Risk Prediction Tools to Improve Patient Selection for Carotid Endarterectomy among Patients with Asymptomatic Carotid Stenosis

Salomeh Keyhani, Erin Madden, Eric M. Cheng, Dawn Bravata, Ethan Halm, Peter C. Austin, Mehrnaz Ghasemiesfe, Ann S. Abraham, Alysandra J. Zhang, Jason M. Johanning

Research output: Contribution to journalArticle

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Abstract

Importance: Randomized clinical trials have demonstrated that patients with asymptomatic carotid stenosis are eligible for carotid endarterectomy (CEA) if the 30-day surgical complication rate is less than 3% and the patient's life expectancy is at least 5 years. Objective: To develop a risk prediction tool to improve patient selection for CEA among patients with asymptomatic carotid stenosis. Design, Setting, and Participants: In this cohort study, veterans 65 years and older who received both carotid imaging and CEA in the Veterans Administration between January 1, 2005, and December 31, 2009 (n = 2325) were followed up for 5 years. Data were analyzed from January 2005 to December 2015. A risk prediction tool (the Carotid Mortality Index [CMI]) based on 23 candidate variables identified in the literature was developed using Veterans Administration and Medicare data. A simpler model based on the number of 4 key comorbidities that were prevalent and strongly associated with 5-year mortality was also developed (any cancer in the past 5 years, chronic obstructive pulmonary disease, congestive heart failure, and chronic kidney disease [the 4C model]). Model performance was assessed using measures of discrimination (eg, area under the curve [AUC]) and calibration. Internal validation was performed by correcting for optimism using 500 bootstrapped samples. Main Outcome and Measure: Five-year mortality. Results: Among 2325 veterans, the mean (SD) age was 73.74 (5.92) years. The cohort was predominantly male (98.8%) and of white race/ethnicity (94.4%). Overall, 29.5% (n = 687) of patients died within 5 years of CEA. On the basis of a backward selection algorithm, 9 patient characteristics were selected (age, chronic kidney disease, diabetes, chronic obstructive pulmonary disease, any cancer diagnosis in the past 5 years, congestive heart failure, atrial fibrillation, remote stroke or transient ischemic attack, and body mass index) for the final logistic model, which yielded an optimism-corrected AUC of 0.687 for the CMI. The 4C model had slightly worse discrimination (AUC, 0.657) compared with the CMI model; however, the calibration curve was similar to the full model in most of the range of predicted probabilities. Conclusions and Relevance: According to results of this study, use of the CMI or the simpler 4C model may improve patient selection for CEA among patients with asymptomatic carotid stenosis..

Original languageEnglish (US)
JournalJAMA Surgery
DOIs
StateAccepted/In press - Jan 1 2018

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Carotid Endarterectomy
Carotid Stenosis
Patient Selection
Mortality
Area Under Curve
United States Department of Veterans Affairs
Veterans
Chronic Renal Insufficiency
Chronic Obstructive Pulmonary Disease
Calibration
Heart Failure
Transient Ischemic Attack
Medicare
Life Expectancy
Atrial Fibrillation
Comorbidity
Neoplasms
Body Mass Index
Cohort Studies
Randomized Controlled Trials

ASJC Scopus subject areas

  • Surgery

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Risk Prediction Tools to Improve Patient Selection for Carotid Endarterectomy among Patients with Asymptomatic Carotid Stenosis. / Keyhani, Salomeh; Madden, Erin; Cheng, Eric M.; Bravata, Dawn; Halm, Ethan; Austin, Peter C.; Ghasemiesfe, Mehrnaz; Abraham, Ann S.; Zhang, Alysandra J.; Johanning, Jason M.

In: JAMA Surgery, 01.01.2018.

Research output: Contribution to journalArticle

Keyhani, Salomeh ; Madden, Erin ; Cheng, Eric M. ; Bravata, Dawn ; Halm, Ethan ; Austin, Peter C. ; Ghasemiesfe, Mehrnaz ; Abraham, Ann S. ; Zhang, Alysandra J. ; Johanning, Jason M. / Risk Prediction Tools to Improve Patient Selection for Carotid Endarterectomy among Patients with Asymptomatic Carotid Stenosis. In: JAMA Surgery. 2018.
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abstract = "Importance: Randomized clinical trials have demonstrated that patients with asymptomatic carotid stenosis are eligible for carotid endarterectomy (CEA) if the 30-day surgical complication rate is less than 3{\%} and the patient's life expectancy is at least 5 years. Objective: To develop a risk prediction tool to improve patient selection for CEA among patients with asymptomatic carotid stenosis. Design, Setting, and Participants: In this cohort study, veterans 65 years and older who received both carotid imaging and CEA in the Veterans Administration between January 1, 2005, and December 31, 2009 (n = 2325) were followed up for 5 years. Data were analyzed from January 2005 to December 2015. A risk prediction tool (the Carotid Mortality Index [CMI]) based on 23 candidate variables identified in the literature was developed using Veterans Administration and Medicare data. A simpler model based on the number of 4 key comorbidities that were prevalent and strongly associated with 5-year mortality was also developed (any cancer in the past 5 years, chronic obstructive pulmonary disease, congestive heart failure, and chronic kidney disease [the 4C model]). Model performance was assessed using measures of discrimination (eg, area under the curve [AUC]) and calibration. Internal validation was performed by correcting for optimism using 500 bootstrapped samples. Main Outcome and Measure: Five-year mortality. Results: Among 2325 veterans, the mean (SD) age was 73.74 (5.92) years. The cohort was predominantly male (98.8{\%}) and of white race/ethnicity (94.4{\%}). Overall, 29.5{\%} (n = 687) of patients died within 5 years of CEA. On the basis of a backward selection algorithm, 9 patient characteristics were selected (age, chronic kidney disease, diabetes, chronic obstructive pulmonary disease, any cancer diagnosis in the past 5 years, congestive heart failure, atrial fibrillation, remote stroke or transient ischemic attack, and body mass index) for the final logistic model, which yielded an optimism-corrected AUC of 0.687 for the CMI. The 4C model had slightly worse discrimination (AUC, 0.657) compared with the CMI model; however, the calibration curve was similar to the full model in most of the range of predicted probabilities. Conclusions and Relevance: According to results of this study, use of the CMI or the simpler 4C model may improve patient selection for CEA among patients with asymptomatic carotid stenosis..",
author = "Salomeh Keyhani and Erin Madden and Cheng, {Eric M.} and Dawn Bravata and Ethan Halm and Austin, {Peter C.} and Mehrnaz Ghasemiesfe and Abraham, {Ann S.} and Zhang, {Alysandra J.} and Johanning, {Jason M.}",
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AU - Keyhani, Salomeh

