Department of Orthopedics, The Affiliated Hospital of Chengde Medical University, Chengde, China.
Department of Preventive Medicine, Chengde Medical University, Chengde, China.
Medicine (Baltimore). 2023 Aug 4;102(31):e34481. doi: 10.1097/MD.0000000000034481.
Knee osteoarthritis (KOA) is a common bone disease in older patients. Medication adherence is of great significance in the prognosis of this disease. Therefore, this study analyzed the high-risk factors that lead to medication nonadherence in patients with KOA and constructed a nomogram risk prediction model. The basic information and clinical characteristics of inpatients diagnosed with KOA at the Department of Orthopedics, The Affiliated Hospital of Chengde Medical University, were collected from January 2020 to January 2022. The Chinese version of the eight-item Morisky scale was used to evaluate medication adherence. The Kellgren-Lawrence (KL) classification was performed in combination with the imaging data of patients. Least absolute shrinkage and selection operator regression analysis and logistic multivariate regression analysis were used to analyze high-risk factors leading to medication nonadherence, and a prediction model of the nomogram was constructed. The model was internally verified using bootstrap self-sampling. The index of concordance (C-index), area under the operating characteristic curve (AUC), decision curve, correction curve, and clinical impact curve were used to evaluate the model. A total of 236 patients with KOA were included in this study, and the non-adherence rate to medication was 55.08%. Seven influencing factors were included in the nomogram prediction: age, underlying diseases, diabetes, age-adjusted Charlson comorbidity index (aCCI), payment method, painkillers, and use of traditional Chinese medicine. The C-index and AUC was 0.935. The threshold probability of the decision curve analysis was 0.02-0.98. The nomogram model can be effectively applied to predict the risk of medication adherence in patients with KOA, which is helpful for medical workers to identify and predict the risk of individualized medication adherence in patients with KOA at an early stage of treatment, and then carry out early intervention.
膝骨关节炎（KOA）是老年患者常见的骨骼疾病。药物依从性对该疾病的预后具有重要意义。因此，本研究分析了导致 KOA 患者药物不依从的高危因素，并构建了列线图风险预测模型。收集 2020 年 1 月至 2022 年 1 月于承德医学院附属医院骨科住院诊断为 KOA 的患者的基本信息和临床特征。采用中文版 Morisky 八条目用药依从性量表评估患者的用药依从性。结合患者的影像学资料进行 Kellgren-Lawrence（KL）分级。采用最小绝对收缩和选择算子回归分析和 logistic 多因素回归分析，分析导致药物不依从的高危因素，并构建列线图预测模型。采用自抽样-bootstrap 法对模型进行内部验证。采用一致性指数（C-index）、接受者操作特征曲线下面积（AUC）、决策曲线、校正曲线和临床影响曲线评估模型。本研究共纳入 236 例 KOA 患者，药物不依从率为 55.08%。列线图预测模型纳入的 7 个影响因素包括：年龄、基础疾病、糖尿病、年龄调整 Charlson 合并症指数（aCCI）、付费方式、止痛药和中药使用。C-index 和 AUC 为 0.935。决策曲线分析的阈值概率为 0.02-0.98。列线图模型可有效预测 KOA 患者药物依从性的风险，有助于医务人员在治疗早期识别和预测 KOA 患者个体化药物依从性的风险，并进行早期干预。
