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J Thorac Cardiovasc Surg 2004;128:811-819
© 2004 The American Association for Thoracic Surgery

Statistics for the Rest of Us

Control chart methods for monitoring cardiac surgical performance and their interpretation

^a Bristol Heart Institute, University of Bristol, Bristol, United Kingdom
^b the UK Cardiothoracic Transplant Audit Steering Group, Clinical Effectiveness Unit, The Royal College of Surgeons of England, London, United Kingdom
^c Health Services Research Unit, London School of Hygiene & Tropical Medicine, London, United Kingdom

Received for publication September 19, 2003; revisions received March 4, 2004; accepted for publication March 16, 2004.

^* Address for reprints: G. D. Angelini, FRCS, Bristol Heart Institute, University of Bristol, Bristol Royal Infirmary, Bristol BS2 8HW, United Kingdom
g.d.angelini@bristol.ac.uk

The first 300 words of the full text of this article appear below.

See related articles on pages 807, 820, 823, and 907.

 

For more than a decade, there has been increasing interest in monitoring the quality of cardiac surgical performance, as demonstrated by public dissemination of surgeon-specific mortality for coronary artery bypass grafting (CABG) in The New York Times,¹ introduction of clinical governance strategies into the United Kingdom National Health Service,² mounting pressure for open scrutiny of results after publication of the Bristol Royal Infirmary Inquiry Panel report,^3,4 and numerous applications of quality control methods in medicine, both to monitor individuals' results^5-15 and to compare the performance of individuals or institutions.^16-18 Quality is seen as important not only because of its potential to detect unacceptable surgical results, but also because of the need to ensure quality when training the next generation of surgeons in a high-risk specialty.

All processes, including all aspects of medical care, are assumed to be subject to intrinsic random (common-cause) variation. The purpose of quality control charts is to distinguish between random variation and special-cause variation, which arises from factors extrinsic to the process. Reducing random variation for a process that is in control requires changing the process itself. Reducing special-cause variation requires identifying factors that cause the process to go out of control and taking appropriate corrective action.

A quality control chart can take one of several forms, depending on the type of data (continuous, binary, or count data—eg, blood loss or length of hospital stay [continuous data], mortality [binary data], or complications [count data]), the quantity of interest (eg, average performance or variability in performance), and the primary objective of the monitoring procedure. Shewhart control charts, for example, were designed for monitoring batches of results.¹⁹ In the surgical context, a batch might be a series of operations performed over a . . . [Full Text of this Article]

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