A contemporary prediction rule for esophageal atresia (EA) and tracheo-esophageal fistula (TEF)

Abstract

Background/purpose: Existing prediction models for tracheo-esophageal fistula (TEF) and esophageal atresia (EA) are derived from small single-institution populations treated over long periods. A prediction rule developed in a contemporary, multicenter cohort is important for counseling, tailoring therapy, and benchmarking outcomes.

Methods: Data were obtained from the 2003, 2006, and 2009 editions of the HCUP Kids' Inpatient Database. Subjects included patients with admission age<three days and ICD-9 diagnostic classification of EA or TEF or procedural coding for TEF repair. An internally validated prediction rule for survival to discharge was developed using a stepwise logistic regression selection algorithm. Predictors included were sex, birth weight, gestational age, cardiac anomalies (major and minor), and chromosomal, other gastrointestinal, central nervous system, and renal anomalies. The model was evaluated for discrimination and calibration and compared with that of Spitz.

Results: An integer-based prediction model was created, identifying patients at high, intermediate, and low risk of death with very good discrimination (c=0.723) and calibration. It is particularly effective at identifying the small population at highest risk of death. The model can be summarized as follows with patients first assigned a score for associated abnormalities: chromosomal abnormality=6 points, major cardiac anomaly=3 points, renal anomaly=2 points, and weight less than 1500g=9 points. Point score cut-offs were 0-6 points low risk, 7-14 intermediate risk, and 15-20 high risk.

Conclusions: This model compares well with existing prediction models and more effectively discriminates the highest risk patients who may require tailored therapy. The Spitz model is also validated.

Keywords: Clinical prediction; Esophageal atresia; Population-based research; Tracheo-esophageal fistula.