Li Hongyan, Zheng Yuanhua, Zhang Yuanyuan, Zhang Xiaotian, Luo Wei, Zhu Weiyi, Zhang Yaqing
School of Nursing, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
School of Nursing, Jiangxi Medical College, Nanchang University, Nanchang, China.
Front Nutr. 2024 Dec 13;11:1470669. doi: 10.3389/fnut.2024.1470669. eCollection 2024.
The diagnosis of sarcopenia in patients on peritoneal dialysis (PD) in clinics is limited owing to its relatively complicated process and the need for expensive assessment equipment. This study aimed to develop and validate sex-specific nomogram models based on body mass index (BMI), handgrip strength, and other routine follow-up examination indicators to predict sarcopenia in patients on PD.
From March 2023 to February 2024, 699 eligible patients were recruited from the PD centers of two tertiary hospitals in southeastern China. Routine follow-up examination indicators such as age, BMI, biochemical indicators, dialysis adequacy, handgrip strength, and five-repetition sit-to-stand test, were used as potential predictive variables. Multivariate logistic regression analyses were used to separately determine the predictive factors for men and women. Nomogram models were constructed based on the results of the multivariate analyses, which were internally validated using a bootstrap re-sampling method ( = 2000). Predictive performance was validated using a receiver operating characteristic (ROC) curve.
The prevalence of sarcopenia in Chinese patients on PD was 13.92%. The nomogram models based on multivariate analyses revealed both handgrip strength and BMI as independent predictors of sarcopenia in men and women on PD. The bootstrap-corrected area under the ROC curves of the models was 0.924 (95% CI: 0.888-0.959) and 0.936 (95% CI, 0.906-0.966) for men and women, respectively. The calibration curves of both models demonstrated high consistency between the observed and anticipated values.
The two nomogram models based on BMI and handgrip strength demonstrated good predictive ability for sarcopenia in male and female patients on PD. Subsequently, these may be used as convenient and inexpensive methods for the early detection and timely management of sarcopenia in patients on PD.
由于肌肉减少症的诊断过程相对复杂且需要昂贵的评估设备，临床上腹膜透析（PD）患者的肌肉减少症诊断受到限制。本研究旨在基于体重指数（BMI）、握力和其他常规随访检查指标，开发并验证性别特异性列线图模型，以预测PD患者的肌肉减少症。
2023年3月至2024年2月，从中国东南部两家三级医院的PD中心招募了699例符合条件的患者。年龄、BMI、生化指标、透析充分性、握力和五次坐立试验等常规随访检查指标被用作潜在预测变量。采用多因素逻辑回归分析分别确定男性和女性的预测因素。基于多因素分析结果构建列线图模型，采用自抽样法（=2000）进行内部验证。使用受试者工作特征（ROC）曲线验证预测性能。
中国PD患者的肌肉减少症患病率为13.92%。基于多因素分析的列线图模型显示，握力和BMI都是PD男性和女性肌肉减少症的独立预测因素。模型的ROC曲线下经自抽样校正的面积，男性为0.924（95%CI：0.888-0.959），女性为0.936（95%CI，0.906-0.966）。两个模型的校准曲线均显示观察值与预期值之间具有高度一致性。
基于BMI和握力的两个列线图模型对PD男性和女性患者的肌肉减少症具有良好的预测能力。随后，这些模型可作为方便且廉价的方法，用于PD患者肌肉减少症的早期检测和及时管理。
