Rheumatology Clinic, Ospedale "Carlo Urbani", Università Politecnica delle Marche, Jesi (Ancona), Italy.
Department of Radiology, Ospedali Riuniti, Università Politecnica delle Marche, Ancona, Italy.
J Med Syst. 2021 Oct 9;45(11):100. doi: 10.1007/s10916-021-01778-9.
To investigate the relationship between handgrip strength (HGs) features, evaluated with an innovative cylindrical-shaped grip device, and demographic, anthropometric and clinical variables, in patients with rheumatoid arthritis (RA). Consecutive RA patients were prospectively enrolled for this cross-sectional study. For each patient were collected demographic, anthropometric, clinical data related to disease activity. HGs was assessed in terms of area under the force-time curve (AUC-FeT), peak grip force and time to reach the curve plateau. The correlations between the variables were studied with the Spearman's rho correlation coefficient. The receiver operating characteristic (ROC) curve analysis was used to test the discriminant accuracy of HGs features in identifying patients in moderate/high disease activity. A multivariate analysis was performed to estimate the contribution of covariates on the AUC-FeT. A significant correlation was found among AUC-FeT, age, Simplified Disease Activity Index (SDAI), Ultrasound-Clinical Arthritis Activity (US-CLARA) (all at p < 0.0001), and body mass index (BMI) (p = 0.0001). Any correlation was found between HGs and radiographic damage. The discriminatory power of AUC-FeT was good [area under-ROC curve = 0.810 (95% CI 0.746-0.864)]. Variables significantly associated with AUC-FeT in multivariate analysis were age (p = 0.0006), BMI (p = 0.012), gender (p = 0.004), SDAI (p = 0.047) and US-CLARA (p = 0.023). HGs is negatively influenced by demographic (gender and age), anthropometric (BMI), and disease activity variables (SDAI and US-CLARA). These findings highlight the role of HGs in RA patients' functional impairment and disability.
探讨使用创新的圆柱形握力装置评估的握力(HG)特征与类风湿关节炎(RA)患者的人口统计学、人体测量学和临床变量之间的关系。
这是一项横断面研究,连续纳入了 RA 患者。为每位患者收集了与疾病活动度相关的人口统计学、人体测量学和临床数据。HG 评估包括力-时间曲线下面积(AUC-FeT)、峰值握力和达到曲线平台的时间。使用 Spearman's rho 相关系数研究变量之间的相关性。使用接收者操作特征(ROC)曲线分析测试 HG 特征在识别中/高度疾病活动患者中的鉴别准确性。进行多变量分析以估计协变量对 AUC-FeT 的贡献。
AUC-FeT 与年龄、简化疾病活动指数(SDAI)、超声临床关节炎活动度(US-CLARA)(均 p<0.0001)和体重指数(BMI)(p=0.0001)呈显著相关性。HG 与放射学损伤之间未发现任何相关性。AUC-FeT 的鉴别力良好[ROC 曲线下面积=0.810(95%CI 0.746-0.864)]。多变量分析中与 AUC-FeT 显著相关的变量为年龄(p=0.0006)、BMI(p=0.012)、性别(p=0.004)、SDAI(p=0.047)和 US-CLARA(p=0.023)。HG 受到人口统计学(性别和年龄)、人体测量学(BMI)和疾病活动度变量(SDAI 和 US-CLARA)的负面影响。
HG 受到 RA 患者功能障碍和残疾的影响。