Contrast enema in Hirschsprung disease: radiological signs and clinical symptoms as predictors in a logistic regression model.

BACKGROUND

Contrast enema is a key imaging tool in the evaluation of children with suspected Hirschsprung disease, although the diagnostic accuracy of individual radiologic signs remains debated.

OBJECTIVE

This study aimed to evaluate the diagnostic performance of specific radiological signs and clinical symptoms in Hirschsprung disease and to develop a simple predictive model based on logistic regression.

MATERIALS AND

METHODS

We retrospectively reviewed 247 contrast enemas from 237 children evaluated for suspected Hirschsprung disease, including 59 studies from 49 histologically confirmed cases. Radiologic and clinical variables were analysed to assess the diagnostic accuracy of specific signs using standard performance metrics and receiver operating characteristic (ROC) curve analysis. A multivariable logistic regression model was developed and optimised to identify the minimal combination of predictors yielding the best diagnostic performance for Hirschsprung disease.

RESULTS

Children with Hirschsprung disease presented with earlier symptom onset and earlier contrast enema evaluation compared with non-Hirschsprung disease patients (both P<0.001). Among radiologic findings, an abnormal rectosigmoid index and a visible transition zone were the strongest predictors of Hirschsprung disease. Clinical features such as abdominal distension, vomiting, and need for rectal irrigations showed significant associations. The final multivariable model demonstrated excellent diagnostic performance (sensitivity 0.83, specificity 0.87, area under the ROC curve (AUC-ROC)=0.85) and was transformed into a simplified clinical score (0-9 points) for practical application.

CONCLUSION

Specific radiological signs, particularly rectosigmoid index and transition zone, retain significant diagnostic value in Hirschsprung disease. When combined with clinical symptoms, they allow development of a simple predictive model that may support clinical decision-making.