Osteoarthritis (OA) is the most common joint disease associated with pain and disability. OA patients are at a high risk for venous thrombosis (VTE). Here, we developed an interpretable machine learning (ML)-based model to predict VTE risk in patients with OA. To establish a prediction model, we used six ML algorithms, of which 35 variables were employed. Recursive feature elimination (RFE) was used to screen the most related clinical variables associated with VTE. SHapley additive exPlanations (SHAP) were applied to interpret the ML mode and determine the importance of the selected features. Overall, 3169 patients with OA (average age: 66.52 ± 7.28 years) were recruited from Xi’an Honghui Hospital. Of these, 352 and 2817 patients were diagnosed with and without VTE, respectively. The XGBoost algorithm showed the best performance. According to the RFE algorithms, 15 variables were retained for further modeling with the XGBoost algorithm. The top three predictors were Kellgren–Lawrence grade, age, and hypertension. Our study showed that the XGBoost model with 15 variables has a high potential to predict VTE risk in patients with OA.

Background: Steroid-induced osteonecrosis of the femoral head (SONFH) is a common clinical disease caused by massive or prolonged use of steroids. Its pathogenesis is unclear, but its incidence is increasing annually. It is characterized by an insidious and rapid onset, and high disability rate, causing a great burden on patients’ daily life. Therefore, clarifying its pathogenesis and providing early and effective treatment for steroid osteonecrosis is important. Methods: In vivo, we used methylprednisolone (MPS) to construct a SONFH rat model and employed Mirco-ct, Hematoxylin and eosin (H&E) staining, and TdT-mediated dUTP nick end labeling (TUNEL) staining analysis to evaluate the therapeutic effects of proanthocyanidins (PACs). Network pharmacology analysis was conducted to mine targets associated with femoral head necrosis, and PACs analyzed possible molecular mechanisms. In vitro, PACs were added at different doses after treatment of cells with dexamethasone (DEX), and human osteoblast-like sarcoma(MG-63) cell apoptosis was determined by Annexin V-FITC-PI. The mechanisms by which PACs regulate bone metabolism via the Phosphoinositide 3-kinase(PI3K)/protein kinase B(AKT)/Recombinant Human B-Cell Leukemia/Lymphoma 2 XL(Bcl-xL) axis were explored by Western blotting. Result: In vivo studies showed that PACs prevented SONFH in rat model. The PI3K/AKT/Bcl-xL signaling pathway was selected by network pharmacology approach; in vitro studies showed that proanthocyanidin-activated AKT and Bcl-xL inhibited osteoblast apoptosis. Conclusions: PACs can inhibit excessive osteoblast apoptosis in SONFH via the PI3K/AKT/Bcl-xL signaling axis and have potential therapeutic effects.