Katherine L Brown1, Libby Rogers2, David J Barron3, Victor Tsang4, David Anderson5, Shane Tibby5, Thomas Witter5, John Stickley3, Sonya Crowe2, Kate English6, Rodney C Franklin7, Christina Pagel2. 1. Great Ormond Street Hospital NHS Foundation Trust, London, United Kingdom. Electronic address: katherine.brown@gosh.nhs.uk. 2. Clinical Operational Research Unit, University College London, London, United Kingdom. 3. Department of Cardiac Surgery, Birmingham Children's Hospital, Birmingham, United Kingdom. 4. Great Ormond Street Hospital NHS Foundation Trust, London, United Kingdom. 5. Department of Paediatric Cardiology and Cardiac Surgery, Evelina London Children's Hospital, London, United Kingdom. 6. Department of Cardiology, Leeds General Infirmary, Leeds, United Kingdom. 7. Paediatric Cardiology Department, Royal Brompton and Harefield NHS Foundation Trust, London, United Kingdom.
Abstract
BACKGROUND: When considering early survival rates after pediatric cardiac surgery it is essential to adjust for risk linked to case complexity. An important but previously less well understood component of case mix complexity is comorbidity. METHODS: The National Congenital Heart Disease Audit data representing all pediatric cardiac surgery procedures undertaken in the United Kingdom and Ireland between 2009 and 2014 was used to develop and test groupings for comorbidity and additional non-procedure-based risk factors within a risk adjustment model for 30-day mortality. A mixture of expert consensus based opinion and empiric statistical analyses were used to define and test the new comorbidity groups. RESULTS: The study dataset consisted of 21,838 pediatric cardiac surgical procedure episodes in 18,834 patients with 539 deaths (raw 30-day mortality rate, 2.5%). In addition to surgical procedure type, primary cardiac diagnosis, univentricular status, age, weight, procedure type (bypass, nonbypass, or hybrid), and era, the new risk factor groups of non-Down congenital anomalies, acquired comorbidities, increased severity of illness indicators (eg, preoperative mechanical ventilation or circulatory support) and additional cardiac risk factors (eg, heart muscle conditions and raised pulmonary arterial pressure) all independently increased the risk of operative mortality. CONCLUSIONS: In an era of low mortality rates across a wide range of operations, non-procedure-based risk factors form a vital element of risk adjustment and their presence leads to wide variations in the predicted risk of a given operation.
BACKGROUND: When considering early survival rates after pediatric cardiac surgery it is essential to adjust for risk linked to case complexity. An important but previously less well understood component of case mix complexity is comorbidity. METHODS: The National Congenital Heart Disease Audit data representing all pediatric cardiac surgery procedures undertaken in the United Kingdom and Ireland between 2009 and 2014 was used to develop and test groupings for comorbidity and additional non-procedure-based risk factors within a risk adjustment model for 30-day mortality. A mixture of expert consensus based opinion and empiric statistical analyses were used to define and test the new comorbidity groups. RESULTS: The study dataset consisted of 21,838 pediatric cardiac surgical procedure episodes in 18,834 patients with 539 deaths (raw 30-day mortality rate, 2.5%). In addition to surgical procedure type, primary cardiac diagnosis, univentricular status, age, weight, procedure type (bypass, nonbypass, or hybrid), and era, the new risk factor groups of non-Down congenital anomalies, acquired comorbidities, increased severity of illness indicators (eg, preoperative mechanical ventilation or circulatory support) and additional cardiac risk factors (eg, heart muscle conditions and raised pulmonary arterial pressure) all independently increased the risk of operative mortality. CONCLUSIONS: In an era of low mortality rates across a wide range of operations, non-procedure-based risk factors form a vital element of risk adjustment and their presence leads to wide variations in the predicted risk of a given operation.
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