Eskelinen Maaret, Meklin Jannica, Selander Tuomas, Syrjänen Kari, Eskelinen Matti
Department of Surgery, Kuopio University Hospital and School of Medicine,University of Eastern Finland, Kuopio, Finland.
Science Service Center, Kuopio University Hospital and School of Medicine,University of Eastern Finland, Kuopio, Finland.
Cancer Diagn Progn. 2021 Jul 3;1(4):265-274. doi: 10.21873/cdp.10034. eCollection 2021 Sep-Oct.
BACKGROUND/AIM: The diagnostic accuracy of history-taking, clinical signs and tests and diagnostic scores (DSs) for patients with non-organic dyspepsia (NOD) have been rarely evaluated.
A cohort of 1333 patients presenting with acute abdominal pain (AAP) were studied, including 50 patients with confirmed NOD. The most significant diagnostic variables (in multivariate logistic regression analysis) were used to construct six different DS models and their diagnostic accuracy was compared with clinical symptoms and signs and tests. Meta-analytical techniques were used to detect the summary sensitivity (Se) and specificity (Sp) estimates for each data set (symptoms, signs and tests as well as DS models).
In hierarchical summary receiver operating characteristic (HSROC) analysis, the area under curve (AUC) values for i) symptoms ii) signs and tests iii) DS were as follows: i) AUC=0.608 [95% confidence interval (CI)=0.550-0.666]; ii) AUC=0.621 (95% CI=0.570-0.672) and iii) AUC=0.877 (95% CI=0.835-0.919). The differences between these AUC values (roccomp analysis) are as follows: between i) and ii) p=0.715; between i) and iii) p<0.0001; between ii) and iii) p<0.0001.
The present study is the first to provide evidence that the DS could be used in diagnosis of NOD. The major advantage of our DS is that this model does not need radiology or endoscopy to reach high diagnostic accuracy.
背景/目的：对于非器质性消化不良（NOD）患者，很少评估病史采集、临床体征和检查以及诊断评分（DS）的诊断准确性。
对1333例急性腹痛（AAP）患者进行了研究，其中包括50例确诊为NOD的患者。使用多变量逻辑回归分析中最显著的诊断变量构建六种不同的DS模型，并将其诊断准确性与临床症状、体征和检查进行比较。采用荟萃分析技术检测每个数据集（症状、体征和检查以及DS模型）的汇总敏感性（Se）和特异性（Sp）估计值。
在分层汇总接受者操作特征（HSROC）分析中，i）症状、ii）体征和检查、iii）DS的曲线下面积（AUC）值如下：i）AUC = 0.608 [95%置信区间（CI）= 0.550 - 0.666]；ii）AUC = 0.621（95% CI = 0.570 - 0.672）；iii）AUC = 0.877（95% CI = 0.835 - 0.919）。这些AUC值之间的差异（roccomp分析）如下：i）和ii）之间p = 0.715；i）和iii）之间p < 0.0001；ii）和iii）之间p < 0.0001。
本研究首次提供证据表明DS可用于NOD的诊断。我们的DS的主要优点是该模型无需放射学或内镜检查即可达到较高的诊断准确性。
