Causal Discovery in Radiographic Markers of Knee Osteoarthritis and Prediction for Knee Osteoarthritis Severity With Attention–Long Short-Term Memory

Frontiers in Public Health

Philip Noble¹  Lei You²  Jacqueline Chyr²  Yanfei Wang²  Lan Lan²  Weiling Zhao²  Hua Xu²  Yujia Zhou²  Xiaobo Zhou³ 

[1] McGovern Medical School, University of Texas Health Science Center at Houston, Houston, TX, United States;School of Biomedical Informatics, University of Texas Health Science Center at Houston, Houston, TX, United States;School of Biomedical Informatics, University of Texas Health Science Center at Houston, Houston, TX, United States;McGovern Medical School, University of Texas Health Science Center at Houston, Houston, TX, United States;

关键词: LSTM – Long Short-Term Memory;    attention-LSTM;    causal inference;    prediction model;    disease progression;   

DOI  :  10.3389/fpubh.2020.604654

来源: Frontiers

【 摘 要 】

The goal of this study is to build a prognostic model to predict the severity of radiographic knee osteoarthritis (KOA) and to identify long-term disease progression risk factors for early intervention and treatment. We designed a long short-term memory (LSTM) model with an attention mechanism to predict Kellgren/Lawrence (KL) grade for knee osteoarthritis patients. The attention scores reveal a time-associated impact of different variables on KL grades. We also employed a fast causal inference (FCI) algorithm to estimate the causal relation of key variables, which will aid in clinical interpretability. Based on the clinical information of current visits, we accurately predicted the KL grade of the patient's next visits with 90% accuracy. We found that joint space narrowing was a major contributor to KOA progression. Furthermore, our causal structure model indicated that knee alignments may lead to joint space narrowing, while symptoms (swelling, grinding, catching, and limited mobility) have little impact on KOA progression. This study evaluated a broad spectrum of potential risk factors from clinical data, questionnaires, and radiographic markers that are rarely considered in previous studies. Using our statistical model, providers are able to predict the risk of the future progression of KOA, which will provide a basis for selecting proper interventions, such as proceeding to joint arthroplasty for patients. Our causal model suggests that knee alignment should be considered in the primary treatment and KOA progression was independent of clinical symptoms.

【 授权许可】

CC BY