Cargando…
A warning machine learning algorithm for early knee osteoarthritis structural progressor patient screening
AIM: In osteoarthritis (OA) there is a need for automated screening systems for early detection of structural progressors. We built a comprehensive machine learning (ML) model that bridges major OA risk factors and serum levels of adipokines/related inflammatory factors at baseline for early predict...
Autores principales: | , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
SAGE Publications
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7905723/ https://www.ncbi.nlm.nih.gov/pubmed/33747150 http://dx.doi.org/10.1177/1759720X21993254 |
_version_ | 1783655161734889472 |
---|---|
author | Bonakdari, Hossein Jamshidi, Afshin Pelletier, Jean-Pierre Abram, François Tardif, Ginette Martel-Pelletier, Johanne |
author_facet | Bonakdari, Hossein Jamshidi, Afshin Pelletier, Jean-Pierre Abram, François Tardif, Ginette Martel-Pelletier, Johanne |
author_sort | Bonakdari, Hossein |
collection | PubMed |
description | AIM: In osteoarthritis (OA) there is a need for automated screening systems for early detection of structural progressors. We built a comprehensive machine learning (ML) model that bridges major OA risk factors and serum levels of adipokines/related inflammatory factors at baseline for early prediction of at-risk knee OA patient structural progressors over time. METHODS: The patient- and gender-based model development used baseline serum levels of six adipokines, three related inflammatory factors and their ratios (36), as well as major OA risk factors [age and bone mass index (BMI)]. Subjects (677) were selected from the Osteoarthritis Initiative (OAI) progression subcohort. The probability values of being structural progressors (PVBSP) were generated using our previously published prediction model, including five baseline structural features of the knee, i.e. two X-rays and three magnetic resonance imaging variables. To identify the most important variables amongst the 47 studied in relation to PVBSP, we employed the ML feature classification methodology. Among five supervised ML algorithms, the support vector machine (SVM) demonstrated the best accuracy and use for gender-based classifiers development. Performance and sensitivity of the models were assessed. A reproducibility analysis was performed with clinical trial OA patients. RESULTS: Feature selections revealed that the combination of age, BMI, and the ratios CRP/MCP-1 and leptin/CRP are the most important variables in predicting OA structural progressors in both genders. Classification accuracies for both genders in the testing stage (OAI) were >80%, with the highest sensitivity of CRP/MCP-1. Reproducibility analysis showed an accuracy ⩾92%; the ratio CRP/MCP-1 demonstrated the highest sensitivity in women and leptin/CRP in men. CONCLUSION: This is the first time that such a framework was built for predicting knee OA structural progressors. Using this automated ML patient- and gender-based model, early prediction of knee structural OA progression can be performed with high accuracy using only three baseline serum biomarkers and two risk factors. PLAIN LANGUAGE SUMMARY: Machine learning model for early knee osteoarthritis structural progression Knee osteoarthritis is a well-known debilitating disease leading to reduced mobility and quality of life – the main causes of chronic invalidity. Disease evolution can be slow and span many years; however, for some individuals, the progression/evolution can be fast. Current treatments are only symptomatic and conventional diagnosis of osteoarthritis is not very effective in early identification of patients who will progress rapidly. To improve therapeutic approaches, we need a robust prediction model to stratify osteoarthritis patients at an early stage according to risk of joint structure disease progression. We hypothesize that a prediction model using a machine learning system would enable such an early identification of individuals for whom osteoarthritis knee structure will degrade rapidly. Data were from the Osteoarthritis Initiative, a National Institute of Health (United States) databank, and the robustness and generalizability of the developed model was further evaluated using osteoarthritis patients from an external cohort. Using the supervised machine learning system (support vector machine), we developed an automated patient- and gender-based model enabling an early clinical prognosis for individuals at high risk of structural progressive osteoarthritis. In brief, this model employed at baseline (when the subject sees a physician) easily obtained features consisting of the two main osteoarthritis risk factors, age and bone mass index (BMI), in addition to the serum levels of three molecules. Two of these molecules belong to a family of factors names adipokines and one to a related inflammatory factor. In brief, the model comprising a combination of age, BMI, and the ratios CRP/MCP-1 and leptin/CRP were found very robust for both genders, and the high accuracy persists when tested with an external cohort conferring the gender-based model generalizability. This study offers a new automated system for identifying early knee osteoarthritis structural progressors, which will significantly improve clinical prognosis with real time patient monitoring. |
format | Online Article Text |
id | pubmed-7905723 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | SAGE Publications |
record_format | MEDLINE/PubMed |
spelling | pubmed-79057232021-03-18 A warning machine learning algorithm for early knee osteoarthritis structural progressor patient screening Bonakdari, Hossein Jamshidi, Afshin Pelletier, Jean-Pierre Abram, François Tardif, Ginette Martel-Pelletier, Johanne Ther Adv Musculoskelet Dis Original Research AIM: In osteoarthritis (OA) there is a need for automated screening systems for early detection of structural progressors. We built a comprehensive machine learning (ML) model that bridges major OA risk factors and serum levels of adipokines/related inflammatory factors at baseline for early prediction of at-risk knee OA patient structural progressors over time. METHODS: The patient- and gender-based model development used baseline serum levels of six adipokines, three related inflammatory factors and their ratios (36), as well as major OA risk factors [age and bone mass index (BMI)]. Subjects (677) were selected from the Osteoarthritis Initiative (OAI) progression subcohort. The probability values of being structural progressors (PVBSP) were generated using