Jin Xiaxia, Hu Lingling, Fang Meidan, Zheng Qiaofei, Yuan Yuan, Lu Guoguang, Li Tao
Department of Clinical Laboratory, Taizhou Hospital of Zhejiang Province, Taizhou Enze Medical Center (Group), Linhai, Zhejiang, China.
Department of Cardiovascular medicine, Taizhou Hospital of Zhejiang Province, Taizhou Enze Medical Center (Group), Linhai, Zhejiang, China.
Front Oncol. 2022 Dec 1;12:1012664. doi: 10.3389/fonc.2022.1012664. eCollection 2022.
Malignant pericardial effusion (MPE) is a serious complication in patients with advanced malignant tumors, which indicates a poor prognosis. However, its clinical manifestations lack specificity, making it challenging to distinguish MPE from benign pericardial effusion (BPE). The aim of this study was to develop and validate a scoring system based on a nomogram to discriminate MPE from BPE through easy-to-obtain clinical parameters.
In this study, the patients with pericardial effusion who underwent diagnostic pericardiocentesis in Taizhou Hospital of Zhejiang Province from February 2013 to December 2021 were retrospectively analyzed. The eligible patients were divided into a training group (n = 161) and a validation group (n = 66) according to the admission time. The nomogram model was established using the meaningful indicators screened by the least absolute shrinkage and selection operator (LASSO) and multivariate logistic regression. Then, a new scoring system was constructed based on this nomogram model.
The new scoring system included loss of weight (3 points), no fever (4 points), mediastinal lymph node enlargement (2 points), pleural effusion (6 points), effusion adenosine deaminase (ADA≦18U/L) (5 points), effusion lactate dehydrogenase (LDH>1033U/L) (7 points), and effusion carcinoembryonic antigen (CEA>4.9g/mL) (10 points). With the optimal cut-off value was 16 points, the area under the curve (AUC), specificity, sensitivity, positive predictive value (PPV), negative predictive value (NPV), positive likelihood ratio (PLR), negative likelihood ratio (NLR) for identifying MPE were 0.974, 95.1%, 91.0%, 85.6%, 96.8%, 10.56 and 0.05, respectively, in the training set and 0.950, 83.3%, 95.2%, 90.9%, 90.9%, 17.50, and 0.18, respectively, in the validation set. The scoring system also showed good diagnostic accuracy in differentiating MPE caused by lung cancer from tuberculous pericardial effusion (TPE) and MPE including atypical cell from BPE.
The new scoring system based on seven easily available variables has good diagnostic value in distinguishing MPE from BPE.
恶性心包积液（MPE）是晚期恶性肿瘤患者的一种严重并发症，提示预后不良。然而，其临床表现缺乏特异性，使得区分MPE与良性心包积液（BPE）具有挑战性。本研究的目的是开发并验证一种基于列线图的评分系统，通过易于获取的临床参数来鉴别MPE和BPE。
本研究回顾性分析了2013年2月至2021年12月在浙江省台州医院接受诊断性心包穿刺术的心包积液患者。根据入院时间将符合条件的患者分为训练组（n = 161）和验证组（n = 66）。使用最小绝对收缩和选择算子（LASSO）和多因素逻辑回归筛选出的有意义指标建立列线图模型。然后，基于该列线图模型构建了一个新的评分系统。
新的评分系统包括体重减轻（3分）、无发热（4分）、纵隔淋巴结肿大（2分）、胸腔积液（6分）、积液腺苷脱氨酶（ADA≤18U/L）（5分）、积液乳酸脱氢酶（LDH>1033U/L）（7分）和积液癌胚抗原（CEA>4.9μg/mL）（10分）。最佳截断值为16分，训练集中识别MPE的曲线下面积（AUC）、特异性、敏感性、阳性预测值（PPV）、阴性预测值（NPV）、阳性似然比（PLR）、阴性似然比（NLR）分别为0.974、95.1%、91.0%、85.6%、96.8%、10.56和0.05，验证集中分别为0.950、83.3%、95.2%、90.9%、90.9%、17.50和0.18。该评分系统在鉴别肺癌所致MPE与结核性心包积液（TPE）以及非典型细胞性MPE与BPE方面也显示出良好的诊断准确性。
基于七个易于获得的变量的新评分系统在区分MPE和BPE方面具有良好的诊断价值。
