Improving Risk Adjustment for Mortality After Pediatric Cardiac Surgery: The UK PRAiS2 Model

Libby Rogers1, Katherine L Brown2, Rodney C Franklin3

  • 1Clinical Operational Research Unit, University College London, London, United Kingdom.

Insights

The updated Partial Risk Adjustment in Surgery (PRAiS2) model improves 30-day mortality prediction in pediatric heart surgery. It incorporates more diagnostic and comorbidity data for better accuracy in the UK and Ireland.

Area of Science:

  • Pediatric Cardiac Surgery
  • Risk Modeling
  • Health Outcomes Research

Background:

  • The Partial Risk Adjustment in Surgery (PRAiS) model has been used since 2013 for 30-day mortality risk in pediatric heart surgery.
  • The UK National Congenital Heart Disease Audit utilizes this model for reporting risk-adjusted survival.
  • There was a need to enhance the model's predictive power by including more detailed patient information.

Purpose of the Study:

  • To develop an improved risk adjustment model for 30-day mortality following pediatric cardiac surgery.
  • To incorporate additional comorbidity and diagnostic data into the existing PRAiS model.
  • To validate the enhanced model's performance in a UK and Ireland cohort.

Main Methods:

  • Utilized a dataset of all procedures from UK and Ireland congenital cardiac centers (2009-2014).
  • Employed logistic regression with 25x5 cross-validation for model development.
  • Assessed model performance using Akaike information criterion, Area Under the Curve (AUC), and calibration, with external validation.

Main Results:

  • The development dataset included 21,838 surgical episodes with 2.5% mortality; validation dataset had 4,207 episodes with 2.3% mortality.
  • The updated PRAiS2 model incorporated 15 procedural, 11 diagnostic, and 4 comorbidity groupings, plus nonlinear age/weight functions.
  • Cross-validation showed a median AUC of 0.83 and excellent performance in the validation dataset (AUC 0.86).

Conclusions:

  • A more sophisticated PRAiS2 risk model was developed for UK use.
  • The enhanced model integrates additional comorbidity and diagnostic information.
  • Nonlinear functions of age and weight were included, improving risk prediction accuracy.
Abstract

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