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

Heart Lung Circ. 2013 Mar;22(3):193-203. doi: 10.1016/j.hlc.2012.10.001. Epub 2012 Nov 12.

Abstract

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.

MeSH terms

  • Adult
  • Age Factors
  • Aged
  • Aged, 80 and over
  • Angina, Unstable / complications
  • Area Under Curve
  • Bayes Theorem
  • 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
  • ROC Curve
  • Retrospective Studies
  • Risk Adjustment*
  • Risk Factors
  • Treatment Failure