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.

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; m

SUBMITTER: Wimmer NJ 

PROVIDER: S-EPMC4309056 | biostudies-literature | 2014 Jun

REPOSITORIES: biostudies-literature