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Appropriateness guidelines and predictive rules to select patients for upper endoscopy: a nationwide multicenter
Luigi Buri1, Cesare Hassan, Gianluca Bersani
1Gastroenterology and Digestive Endoscopy Unit, Cattinara Hospital, Trieste, Italy.
The American Journal of Gastroenterology
|December 24, 2009
Summary
A simple rule using age and alarm features effectively selects patients for upper endoscopy (EGD), similar to complex guidelines. Advanced models like logistic regression and artificial neural networks (ANNs) show promise in identifying high-risk cancer patients.
Area of Science:
- Gastroenterology
- Medical Informatics
- Clinical Decision Support
Background:
- Appropriate patient selection for esophagogastroduodenoscopy (EGD) is essential for optimizing resource utilization in endoscopy.
- Current methods include appropriateness guidelines and clinical judgment, but their efficiency varies.
Purpose of the Study:
- To compare the effectiveness of different strategies for selecting patients for EGD.
- To evaluate appropriateness guidelines, a simplified rule (age/alarm features), logistic regression, and artificial neural network (ANN) models.
Main Methods:
- A nationwide, multicenter, prospective study enrolled 8,252 patients referred for EGD.
- Referral appropriateness was assessed using American Society for Gastrointestinal Endoscopy (ASGE) guidelines.
- The study compared the diagnostic accuracy of ASGE guidelines, a simplified rule, logistic regression, and ANN models for detecting relevant findings and malignancy.
Main Results:
- The simplified rule (age/alarm features) demonstrated similar sensitivity and specificity to ASGE guidelines for detecting relevant findings and cancer.
- Logistic regression and ANN models showed significantly higher accuracy (AUC 0.82 and 0.87, respectively) in predicting new cases of malignancy compared to guidelines and the simplified rule.
Conclusions:
- A simplified rule based on age and alarm features is a practical alternative to ASGE guidelines for EGD patient selection.
- Multivariate models, particularly ANNs, offer enhanced accuracy in identifying patients at higher risk for malignancy, warranting further investigation.
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