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Editorial
Fair comparison of mortality data following cardiac surgery
  1. R ECOCHARD
  1. G DE GEVIGNEY
  1. C COLIN
  1. Unité de Biostatistique
  2. Département d'Information Médicale des Hospices Civils de Lyon
  3. 162 avenue Lacassagne
  4. 69424 Lyon Cedex 03, France
  5. rene.ecochard@chu-lyon.fr
  6. Service de cardiologie
  7. Hôpital cardiologique des Hospices Civils de Lyon
  8. 59 boulevard Pinel
  9. 69500 Bron, France
  10. Unité de Méthodologie en Evaluation Médicale
  11. Département d'Information Médicale des Hospices Civils de Lyon
  12. Lyon, France

https://doi.org/10.1136/heart.84.1.5

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    Outcome based quality of care monitoring is currently the object of a lively debate, particularly in cardiac surgery. Medical, technical, and economical reasons subtend comparisons between surgeons, hospitals, or regions. This raises three major questions. What is the reference to compare with? In what form is information on surgical outcomes disclosed? How are random variations dealt with?

    What is the reference to compare with?

    The surgical outcomes of a given centre are compared either to a recognised standard or to other centres' results.

    Comparing to a standard, predictive models are used to compute risk adjusted rates: the predicted risk is thus considered as a yardstick for acceptable practice. The Parsonnet scoring system is the most widely used for risk stratification in open heart surgery. The scores were calculated more than 10 years ago (1982–1987) in the USA, and involved over 3500 consecutive surgical procedures.1 The system has proved its validity in predicting coronary artery surgery mortality in the UK.2 However, it does not seem applicable to the present European practice. Studies carried in the UK3 ,4 and France5 have shown that current European mortality figures are 30–50% lower than those predicted by the Parsonnet score. Besides, statisticians criticised the methodology used in the original paper and stated that numerical risks obtained with the Parsonnet index should not be taken literally.6

    In this issue, Wynne-Jones and colleagues propose a new predictive model …

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