Risk models for monitoring post-operative complication rates after pediatric cardiac surgery
Mitchell, H. K., Espuny-Pujol, F.
It is advisable to refer to the publisher's version if you intend to cite from this work. See Guidance on citing. To link to this item DOI: 10.1093/ejcts/ezaf317 Abstract/SummaryOBJECTIVES As post-operative mortality for paediatric cardiac surgery is very low, we aimed to develop methods for monitoring of post-operative complication rates, given their impact upon children’s health and wellbeing. METHODS We used national registry data to develop and evaluate a suite of risk adjustment models for the outcomes of 6 defined post-operative complications, designed for use in complication monitoring for quality assurance. RESULTS There were 23,423 30-day post-operative episodes in children under the age of 18-years undergoing cardiac surgery between 2015–2021 in England and Wales, with 361 (1.5%) deaths <30-days. 257 (1.9%) of 13,556 post-operative episodes in infants (<1 year) involved necrotising enterocolitis; 158 (1.3%) of 12,408 post-operative episodes between 2018 and 2021 involved prolonged pleural effusion; and amongst the full sample of post-operative episodes there were 526 (2.2%) acute neurological events, 446 (1.9%) extracorporeal life supports, 740 (3.6%) renal replacement therapies and 1,006 (4.3%) unplanned reinterventions within 30-days of surgery. The risk adjustment models were developed using clinical factors first defined for mortality monitoring. The models for prolonged pleural effusion, extracorporeal life support and renal replacement performed very well with area under the curve (AUC) statistics >0.85. The performance of the models for necrotising enterocolitis, acute neurological event and unplanned reintervention were less good (AUC statistics 0.74–0.79). CONCLUSIONS Although complications are more complex outcome measures than mortality, national registry data can be used to capture them and to evaluate methods for risk adjustment of these outcomes. These methods may enable future risk-adjusted monitoring of complication metrics for quality assurance.
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