Akash A Shah1, Sai K Devana1, Changhee Lee2, Reza Kianian1, Mihaela van der Schaar3, Nelson F SooHoo1. 1. Department of Orthopaedic Surgery, David Geffen School of Medicine at UCLA, Los Angeles, CA. 2. Department of Electrical and Computer Engineering, University of California, Los Angeles, CA. 3. Department of Electrical and Computer Engineering, University of California, Los Angeles, CA; Department of Applied Mathematics and Theoretical Physics, University of Cambridge, Cambridge, United Kingdom.
Abstract
BACKGROUND: As the prevalence of hip osteoarthritis increases, the number of total hip arthroplasty (THA) procedures performed is also projected to increase. Accurately risk-stratifying patients who undergo THA would be of great utility, given the significant cost and morbidity associated with developing perioperative complications. We aim to develop a novel machine learning (ML)-based ensemble algorithm for the prediction of major complications after THA, as well as compare its performance against standard benchmark ML methods. METHODS: This is a retrospective cohort study of 89,986 adults who underwent primary THA at any California-licensed hospital between 2015 and 2017. The primary outcome was major complications (eg infection, venous thromboembolism, cardiac complication, pulmonary complication). We developed a model predicting complication risk using AutoPrognosis, an automated ML framework that configures the optimally performing ensemble of ML-based prognostic models. We compared our model with logistic regression and standard benchmark ML models, assessing discrimination and calibration. RESULTS: There were 545 patients who had major complications (0.61%). Our novel algorithm was well-calibrated and improved risk prediction compared to logistic regression, as well as outperformed the other four standard benchmark ML algorithms. The variables most important for AutoPrognosis (eg malnutrition, dementia, cancer) differ from those that are most important for logistic regression (eg chronic atherosclerosis, renal failure, chronic obstructive pulmonary disease). CONCLUSION: We report a novel ensemble ML algorithm for the prediction of major complications after THA. It demonstrates superior risk prediction compared to logistic regression and other standard ML benchmark algorithms. By providing accurate prognostic information, this algorithm may facilitate more informed preoperative shared decision-making.
BACKGROUND: As the prevalence of hip osteoarthritis increases, the number of total hip arthroplasty (THA) procedures performed is also projected to increase. Accurately risk-stratifying patients who undergo THA would be of great utility, given the significant cost and morbidity associated with developing perioperative complications. We aim to develop a novel machine learning (ML)-based ensemble algorithm for the prediction of major complications after THA, as well as compare its performance against standard benchmark ML methods. METHODS: This is a retrospective cohort study of 89,986 adults who underwent primary THA at any California-licensed hospital between 2015 and 2017. The primary outcome was major complications (eg infection, venous thromboembolism, cardiac complication, pulmonary complication). We developed a model predicting complication risk using AutoPrognosis, an automated ML framework that configures the optimally performing ensemble of ML-based prognostic models. We compared our model with logistic regression and standard benchmark ML models, assessing discrimination and calibration. RESULTS: There were 545 patients who had major complications (0.61%). Our novel algorithm was well-calibrated and improved risk prediction compared to logistic regression, as well as outperformed the other four standard benchmark ML algorithms. The variables most important for AutoPrognosis (eg malnutrition, dementia, cancer) differ from those that are most important for logistic regression (eg chronic atherosclerosis, renal failure, chronic obstructive pulmonary disease). CONCLUSION: We report a novel ensemble ML algorithm for the prediction of major complications after THA. It demonstrates superior risk prediction compared to logistic regression and other standard ML benchmark algorithms. By providing accurate prognostic information, this algorithm may facilitate more informed preoperative shared decision-making.
Authors: Akash A Shah; Sai K Devana; Changhee Lee; Amador Bugarin; Elizabeth L Lord; Arya N Shamie; Don Y Park; Mihaela van der Schaar; Nelson F SooHoo Journal: Eur Spine J Date: 2021-08-15 Impact factor: 2.721
Authors: Sai K Devana; Akash A Shah; Changhee Lee; Andrew R Jensen; Edward Cheung; Mihaela van der Schaar; Nelson F SooHoo Journal: J Shoulder Elb Arthroplast Date: 2022-04-19
Authors: Igor Lazic; Florian Hinterwimmer; Severin Langer; Florian Pohlig; Christian Suren; Fritz Seidl; Daniel Rückert; Rainer Burgkart; Rüdiger von Eisenhart-Rothe Journal: J Clin Med Date: 2022-04-12 Impact factor: 4.964