Risk modelling in quality clinical registries: monitoring lesion treatment failure rate in percutaneous coronary interventions.

AIMS: This paper describes the development of a risk adjustment (RA) model predictive of individual lesion treatment failure in percutaneous coronary interventions (PCI) for use in a quality monitoring and improvement program.

METHODS AND RESULTS: Prospectively collected data for 3972 consecutive revascularisation procedures (5601 lesions) performed between January 2003 and September 2011 were studied. Data on procedures to September 2009 (n=3100) were used to identify factors predictive of lesion treatment failure. Factors identified included lesion risk class (p<0.001), occlusion type (p<0.001), patient age (p=0.001), vessel system (p<0.04), vessel diameter (p<0.001), unstable angina (p=0.003) and presence of major cardiac risk factors (p=0.01). A Bayesian RA model was built using these factors with predictive performance of the model tested on the remaining procedures (area under the receiver operating curve: 0.765, Hosmer-Lemeshow p value: 0.11). Cumulative sum, exponentially weighted moving average and funnel plots were constructed using the RA model and subjectively evaluated.

CONCLUSION: A RA model was developed and applied to SPC monitoring for lesion failure in a PCI database. If linked to appropriate quality improvement governance response protocols, SPC using this RA tool might improve quality control and risk management by identifying variation in performance based on a comparison of observed and expected outcomes.