Clinical prediction model suitable for assessing hospital quality for patients undergoing carotid endarterectomy
Neil J Wimmer1, John A Spertus2, Kevin F Kennedy2
1Brigham and Women's Hospital and Harvard Medical School, Boston, MA (N.J.W.).
Insights
A new NCDR CEA score predicts stroke or death after carotid endarterectomy (CEA). This tool helps assess hospital quality by adjusting for patient risk factors, improving quality assessment.
Area of Science:
- Cardiovascular Surgery
- Health Services Research
- Medical Informatics
Background:
- Assessing hospital quality for carotid endarterectomy (CEA) requires risk adjustment for varying patient case mixes.
- Developing accurate prediction models is crucial for evaluating hospital performance in CEA procedures.
- Existing methods may not adequately account for patient complexity, impacting quality assessments.
Purpose of the Study:
- To develop and validate a prediction model for in-hospital stroke or death after CEA.
- To create a tool that aids in the objective assessment of hospital quality for CEA.
- To enable risk adjustment for comparing outcomes across hospitals with different patient populations.
Main Methods:
- Utilized data from the National Cardiovascular Data Registry (NCDR) Carotid Artery Revascularization and Endarterectomy (CARE) Registry (2005-2013).
- Employed hierarchical logistic regression with 20 candidate variables, accounting for hospital-level clustering.
- Validated the model internally using bootstrapping, assessing discrimination and calibration (c-statistic 0.65).
Main Results:
- Identified 7 independent predictors of in-hospital stroke or death: age, prior peripheral artery disease, diabetes, prior coronary artery disease, symptomatic carotid lesion, contralateral carotid occlusion, and advanced heart failure (NYHA Class III/IV).
- Observed 213 (1.7%) primary endpoint events in 12,889 CEA procedures.
- The developed model demonstrated good calibration and moderate discriminative ability.
Conclusions:
- The NCDR CEA score, based on 7 clinical variables, effectively predicts in-hospital stroke or death following CEA.
- This score facilitates the estimation of risk-adjusted hospital outcomes for CEA.
- The NCDR CEA score serves as a valuable tool for assessing and improving hospital quality in CEA procedures.
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.
