Morbidity and mortality prediction in pediatric heart surgery: Physiological profiles and surgical complexity

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Abstract

Objectives

Outcome prediction for pediatric heart surgery has focused on mortality but mortality has been significantly reduced over the past 2 decades. Clinical care practices now emphasize reducing morbidity. Physiology-based profiles assessed by the Pediatric Risk of Mortality (PRISM) score are associated with new significant functional morbidity detected at hospital discharge. Our aims were to assess the relationship between new functional morbidity and surgical risk categories (Risk Adjustment for Congenital Heart Surgery [RACHS] and Society for Thoracic Surgery Congenital Heart Surgery Database Mortality Risk [STAT]), measure the performance of 3-level (intact survival, survival with new functional morbidity, or death) and 2-level (survival or death) PRISM prediction algorithms, and assess whether including RACHS or STAT complexity categories improves the PRISM predictive performance.

Methods

Patients (newborn to age 18 years) were randomly selected from 7 sites (December 2011-April 2013). Morbidity (using the Functional Status Scale) and mortality were assessed at hospital discharge. The most recently published PRISM algorithms were tested for goodness of fit, and discrimination with and without the RACHS and STAT complexity categories.

Results

The mortality rate in the 1550 patients was 3.2%. Significant new functional morbidity rate occurred in 4.8%, increasing from 1.8% to 13.9%, 1.7%, and 12.9% from the lowest to the highest RACHS and STAT categories, respectively. The 3-level and 2-level PRISM models had satisfactory goodness of fit and substantial discriminative ability. Inclusion of RACHS and STAT complexity categories did not improve model performance.

Conclusions

Both mortality and new, functional morbidity are important outcomes associated with surgical complexity and can be predicted using PRISM algorithms. Adding surgical complexity to the physiologic profiles does not improve predictor performance.

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