Details

Autor(en) / Beteiligte

• BURI, Luigi
• HASSAN, Cesare
• LORIGA, Pietro
• MORINI, Sergio
• PIETROPAOLO, Vincenzo
• ZAMBELLI, Alessandro
• GROSSI, Enzo
• INTRALIGI, Marco
• BUSCEMA, Massimo
• BERSANI, Gianluca
• ANTI, Marcello
• BIANCO, Maria Antonietta
• CIPOLLETTA, Livio
• DI GIULIO, Emilio
• DI MATTEO, Giovanni
• FAMILIARI, Luigi
• FICANO, Leonardo

Titel

Appropriateness Guidelines and Predictive Rules to Select Patients for Upper Endoscopy: A Nationwide Multicenter Study

Ist Teil von

• The American journal of gastroenterology, 2010-06, Vol.105 (6), p.1327-1337

Ort / Verlag

Basingstoke: Nature Publishing Group

Erscheinungsjahr

2010

Quelle

MEDLINE

Beschreibungen/Notizen

• 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.