Gastroenterology and Digestive Endoscopy Unit, Cattinara Hospital, Trieste, Italy.
Am J Gastroenterol. 2010 Jun;105(6):1327-37. doi: 10.1038/ajg.2009.675. Epub 2009 Dec 22.
Selecting patients appropriately for upper endoscopy (EGD) is crucial for efficient use of endoscopy. The objective of this study was to compare different clinical strategies and statistical methods to select patients for EGD, namely appropriateness guidelines, age and/or alarm features, and multivariate and artificial neural network (ANN) models.
A nationwide, multicenter, prospective study was undertaken in which consecutive patients referred for EGD during a 1-month period were enrolled. Before EGD, the endoscopist assessed referral appropriateness according to the American Society for Gastrointestinal Endoscopy (ASGE) guidelines, also collecting clinical and demographic variables. Outcomes of the study were detection of relevant findings and new diagnosis of malignancy at EGD. The accuracy of the following clinical strategies and predictive rules was compared: (i) ASGE appropriateness guidelines (indicated vs. not indicated), (ii) simplified rule (>or=45 years or alarm features vs. <45 years without alarm features), (iii) logistic regression model, and (iv) ANN models.
A total of 8,252 patients were enrolled in 57 centers. Overall, 3,803 (46%) relevant findings and 132 (1.6%) new malignancies were detected. Sensitivity, specificity, and area under the receiver-operating characteristic curve (AUC) of the simplified rule were similar to that of the ASGE guidelines for both relevant findings (82%/26%/0.55 vs. 88%/27%/0.52) and cancer (97%/22%/0.58 vs. 98%/20%/0.58). Both logistic regression and ANN models seemed to be substantially more accurate in predicting new cases of malignancy, with an AUC of 0.82 and 0.87, respectively.
A simple predictive rule based on age and alarm features is similarly effective to the more complex ASGE guidelines in selecting patients for EGD. Regression and ANN models may be useful in identifying a relatively small subgroup of patients at higher risk of cancer.
对上消化道内镜检查（EGD）进行适当的患者选择对于内镜的有效利用至关重要。本研究的目的是比较不同的临床策略和统计方法来选择接受 EGD 的患者，即适当性指南、年龄和/或报警特征以及多变量和人工神经网络（ANN）模型。
进行了一项全国性、多中心、前瞻性研究，在此期间，连续招募了一个月内接受 EGD 的患者。在 EGD 之前，内镜医生根据美国胃肠内镜学会（ASGE）指南评估转诊的适当性，同时收集临床和人口统计学变量。该研究的结果是在 EGD 中检测到相关发现和新诊断的恶性肿瘤。比较了以下临床策略和预测规则的准确性：（i）ASGE 适当性指南（指示与非指示），（ii）简化规则（> = 45 岁或报警特征与< 45 岁无报警特征），（iii）逻辑回归模型和（iv）ANN 模型。
共纳入 57 个中心的 8252 例患者。总体而言，检测到 3803 例（46%）相关发现和 132 例（1.6%）新恶性肿瘤。简化规则的敏感性、特异性和受试者工作特征曲线（ROC）下面积（AUC）对于相关发现（82%/26%/0.55 与 88%/27%/0.52）和癌症（97%/22%/0.58 与 98%/20%/0.58）与 ASGE 指南相似。逻辑回归和 ANN 模型在预测新的恶性肿瘤病例方面似乎都更准确，AUC 分别为 0.82 和 0.87。
基于年龄和报警特征的简单预测规则与更复杂的 ASGE 指南在选择接受 EGD 的患者方面同样有效。回归和 ANN 模型可能有助于识别癌症风险较高的相对较小的亚组患者。
