The Dutch Audit of Carotid Interventions: Transparency in Quality of Carotid Endarterectomy in Symptomatic Patients in the Netherlands.

Abstract

BACKGROUND

The Dutch Audit for Carotid Interventions (DACI) registers all patients undergoing interventions for carotid artery stenosis in the Netherlands. This study describes the design of the DACI and results of patients with a symptomatic stenosis undergoing carotid endarterectomy (CEA). It aimed to evaluate variation between hospitals in process of care and (adjusted) outcomes, as well as predictors of major stroke/death after CEA.

CONCLUSION

CEA in The Netherlands is associated with an overall low mortality and (major) stroke/death rate. Whereas the indicator time to intervention varied between hospitals, mortality and (major) stroke/death were not significantly distinctive enough to identify worse practices and therefore were unsuitable for hospital comparison in the Dutch setting. Additionally, predictors of major stroke/death at population level could be identified.

RESULTS

A total of 6459 patients, registered by 52 hospitals, were included. The majority (4,832, 75%) were treated <2 weeks after their first hospital consultation, varying from 40% to 93% between hospitals. Mortality, stroke/death, and major stroke/death were, respectively, 1.1%, 3.6%, and 1.8%. Adjusted major stroke/death rates for hospital comparison varied between 0 and 6.5%. Nine hospitals performed significantly better, none performed significantly worse. Predictors of major stroke/death were sex, age, pulmonary disease, presenting neurological symptoms, and peri-operative shunt.

METHODS

All patients with a symptomatic stenosis, who underwent CEA and were registered in the DACI between 2014 and 2016 were included in this cohort. Descriptive analyses of patient characteristics, process of care, and outcomes were performed. Casemix adjusted hospital procedural outcomes as (30 day/in hospital) mortality, stroke/death, and major stroke/death, were compared with the national mean. A multivariable logistic regression model (backward elimination at p > 0.10) was used to identify predictors of major stroke/death.