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Ann Thorac Surg 2003;76:663-667
© 2003 The Society of Thoracic Surgeons


Statistician's page

Cumulative sum techniques for assessing surgical results

Gary L. Grunkemeier, PhDa*, Ying Xing Wu, MDa, Anthony P. Furnary, MDa

a Providence Health System, Portland, Oregon, USA

* Address reprint requests to Dr Grunkemeier, 9205 SW Barnes, #33, Portland, OR 97225, USA.
e-mail: gary.grunkemeier@providence.org

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

The report by Novick and colleagues [1], in this issue of The Annals of Thoracic Surgery uses a cumulative sum (CUSUM) technique to assess the learning curve in telerobotic surgery. Novick and associates [2] have previously used CUSUMs to describe the learning curve of an academic surgeon, the change from on-pump to off-pump coronary bypass surgery [3], and the learning curve for off-pump surgery [4]. From these studies, they found that CUSUM provided " ... a more sensitive indicator of a cluster of surgical failures than standard statistical techniques" [1].

Background

CUSUM analysis was introduced 50 years ago in the United Kingdom (UK) using the terminology of industrial quality control [5], and was first used to monitor surgical performance 10 years ago [6]. Since then, several authors from the UK have extended the theory to accommodate the varying risk of cardiac surgery mortality [7–13]

Constant risk of failure
The original idea, as used by Novick and coworkers [1], is to plot the cumulative sum of "adjusted" failures by patient number, where the adjustment consists of subtracting a fraction of a failure for each patient, representing the expected or acceptable failure rate. The units on the vertical axis are then "excess failures." If the "process" is performing as expected, the resulting cumulative sum will be a jagged line hovering around the horizontal axis. For example, if the expected failure rate is 10%, then 0.1 (10% of a failure) is subtracted from the cumulative sum for each patient. When a patient fails, 1.0 (100% of a failure) is added, resulting in a net rise of 0.9 (1.0 to 0.1) at that point. Nothing is added to the expected risk if the patient does not fail; the drop stays at 0.1. Thus, if exactly 1 of the first . . . [Full Text of this Article]




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