■肌肉减少症与类风湿关节炎(RA)患者的不良预后有关。早期识别和治疗肌肉减少症具有临床意义。这项研究旨在创建和验证一个列线图来预测RA患者的肌肉减少症风险。为临床医生提供早期识别高危患者的可靠工具。
■在2022年8月至2024年1月期间诊断为RA的患者被纳入并以7:3的比例随机分为训练集和验证集。使用最小绝对收缩和选择算子(LASSO)回归分析和多因素逻辑回归分析来筛选RA相关肌肉损失的风险变量并创建RA肌肉减少症风险评分。通过绘制受试者工作特征曲线并计算曲线下面积(AUC)评估风险模型的预测性能和临床实用性,以及校准曲线和临床决策曲线(DCA)。
■本研究共纳入480例RA患者(90%为女性,45-59岁年龄组中人数最多,约50%)。在这项研究中,四个变量(体重指数,疾病持续时间,血红蛋白,和握力)被纳入其中,以构建预测RA肌肉减少症的列线图。训练集和验证集的AUC分别为0.915(95%CI:0.8795-0.9498)和0.907(95%CI:0.8552-0.9597),分别,证明了预测模型的区分度很好。校正曲线表明,模型预测值与实际值基本吻合,显示良好的校准。DCA表明,几乎所有RA患者都可以从这种新颖的预测模型中受益。提示良好的临床效用。
■这项研究开发并验证了一种列线图预测模型,以预测RA患者的肌少症风险。该模型可以帮助临床医生增强其筛查RA肌肉减少症的能力,评估患者预后,做出早期决定,改善RA患者的生活质量。
UNASSIGNED: Sarcopenia is linked to an unfavorable prognosis in individuals with rheumatoid arthritis (RA). Early identification and treatment of sarcopenia are clinically significant. This study aimed to create and validate a nomogram for predicting sarcopenia risk in RA patients, providing clinicians with a reliable tool for the early identification of high-risk patients.
UNASSIGNED: Patients with RA diagnosed between August 2022 and January 2024 were included and randomized into training and validation sets in a 7:3 ratio. Least Absolute Shrinkage and Selection Operator (LASSO) regression analysis and multifactorial logistic regression analysis were used to screen the risk variables for RA-associated muscle loss and to create an RA sarcopenia risk score. The predictive performance and clinical utility of the risk model were evaluated by plotting the receiver operating characteristic curve and calculating the area under the curve (AUC), along with the calibration curve and clinical decision curve (DCA).
UNASSIGNED: A total of 480 patients with RA were included in the study (90% female, with the largest number in the 45-59 age group, about 50%). In this study, four variables (body mass index, disease duration, hemoglobin, and grip strength) were included to construct a nomogram for predicting RA sarcopenia. The training and validation set AUCs were 0.915 (95% CI: 0.8795-0.9498) and 0.907 (95% CI: 0.8552-0.9597), respectively, proving that the predictive model was well discriminated. The calibration curve showed that the predicted values of the model were basically in line with the actual values, demonstrating good calibration. The DCA indicated that almost the entire range of patients with RA can benefit from this novel prediction model, suggesting good clinical utility.
UNASSIGNED: This study developed and validated a nomogram prediction model to predict the risk of sarcopenia in RA patients. The model can assist clinicians in enhancing their ability to screen for RA sarcopenia, assess patient prognosis, make early decisions, and improve the quality of life for RA patients.