Risk analysis of carotid stent from a population-based database in Taiwan. Issue 35 (August 2016)

• Record Type: Journal Article
• Authors: Cheng, Chun-An; Chien, Wu-Chien; Hsu, Chien-Yeh; Lin, Hui-Chen; Chiu, Hung-Wen
• Other Names: L. Hansen. Christopher section editor.
• Is Part Of: Medicine. Volume 95:Issue 35(2016)
• Publication Date: 2016-08
• Subjects: carotid stent -- nomogram -- risk estimate -- validation; Medicine -- Periodicals; Medicine -- Periodicals; Médecine -- Périodiques; Geneeskunde; Medicine; Periodicals; Periodicals; 610.5
• DOI: 10.1097/MD.0000000000004747
• Languages: English
• ISSNs: 0025-7974

Abstract

Abstract : Abstract: Because stroke is the third leading disease that causes mortality in the world, the prevention of stroke from advanced carotid stenosis is an important issue. The carotid stent (CAS) is a less invasive to treat advanced carotid stenosis, but for high-risk patients it may cause some events after the procedure that reduces the benefit of stroke prevention. Because patients and their families have less information about risk of events after CAS and are easy concerned, this study calculates the individual probability of major adverse cardiovascular events including any stroke, myocardial infarction, or death after procedure. The analyzed dataset was composed of patients undergoing CAS from the longitudinal National Health Insurance claim database in Taiwan. The validation dataset was composed of patients undergoing CAS from the Tri-Service General Hospital. We excluded patients under 18 years of age. The prediction model was constructed with a multivariable Cox proportional hazard regression and performed with forward stepwise selection. The nomogram construction was based on the multivariable Cox model. The risk factors were determined as follows: age with a hazard ratio (HR) of 1.027 (95% confidence interval [CI]: 1.002–1.053) for every 1 year older, congestive heart failure with a HR of 2.196 (95% CI: 1.368–3.524), malignant disease with a HR of 1.724 (95% CI: 1.009–2.944), diabetes mellitus with a HR of 1.722 (95% CI: 1.109–2.674), and symptomatic status with a HR of 1.604 (95% CI: 1.027–2.507). The model showed good discrimination with a P < 0.001 (concordance index, 0.681; bootstrap corrected, 0.661) in the derivation data. The concordance index of external validation was 0.66 ( P = 0.048), which indicates acceptable performance. We developed a nomogram with a visual scale method and prognostic information, and it is easy to use in clinical practice. The integer-base method may support communication between clinicians and patients before CAS to reduce the anxiety about making a treatment decision. However, insofar as older patients with multiple comorbidities are at high risk, the option of an alternative treatment strategy with medical therapy should be suggested. In the future, prospective tests should be performed to validate whether this model helps patients to prevent events. Abstract : Supplemental Digital Content is available in the text