Risk Modelling in Quality Clinical Registries: Monitoring Lesion Treatment Failure Rate in Percutaneous Coronary Interventions

• Published in: Heart, lung & circulation lung & circulation, 2013-03, Vol.22 (3), p.193-203
• Main Authors: Smith, Ian R., MAppSc, Cameron, James, FRACP, Mengersen, Kerrie L., PhD, Foster, Kelley A., PhD, Rivers, John T., FRACP
• Format: Article
• Language: English
• Subjects: Adult; Age Factors; Aged; Aged, 80 and over; Angina, Unstable - complications; Angioplasty; Area Under Curve; Bayes Theorem; Cardiovascular; Case mix or risk adjustment; Clinical governance; Coronary Occlusion - classification; Coronary Occlusion - surgery; Female; Humans; Male; Middle Aged; Models, Statistical; Percutaneous Coronary Intervention - standards; Predictive Value of Tests; Quality Assurance, Health Care; Quality Improvement; Retrospective Studies; Risk Adjustment; Risk Factors; ROC Curve; Statistical process control; Treatment Failure
• ISSN: 1443-9506; 1444-2892
• DOI: 10.1016/j.hlc.2012.10.001

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.