Christian Klemt1, Venkatsaiakhil Tirumala1, Ameen Barghi1, Wayne Brian Cohen-Levy1, Matthew Gerald Robinson1, 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
PURPOSE: Although the average length of hospital stay following revision total knee arthroplasty (TKA) has decreased over recent years due to improved perioperative and intraoperative techniques and planning, prolonged length of stay (LOS) continues to be a substantial driver of hospital costs. The purpose of this study was to develop and validate artificial intelligence algorithms for the prediction of prolonged length of stay for patients following revision TKA. METHODS: A total of 2512 consecutive patients who underwent revision TKA were evaluated. Those patients with a length of stay greater than 75th percentile for all length of stays were defined as patients with prolonged LOS. Three artificial intelligence algorithms were developed to predict prolonged LOS following revision TKA and these models were assessed by discrimination, calibration and decision curve analysis. RESULTS: The strongest predictors for prolonged length of stay following revision TKA were age (> 75 years; p < 0.001), Charlson Comorbidity Index (> 6; p < 0.001) and body mass index (> 35 kg/m2; p < 0.001). The three artificial intelligence algorithms all achieved excellent performance across discrimination (AUC > 0.84) and decision curve analysis (p < 0.01). CONCLUSION: The study findings demonstrate excellent performance on discrimination, calibration and decision curve analysis for all three candidate algorithms. This highlights the potential of these artificial intelligence algorithms to assist in the preoperative identification of patients with an increased risk of prolonged LOS following revision TKA, which may aid in strategic discharge planning. LEVEL OF EVIDENCE: IV.
PURPOSE: Although the average length of hospital stay following revision total knee arthroplasty (TKA) has decreased over recent years due to improved perioperative and intraoperative techniques and planning, prolonged length of stay (LOS) continues to be a substantial driver of hospital costs. The purpose of this study was to develop and validate artificial intelligence algorithms for the prediction of prolonged length of stay for patients following revision TKA. METHODS: A total of 2512 consecutive patients who underwent revision TKA were evaluated. Those patients with a length of stay greater than 75th percentile for all length of stays were defined as patients with prolonged LOS. Three artificial intelligence algorithms were developed to predict prolonged LOS following revision TKA and these models were assessed by discrimination, calibration and decision curve analysis. RESULTS: The strongest predictors for prolonged length of stay following revision TKA were age (> 75 years; p < 0.001), Charlson Comorbidity Index (> 6; p < 0.001) and body mass index (> 35 kg/m2; p < 0.001). The three artificial intelligence algorithms all achieved excellent performance across discrimination (AUC > 0.84) and decision curve analysis (p < 0.01). CONCLUSION: The study findings demonstrate excellent performance on discrimination, calibration and decision curve analysis for all three candidate algorithms. This highlights the potential of these artificial intelligence algorithms to assist in the preoperative identification of patients with an increased risk of prolonged LOS following revision TKA, which may aid in strategic discharge planning. LEVEL OF EVIDENCE: IV.
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: Gilbert M Schwarz; Sebastian Simon; Jennyfer A Mitterer; Bernhard J H Frank; Alexander Aichmair; Martin Dominkus; Jochen G Hofstaetter Journal: Knee Surg Sports Traumatol Arthrosc Date: 2022-07-11 Impact factor: 4.114
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