Dataset Information

Validation of Risk Prediction Models to Detect Asymptomatic Carotid Stenosis.

ABSTRACT: Background Significant asymptomatic carotid stenosis (ACS) is associated with higher risk of strokes. While the prevalence of moderate and severe ACS is low in the general population, prediction models may allow identification of individuals at increased risk, thereby enabling targeted screening. We identified established prediction models for ACS and externally validated them in a large screening population. Methods and Results Prediction models for prevalent cases with ?50% ACS were identified in a systematic review (975 studies reviewed and 6 prediction models identified [3 for moderate and 3 for severe ACS]) and then validated using data from 596 469 individuals who attended commercial vascular screening clinics in the United States and United Kingdom. We assessed discrimination and calibration. In the validation cohort, 11 178 (1.87%) participants had ?50% ACS and 2033 (0.34%) had ?70% ACS. The best model included age, sex, smoking, hypertension, hypercholesterolemia, diabetes mellitus, vascular and cerebrovascular disease, measured blood pressure, and blood lipids. The area under the receiver operating characteristic curve for this model was 0.75 (95% CI, 0.74-0.75) for ?50% ACS and 0.78 (95% CI, 0.77-0.79) for ?70% ACS. The prevalence of ?50% ACS in the highest decile of risk was 6.51%, and 1.42% for ?70% ACS. Targeted screening of the 10% highest risk identified 35% of cases with ?50% ACS and 42% of cases with ?70% ACS. Conclusions Individuals at high risk of significant ACS can be selected reliably using a prediction model. The best-performing prediction models identified over one third of all cases by targeted screening of individuals in the highest decile of risk only.

SUBMITTER: Poorthuis MHF

PROVIDER: S-EPMC7428515 | biostudies-literature | 2020 Apr

REPOSITORIES: biostudies-literature

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Validation of Risk Prediction Models to Detect Asymptomatic Carotid Stenosis.

Poorthuis Michiel H F MHF Halliday Alison A Massa M Sofia MS Sherliker Paul P Clack Rachel R Morris Dylan R DR Clarke Robert R de Borst Gert J GJ Bulbulia Richard R Lewington Sarah S

Journal of the American Heart Association 20200420 8

Background Significant asymptomatic carotid stenosis (ACS) is associated with higher risk of strokes. While the prevalence of moderate and severe ACS is low in the general population, prediction models may allow identification of individuals at increased risk, thereby enabling targeted screening. We identified established prediction models for ACS and externally validated them in a large screening population. Methods and Results Prediction models for prevalent cases with ≥50% ACS were identified ...[more]

PMID: 32310014

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Project description:ObjectiveAsymptomatic carotid stenosis (ACS) is associated with an increased risk of ischaemic stroke and myocardial infarction. Risk scores have been developed to detect individuals at high risk of ACS, thereby enabling targeted screening, but previous external validation showed scope for refinement of prediction by adding additional predictors. The aim of this study was to develop a novel risk score in a large contemporary screened population.MethodsA prediction model was developed for moderate (≥50%) and severe (≥70%) ACS using data from 596 469 individuals who attended screening clinics. Variables that predicted the presence of ≥50% and ≥70% ACS independently were determined using multivariable logistic regression. Internal validation was performed using bootstrapping techniques. Discrimination was assessed using area under the receiver operating characteristic curves (AUROCs) and agreement between predicted and observed cases using calibration plots.ResultsPredictors of ≥50% and ≥70% ACS were age, sex, current smoking, diabetes mellitus, prior stroke/transient ischaemic attack, coronary artery disease, peripheral arterial disease, blood pressure, and blood lipids. Models discriminated between participants with and without ACS reliably, with an AUROC of 0.78 (95% confidence interval [CI] 0.77-0.78) for ≥ 50% ACS and 0.82 (95% CI 0.81-0.82) for ≥ 70% ACS. The number needed to screen in the highest decile of predicted risk to detect one case with ≥50% ACS was 13 and that of ≥70% ACS was 58. Targeted screening of the highest decile identified 41% of cases with ≥50% ACS and 51% with ≥70% ACS.ConclusionThe novel risk model predicted the prevalence of ACS reliably and performed better than previous models. Targeted screening among the highest decile of predicted risk identified around 40% of all cases with ≥50% ACS. Initiation or intensification of cardiovascular risk management in detected cases might help to reduce both carotid related ischaemic strokes and myocardial infarctions.

