Open AccessClinical Prediction Model Suitable for Assessing Hospital Quality for Patients Undergoing Carotid Endarterectomy
Author(s) -
Wimmer Neil J.,
Spertus John A.,
Kennedy Kevin F.,
Anderson H. Ver,
Curtis Jeptha P.,
Weintraub William S.,
Singh Mandeep,
Rumsfeld John S.,
Masoudi Frederick A.,
Yeh Robert W.
Publication year - 2014
Publication title -
journal of the american heart association
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.494
H-Index - 85
ISSN - 2047-9980
DOI - 10.1161/jaha.113.000728
Subject(s) - medicine , carotid endarterectomy , stroke (engine) , endarterectomy , logistic regression , emergency medicine , revascularization , myocardial infarction , cardiology , stenosis , mechanical engineering , engineering
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.