Identification of the most important features of knee osteoarthritis structural progressors using machine learning methods

Jamshidi, Afshin; Leclercq, Mickaël; Labbe, Aurélie; Pelletier, Jean‐Pierre; Abram, F.; Droit, Arnaud; Martel‐Pelletier, Johanne

doi:10.1177/1759720x20933468

Cited by 35 publications

(31 citation statements)

References 43 publications

(63 reference statements)

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“…In order to assign a label for each participant, the PVBSP were generated using the prediction model developed in our previous study. 20 As this model consists of features that could be difficult to obtain for a health care professional, we chose to use this knowledge and build a model that would be more accessible, that is, biomarkers and OA risk factors. In brief, we used to label PVBSP for each participant the baseline medial minimum joint space width, mean cartilage thickness of peripheral, medial and central tibial plateaus as assessed by quantitative MRI, the medial joint space narrowing (JSN) as a score, 22 and the outcome JSN ⩾ 1 at 48 months.…”

Section: Methodsmentioning

confidence: 99%

A warning machine learning algorithm for early knee osteoarthritis structural progressor patient screening

Bonakdari

Jamshidi

Pelletier

et al. 2021

Therapeutic Advances in Musculoskeletal

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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.

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Section: Methodsmentioning

confidence: 99%

A warning machine learning algorithm for early knee osteoarthritis structural progressor patient screening

Bonakdari

Jamshidi

Pelletier

et al. 2021

Therapeutic Advances in Musculoskeletal

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“…Then, three imaging outcomes were selected on all three cases. In total, 21,19, and 20 features of the first 40 selected in the left knee, right knee, and both knees combined, respectively, come from either the symptoms or the imaging outcomes category. Other contributing factors proved to be the nutrition and physical exam outcomes since approximately 20 out of the 100 features were selected in each case.…”

Section: Clustering Resultsmentioning

confidence: 99%

“…The predictive models with the higher accuracy proved to be an SVM model for the right knee (77.7% accuracy) and a linear regression model for the left knee (78.3% accuracy). In addition, Jamshidi et al proposed a ML methodology for the identification of important risk factors that are associated to KOA incidents [ 19 ]. They used data from OAI and they concluded that baseline X-ray and MRI-based features could identify early OA knee progressors.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Joint Space Narrowing Progression in Knee Osteoarthritis Patients

Ntakolia

Kokkotis

Moustakidis³

et al. 2021

Diagnostics

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Osteoarthritis is a joint disease that commonly occurs in the knee (KOA). The continuous increase in medical data regarding KOA has triggered researchers to incorporate artificial intelligence analytics for KOA prognosis or treatment. In this study, two approaches are presented to predict the progression of knee joint space narrowing (JSN) in each knee and in both knees combined. A machine learning approach is proposed with the use of multidisciplinary data from the osteoarthritis initiative database. The proposed methodology employs: (i) A clustering process to identify groups of people with progressing and non-progressing JSN; (ii) a robust feature selection (FS) process consisting of filter, wrapper, and embedded techniques that identifies the most informative risk factors; (iii) a decision making process based on the evaluation and comparison of various classification algorithms towards the selection and development of the final predictive model for JSN; and (iv) post-hoc interpretation of the features’ impact on the best performing model. The results showed that bounding the JSN progression of both knees can result to more robust prediction models with a higher accuracy (83.3%) and with fewer risk factors (29) compared to the right knee (77.7%, 88 risk factors) and the left knee (78.3%, 164 risk factors), separately.

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“…For the feature selection, as the knees with accelerated OA (progressors) have a higher chance of needing a TKR compared to knees without accelerated OA (3), we considered 2 groups: progressors and non‐progressors. The OA knee progressor and non‐progressor definition from this cohort has been previously described and discussed (16). Data from the knees of 733 progressors and 698 non‐progressors were included (Figure 1).…”

Section: Methodsmentioning

confidence: 99%

Machine Learning–Based Individualized Survival Prediction Model for Total Knee Replacement in Osteoarthritis: Data From the Osteoarthritis Initiative

Jamshidi

Pelletier

Labbe

et al. 2021

Arthritis Care & Research

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Objective By using machine learning, our study aimed to build a model to predict risk and time to total knee replacement (TKR) of an osteoarthritic knee. Methods Features were from the Osteoarthritis Initiative (OAI) cohort at baseline. Using the lasso method for variable selection in the Cox regression model, we identified the 10 most important characteristics among 1,107 features. The prognostic power of the selected features was assessed by the Kaplan‐Meier method and applied to 7 machine learning methods: Cox, DeepSurv, random forests algorithm, linear/kernel support vector machine (SVM), and linear/neural multi‐task logistic regression models. As some of the 10 first‐found features included similar radiographic measurements, we further looked at using the least number of features without compromising the accuracy of the model. Prediction performance was assessed by the concordance index, Brier score, and time‐dependent area under the curve (AUC). Results Ten features were identified and included radiographs, bone marrow lesions of the medial condyle on magnetic resonance imaging, hyaluronic acid injection, performance measure, medical history, and knee‐related symptoms. The methodologies Cox, DeepSurv, and linear SVM demonstrated the highest accuracy (concordance index scores of 0.85, Brier score of 0.02, and an AUC of 0.87). DeepSurv was chosen to build the prediction model to estimate the time to TKR for a given knee. Moreover, we were able to decrease the features to only 3 and maintain the high accuracy (concordance index of 0.85, Brier score of 0.02, and AUC of 0.86), which included bone marrow lesions, Kellgren/Lawrence grade, and knee‐related symptoms, to predict risk and time of a TKR event. Conclusion For the first time, we developed a model using the OAI cohort to predict with high accuracy if a given osteoarthritic knee would require TKR, when a TKR would be required, and who would likely progress fast toward this event.

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Identification of the most important features of knee osteoarthritis structural progressors using machine learning methods

Cited by 35 publications

References 43 publications

A warning machine learning algorithm for early knee osteoarthritis structural progressor patient screening

A warning machine learning algorithm for early knee osteoarthritis structural progressor patient screening

Prediction of Joint Space Narrowing Progression in Knee Osteoarthritis Patients

Machine Learning–Based Individualized Survival Prediction Model for Total Knee Replacement in Osteoarthritis: Data From the Osteoarthritis Initiative

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