[Clinical prediction of endoscopic signs in active or recent XXXX ].

@entity1 @entity548 @entity167 @entity5507 @entity341 @entity360
BACKGROUND AND OBJECTIVE: The aim of this study was to construct and validate a mathematical model, based on clinical and laboratory data, that could be useful in the @entity341 ( @entity341 ) to predict which @entity1 with @entity360 ( @entity360 ) have an @entity548 . @entity1 AND METHOD: During a period of 12 months, we included all consecutive cases of @entity360 that came to the @entity341 of an urban hospital. These @entity1 made up the primary or model obtaining series. During the 12 following months, we selected a sample of @entity360 @entity1 who made up the secondary series. The mathematical predictive model was built using logistic regression analysis. RESULTS: 623 @entity1 made up the primary series and 251 the secondary series. A score equal or higher than 4 indicated the best diagnosis accuracy (63.6%) with positive and negative predictive values of 65.1 and 40.0%, respectively. The discriminative power of the model was significant (p < 0.001) but it displayed little accuracy. CONCLUSIONS: Shock, positive tilt test, increased @entity5507 , previous hematemesis, to be an usual @entity167 drinker, presence of sweating, and no previous treatment with antiulcer drugs were independent predictors for @entity548 . The diagnostic accuracy of the model does not not allow its systematic use in clinical practice. However, it may be helpful for the triage of @entity360 in the @entity341 .
