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Published on: May 26, 2023
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.
Background:
Partial Risk Adjustment in Surgery (PRAiS), a risk model for 30-day mortality after children's heart surgery, has been used by the UK National Congenital Heart Disease Audit to report expected risk-adjusted survival since 2013. This study aimed to improve the model by incorporating additional comorbidity and diagnostic information.
Methods:
The model development dataset was all procedures performed between 2009 and 2014 in all UK and Ireland congenital cardiac centers. The outcome measure was death within each 30-day surgical episode. Model development followed an iterative process of clinical discussion and development and assessment of models using logistic regression under 25 × 5 cross-validation. Performance was measured using Akaike information criterion, the area under the receiver-operating characteristic curve (AUC), and calibration. The final model was assessed in an external 2014 to 2015 validation dataset.
Results:
The development dataset comprised 21,838 30-day surgical episodes, with 539 deaths (mortality, 2.5%). The validation dataset comprised 4,207 episodes, with 97 deaths (mortality, 2.3%). The updated risk model included 15 procedural, 11 diagnostic, and 4 comorbidity groupings, and nonlinear functions of age and weight. Performance under cross-validation was: median AUC of 0.83 (range, 0.82 to 0.83), median calibration slope and intercept of 0.92 (range, 0.64 to 1.25) and -0.23 (range, -1.08 to 0.85) respectively. In the validation dataset, the AUC was 0.86 (95% confidence interval [CI], 0.82 to 0.89), and the calibration slope and intercept were 1.01 (95% CI, 0.83 to 1.18) and 0.11 (95% CI, -0.45 to 0.67), respectively, showing excellent performance.
Conclusions:
A more sophisticated PRAiS2 risk model for UK use was developed with additional comorbidity and diagnostic information, alongside age and weight as nonlinear variables.
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