A contemporary prediction rule for XXXX ( @entity1152 ) and @entity1727 ( @entity1727 ).

@entity1 @entity8 @entity1152 @entity344 @entity1727 @entity299 @entity2008 @entity204
BACKGROUND/PURPOSE: Existing prediction models for @entity1727 ( @entity1727 ) and @entity1152 ( @entity1152 ) 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 @entity1 with admission age<three days and ICD-9 diagnostic classification of @entity1152 or @entity1727 or procedural coding for @entity1727 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, @entity299 (major and minor), and chromosomal, other @entity344 , and @entity8 . The model was evaluated for discrimination and calibration and compared with that of Spitz. RESULTS: An integer-based prediction model was created, identifying @entity1 at high, intermediate, and low risk of @entity204 with very good discrimination (c=0.723) and calibration. It is particularly effective at identifying the small population at highest risk of @entity204 . The model can be summarized as follows with @entity1 first assigned a score for associated abnormalities: @entity2008 =6 points, major @entity299 =3 points, @entity8 =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 @entity1 who may require tailored therapy. The Spitz model is also validated.