our previously published prediction model, including five baseline structural features of the knee, i.e. two X-rays and three magnetic resonance imaging variables. To identify the most important variables amongst the 47 studied in relation to PVBSP, we employed the ML feature classification methodology. Among five supervised ML algorithms, the support vector machine (SVM) demonstrated the best accuracy and use for gender-based classifiers development. Performance and sensitivity of the models were assessed. A reproducibility analysis was performed with clinical trial OA patients. RESULTS: Feature selections revealed that the combination of age, BMI, and the ratios CRP/MCP-1 and leptin/CRP are the most important variables in predicting OA structural progressors in both genders. Classification accuracies for both genders in the testing stage (OAI) were >80%, with the highest sensitivity of CRP/MCP-1. Reproducibility analysis showed an accuracy ⩾92%; the ratio CRP/MCP-1 demonstrated the highest sensitivity in women and leptin/CRP in men. CONCLUSION: This is the first time that such a framework was built for predicting knee OA structural progressors. Using this automated ML patient- and gender-based model, early prediction of knee structural OA progression can be performed with high accuracy using only three baseline serum biomarkers and two risk factors. PLAIN LANGUAGE SUMMARY: Machine learning model for early knee osteoarthritis structural progression Knee osteoarthritis is a well-known debilitating disease leading to reduced mobility and quality of life – the main causes of chronic invalidity. Disease evolution can be slow and span many years; however, for some individuals, the progression/evolution can be fast. Current treatments are only symptomatic and conventional diagnosis of osteoarthritis is not very effective in early identification of patients who will progress rapidly. To improve therapeutic approaches, we need a robust prediction model to stratify osteoarthritis patients at an early stage according to risk of joint structure disease progression. We hypothesize that a prediction model using a machine learning system would enable such an early identification of individuals for whom osteoarthritis knee structure will degrade rapidly. Data were from the Osteoarthritis Initiative, a National Institute of Health (United States) databank, and the robustness and generalizability of the developed model was further evaluated using osteoarthritis patients from an external cohort. Using the supervised machine learning system (support vector machine), we developed an automated patient- and gender-based model enabling an early clinical prognosis for individuals at high risk of structural progressive osteoarthritis. In brief, this model employed at baseline (when the subject sees a physician) easily obtained features consisting of the two main osteoarthritis risk factors, age and bone mass index (BMI), in addition to the serum levels of three molecules. Two of these molecules belong to a family of factors names adipokines and one to a related inflammatory factor. In brief, the model comprising a combination of age, BMI, and the ratios CRP/MCP-1 and leptin/CRP were found very robust for both genders, and the high accuracy persists when tested with an external cohort conferring the gender-based model generalizability. This study offers a new automated system for identifying early knee osteoarthritis structural progressors, which will significantly improve clinical prognosis with real time patient monitoring. SAGE Publications 2021-02-23 /pmc/articles/PMC7905723/ /pubmed/33747150 http://dx.doi.org/10.1177/1759720X21993254 Text en © The Author(s), 2021 https://creativecommons.org/licenses/by-nc/4.0/ This article is distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 License (https://creativecommons.org/licenses/by-nc/4.0/) which permits non-commercial use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access page (https://us.sagepub.com/en-us/nam/open-access-at-sage). |
spellingShingle | Original Research Bonakdari, Hossein Jamshidi, Afshin Pelletier, Jean-Pierre Abram, François Tardif, Ginette Martel-Pelletier, Johanne A warning machine learning algorithm for early knee osteoarthritis structural progressor patient screening |
title | A warning machine learning algorithm for early knee osteoarthritis structural progressor patient screening |
title_full | A warning machine learning algorithm for early knee osteoarthritis structural progressor patient screening |
title_fullStr | A warning machine learning algorithm for early knee osteoarthritis structural progressor patient screening |
title_full_unstemmed | A warning machine learning algorithm for early knee osteoarthritis structural progressor patient screening |
title_short | A warning machine learning algorithm for early knee osteoarthritis structural progressor patient screening |
title_sort | warning machine learning algorithm for early knee osteoarthritis structural progressor patient screening |
topic | Original Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7905723/ https://www.ncbi.nlm.nih.gov/pubmed/33747150 http://dx.doi.org/10.1177/1759720X21993254 |
work_keys_str_mv | AT bonakdarihossein awarningmachinelearningalgorithmforearlykneeosteoarthritisstructuralprogressorpatientscreening AT jamshidiafshin awarningmachinelearningalgorithmforearlykneeosteoarthritisstructuralprogressorpatientscreening AT pelletierjeanpierre awarningmachinelearningalgorithmforearlykneeosteoarthritisstructuralprogressorpatientscreening AT abramfrancois awarningmachinelearningalgorithmforearlykneeosteoarthritisstructuralprogressorpatientscreening AT tardifginette awarningmachinelearningalgorithmforearlykneeosteoarthritisstructuralprogressorpatientscreening AT martelpelletierjohanne awarningmachinelearningalgorithmforearlykneeosteoarthritisstructuralprogressorpatientscreening AT bonakdarihossein warningmachinelearningalgorithmforearlykneeosteoarthritisstructuralprogressorpatientscreening AT jamshidiafshin warningmachinelearningalgorithmforearlykneeosteoarthritisstructuralprogressorpatientscreening AT pelletierjeanpierre warningmachinelearningalgorithmforearlykneeosteoarthritisstructuralprogressorpatientscreening AT abramfrancois warningmachinelearningalgorithmforearlykneeosteoarthritisstructuralprogressorpatientscreening AT tardifginette warningmachinelearningalgorithmforearlykneeosteoarthritisstructuralprogressorpatientscreening AT martelpelletierjohanne warningmachinelearningalgorithmforearlykneeosteoarthritisstructuralprogressorpatientscreening |