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Utilization of machine learning methods for predicting surgical outcomes after total knee arthroplasty

BACKGROUND: Predictive models could help clinicians identify risk factors that cause adverse events after total knee arthroplasty (TKA), allowing for appropriate preoperative preventive interventions and allocation of resources. METHODS: The National Inpatient Sample datasets from 2010–2014 were use...

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Autores principales: Mohammed, Hina, Huang, Yihe, Memtsoudis, Stavros, Parks, Michael, Huang, Yuxiao, Ma, Yan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8939835/
https://www.ncbi.nlm.nih.gov/pubmed/35316270
http://dx.doi.org/10.1371/journal.pone.0263897
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author Mohammed, Hina
Huang, Yihe
Memtsoudis, Stavros
Parks, Michael
Huang, Yuxiao
Ma, Yan
author_facet Mohammed, Hina
Huang, Yihe
Memtsoudis, Stavros
Parks, Michael
Huang, Yuxiao
Ma, Yan
author_sort Mohammed, Hina
collection PubMed
description BACKGROUND: Predictive models could help clinicians identify risk factors that cause adverse events after total knee arthroplasty (TKA), allowing for appropriate preoperative preventive interventions and allocation of resources. METHODS: The National Inpatient Sample datasets from 2010–2014 were used to build Logistic Regression (LR), Gradient Boosting Method (GBM), Random Forest (RF), and Artificial Neural Network (ANN) predictive models for three clinically relevant outcomes after TKA—disposition at discharge, any post-surgical complications, and blood transfusion. Model performance was evaluated using the Brier scores as calibration measures, and area under the ROC curve (AUC) and F1 scores as discrimination measures. RESULTS: GBM-based predictive models were observed to have better calibration and discrimination than the other models; thus, indicating comparatively better overall performance. The Brier scores for GBM models predicting the outcomes under investigation ranged from 0.09–0.14, AUCs ranged from 79–87%, and F1-scores ranged from 41–73%. Variable importance analysis for GBM models revealed that admission month, patient location, and patient’s income level were significant predictors for all the outcomes. Additionally, any post-surgical complications and blood transfusions were significantly predicted by deficiency anemias, and discharge disposition by length of stay and age groups. Notably, any post-surgical complications were also significantly predicted by the patient undergoing blood transfusion. CONCLUSIONS: The predictive abilities of the ML models were successfully demonstrated using data from the National Inpatient Sample (NIS), indicating a wide range of clinical applications for obtaining accurate prognoses of complications following orthopedic surgical procedures.
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spelling pubmed-89398352022-03-23 Utilization of machine learning methods for predicting surgical outcomes after total knee arthroplasty Mohammed, Hina Huang, Yihe Memtsoudis, Stavros Parks, Michael Huang, Yuxiao Ma, Yan PLoS One Research Article BACKGROUND: Predictive models could help clinicians identify risk factors that cause adverse events after total knee arthroplasty (TKA), allowing for appropriate preoperative preventive interventions and allocation of resources. METHODS: The National Inpatient Sample datasets from 2010–2014 were used to build Logistic Regression (LR), Gradient Boosting Method (GBM), Random Forest (RF), and Artificial Neural Network (ANN) predictive models for three clinically relevant outcomes after TKA—disposition at discharge, any post-surgical complications, and blood transfusion. Model performance was evaluated using the Brier scores as calibration measures, and area under the ROC curve (AUC) and F1 scores as discrimination measures. RESULTS: GBM-based predictive models were observed to have better calibration and discrimination than the other models; thus, indicating comparatively better overall performance. The Brier scores for GBM models predicting the outcomes under investigation ranged from 0.09–0.14, AUCs ranged from 79–87%, and F1-scores ranged from 41–73%. Variable importance analysis for GBM models revealed that admission month, patient location, and patient’s income level were significant predictors for all the outcomes. Additionally, any post-surgical complications and blood transfusions were significantly predicted by deficiency anemias, and discharge disposition by length of stay and age groups. Notably, any post-surgical complications were also significantly predicted by the patient undergoing blood transfusion. CONCLUSIONS: The predictive abilities of the ML models were successfully demonstrated using data from the National Inpatient Sample (NIS), indicating a wide range of clinical applications for obtaining accurate prognoses of complications following orthopedic surgical procedures. Public Library of Science 2022-03-22 /pmc/articles/PMC8939835/ /pubmed/35316270 http://dx.doi.org/10.1371/journal.pone.0263897 Text en © 2022 Mohammed et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Mohammed, Hina
Huang, Yihe
Memtsoudis, Stavros
Parks, Michael
Huang, Yuxiao
Ma, Yan
Utilization of machine learning methods for predicting surgical outcomes after total knee arthroplasty
title Utilization of machine learning methods for predicting surgical outcomes after total knee arthroplasty
title_full Utilization of machine learning methods for predicting surgical outcomes after total knee arthroplasty
title_fullStr Utilization of machine learning methods for predicting surgical outcomes after total knee arthroplasty
title_full_unstemmed Utilization of machine learning methods for predicting surgical outcomes after total knee arthroplasty
title_short Utilization of machine learning methods for predicting surgical outcomes after total knee arthroplasty
title_sort utilization of machine learning methods for predicting surgical outcomes after total knee arthroplasty
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8939835/
https://www.ncbi.nlm.nih.gov/pubmed/35316270
http://dx.doi.org/10.1371/journal.pone.0263897
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