Libby Rogers1, Katherine L Brown2, Rodney C Franklin3, Gareth Ambler4, David Anderson5, David J Barron6, Sonya Crowe7, Kate English8, John Stickley6, Shane Tibby5, Victor Tsang2, Martin Utley7, Thomas Witter5, Christina Pagel7. 1. Clinical Operational Research Unit, University College London, London, United Kingdom. Electronic address: libby.rogers@ucl.ac.uk. 2. Cardiac, Critical Care and Respiratory Division, Great Ormond Street Hospital NHS Foundation Trust, London, United Kingdom. 3. Pediatric Cardiology, Royal Brompton and Harefield NHS Foundation Trust, London, United Kingdom. 4. Department of Statistical Science, University College London, London, United Kingdom. 5. Cardiology and Critical Care, Evelina London Children's Hospital, London, United Kingdom. 6. Cardiothoracic Surgery, Birmingham Children's Hospital, Birmingham, United Kingdom. 7. Clinical Operational Research Unit, University College London, London, United Kingdom. 8. Department of Congenital Cardiology, Leeds Teaching Hospitals NHS Trust, Leeds, United Kingdom.
Abstract
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.
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.
Authors: Ferran Espuny Pujol; Christina Pagel; Katherine L Brown; James C Doidge; Richard G Feltbower; Rodney C Franklin; Arturo Gonzalez-Izquierdo; Doug W Gould; Lee J Norman; John Stickley; Julie A Taylor; Sonya Crowe Journal: BMJ Open Date: 2022-05-19 Impact factor: 3.006
Authors: Elena Hadjicosta; Rodney Franklin; Anna Seale; Oliver Stumper; Victor Tsang; David R Anderson; Christina Pagel; Sonya Crowe; Ferran Espuny Pujol; Deborah Ridout; Kate L Brown Journal: Heart Date: 2022-06-10 Impact factor: 7.365
Authors: Katherine L Brown; Christina Pagel; Deborah Ridout; Jo Wray; David Anderson; David J Barron; Jane Cassidy; Peter Davis; Emma Hudson; Alison Jones; Andrew Mclean; Stephen Morris; Warren Rodrigues; Karen Sheehan; Serban Stoica; Shane M Tibby; Thomas Witter; Victor T Tsang Journal: BMJ Open Date: 2019-09-09 Impact factor: 2.692
Authors: Jo Wray; Deborah Ridout; Alison Jones; Peter Davis; Paul Wellman; Warren Rodrigues; Emma Hudson; Victor Tsang; Christina Pagel; Katherine L Brown Journal: Ann Thorac Surg Date: 2020-11-27 Impact factor: 4.330