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Use of diagnostic information submitted to the United Kingdom Central Cardiac Audit Database: development of categorisation and allocation algorithms

Published online by Cambridge University Press:  02 October 2012

Kate L. Brown*
Affiliation:
Cardiac Unit, Great Ormond Street Hospital NHS Trust, London, United Kingdom
Sonya Crowe
Affiliation:
Clinical Operational Research Unit, University College London, London, United Kingdom
Christina Pagel
Affiliation:
Clinical Operational Research Unit, University College London, London, United Kingdom
Catherine Bull
Affiliation:
Cardiac Unit, Great Ormond Street Hospital NHS Trust, London, United Kingdom
Nagarajan Muthialu
Affiliation:
Cardiac Unit, Great Ormond Street Hospital NHS Trust, London, United Kingdom
John Gibbs
Affiliation:
National Institute Cardiovascular Outcomes Research, Central Cardiac Audit Database, University College London, London, United Kingdom
David Cunningham
Affiliation:
National Institute Cardiovascular Outcomes Research, Central Cardiac Audit Database, University College London, London, United Kingdom
Martin Utley
Affiliation:
Clinical Operational Research Unit, University College London, London, United Kingdom
Victor T. Tsang
Affiliation:
Cardiac Unit, Great Ormond Street Hospital NHS Trust, London, United Kingdom
Rodney Franklin
Affiliation:
Paediatric Cardiology, Royal Brompton and Harefield Hospitals, London, United Kingdom
*
Correspondence to: Dr K. L. Brown, Consultant, Cardiac Unit, Great Ormond Street Hospital NHS Trust, London WC1N 3JH, United Kingdom. Tel: +44 207 813 8180; Fax: +44 207 829 8673; E-mail: brownk@gosh.nhs.uk

Abstract

Objective

To categorise records according to primary cardiac diagnosis in the United Kingdom Central Cardiac Audit Database in order to add this information to a risk adjustment model for paediatric cardiac surgery.

Design

Codes from the International Paediatric Congenital Cardiac Code were mapped to recognisable primary cardiac diagnosis groupings, allocated using a hierarchy and less refined diagnosis groups, based on the number of functional ventricles and presence of aortic obstruction.

Setting

A National Clinical Audit Database.

Patients

Children undergoing cardiac interventions: the proportions for each diagnosis scheme are presented for 13,551 first patient surgical episodes since 2004.

Results

In Scheme 1, the most prevalent diagnoses nationally were ventricular septal defect (13%), patent ductus arteriosus (10.4%), and tetralogy of Fallot (9.5%). In Scheme 2, the prevalence of a biventricular heart without aortic obstruction was 64.2% and with aortic obstruction was 14.1%; the prevalence of a functionally univentricular heart without aortic obstruction was 4.3% and with aortic obstruction was 4.7%; the prevalence of unknown (ambiguous) number of ventricles was 8.4%; and the prevalence of acquired heart disease only was 2.2%. Diagnostic groups added to procedural information: of the 17% of all operations classed as “not a specific procedure”, 97.1% had a diagnosis identified in Scheme 1 and 97.2% in Scheme 2.

Conclusions

Diagnostic information adds to surgical procedural data when the complexity of case mix is analysed in a national database. These diagnostic categorisation schemes may be used for future investigation of the frequency of conditions and evaluation of long-term outcome over a series of procedures.

