Dataset Information

Modeling and predicting osteoarthritis progression: data from the osteoarthritis initiative.

ABSTRACT: OBJECTIVE:The goal of this study was to model the longitudinal progression of knee osteoarthritis (OA) and build a prognostic tool that uses data collected in 1 year to predict disease progression over 8 years. DESIGN:To model OA progression, we used a mixed-effects mixture model and 8-year data from the Osteoarthritis Initiative (OAI)-specifically, joint space width measurements from X-rays and pain scores from the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) questionnaire. We included 1243 subjects who at enrollment were classified as being at high risk of developing OA based on age, body mass index (BMI), and medical and occupational histories. After clustering subjects based on radiographic and pain progression, we used clinical variables collected within the first year to build least absolute shrinkage and selection (LASSO) regression models for predicting the probabilities of belonging to each cluster. Areas under the receiver operating characteristic curve (AUC) represent predictive performance on held-out data. RESULTS:Based on joint space narrowing, subjects clustered as progressing or non-progressing. Based on pain scores, they clustered as stable, improving, or worsening. Radiographic progression could be predicted with high accuracy (AUC = .86) using data from two visits spanning 1 year, whereas pain progression could be predicted with high accuracy (AUC = .95) using data from a single visit. Joint space narrowing and pain progression were not associated. CONCLUSION:Statistical models for characterizing and predicting OA progression promise to improve clinical trial design and OA prevention efforts in the future.

SUBMITTER: Halilaj E

PROVIDER: S-EPMC6469859 | biostudies-literature | 2018 Dec

REPOSITORIES: biostudies-literature

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Modeling and predicting osteoarthritis progression: data from the osteoarthritis initiative.

Halilaj E E Le Y Y Hicks J L JL Hastie T J TJ Delp S L SL

Osteoarthritis and cartilage 20180818 12

<h4>Objective</h4>The goal of this study was to model the longitudinal progression of knee osteoarthritis (OA) and build a prognostic tool that uses data collected in 1 year to predict disease progression over 8 years.<h4>Design</h4>To model OA progression, we used a mixed-effects mixture model and 8-year data from the Osteoarthritis Initiative (OAI)-specifically, joint space width measurements from X-rays and pain scores from the Western Ontario and McMaster Universities Osteoarthritis Index (W ...[more]

PMID: 30130590

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Project description:OBJECTIVE:Osteoarthritis (OA) is a disease with a substantial public health burden. Quantitative assessments of periarticular bone may be a biomarker capable of monitoring early disease progression. The purpose of this study was to evaluate whether measures of periarticular bone associate with longitudinal structural progression. METHODS:We conducted a 12-18 months longitudinal study using the Osteoarthritis Initiative (OAI). Participants received knee dual-energy x-ray absorptiometry (DXA), trabecular magnetic resonance (MR) imaging, and x-rays. Knee DXAs generated proximal tibial medial:lateral periarticular bone mineral density (paBMD) measures. Proximal tibial trabecular MR images were assessed for trabecular morphometry: apparent bone volume fraction (BVF), trabecular number, thickness, and spacing. Weight-bearing x-rays were assessed for medial tibiofemoral joint space narrowing (JSN). Chi-squared analyses assessed whether periarticular bone measures were predictive of worsening medial tibiofemoral JSN, adjusted for age, sex, and BMI. RESULTS:In all, 444 participants, mean age 64.2 ± 9.2 years, BMI 29.5 ± 4.6kg/m2, and 52% male at baseline. Medial JSN (radiographic progression) occurred in 40 participants (9%). Higher baseline medial:lateral paBMD, apparent BVF, trabecular number and thickness, and lower baseline and decreased trabecular spacing were all associated with more progression of JSN in the medial compartment. From lowest to highest baseline medial:lateral paBMD quartile groups, 2%, 5%, 11%, and 18% had medial JSN progression, respectively, between the 36- and 48-month visits, p-values = 0.001 and 0.002 unadjusted and adjusted. The rate of change in medial:lateral paBMD, apparent BVF, and spacing were associated with more medial JSN. For rate of medial:lateral paBMD change from lowest to highest quartile, the proportion of each group that experienced medial JSN progression were 5%, 5%, 11%, and 18%, with an unadjusted and adjusted p-value of 0.005. CONCLUSION:Baseline and most rates of periarticular bone change associate with knee OA structural progression, highlighting the close relationship between subchondral bone and JSN. Future studies should focus on developing these measures as predictive and pathophysiological biomarkers, and evaluating their deployment in clinical trials testing bone-targeted therapeutics.

Project description:BackgroundWe conducted an exploratory analysis of osteoarthritis progression among medication users in the Osteoarthritis Initiative to identify interventions or pathways that may be associated with disease modification and therefore of interest for future clinical trials.MethodsWe used participants from the Osteoarthritis Initiative with annual medication inventory data between the baseline and 36-month follow-up visit (n = 2938). Consistent medication users were defined for each medication classification as a participant reporting at all four annual visits that they were regularly using an oral prescription medication at the time of the visit. The exploratory analysis focused on medication classes with 40 or more users. The primary outcome measures were medial tibiofemoral joint space width change and the Western Ontario and McMaster Universities Arthritis Index (WOMAC) knee pain score change (12-36-month visits). Within each knee, we explored eight comparisons between users and matched or unmatched nonusers (defined two ways). An effect size of each comparison was calculated. Medication classes had potential signals if (a) both knees had less progression among users compared with nonusers, or (b) there was less progression based on structure and symptoms in one knee.ResultsWe screened 28 medication classes. Six medication classes had signals for fewer structural changes and better knee pain changes: alpha-adrenergic blockers, antilipemic (excluding statins and fibric acid), anticoagulants, selective serotonin reuptake inhibitors, antihistamines, and antineoplastic agents. Four medication classes had signals for structural changes alone: anti-estrogen (median effect size = 0.28; range = -0.41-0.64), angiotensin-converting enzyme inhibitors (median effect size = 0.13; range = -0.08-0.28), beta-adrenergic blockers (median effect size = 0.09; range = 0.01-0.30), and thyroid agents (median effect size = 0.04; range = -0.05-0.14). Thiazide diuretics had evidence for symptom modification (median effect size = -0.12; range = -0.24-0.04).ConclusionsUsers of neurovascular, antilipemic, or hormonal interventions may have less disease progression compared with nonusers.

