Year: 2016
Type: editorial
Abstract: The art and science of outcome analysis of paediatric and congenital cardiac care continue to evolve [ 1 , 2 ]. Ranucci et al. [ 3 ] are to be congratulated for their manuscript titled: ‘Re-tuning mortality risk prediction in pediatric cardiac surgery: the additional role of early postoperative metabolic and respiratory profile’, which reports the results of a study designed to investigate whether early postoperative parameters may be used to improve the prediction of risk associated with paediatric cardiac surgery. The authors concluded that early postoperative respiratory and metabolic parameters increased the accuracy and discrimination of prediction of risk associated with paediatric cardiac surgery. Three major multi-institutional efforts have attempted to stratify the risk of congenital cardiac operations and have been used in the congenital cardiac surgical databases of The European Association for Cardio-Thoracic Surgery (EACTS)/European Congenital Heart Surgeons Association (ECHSA), The Society of Thoracic Surgeons (STS), and The Japan Congenital Cardiovascular Surgery Database (JCCVSD): R isk A djustment in C ongenital H eart S urgery-1 methodology (RACHS-1 method) [ 4 ] A ristotle B asic C omplexity Score (ABC Score) [ 4 , 5 ], and ST S-E A C T S Congenital Heart Surgery Mortality Categories (STS-EACTS Mortality Categories) (STAT Mortality Categories) [ 6 , 7 ]. RACHS-1 and the ABC Score were developed at a time when limited multi-institutional clinical data were available and were therefore based in a large part on subjective probability (expert opinion). ABC was introduced into the STS Congenital Heart Surgery Database (STS-CHSD) in 2002. RACHS-1 was introduced into the STS-CHSD in 2006. STAT Mortality Categories were introduced into the STS-CHSD in 2010. In the analysis reported by Ranucci and colleagues, ABC Score yielded a c -statistic of 0.746. Additional independent predictors of operative mortality were postoperative hypoxia (PaO 2 /FiO 2 <200) and arterial blood lactates. In a multivariable model including ABC Score, postoperative hypoxia, and arterial blood lactates, all factors remained independently associated with operative mortality. A modified ABC Score was created, consisting of the ABC Score plus 1.5 points in case of postoperative hypoxia plus 1 point per each 1 mmol/l of arterial blood lactates. The new model was significantly ( P = 0.043) more discriminative than the ABC Score ( c -statistic = 0.803). In 2014, the STS-CHSD began assessing outcomes using the 2014 STS-CHSD Mortality Risk Model [ 8–10 ], which facilitates the description of operative mortality adjusted for the following factors: Age group Primary procedure (The model adjusts for each combination of primary procedure and age group. Coefficients are obtained via shrinkage estimation with STAT Mortality Category as an auxiliary variable.) Weight (neonates and infants) Prior cardiothoracic operation Any non-cardiac congenital anatomical abnormality Any chromosomal abnormality or syndrome Prematurity (neonates and infants) Preoperative factors including: Preoperative/preprocedural mechanical circulatory support [Includes intraaortic balloon pump (IABP), ventricular assist device (VAD), extracorporeal membrane oxygenation (ECMO), and mechanical cardiopulmonary support system (CPS)] Shock, persistent at time of surgery Mechanical ventilation to treat cardiorespiratory failure Renal failure requiring dialysis and/or renal dysfunction Preoperative neurological deficit Any other preoperative factor that is coded in STS-CHSD. The 2014 STS-CHSD Mortality Risk Model was developed from an analysis of 52 224 index cardiac operations from 86 centres during the 4-year analytical window of 1 January 2010–31 December 2013. The 2014 STS-CHSD Risk Model has the highest c -statistic of any paediatric and congenital cardiac surgical risk model to date ( c -statistic in developmental sample = 0.875, c -statistic in validation sample = 0.858) and is the state of the art in paediatric and congenital cardiac surgical risk adjustment. This model is the basis of transparency and public reporting with the STS-CHSD. The model undergoes recalibration with updating of the coefficients in the model on a twice-yearly basis to coincide with the production of each STS-CHSD Participant Feedback Report. Although the end-point of the 2014 STS-CHSD Risk Model is Operative Mortality, efforts are ongoing to develop a multidomain composite quality metric that incorporates both mortality and morbidity and adjusts for the operation performed and patient-specific factors. Funded by the USA National Heart, Lung, and Blood Institute (NHLBI), an NIH/NHLBI R01 Grant (R01 HL122261) is actively developing this multidomain composite (Principal Investigator: Sara K. Pasquali, STS Principal Investigator: Jeffrey P. Jacobs). Titled ‘Understanding Quality and Costs in Congenital Heart Surgery’, this grant has two specific aims: Develop and validate a composite quality metric in congenital heart surgery and Examine the relationship between our composite measure of quality and cost. This new multidomain composite will add to the portfolio of measures available for risk adjustment in paediatric and congenital cardiac surgery and complement currently available measures including the 2014 STS-CHSD Mortality Risk Model. Whenever one is developing or using a risk model, it is important to consider the purpose of the analysis. Risk models can be used to predict the risk of a given patient, assess the case-mix of a given programme, or even assess the performance of a given programme in comparison to multi-institutional aggregate outcome data. The point in time of assessing this risk is critical in each of these applications. For example, the 2014 STS-CHSD Mortality Risk Model is designed to assess risk at the time when the patient enters the operating theatre. Meanwhile, the addition to a risk model of early postoperative parameters present on arrival to the intensive care unit may better inform a model designed to assess risk at the time when the patient enters the intensive care unit after surgery. Ranucci and colleagues are to be congratulated for demonstrating a methodology that can better inform a model designed to assess the risk of a paediatric cardiac surgical patient at the time that the patient enters the intensive care unit after surgery. The authors stated: ‘The present study is aimed to verify the hypothesis that a number of metabolic and respiratory parameters collected at the arrival in the intensive care unit after cardiac surgery in pediatric patients may be associated with postoperative mortality risk. Based on this hypothesis, we aim to re-tune the mortality risk prediction based on the ABC Score by incorporating one or more of these early postoperative parameters into a new predictive model’. The authors have successfully proved their hypothesis and in the process documented a useful method for risk adjustment in paediatric and congenital cardiac surgery.
DOI: 10.1093/ejcts/ezw166
Author: Jeffrey P. Jacobs
Language: English
Subfield: Epidemiology
Field: Medicine
Domain: Health Sciences
Open Access status: bronze
OpenAlex ID: W2420656699