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Development of Machine Learning Algorithms to Predict Being Lost to Follow-up After Hip Arthroscopy for Femoroacetabular Impingement Syndrome

PURPOSE: To determine factors predictive of patients who are at risk for being lost to follow-up after hip arthroscopy for femoroacetabular impingement syndrome (FAIS). METHODS: A prospective clinical repository was queried between January 2012 and October 2017 and all patients who underwent hip art...

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Autores principales: Kunze, Kyle N., Burnett, Robert A., Lee, Elaine K., Rasio, Jonathan P., Nho, Shane J.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7588627/
https://www.ncbi.nlm.nih.gov/pubmed/33134999
http://dx.doi.org/10.1016/j.asmr.2020.07.007
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author Kunze, Kyle N.
Burnett, Robert A.
Lee, Elaine K.
Rasio, Jonathan P.
Nho, Shane J.
author_facet Kunze, Kyle N.
Burnett, Robert A.
Lee, Elaine K.
Rasio, Jonathan P.
Nho, Shane J.
author_sort Kunze, Kyle N.
collection PubMed
description PURPOSE: To determine factors predictive of patients who are at risk for being lost to follow-up after hip arthroscopy for femoroacetabular impingement syndrome (FAIS). METHODS: A prospective clinical repository was queried between January 2012 and October 2017 and all patients who underwent hip arthroscopy for primary or revision FAIS with minimum 2-year follow-up were included. A total of 27 potential risk factors for loss to follow-up were available and tested for predictive value. An 80:20 random sample split of all patients was performed to create training and testing sets. Cross-validation, minimum Bayes information criteria, and adaptive machine-learning algorithms were used to develop the predictive model. The model with the best predictive performance was selected based off of the lowest postestimation deviance between the training and testing samples. The c-statistic is a measure of discrimination. It ranges from 0.5 to 1.0, with 1.0 being perfect discrimination and 0.5 indicating the model is no better than chance. A log-likelihood χ(2) test was used to evaluate the goodness-of-fit of the logistic regression model. RESULTS: A total of 2113 patients were included. Inference of minimum Bayes information criteria model indicated that male sex (odds ratio [OR] 1.82, P = .028), non-white race (African American OR 2.41, P = .013; other non-white OR 1.42, P = .042), smoking (OR 1.07, P = .021), and failure to provide a phone number (OR 1.78, P = .032) increased the risk for being lost to follow-up. Furthermore, greater preoperative International Hip Outcome Tool 12-item component questionnaire (OR 1.03, P = .004), and modified Harris Hip Score (OR 1.05, P = .014) scores increased the risk of being lost to follow-up. The c-statistic was 0.76 (95% confidence interval 0.701-0.848). The log-likelihood indicated that the regression model as a whole was statistically significant (P = .002). CONCLUSIONS: Patients who are male, non-white, smokers, fail to provide a telephone number, and have greater preoperative modified Harris Hip Score and International Hip Outcome Tool 12-item component questionnaire scores are at an increased risk for being lost to follow-up 2 years after hip arthroscopy for FAIS. LEVEL OF EVIDENCE: Level III, case control study
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spelling pubmed-75886272020-10-30 Development of Machine Learning Algorithms to Predict Being Lost to Follow-up After Hip Arthroscopy for Femoroacetabular Impingement Syndrome Kunze, Kyle N. Burnett, Robert A. Lee, Elaine K. Rasio, Jonathan P. Nho, Shane J. Arthrosc Sports Med Rehabil Original Article PURPOSE: To determine factors predictive of patients who are at risk for being lost to follow-up after hip arthroscopy for femoroacetabular impingement syndrome (FAIS). METHODS: A prospective clinical repository was queried between January 2012 and October 2017 and all patients who underwent hip arthroscopy for primary or revision FAIS with minimum 2-year follow-up were included. A total of 27 potential risk factors for loss to follow-up were available and tested for predictive value. An 80:20 random sample split of all patients was performed to create training and testing sets. Cross-validation, minimum Bayes information criteria, and adaptive machine-learning algorithms were used to develop the predictive model. The model with the best predictive performance was selected based off of the lowest postestimation deviance between the training and testing samples. The c-statistic is a measure of discrimination. It ranges from 0.5 to 1.0, with 1.0 being perfect discrimination and 0.5 indicating the model is no better than chance. A log-likelihood χ(2) test was used to evaluate the goodness-of-fit of the logistic regression model. RESULTS: A total of 2113 patients were included. Inference of minimum Bayes information criteria model indicated that male sex (odds ratio [OR] 1.82, P = .028), non-white race (African American OR 2.41, P = .013; other non-white OR 1.42, P = .042), smoking (OR 1.07, P = .021), and failure to provide a phone number (OR 1.78, P = .032) increased the risk for being lost to follow-up. Furthermore, greater preoperative International Hip Outcome Tool 12-item component questionnaire (OR 1.03, P = .004), and modified Harris Hip Score (OR 1.05, P = .014) scores increased the risk of being lost to follow-up. The c-statistic was 0.76 (95% confidence interval 0.701-0.848). The log-likelihood indicated that the regression model as a whole was statistically significant (P = .002). CONCLUSIONS: Patients who are male, non-white, smokers, fail to provide a telephone number, and have greater preoperative modified Harris Hip Score and International Hip Outcome Tool 12-item component questionnaire scores are at an increased risk for being lost to follow-up 2 years after hip arthroscopy for FAIS. LEVEL OF EVIDENCE: Level III, case control study Elsevier 2020-09-22 /pmc/articles/PMC7588627/ /pubmed/33134999 http://dx.doi.org/10.1016/j.asmr.2020.07.007 Text en © 2020 The Authors https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
spellingShingle Original Article
Kunze, Kyle N.
Burnett, Robert A.
Lee, Elaine K.
Rasio, Jonathan P.
Nho, Shane J.
Development of Machine Learning Algorithms to Predict Being Lost to Follow-up After Hip Arthroscopy for Femoroacetabular Impingement Syndrome
title Development of Machine Learning Algorithms to Predict Being Lost to Follow-up After Hip Arthroscopy for Femoroacetabular Impingement Syndrome
title_full Development of Machine Learning Algorithms to Predict Being Lost to Follow-up After Hip Arthroscopy for Femoroacetabular Impingement Syndrome
title_fullStr Development of Machine Learning Algorithms to Predict Being Lost to Follow-up After Hip Arthroscopy for Femoroacetabular Impingement Syndrome
title_full_unstemmed Development of Machine Learning Algorithms to Predict Being Lost to Follow-up After Hip Arthroscopy for Femoroacetabular Impingement Syndrome
title_short Development of Machine Learning Algorithms to Predict Being Lost to Follow-up After Hip Arthroscopy for Femoroacetabular Impingement Syndrome
title_sort development of machine learning algorithms to predict being lost to follow-up after hip arthroscopy for femoroacetabular impingement syndrome
topic Original Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7588627/
https://www.ncbi.nlm.nih.gov/pubmed/33134999
http://dx.doi.org/10.1016/j.asmr.2020.07.007
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