Project description:ImportanceRandomized 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.ObjectiveTo develop a risk prediction tool to improve patient selection for CEA among patients with asymptomatic carotid stenosis.Design, setting, and participantsIn 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 measureFive-year mortality.ResultsAmong 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 relevanceAccording 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.

Project description:ObjectiveWe have shown that almost 50% of patients with asymptomatic carotid stenosis (ACS) will demonstrate cognitive impairment. Recent evidence has suggested that cerebral hypoperfusion is an important cause of cognitive impairment. Carotid stenosis can restrict blood flow to the brain, with consequent cerebral hypoperfusion. In contrast, cross-hemispheric collateral compensation through the Circle of Willis, and cerebrovascular vasodilation can also mitigate the effects of flow restriction. It is, therefore, critical to develop a clinically relevant measure of net brain perfusion in patients with ACS that could help in risk stratification and in determining the appropriate treatment. To determine whether ACS results in cerebral hypoperfusion, we developed a novel approach to quantify interhemispheric cerebral perfusion differences, measured as the time to peak (TTP) and mean transit time (MTT) delays using perfusion-weighted magnetic resonance imaging (PWI) of the whole brain. To evaluate the utility of using clinical duplex ultrasonography (DUS) to infer brain perfusion, we also assessed the relationship between the PWI findings and ultrasound-based peak systolic velocity (PSV).MethodsStructural and PWI of the brain and magnetic resonance angiography of the carotid arteries were performed in 20 patients with ≥70% ACS. DUS provided the PSV, and magnetic resonance angiography provided plaque geometric measures at the stenosis. Volumetric perfusion maps of the entire brain from PWI were analyzed to obtain the mean interhemispheric differences for the TTP and MTT delays. In addition, the proportion of brain volume that demonstrated a delay in TTP and MTT was also measured. These proportions were measured for increasing severity of perfusion delays (0.5, 1.0, and 2.0 seconds). Finally, perfusion asymmetries on PWI were correlated with the PSV and stenosis features on DUS using Pearson's correlation coefficients.ResultsOf the 20 patients, 18 had unilateral stenosis (8 right and 10 left) and 2 had bilateral stenoses. The interhemispheric (left-right) TTP delays measured for the whole brain volume identified impaired perfusion in the hemisphere ipsilateral to the stenosis in 16 of the 18 patients. More than 45% of the patients had had ischemia in at least one half of their brain volume, with a TTP delay >0.5 second. The TTP and MTT delays showed strong correlations with PSV. In contrast, the correlations with the percentage of stenosis were weaker. The correlations for the PSV were strongest with the perfusion deficits (TTP and MTT delays) measured for the whole brain using our proposed algorithm (r = 0.80 and r = 0.74, respectively) rather than when measured on a single magnetic resonance angiography slice as performed in current clinical protocols (r = 0.31 and r = 0.58, respectively).ConclusionsInterhemispheric TTP and MTT delay measured for the whole brain using PWI has provided a new tool for assessing cerebral perfusion deficits in patients with ACS. Carotid stenosis was associated with a detectable reduction in ipsilateral brain perfusion compared with the opposite hemisphere in >80% of patients. The PSV measured at the carotid stenosis using ultrasonography correlated with TTP and MTT delays and might serve as a clinically useful surrogate to brain hypoperfusion in these patients.

Dataset Information

Validation of Risk Prediction Models to Detect Asymptomatic Carotid Stenosis.

Publications

Validation of Risk Prediction Models to Detect Asymptomatic Carotid Stenosis.

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