Cumulative funnel plots for the early detection of interoperator variation: retrospective database analysis of observed versus predicted results of percutaneous coronary intervention

Objective To use funnel plots and cumulative funnel plots to compare in-hospital outcome data for operators undertaking percutaneous coronary interventions with predicted results derived from a validated risk score to allow for early detection of variation in performance.Design Analysis of prospectively collected data.Setting Tertiary centre NHS hospital in the north east of England.Participants Five cardiologists carrying out percutaneous coronary interventions between January 2003 and December 2006.Main outcome measures In-hospital major adverse cardiovascular and cerebrovascular events (in-hospital death, Q wave myocardial infarction, emergency coronary artery bypass graft surgery, and cerebrovascular accident) analysed against the logistic north west quality improvement programme predicted risk, for each operator. Results are displayed as funnel plots summarising overall performance for each operator and cumulative funnel plots for an individual operator’s performance on a case series basis.Results The funnel plots for 5198 patients undergoing percutaneous coronary interventions showed an average observed rate for major adverse cardiovascular and cerebrovascular events of 1.96% overall. This was below the predicted risk of 2.06% by the logistic north west quality improvement programme risk score. Rates of in-hospital major adverse cardiovascular and cerebrovascular events for all operators were within the 3σ upper control limit of 2.75% and 2σ upper warning limit of 2.49%.Conclusion The overall in-hospital major adverse cardiovascular and cerebrovascular events rates were under the predicted event rate. In-hospital rates after percutaneous coronary intervention procedure can be monitored successfully using funnel and cumulative funnel plots with 3σ control limits to display and publish each operator’s outcomes. The upper warning limit (2σ control limit) could be used for internal monitoring. The main advantage of these charts is their transparency, as they show observed and predicted events separately. By this approach individual operators can monitor their own performance, using the predicted risk for their patients but in a way that is compatible with benchmarking to colleagues, encapsulated by the funnel plot. This methodology is applicable regardless of variations in individual operator case volume and case mix.