Type
Original Articles
Copyright
Copyright © Cambridge University Press 2012 

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References

1. O'Brien, SM, Clarke, DR, Jacobs, JP, et al. An empirically based tool for analyzing mortality associated with congenital heart surgery. J Thorac Cardiovasc Surg 2009; 138: 11391153.CrossRefGoogle ScholarPubMed
2. O'Brien, SM, Jacobs, JP, Clarke, DR, et al. Accuracy of the aristotle basic complexity score for classifying the mortality and morbidity potential of congenital heart surgery operations. Ann Thorac Surg 2007; 84: 20 272037; discussion 27-37.Google Scholar
3. Lacour-Gayet, F, Clarke, D, Jacobs, J, et al. The Aristotle score for congenital heart surgery. Semin Thorac Cardiovasc Surg Pediatr Card Surg Annu 2004; 7: 185191.CrossRefGoogle ScholarPubMed
4. Jacobs, JP, Lacour-Gayet, FG, Jacobs, ML, et al. Initial application in the STS congenital database of complexity adjustment to evaluate surgical case mix and results. Ann Thorac Surg 2005; 79: 16351649; ; discussion 35-49.Google Scholar
5. Lacour-Gayet, F. Risk stratification theme for congenital heart surgery. Semin Thorac Cardiovasc Surg Pediatr Card Surg Annu 2002; 5: 148152.CrossRefGoogle ScholarPubMed
6. Lacour-Gayet, F, Clarke, D, Jacobs, J, et al. The Aristotle score: a complexity-adjusted method to evaluate surgical results. Eur J Cardiothorac Surg 2004; 25: 911924.Google Scholar
7. Jacobs, JP, Jacobs, ML, Lacour-Gayet, FG, et al. Stratification of complexity improves the utility and accuracy of outcomes analysis in a Multi-Institutional Congenital Heart Surgery Database: Application of the Risk Adjustment in Congenital Heart Surgery (RACHS-1) and Aristotle Systems in the Society of Thoracic Surgeons (STS) Congenital Heart Surgery Database. Pediatr Cardiol 2009; 30: 11171130.CrossRefGoogle Scholar
8. Jenkins, KJ, Gauvreau, K, Newburger, JW, Spray, TL, Moller, JH, Iezzoni, LI. Consensus-based method for risk adjustment for surgery for congenital heart disease. J Thorac Cardiovasc Surg 2002; 123: 110118.CrossRefGoogle ScholarPubMed
9. Jenkins, KJ. Risk adjustment for congenital heart surgery: the RACHS-1 method. Semin Thorac Cardiovasc Surg Pediatr Card Surg Annu 2004; 7: 180184.Google Scholar
10. Gibbs, JL, Monro, JL, Cunningham, D, Rickards, A. Survival after surgery or therapeutic catheterisation for congenital heart disease in children in the United Kingdom: analysis of the central cardiac audit database for 2000–1. BMJ 2004; 328: 611.Google Scholar
11. Central Cardiac Audit Database (CCAD). Central Cardiac Audit Database: Paediatric Analysis Home Page. Congenital Heart Disease Website. The Information Centre, London, 2011.Google Scholar
12. National Institute for Health Research (NIHR). NIHR Health Services Research: Funded Projects, Projects in Progress, 2011:Protocol.Google Scholar
13. Franklin, RC, Jacobs, JP, Krogmann, ON, et al. Nomenclature for congenital and paediatric cardiac disease: historical perspectives and The International Pediatric and Congenital Cardiac Code. Cardiol Young 2008; 18 (Suppl 2): 7080.CrossRefGoogle ScholarPubMed
14. Jacobs, JP, Jacobs, ML, Mavroudis, C, et al. Nomenclature and databases for the surgical treatment of congenital cardiac disease – an updated primer and an analysis of opportunities for improvement. Cardiol Young 2008; 18 (Suppl 2): 3862.CrossRefGoogle Scholar
15. Fyler, DC. Report of the New England regional infant cardiac program. Pediatrics 1980; 65 (Pt 2): 375461.Google Scholar
16. Ferencz, C, Rubin, JD, McCarter, RJ, et al. Congenital heart disease: prevalence at livebirth. The Baltimore-Washington Infant Study. Am J Epidemiol 1985; 121: 3136.Google Scholar
17. Knowles, R, Griebsch, I, Dezateux, C, Brown, J, Bull, C, Wren, C. Newborn screening for congenital heart defects: a systematic review and cost-effectiveness analysis. Health Technol Assess 2005; 9: 1152, iii-iv.Google Scholar
18. Moller, JH, Moodie, DS, Blees, M, Norton, JB, Nouri, S. Symptomatic heart disease in infants: comparison of three studies performed during 1969–1987. Pediatr Cardiol 1995; 16: 216222.Google Scholar
19. Extracorporeal Life Support Organisation (ELSO). Registry of the Extracorporeal Life Support Organisation. ELSO, Ann Arbor, Michigan, 2011.Google Scholar
20. Wren, C, Richmond, S, Donaldson, L. Temporal variability in birth prevalence of cardiovascular malformations. Heart 2000; 83: 414419.Google Scholar
21. Wren, C, O'Sullivan, JJ. Survival with congenital heart disease and need for follow up in adult life. Heart 2001; 85: 438443.CrossRefGoogle ScholarPubMed
22. Wren, C, Reinhardt, Z, Khawaja, K. Twenty-year trends in diagnosis of life-threatening neonatal cardiovascular malformations. Arch Dis Child Fetal Neonatal Ed 2008; 93: F33F35.Google Scholar
23. Clancy, RR, McGaurn, SA, Wernovsky, G, et al. Preoperative risk-of-death prediction model in heart surgery with deep hypothermic circulatory arrest in the neonate. J Thorac Cardiovasc Surg 2000; 119: 347357.Google Scholar
24. Kang, N, Cole, T, Tsang, V, Elliott, M, de Leval, M. Risk stratification in paediatric open-heart surgery. Eur J Cardiothorac Surg 2004; 26: 311.Google Scholar
25. Knowles, RL, Griebsch, I, Bull, C, Brown, J, Wren, C, Dezateux, C. Quality of life and congenital heart defects: comparing parent and professional values. Arch Dis Child 2007; 92: 388393.Google Scholar
26. National Health Service (NHS). Safe and Sustainable: Childrens Congenital Cardiac Services. NHS Specialist Services, England, 2011.Google Scholar
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