroplasty (THA) represents a technically demanding surgical procedure which is associated with significant morbidity and mortality. Understanding risk factors for failure of revision THA is of clinical importance to identify at-risk patients. This study aimed to develop and validate novel machine learning algorithms for the prediction of re-revision surgery for patients following revision total hip arthroplasty. METHODS: A total of 2588 consecutive patients that underwent revision THA was evaluated, including 408 patients (15.7%) with confirmed re-revision THA. Electronic patient records were manually reviewed to identify patient demographics, implant characteristics and surgical variables that may be associated with re-revision THA. Machine learning algorithms were developed to predict re-revision THA and these models were assessed by discrimination, calibration and decision curve analysis. RESULTS: The strongest predictors for re-revision THA as predicted by the four validated machine learning models were the American Society of Anaesthesiology score, obesity (> 35 kg/m2) and indication for revision THA. The four machine learning models all achieved excellent performance across discrimination (AUC > 0.80), calibration and decision curve analysis. Higher net benefits for all machine learning models were demonstrated, when compared to the default strategies of changing management for all patients or no patients. CONCLUSION: This study developed four machine learning models for the prediction of re-revision surgery for patients following revision total hip arthroplasty. The study findings show excellent model performance, highlighting the potential of these computational models to assist in preoperative patient optimization and counselling to improve revision THA patient outcomes. LEVEL OF EVIDENCE: Level III, case-control retrospective analysis.
INTRODUCTION: Revision total hip arthroplasty (THA) represents a technically demanding surgical procedure which is associated with significant morbidity and mortality. Understanding risk factors for failure of revision THA is of clinical importance to identify at-risk patients. This study aimed to develop and validate novel machine learning algorithms for the prediction of re-revision surgery for patients following revision total hip arthroplasty. METHODS: A total of 2588 consecutive patients that underwent revision THA was evaluated, including 408 patients (15.7%) with confirmed re-revision THA. Electronic patient records were manually reviewed to identify patient demographics, implant characteristics and surgical variables that may be associated with re-revision THA. Machine learning algorithms were developed to predict re-revision THA and these models were assessed by discrimination, calibration and decision curve analysis. RESULTS: The strongest predictors for re-revision THA as predicted by the four validated machine learning models were the American Society of Anaesthesiology score, obesity (> 35 kg/m2) and indication for revision THA. The four machine learning models all achieved excellent performance across discrimination (AUC > 0.80), calibration and decision curve analysis. Higher net benefits for all machine learning models were demonstrated, when compared to the default strategies of changing management for all patients or no patients. CONCLUSION: This study developed four machine learning models for the prediction of re-revision surgery for patients following revision total hip arthroplasty. The study findings show excellent model performance, highlighting the potential of these computational models to assist in preoperative patient optimization and counselling to improve revision THA patient outcomes. LEVEL OF EVIDENCE: Level III, case-control retrospective analysis.
Authors: Rasesh R Desai; Arthur L Malkani; Kirby D Hitt; Fredrick F Jaffe; John R Schurman; Jianhua Shen Journal: J Arthroplasty Date: 2012-06-27 Impact factor: 4.757
Authors: Monti Khatod; Guy Cafri; Maria C S Inacio; Alan L Schepps; Elizabeth W Paxton; Stefano A Bini Journal: J Bone Joint Surg Am Date: 2015-03-04 Impact factor: 5.284
Authors: Julian Jz Prokopetz; Elena Losina; Robin L Bliss; John Wright; John A Baron; Jeffrey N Katz Journal: BMC Musculoskelet Disord Date: 2012-12-15 Impact factor: 2.362
Authors: Christian Klemt; Akachimere Cosmas Uzosike; John G Esposito; Michael Joseph Harvey; Ingwon Yeo; Murad Subih; Young-Min Kwon Journal: Arch Orthop Trauma Surg Date: 2022-06-29 Impact factor: 3.067
Authors: Ingwon Yeo; Christian Klemt; Christopher M Melnic; Meghan H Pattavina; Bruna M Castro De Oliveira; Young-Min Kwon Journal: Arch Orthop Trauma Surg Date: 2022-08-22 Impact factor: 2.928