Comparative Study
Journal Article
Multicenter Study
Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't
Validation Study
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Reducing Underdiagnosis of Hirschsprung-Associated Enterocolitis: A Novel Scoring System.

BACKGROUND: Hirschsprung-Associated Enterocolitis (HAEC) is a life-threatening and difficult to diagnose complication of Hirschsprung Disease (HSCR). The goal of this study was to evaluate existing HAEC scoring systems and develop a new scoring system.

METHODS: Retrospective, multi-institutional data collection was performed. For each patient, all encounters were analyzed. Data included demographics, symptomatology, laboratory and radiographic findings, and treatments received. A "true" diagnosis of HAEC was defined as receipt of treatment with rectal irrigations, antibiotics, and bowel rest. The Pastor and Frykman scoring systems were evaluated for sensitivity/specificity and univariate and multivariate logistic regression performed to create a new scoring system.

RESULTS: Four centers worldwide provided data on 200 patients with 1450 encounters and 369 HAEC episodes. Fifty-seven percent of patients experienced one or more episodes of HAEC. Long-segment colonic disease was associated with a higher risk of HAEC on univariate analysis (OR 1.92, 95% CI 1.43-2.57). Six variables were significantly associated with HAEC on multivariate analysis. Using published diagnostic cutoffs, sensitivity/specificity for existing systems were found to be 38.2%/96% for Pastor's and 56.4%/86.9% for Frykman's score. A new scoring system with a sensitivity/specificity of 67.8%/87.9% was created by stepwise multivariate analysis. The new score outperformed the existing scores by decreasing underdiagnosis in this patient cohort.

CONCLUSIONS: Existing scoring systems perform poorly in identifying episodes of HAEC, resulting in significant underdiagnosis. The proposed scoring system may be better at identifying those underdiagnosed in the clinical setting. Head-to-head comparison of HAEC scoring systems using prospective data collection may be beneficial to achieve standardization in the field.

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