AU - Madden, Erin

AU - Cheng, Eric M.

AU - Bravata, Dawn

AU - Halm, Ethan

AU - Austin, Peter C.

AU - Ghasemiesfe, Mehrnaz

AU - Abraham, Ann S.

AU - Zhang, Alysandra J.

AU - Johanning, Jason M.

PY - 2018/1/1

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N2 - Importance: Randomized clinical trials have demonstrated that patients with asymptomatic carotid stenosis are eligible for carotid endarterectomy (CEA) if the 30-day surgical complication rate is less than 3% and the patient's life expectancy is at least 5 years. Objective: To develop a risk prediction tool to improve patient selection for CEA among patients with asymptomatic carotid stenosis. Design, Setting, and Participants: In this cohort study, veterans 65 years and older who received both carotid imaging and CEA in the Veterans Administration between January 1, 2005, and December 31, 2009 (n = 2325) were followed up for 5 years. Data were analyzed from January 2005 to December 2015. A risk prediction tool (the Carotid Mortality Index [CMI]) based on 23 candidate variables identified in the literature was developed using Veterans Administration and Medicare data. A simpler model based on the number of 4 key comorbidities that were prevalent and strongly associated with 5-year mortality was also developed (any cancer in the past 5 years, chronic obstructive pulmonary disease, congestive heart failure, and chronic kidney disease [the 4C model]). Model performance was assessed using measures of discrimination (eg, area under the curve [AUC]) and calibration. Internal validation was performed by correcting for optimism using 500 bootstrapped samples. Main Outcome and Measure: Five-year mortality. Results: Among 2325 veterans, the mean (SD) age was 73.74 (5.92) years. The cohort was predominantly male (98.8%) and of white race/ethnicity (94.4%). Overall, 29.5% (n = 687) of patients died within 5 years of CEA. On the basis of a backward selection algorithm, 9 patient characteristics were selected (age, chronic kidney disease, diabetes, chronic obstructive pulmonary disease, any cancer diagnosis in the past 5 years, congestive heart failure, atrial fibrillation, remote stroke or transient ischemic attack, and body mass index) for the final logistic model, which yielded an optimism-corrected AUC of 0.687 for the CMI. The 4C model had slightly worse discrimination (AUC, 0.657) compared with the CMI model; however, the calibration curve was similar to the full model in most of the range of predicted probabilities. Conclusions and Relevance: According to results of this study, use of the CMI or the simpler 4C model may improve patient selection for CEA among patients with asymptomatic carotid stenosis..

AB - Importance: Randomized clinical trials have demonstrated that patients with asymptomatic carotid stenosis are eligible for carotid endarterectomy (CEA) if the 30-day surgical complication rate is less than 3% and the patient's life expectancy is at least 5 years. Objective: To develop a risk prediction tool to improve patient selection for CEA among patients with asymptomatic carotid stenosis. Design, Setting, and Participants: In this cohort study, veterans 65 years and older who received both carotid imaging and CEA in the Veterans Administration between January 1, 2005, and December 31, 2009 (n = 2325) were followed up for 5 years. Data were analyzed from January 2005 to December 2015. A risk prediction tool (the Carotid Mortality Index [CMI]) based on 23 candidate variables identified in the literature was developed using Veterans Administration and Medicare data. A simpler model based on the number of 4 key comorbidities that were prevalent and strongly associated with 5-year mortality was also developed (any cancer in the past 5 years, chronic obstructive pulmonary disease, congestive heart failure, and chronic kidney disease [the 4C model]). Model performance was assessed using measures of discrimination (eg, area under the curve [AUC]) and calibration. Internal validation was performed by correcting for optimism using 500 bootstrapped samples. Main Outcome and Measure: Five-year mortality. Results: Among 2325 veterans, the mean (SD) age was 73.74 (5.92) years. The cohort was predominantly male (98.8%) and of white race/ethnicity (94.4%). Overall, 29.5% (n = 687) of patients died within 5 years of CEA. On the basis of a backward selection algorithm, 9 patient characteristics were selected (age, chronic kidney disease, diabetes, chronic obstructive pulmonary disease, any cancer diagnosis in the past 5 years, congestive heart failure, atrial fibrillation, remote stroke or transient ischemic attack, and body mass index) for the final logistic model, which yielded an optimism-corrected AUC of 0.687 for the CMI. The 4C model had slightly worse discrimination (AUC, 0.657) compared with the CMI model; however, the calibration curve was similar to the full model in most of the range of predicted probabilities. Conclusions and Relevance: According to results of this study, use of the CMI or the simpler 4C model may improve patient selection for CEA among patients with asymptomatic carotid stenosis..

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