Project description:To evaluate the influence of the mtDNA haplogroups on knee osteoarthritis progression in Osteoarthritis Initiative (OAI) participants through longitudinal data from radiographs and magnetic resonance imaging (MRI).Four-year knee osteoarthritis progression was analyzed as increase in Kellgren and Lawrence (KL) grade, in addition to increase in OARSI atlas grade for joint space narrowing (JSN), osteophytes and subchondral sclerosis in the tibia medial compartment of 891 Caucasian individuals from the progression subcohort. The influence of the haplogroups on the rate of structural progression was also assessed as the four-year change in minimum joint space width (mJSW in millimetres) in both knees of (n?=?216) patients with baseline unilateral medial-tibiofemoral JSN. Quantitative cartilage measures from longitudinal MRI data were those related to cartilage thickness and volume with a 24 month follow-up period (n?=?381).During the four-year follow-up period, knee OA patients with the haplogroup T showed the lowest increase in KL grade (Hazard Risk [HR]?=?0.499; 95% Confidence Interval [CI]: 0.261-0.819; p<0.05) as well as the lowest cumulative probability of progression for JSN (HR?=?0.547; 95% CI: 0.280-0.900; p<0.05), osteophytes (HR?=?0.573; 95% CI: 0.304-0.893; p<0.05) and subchondral sclerosis (HR?=?0.549; 95% CI: 0.295-0.884; p<0.05). They also showed the lowest decline in mJSW (standardized response means (SRM)?=?-0.39; p?=?0.037) in those knees without baseline medial JSN (no-JSN knees). Normalized cartilage volume loss was significantly lower in patients carrying the haplogroup T at medial tibia femoral (SRM?=?-0.33; p?=?0.023) and central medial femoral (SRM?=?-0.27; p?=?0.031) compartments. Cartilage thickness loss was significantly lower in carriers of haplogroup T at central medial tibia-femoral (SRM?=?-0.42; p?=?0.011), medial tibia femoral (SRM?=?-0.32; p?=?0.018), medial tibia anterior (SRM?=?+0.31; p?=?0.013) and central medial femoral (SRM?=?-0.19; p?=?0.013) compartments.Mitochondrial genome seems to play a role in the progression of knee osteoarthritis. mtDNA variation could improve identification of patients predisposed to faster or severe progression of the disease.

Project description:Background No investigations have thoroughly explored the feasibility of combining magnetic resonance (MR) images and deep-learning methods for predicting the progression of knee osteoarthritis (KOA). We thus aimed to develop a potential deep-learning model for predicting OA progression based on MR images for the clinical setting. Methods A longitudinal case-control study was performed using data from the Foundation for the National Institutes of Health (FNIH), composed of progressive cases [182 osteoarthritis (OA) knees with both radiographic and pain progression for 24–48 months] and matched controls (182 OA knees not meeting the case definition). DeepKOA was developed through 3-dimensional (3D) DenseNet169 to predict KOA progression over 24–48 months based on sagittal intermediate-weighted turbo-spin echo sequences with fat-suppression (SAG-IW-TSE-FS), sagittal 3D dual-echo steady-state water excitation (SAG-3D-DESS-WE) and its axial and coronal multiplanar reformation, and their combined MR images with patient-level labels at baseline, 12, and 24 months to eventually determine the probability of progression. The classification performance of the DeepKOA was evaluated using 5-fold cross-validation. An X-ray-based model and traditional models that used clinical variables via multilayer perceptron were built. Combined models were also constructed, which integrated clinical variables with DeepKOA. The area under the curve (AUC) was used as the evaluation metric. Results The performance of SAG-IW-TSE-FS in predicting OA progression was similar or higher to that of other single and combined sequences. The DeepKOA based on SAG-IW-TSE-FS achieved an AUC of 0.664 (95% CI: 0.585–0.743) at baseline, 0.739 (95% CI: 0.703–0.775) at 12 months, and 0.775 (95% CI: 0.686–0.865) at 24 months. The X-ray-based model achieved an AUC ranging from 0.573 to 0.613 at 3 time points. However, adding clinical variables to DeepKOA did not improve performance (P>0.05). Initial visualizations from gradient-weighted class activation mapping (Grad-CAM) indicated that the frequency with which the patellofemoral joint was highlighted increased as time progressed, which contrasted the trend observed in the tibiofemoral joint. The meniscus, the infrapatellar fat pad, and muscles posterior to the knee were highlighted to varying degrees. Conclusions This study initially demonstrated the feasibility of DeepKOA in the prediction of KOA progression and identified the potential responsible structures which may enlighten the future development of more clinically practical methods.

Dataset Information

Modeling and predicting osteoarthritis progression: data from the osteoarthritis initiative.

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Modeling and predicting osteoarthritis progression: data from the osteoarthritis initiative.

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