Department of Nutrition and Dietetics, Internal Medicine, VU University Medical Center, Amsterdam, The Netherlands.
Clin Nutr. 2012 Oct;31(5):647-51. doi: 10.1016/j.clnu.2012.01.010. Epub 2012 Feb 26.
BACKGROUND & AIMS: Low handgrip strength by dynamometry is associated with increased postoperative morbidity, higher mortality and reduced quality of life. The aim of this study was to evaluate the accuracy of four algorithms in diagnosing malnutrition by measuring handgrip strength.
We included 504 consecutive preoperative outpatients. Reference standard for malnutrition was defined based on percentage involuntary weight loss and BMI. Diagnostic characteristics of the handgrip strength algorithms (Álvares-da-Silva, Klidjian, Matos, Webb) were expressed by sensitivity, specificity, positive and negative predictive value, false positive and negative rate.
The prevalence of malnutrition was 5.8%. Although Klidjian showed the highest sensitivity (79%, 95% CI 62%-90%), 6 out of 29 malnourished patients were falsely identified as well-nourished (false positive rate 21%, 95% CI 9%-38%). In contrast, this algorithm showed the lowest positive predictive value (8%, 95% CI 5%-13%). Matos presented the highest positive predictive value; the post-test probability increased to 13% (95% CI 8%-20%). The 1-minus negative predictive value ranged between 3% and 5% for all algorithms.
None of the algorithms derived from handgrip strength measurements was found to have a diagnostic accuracy good enough to introduce handgrip strength as a systematic institutional screening tool to detect malnutrition in individual adult preoperative elective outpatients.
握力计测量的低握力与术后发病率增加、死亡率升高和生活质量降低有关。本研究旨在评估通过测量握力来诊断营养不良的 4 种算法的准确性。
我们纳入了 504 例连续的术前门诊患者。营养不良的参考标准基于非自愿体重减轻百分比和 BMI 定义。握力强度算法（Álvares-da-Silva、Klidjian、Matos、Webb）的诊断特征通过灵敏度、特异性、阳性预测值、阴性预测值、假阳性率和假阴性率来表示。
营养不良的患病率为 5.8%。虽然 Klidjian 显示出最高的灵敏度（79%，95%CI 62%-90%），但 29 名营养不良患者中有 6 名被错误地识别为营养良好（假阳性率 21%，95%CI 9%-38%）。相比之下，该算法显示出最低的阳性预测值（8%，95%CI 5%-13%）。Matos 呈现出最高的阳性预测值；后验概率增加到 13%（95%CI 8%-20%）。所有算法的 1 分钟阴性预测值范围在 3%至 5%之间。
没有一种从握力测量中得出的算法被发现具有足够的诊断准确性，可以将握力作为一种系统的机构筛查工具引入，以检测个体成年术前择期门诊患者的营养不良。
