Validation of risk adjustment models for in-hospital percutaneous transluminal coronary angioplasty mortality on an independent data set

Abstract

Predictive models for PTCA mortality yield comparable results when applied to patient groups other than the one on which the original model was developed. The accuracy of the two models tested in adjusting for the relatively high mortality rate observed in this patient group supports their application in quality assessment or quality improvement efforts.

Quality assessment and improvement in interventional cardiology: A position statement of the society of cardiovascular angiography and interventions, Part II: Public reporting and risk adjustment

“…Further, there is only a weak relationship with unplanned CABG [38][39][40][41][42][43][44], which is likely due to the many clinical variables that impact outcomes (Table III). In the Northern New England Registry [26] there was no relationship between operator volume and clinical success, MI as a complication, mortality (low or high-risk patients) or in-hospital CABG.…”

“…Risk adjustment models, developed from diverse patient populations, are strongly predictive of outcomes [39,40]. As this endpoint represents the primary measure of clinical outcome and appropriateness, the cardiology community must ensure the accuracy of the databases and registries from which these models are derived.…”

“…As this endpoint represents the primary measure of clinical outcome and appropriateness, the cardiology community must ensure the accuracy of the databases and registries from which these models are derived. External validation is the critical step in determining the value of any model [39][40][41][42].…”

Tree-structured risk stratification of in-hospital mortality after percutaneous coronary intervention for acute myocardial infarction: A report from the New York State percutaneous coronary intervention database

“…These presenting features were selected from all the available baseline variables in the New York State PCIRS on the basis of their clinical importance, i.e., having been identified in previous studies as being a risk factor for inhospital mortality [5][6][7][8][9][10][11][12][13][14][15][16][17]. Tree construction was carried out using a stopping rule of 350 patients in internal nodes and 175 patients in terminal nodes.…”

“…From 1987 to 2002 the number of procedures had increased 324 percent [4]. Alongside this increasing utilization of the procedure, a number of prediction models that identify important risk factors of mortality and post-procedural complications have been reported [5][6][7][8][9][10][11][12][13][14][15][16][17][18]. The development of these predictive models was partly motivated by the desire to predict an individual's risk of death after PCI and partly by the need for risk adjustment when comparing results from different operators and institutions.…”