Predicting Venous Thrombosis in Osteoarthritis Using a Machine Learning Algorithm: A Population-Based Cohort Study.

Chao Lu,Jiayin Song,Ke Xu,Peng Xu,Yangquan Hao,Wenxing Yu,Hui Li

doi:10.3390/jpm12010114

Abstract

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.

Highlights

Osteoarthritis (OA) is the most common joint disease worldwide, with an age-associated increase in both incidence and prevalence [1,2]
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Our results showed that the XGBoost model demonstrated the best performance, with an area under the curve (AUC) of 0.741 (Figure 2A,B)

Summary

Introduction

Osteoarthritis (OA) is the most common joint disease worldwide, with an age-associated increase in both incidence and prevalence [1,2]. OA, a primary cause of pain, disability, and joint replacement, is characterized by disease affecting the whole joint, including articular cartilage degradation, synovium and ligament inflammation, and changes to the subchondral bone [5,6,7]. A multitude of possible etiologies contribute to the development of OA, including obesity, sedentary lifestyle, trauma, and aging [9,10,11]. Prevention and elimination of risk factors are critical in delaying disease progression [12]. Despite these identifiable underlying causes, OA still cannot be effectively prevented

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