Literature DB >> 34634926

External Validation of Risk Prediction Models to Improve Selection of Patients for Carotid Endarterectomy.

Michiel H F Poorthuis1, Reinier A R Herings2, Kirsten Dansey3, Johanna A A Damen2, Jacoba P Greving2, Marc L Schermerhorn3, Gert J de Borst4.   

Abstract

BACKGROUND AND
PURPOSE: The net benefit of carotid endarterectomy (CEA) is determined partly by the risk of procedural stroke or death. Current guidelines recommend CEA if 30-day risks are <6% for symptomatic stenosis and <3% for asymptomatic stenosis. We aimed to identify prediction models for procedural stroke or death after CEA and to externally validate these models in a large registry of patients from the United States.
METHODS: We conducted a systematic search in MEDLINE and EMBASE for prediction models of procedural outcomes after CEA. We validated these models with data from patients who underwent CEA in the American College of Surgeons National Surgical Quality Improvement Program (2011-2017). We assessed discrimination using C statistics and calibration graphically. We determined the number of patients with predicted risks that exceeded recommended thresholds of procedural risks to perform CEA.
RESULTS: After screening 788 reports, 15 studies describing 17 prediction models were included. Nine were developed in populations including both asymptomatic and symptomatic patients, 2 in symptomatic and 5 in asymptomatic populations. In the external validation cohort of 26 293 patients who underwent CEA, 702 (2.7%) developed a stroke or died within 30-days. C statistics varied between 0.52 and 0.64 using all patients, between 0.51 and 0.59 using symptomatic patients, and between 0.49 to 0.58 using asymptomatic patients. The Ontario Carotid Endarterectomy Registry model that included symptomatic status, diabetes, heart failure, and contralateral occlusion as predictors, had C statistic of 0.64 and the best concordance between predicted and observed risks. This model identified 4.5% of symptomatic and 2.1% of asymptomatic patients with procedural risks that exceeded recommended thresholds.
CONCLUSIONS: Of the 17 externally validated prediction models, the Ontario Carotid Endarterectomy Registry risk model had most reliable predictions of procedural stroke or death after CEA and can inform patients about procedural hazards and help focus CEA toward patients who would benefit most from it.

Entities:  

Keywords:  carotid endarterectomy; clinical prediction rules; ischemic stroke; precision medicine; prognosis; transient ischemic attack

Mesh:

Year:  2021        PMID: 34634926      PMCID: PMC8712365          DOI: 10.1161/STROKEAHA.120.032527

Source DB:  PubMed          Journal:  Stroke        ISSN: 0039-2499            Impact factor:   7.914


  122 in total

1.  Predicting risk of perioperative death and stroke after carotid endarterectomy in asymptomatic patients: derivation and validation of a clinical risk score.

Authors:  Linda Calvillo-King; Lei Xuan; Song Zhang; Stanley Tuhrim; Ethan A Halm
Journal:  Stroke       Date:  2010-11-04       Impact factor: 7.914

Review 2.  Stenting versus surgery in patients with carotid stenosis after previous cervical radiation therapy: systematic review and meta-analysis.

Authors:  Margriet Fokkema; Anne G den Hartog; Michiel L Bots; Ingeborg van der Tweel; Frans L Moll; Gert Jan de Borst
Journal:  Stroke       Date:  2011-12-29       Impact factor: 7.914

3.  Clinical and operative predictors of outcomes of carotid endarterectomy.

Authors:  Ethan A Halm; Edward L Hannan; Mary Rojas; Stanley Tuhrim; Thomas S Riles; Caron B Rockman; Mark R Chassin
Journal:  J Vasc Surg       Date:  2005-09       Impact factor: 4.268

4.  The Vascular Quality Initiative 30-day stroke/death risk score calculator after transfemoral carotid artery stenting.

Authors:  Hanaa Dakour-Aridi; Muhammad Faateh; Pei-Lun Kuo; Devin S Zarkowsky; Adam Beck; Mahmoud B Malas
Journal:  J Vasc Surg       Date:  2019-09-13       Impact factor: 4.268

5.  Preoperative frailty Risk Analysis Index to stratify patients undergoing carotid endarterectomy.

Authors:  Alyson A Melin; Kendra K Schmid; Thomas G Lynch; Iraklis I Pipinos; Steven Kappes; G Matthew Longo; Prateek K Gupta; Jason M Johanning
Journal:  J Vasc Surg       Date:  2014-12-09       Impact factor: 4.268

6.  Efficacy of carotid endarterectomy for asymptomatic carotid stenosis. The Veterans Affairs Cooperative Study Group.

Authors:  R W Hobson; D G Weiss; W S Fields; J Goldstone; W S Moore; J B Towne; C B Wright
Journal:  N Engl J Med       Date:  1993-01-28       Impact factor: 91.245

7.  Development of an individualized scoring system to predict mid-term survival after carotid endarterectomy.

Authors:  Sara M Morales-Gisbert; José M Zaragozá García; Ángel Plaza Martínez; Francisco J Gómez Palonés; Eduardo Ortiz-Monzón
Journal:  J Cardiovasc Surg (Torino)       Date:  2014-07-30       Impact factor: 1.888

8.  External validation of the Vascular Study Group of New England carotid endarterectomy risk predictive model using an independent U.S. national surgical database.

Authors:  Mohammad H Eslami; Zein Saadeddin; Alik Farber; Larry Fish; Efthymios D Avgerinos; Michel S Makaroun
Journal:  J Vasc Surg       Date:  2019-10-30       Impact factor: 4.268

9.  Reduction in early stroke risk in carotid stenosis with transient ischemic attack associated with statin treatment.

Authors:  Áine Merwick; Gregory W Albers; Ethem M Arsava; Hakan Ay; David Calvet; Shelagh B Coutts; Brett L Cucchiara; Andrew M Demchuk; Matthew F Giles; Jean-Louis Mas; Jean Marc Olivot; Francisco Purroy; Peter M Rothwell; Jeffrey L Saver; Vijay K Sharma; Georgios Tsivgoulis; Peter J Kelly
Journal:  Stroke       Date:  2013-08-01       Impact factor: 7.914

10.  Assessing risk factors for major adverse cardiovascular and cerebrovascular events during the perioperative period of carotid angioplasty with stenting patients.

Authors:  Juan Liu; Zhi-Qiang Xu; Min Cui; Ling Li; Yong Cheng; Hua-Dong Zhou
Journal:  Exp Ther Med       Date:  2016-05-18       Impact factor: 2.447

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  1 in total

1.  Predictors of 30-day mortality using machine learning approach following carotid endarterectomy.

Authors:  Ahmed Mohamed; Ashfaq Shuaib; Ayman Z Ahmed; Maher Saqqur; Nida Fatima
Journal:  Neurol Sci       Date:  2022-09-15       Impact factor: 3.830

  1 in total

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