Cui Ruifang, Zhang Shunli, Wang Mo, Jia Tingting, Zhai Yuhua, Yue Yuhong, Zhang Rui, Liang Yufang, Wang Qingtao
Clin Lab. 2020 Aug 1;66(8). doi: 10.7754/Clin.Lab.2019.190832.
Due to the insidious onset of multiple myeloma (MM), missed diagnosis and misdiagnosis have a serious impact on the health of MM patients. Simple, rapid, and valid laboratory screening is critical for MM clinical diagnosis.
We used routine laboratory tests to establish a simple, inexpensive, and non-invasive diagnostic model for MM based on logistic regression. In the retrospective analysis, a total of 273 newly diagnosed MM inpatients and 288 non-MM participants, from January 2016 to December 2018 in Beijing Chaoyang hospital, Capital Medical University, were divided into training set and validation set. Age, gender, and the related routine laboratory tests for MM, including albumin (ALB), globulin (GLB), lactate dehydrogenase (LDH), creatinine (Cr), calcium (Ca2+), hemoglobin (Hb) and platelet (PLT), were analyzed by multivariate logistic regression to develop a diagnostic model.
A diagnostic model was calculated using the formula MM index=-((-18×gender-3×ALB-Hb)/10), based on the logistic regression. The MM index [22 (20 - 25)] of MM patients was significantly lower than that of non-MM [30 (29 - 31)] in the training set (p < 0.001). It showed an excellent diagnostic performance in diagnosing MM through a receiver operating characteristic (ROC) curve, and its corresponding sensitivity, specificity, and area under the curve (AUC) were 95.6%, 96.7%, and 0.982 (0.968, 0.997), respectively. At a diagnostic risk threshold of 28, the model identified MM with a sensitivity of 95.6% and a specificity of 98.1% by using independent validation data. There was a significant positive correlation (r = 0.845, p < 0.001) between the DS grading and the MM index among all the participants.
The established diagnostic model of MM index can successfully identify newly diagnosed MM from healthy controls. The diagnostic model of MM index may also act as a predictor of the severity of MM without therapy.
由于多发性骨髓瘤（MM）发病隐匿，漏诊和误诊对MM患者的健康有严重影响。简单、快速且有效的实验室筛查对MM临床诊断至关重要。
我们使用常规实验室检测，基于逻辑回归建立了一种简单、廉价且非侵入性的MM诊断模型。在回顾性分析中，2016年1月至2018年12月期间首都医科大学附属北京朝阳医院的273例新诊断MM住院患者和288例非MM参与者被分为训练集和验证集。通过多变量逻辑回归分析年龄、性别以及MM相关的常规实验室检测指标，包括白蛋白（ALB）、球蛋白（GLB）、乳酸脱氢酶（LDH）、肌酐（Cr）、钙（Ca2+）、血红蛋白（Hb）和血小板（PLT），以建立诊断模型。
基于逻辑回归，使用公式MM指数 = -((-18×性别 - 3×ALB - Hb)/10)计算出诊断模型。在训练集中，MM患者的MM指数[22(20 - 25)]显著低于非MM患者[30(29 - 31)](p < 0.001)。通过绘制受试者工作特征（ROC）曲线，该模型在诊断MM方面表现出优异的诊断性能，其相应的敏感性、特异性和曲线下面积（AUC）分别为95.6%、96.7%和0.
