Shai Factor1, Yair Neuman2, Matias Vidra3, Moshe Shalom3, Adi Lichtenstein3, Eyal Amar3, Ehud Rath3. 1. Orthopedic Division, Department of Orthopedic Surgery, Affiliated with the Sackler Faculty of Medicine, Tel Aviv Medical Center, 6 Weitzman St., 6423906, Tel Aviv, Israel. factor310@gmail.com. 2. Department of Cognitive and Brain Sciences and the Zlotowski Center for Neuroscience, Ben-Gurion University of the Negev, 84105, Beer-Sheva, Israel. 3. Orthopedic Division, Department of Orthopedic Surgery, Affiliated with the Sackler Faculty of Medicine, Tel Aviv Medical Center, 6 Weitzman St., 6423906, Tel Aviv, Israel.
Abstract
PURPOSE: The mechanism by which preoperative expectations may be associated with patient satisfaction and procedural outcomes following hip preservation surgery (HPS) is far from simple or linear. The purpose of this study is to better understand patient expectations regarding HPS and their relationship with patient-reported outcomes (PROs) and satisfaction using machine learning (ML) algorithms. METHODS: Patients scheduled for hip arthroscopy completed the Hip Preservation Surgery Expectations Survey (HPSES) and the pre- and a minimum 2 year postoperative International Hip Outcome Tool (iHOT-33). Patient demographics, including age, gender, occupation, and body mass index (BMI), were also collected. At the latest follow-up, patients were evaluated for subjective satisfaction and postoperative complications. ML algorithms and standard statistics were used. RESULTS: A total of 69 patients were included in this study (mean age 33.7 ± 13.1 years, 62.3% males). The mean follow-up period was 27 months. The mean HPSES score, patient satisfaction, preoperative, and postoperative iHOT-33 were 83.8 ± 16.5, 75.9 ± 26.9, 31.6 ± 15.8, and 73 ± 25.9, respectively. Fifty-nine patients (86%) reported that they would undergo the surgery again, with no significant difference with regards to expectations. A significant difference was found with regards to expectation violation (p < 0.001). Expectation violation scores were also found to be significantly correlated with satisfaction. CONCLUSION: ML algorithms utilized in this study demonstrate that violation of expectations plays an important predictive role in postoperative outcomes and patient satisfaction and is associated with patients' willingness to undergo surgery again. LEVEL OF EVIDENCE: IV.
PURPOSE: The mechanism by which preoperative expectations may be associated with patient satisfaction and procedural outcomes following hip preservation surgery (HPS) is far from simple or linear. The purpose of this study is to better understand patient expectations regarding HPS and their relationship with patient-reported outcomes (PROs) and satisfaction using machine learning (ML) algorithms. METHODS: Patients scheduled for hip arthroscopy completed the Hip Preservation Surgery Expectations Survey (HPSES) and the pre- and a minimum 2 year postoperative International Hip Outcome Tool (iHOT-33). Patient demographics, including age, gender, occupation, and body mass index (BMI), were also collected. At the latest follow-up, patients were evaluated for subjective satisfaction and postoperative complications. ML algorithms and standard statistics were used. RESULTS: A total of 69 patients were included in this study (mean age 33.7 ± 13.1 years, 62.3% males). The mean follow-up period was 27 months. The mean HPSES score, patient satisfaction, preoperative, and postoperative iHOT-33 were 83.8 ± 16.5, 75.9 ± 26.9, 31.6 ± 15.8, and 73 ± 25.9, respectively. Fifty-nine patients (86%) reported that they would undergo the surgery again, with no significant difference with regards to expectations. A significant difference was found with regards to expectation violation (p < 0.001). Expectation violation scores were also found to be significantly correlated with satisfaction. CONCLUSION: ML algorithms utilized in this study demonstrate that violation of expectations plays an important predictive role in postoperative outcomes and patient satisfaction and is associated with patients' willingness to undergo surgery again. LEVEL OF EVIDENCE: IV.
Authors: Kyle N Kunze; Evan M Polce; Ian Clapp; Benedict U Nwachukwu; Jorge Chahla; Shane J Nho Journal: J Bone Joint Surg Am Date: 2021-03-11 Impact factor: 5.284
Authors: J Matthew Helm; Andrew M Swiergosz; Heather S Haeberle; Jaret M Karnuta; Jonathan L Schaffer; Viktor E Krebs; Andrew I Spitzer; Prem N Ramkumar Journal: Curr Rev Musculoskelet Med Date: 2020-02
Authors: Jorge Chahla; Edward C Beck; Benedict U Nwachukwu; Thomas Alter; Joshua D Harris; Shane J Nho Journal: Arthroscopy Date: 2019-12 Impact factor: 4.772
Authors: R Carter Dunn; David Coz; Yuan Liu; Ayush Jain; Clara Eng; David H Way; Kang Lee; Peggy Bui; Kimberly Kanada; Guilherme de Oliveira Marinho; Jessica Gallegos; Sara Gabriele; Vishakha Gupta; Nalini Singh; Vivek Natarajan; Rainer Hofmann-Wellenhof; Greg S Corrado; Lily H Peng; Dale R Webster; Dennis Ai; Susan J Huang; Yun Liu Journal: Nat Med Date: 2020-05-18 Impact factor: 53.440