Objective: To assess the ability of radiography-based bone texture variables in proximal femur and acetabulum to predict incident radiographic hip osteoarthritis (rHOA) over a 10 years period. Design: Pelvic radiographs from CHECK at baseline (987 hips) were analyzed for bone texture using fractal signature analysis (FSA) in proximal femur and acetabulum. Elastic net (machine learning) was used to predict the incidence of rHOA (including Kellgren–Lawrence grade (KL) ≥ 2 or total hip replacement (THR)), joint space narrowing score (JSN, range 0–3), and osteophyte score (OST, range 0–3) after 10 years. Performance of prediction models was assessed using the area under the receiver operating characteristic curve (ROC AUC). Results: Of the 987 hips without rHOA at baseline, 435 (44%) had rHOA at 10-year follow-up. Of the 667 hips with JSN grade 0 at baseline, 471 (71%) had JSN grade ≥ 1 at 10-year follow-up. Of the 613 hips with OST grade 0 at baseline, 526 (86%) had OST grade ≥ 1 at 10-year follow-up. AUCs for the models including age, gender, and body mass index (BMI) to predict incident rHOA, JSN, and OST were 0.59, 0.54, and 0.51, respectively. The inclusion of bone texture variables in the models improved the prediction of incident rHOA (ROC AUC 0.68 and 0.71 when baseline KL was also included in the model) and JSN (ROC AUC 0.62), but not incident OST (ROC AUC 0.52). Conclusion: Bone texture analysis provides additional information for predicting incident rHOA or THR over 10 years.@en

Objectives: There is sparse evidence for a relationship between cardiovascular disease (CVD) and osteoarthritis (OA). We investigated the association between incidence of arterial calcifications and incidence of radiographic knee and/or hip OA. Design: We used baseline and 8-year follow-up data of Cohort Hip and Cohort Knee (CHECK). Knees and hips were either Kellgren-Lawrence (KL) grade 0 or 1 at baseline. Arterial calcifications were scored on hip and knee radiographs using a four-grade scale. Scores were summed for patient-level analyses. To investigate incidence, participants with arterial calcifications at baseline or missing follow-up were excluded. Incident OA was defined per joint as KL ≥ 2 or prosthesis at year eight. The association between incidenct of arterial calcifications and incident OA was studied using mixed-effects logistic regression. Results: Of 763 participants included, 623 (82%) were women. Mean (sd) age was 56 (5.1) years, mean (sd) body mass index (BMI) 26.2 (4.1) kg/m2. Arterial calcifications developed in 174 participants (283 joints). OA developed in 456 participants (778 joints). Sex modified the association between arterial calcification and OA. In women, incident arterial calcification around a joint was positively associated with incident OA in that joint (adjusted OR 2.51 (95% CI 1.57-4.03)). In men, no association was observed on joint-level, but at patient-level the arterial calcification sum score was negatively associated with incident OA (adjusted OR per point increase 0.70 (95% CI 0.54-0.90)) indicating a systemic effect. Conclusions: We observed sex-dependent associations between incident arterial calcification and incident radiographic knee and/or hip OA, which differs between joint- and patient-level.@en

Objective: To design an automated workflow for hip radiographs focused on joint shape and tests its prognostic value for future hip osteoarthritis. Design: We used baseline and 8-year follow-up data from 1,002 participants of the CHECK-study. The primary outcome was definite radiographic hip osteoarthritis (rHOA) (Kellgren–Lawrence grade ≥2 or joint replacement) at 8-year follow-up. We designed a method to automatically segment the hip joint from radiographs. Subsequently, we applied machine learning algorithms (elastic net with automated parameter optimization) to provide the Shape-Score, a single value describing the risk for future rHOA based solely on joint shape. We built and internally validated prediction models using baseline demographics, physical examination, and radiologists scores and tested the added prognostic value of the Shape-Score using Area-Under-the-Curve (AUC). Missing data was imputed by multiple imputation by chained equations. Only hips with pain in the corresponding leg were included. Results: 84% were female, mean age was 56 (±5.1) years, mean BMI 26.3 (±4.2). Of 1,044 hips with pain at baseline and complete follow-up, 143 showed radiographic osteoarthritis and 42 were replaced. 91.5% of the hips had follow-up data available. The Shape-Score was a significant predictor of rHOA (odds ratio per decimal increase 5.21, 95%-CI (3.74–7.24)). The prediction model using demographics, physical examination, and radiologists scores demonstrated an AUC of 0.795, 95%-CI (0.757–0.834). After addition of the Shape-Score the AUC rose to 0.864, 95%-CI (0.833–0.895). Conclusions: Our Shape-Score, automatically derived from radiographs using a novel machine learning workflow, may strongly improve risk prediction in hip osteoarthritis.@en