Stroke · 2014
Prediction of asymptomatic carotid artery stenosis in the general population: identification of high-risk groups.
СтатьяПоддержка NIH: внешняя программаНазвание и аннотация: Перевод пока не подготовлен
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
de Weerd Marjolein, Greving Jacoba P, Hedblad Bo, Lorenz Matthias W, Mathiesen Ellisiv B, O'Leary Daniel H, Rosvall Maria, Sitzer Matthias, de Borst Gert Jan, Buskens Erik, Bots Michiel L
Аннотация
BACKGROUND AND PURPOSE: Because of a low prevalence of severe carotid stenosis in the general population, screening for presence of asymptomatic carotid artery stenosis (ACAS) is not warranted. Possibly, for certain subgroups, screening is worthwhile. The present study aims to develop prediction rules for the presence of ACAS (>50% and >70%).
METHODS: Individual participant data from 4 population-based cohort studies (Malmö Diet and Cancer Study, Tromsø Study, Carotid Atherosclerosis Progression Study, and Cardiovascular Health Study; totaling 23 706 participants) were pooled. Multivariable logistic regression was performed to determine which variables predict presence of ACAS (>50% and >70%). Calibration and discrimination of the models were assessed, and bootstrapping was used to correct for overfitting.
RESULTS: Age, sex, history of vascular disease, systolic and diastolic blood pressure, total cholesterol/high-density lipoprotein ratio, diabetes mellitus, and current smoking were predictors of stenosis (>50% and >70%). The calibration of the model was good confirmed by a nonsignificant Hosmer and Lemeshow test for moderate (P=0.59) and severe stenosis (P=0.07). The models discriminated well between participants with and without stenosis, with an area under the receiver operating characteristic curve corrected for over optimism of 0.82 (95% confidence interval, 0.80-0.84) for moderate stenosis and of 0.87 (95% confidence interval, 0.85-0.90) for severe stenosis. The regression coefficients of the predictors were converted into a score chart to facilitate practical application.
CONCLUSIONS: A clinical prediction rule was developed that allows identification of subgroups with high prevalence of moderate (>50%) and severe (>70%) ACAS. When confirmed in comparable cohorts, application of the prediction rule may lead to a reduction in the number needed to screen for ACAS.
METHODS: Individual participant data from 4 population-based cohort studies (Malmö Diet and Cancer Study, Tromsø Study, Carotid Atherosclerosis Progression Study, and Cardiovascular Health Study; totaling 23 706 participants) were pooled. Multivariable logistic regression was performed to determine which variables predict presence of ACAS (>50% and >70%). Calibration and discrimination of the models were assessed, and bootstrapping was used to correct for overfitting.
RESULTS: Age, sex, history of vascular disease, systolic and diastolic blood pressure, total cholesterol/high-density lipoprotein ratio, diabetes mellitus, and current smoking were predictors of stenosis (>50% and >70%). The calibration of the model was good confirmed by a nonsignificant Hosmer and Lemeshow test for moderate (P=0.59) and severe stenosis (P=0.07). The models discriminated well between participants with and without stenosis, with an area under the receiver operating characteristic curve corrected for over optimism of 0.82 (95% confidence interval, 0.80-0.84) for moderate stenosis and of 0.87 (95% confidence interval, 0.85-0.90) for severe stenosis. The regression coefficients of the predictors were converted into a score chart to facilitate practical application.
CONCLUSIONS: A clinical prediction rule was developed that allows identification of subgroups with high prevalence of moderate (>50%) and severe (>70%) ACAS. When confirmed in comparable cohorts, application of the prediction rule may lead to a reduction in the number needed to screen for ACAS.