title: Clinical prediction model suitable for assessing hospital quality for patients undergoing carotid endarterectomy.
abstract: 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.
  
  
  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.
authors.affiliation: Brigham and Women's Hospital and Harvard Medical School, Boston, MA (N.J.W.).
authors.forename: Neil J
authors.initials: NJ
authors.lastname: Wimmer
authors.affiliation: Saint Luke's Mid-America Heart Institute, Kansas City, MO (J.A.S., K.F.K.).
authors.forename: John A
authors.initials: JA
authors.lastname: Spertus
authors.affiliation: Saint Luke's Mid-America Heart Institute, Kansas City, MO (J.A.S., K.F.K.).
authors.forename: Kevin F
authors.initials: KF
authors.lastname: Kennedy
authors.affiliation: University of Texas Health Science Center, Houston, TX (V.A.).
authors.forename: H Vernon
authors.initials: HV
authors.lastname: Anderson
authors.affiliation: Yale University School of Medicine, New Haven, CT (J.P.C.).
authors.forename: Jeptha P
authors.initials: JP
authors.lastname: Curtis
authors.affiliation: Christiana Health Care, Newark, DE (W.S.W.).
authors.forename: William S
authors.initials: WS
authors.lastname: Weintraub
authors.affiliation: Mayo Clinic, Rochester, MN (M.S.).
authors.forename: Mandeep
authors.initials: M
authors.lastname: Singh
authors.affiliation: Denver Veterans Affairs Medical Center, Denver, CO (J.S.R.).
authors.forename: John S
authors.initials: JS
authors.lastname: Rumsfeld
authors.affiliation: University of Colorado, Aurora, CO (F.A.M.).
authors.forename: Frederick A
authors.initials: FA
authors.lastname: Masoudi
authors.affiliation: Massachusetts General Hospital and Harvard Medical School, Boston, MA (R.W.Y.).
authors.forename: Robert W
authors.initials: RW
authors.lastname: Yeh
mesh_terms: D000367:Age Factors; D000368:Aged; D000369:Aged, 80 and over; D016894:Endarterectomy, Carotid / Q000401:mortality / Q000592:standards* / Q000706:statistics & numerical data; D005260:Female; D006761:Hospitals / Q000592:standards* / Q000706:statistics & numerical data; D006801:Humans; D016015:Logistic Models; D008297:Male; D015233:Models, Statistical; D011785:Quality Assurance, Health Care / Q000379:methods* / Q000706:statistics & numerical data; D012042:Registries; D020521:Stroke / Q000453:epidemiology / Q000517:prevention & control; D014481:United States / Q000453:epidemiology
keywords: carotid endarterectomy; risk prediction; stroke