Clinical prediction model suitable for assessing hospital quality for patients undergoing carotid endarterectomy

Background-assessing hospital quality in the performance of carotid endarterectomy (cea) requires appropriate risk adjustment across hospitals with varying case mixes. the aim of this study was to develop and validate a prediction model to assess the risk of in-hospital stroke or death after cea that could aid in the assessment of hospital quality. methods and results-patients from national cardiovascular data registry (ncdr)'s carotid artery revascularization and endarterectomy (care) registry undergoing cea without acute evolving stroke from 2005 to 2013 were included. in-hospital stroke or death was modeled using hierarchical logistic regression with 20 candidate variables and accounting for hospital-level clustering. internal validation was achieved with bootstrapping; model discrimination and calibration were assessed. a total of 213 (1.7%) primary end point events occurred during 12 889 procedures. independent predictors of stroke or death included age,prior peripheral artery disease,diabetes mellitus,prior coronary artery disease,having a symptomatic carotid lesion,having a contralateral carotid occlusion,or having new york heart association class iii or iv heart failure. the model was well calibrated and demonstrated moderate discriminative ability (c-statistic 0.65). the ncdr cea score was then developed to support simple,prospective risk quantification in the clinical setting. conclusions-the ncdr cea score,comprising 7 clinical variables,predicts in-hospital stroke or death after cea. this model can be used to estimate hospital risk-adjusted outcomes for cea and to assist with the assessment of hospital quality. © 2014 the authors.