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Machine learning predicts rate of cartilage loss: data from the osteoarthritis initiative (OAI) and the multicenter osteoarthritis (most) studies

INTRODUCTION: Loss of cartilage is one of the hallmark radiographic symptoms of osteoarthritis and the direct cause of much of the disability directly related to OA. The rate of cartilage loss can range from a slow deterioration process, lasting decades, to a very rapid deterioration leading to comp...

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Autores principales: Paixão, T., DiFranco, M., Goetz, C., Ljuhar, R., Meier, P., Nehrer, S.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: SAGE Publications 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7543177/
http://dx.doi.org/10.1177/2325967120S00530
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author Paixão, T.
DiFranco, M.
Goetz, C.
Ljuhar, R.
Meier, P.
Nehrer, S.
author_facet Paixão, T.
DiFranco, M.
Goetz, C.
Ljuhar, R.
Meier, P.
Nehrer, S.
author_sort Paixão, T.
collection PubMed
description INTRODUCTION: Loss of cartilage is one of the hallmark radiographic symptoms of osteoarthritis and the direct cause of much of the disability directly related to OA. The rate of cartilage loss can range from a slow deterioration process, lasting decades, to a very rapid deterioration leading to complete loss in as little as 24 months 1. In fact, evidence has been gathering that a subset of OA patients develops an “accelerated” form of knee osteoarthritis 2. HYPOTHESES: The rate of cartilage loss can vary widely between patients at risk of or suffering from knee osteoarthritis (OA) but its causes remain unknown. We investigate prediction of future joint space width (JSW) loss from single time point quantitative and semi-quantitative radiographic features. METHODS: Bilateral knee radiographs acquired at several time points in the context of the MOST study from 2651 patients (1079 female, 1572 male) were collected. Joint space narrowing (JSN), osteophyte and sclerosis OARSI grades, as well as Kellgren-Lawrence (KL) grade and joint space width were obtained from each image using an automated software algorithm. Individuals were classified as fast progressors if the rate of JSW loss, measured via linear regression, was above 10% baseline JSW. Fast progressors were predicted using a logistic regression model trained with KL and OARSI grades at baseline as independent variables. Independent validation was performed on 1900 individuals (1079 female, 821 male) from the Osteoarthritis Initiative (OAI) study. Performance was characterized by the area under the ROC curve (ROC-AUC). Confidence intervals were calculated by bootstrapping. RESULTS: AUCs of 0.84 (0.82; 0.87) were achieved for classifying individual knees as fast progressors on the validation dataset (OAI). KL and sclerosis OARSI grades were the main predictors of rapid cartilage loss. CONCLUSION: We demonstrate prediction of future rapid cartilage loss from a single plain radiograph with validation on an independent dataset. Sclerosis OARSI grade, but not osteophytes OARSI grade, was a predictor of rapid cartilage loss, suggesting a non-canonical mode of OA progression.
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spelling pubmed-75431772020-10-20 Machine learning predicts rate of cartilage loss: data from the osteoarthritis initiative (OAI) and the multicenter osteoarthritis (most) studies Paixão, T. DiFranco, M. Goetz, C. Ljuhar, R. Meier, P. Nehrer, S. Orthop J Sports Med Article INTRODUCTION: Loss of cartilage is one of the hallmark radiographic symptoms of osteoarthritis and the direct cause of much of the disability directly related to OA. The rate of cartilage loss can range from a slow deterioration process, lasting decades, to a very rapid deterioration leading to complete loss in as little as 24 months 1. In fact, evidence has been gathering that a subset of OA patients develops an “accelerated” form of knee osteoarthritis 2. HYPOTHESES: The rate of cartilage loss can vary widely between patients at risk of or suffering from knee osteoarthritis (OA) but its causes remain unknown. We investigate prediction of future joint space width (JSW) loss from single time point quantitative and semi-quantitative radiographic features. METHODS: Bilateral knee radiographs acquired at several time points in the context of the MOST study from 2651 patients (1079 female, 1572 male) were collected. Joint space narrowing (JSN), osteophyte and sclerosis OARSI grades, as well as Kellgren-Lawrence (KL) grade and joint space width were obtained from each image using an automated software algorithm. Individuals were classified as fast progressors if the rate of JSW loss, measured via linear regression, was above 10% baseline JSW. Fast progressors were predicted using a logistic regression model trained with KL and OARSI grades at baseline as independent variables. Independent validation was performed on 1900 individuals (1079 female, 821 male) from the Osteoarthritis Initiative (OAI) study. Performance was characterized by the area under the ROC curve (ROC-AUC). Confidence intervals were calculated by bootstrapping. RESULTS: AUCs of 0.84 (0.82; 0.87) were achieved for classifying individual knees as fast progressors on the validation dataset (OAI). KL and sclerosis OARSI grades were the main predictors of rapid cartilage loss. CONCLUSION: We demonstrate prediction of future rapid cartilage loss from a single plain radiograph with validation on an independent dataset. Sclerosis OARSI grade, but not osteophytes OARSI grade, was a predictor of rapid cartilage loss, suggesting a non-canonical mode of OA progression. SAGE Publications 2020-09-30 /pmc/articles/PMC7543177/ http://dx.doi.org/10.1177/2325967120S00530 Text en © The Author(s) 2020 https://creativecommons.org/licenses/by-nc-nd/4.0/ This open-access article is published and distributed under the Creative Commons Attribution - NonCommercial - No Derivatives License (https://creativecommons.org/licenses/by-nc-nd/4.0/), which permits the noncommercial use, distribution, and reproduction of the article in any medium, provided the original author and source are credited. You may not alter, transform, or build upon this article without the permission of the Author(s). For article reuse guidelines, please visit SAGE’s website at http://www.sagepub.com/journals-permissions.
spellingShingle Article
Paixão, T.
DiFranco, M.
Goetz, C.
Ljuhar, R.
Meier, P.
Nehrer, S.
Machine learning predicts rate of cartilage loss: data from the osteoarthritis initiative (OAI) and the multicenter osteoarthritis (most) studies
title Machine learning predicts rate of cartilage loss: data from the osteoarthritis initiative (OAI) and the multicenter osteoarthritis (most) studies
title_full Machine learning predicts rate of cartilage loss: data from the osteoarthritis initiative (OAI) and the multicenter osteoarthritis (most) studies
title_fullStr Machine learning predicts rate of cartilage loss: data from the osteoarthritis initiative (OAI) and the multicenter osteoarthritis (most) studies
title_full_unstemmed Machine learning predicts rate of cartilage loss: data from the osteoarthritis initiative (OAI) and the multicenter osteoarthritis (most) studies
title_short Machine learning predicts rate of cartilage loss: data from the osteoarthritis initiative (OAI) and the multicenter osteoarthritis (most) studies
title_sort machine learning predicts rate of cartilage loss: data from the osteoarthritis initiative (oai) and the multicenter osteoarthritis (most) studies
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7543177/
http://dx.doi.org/10.1177/2325967120S00530
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