Self-Reported Stroke Risk Stratification: Reasons for Geographic and Racial Differences in Stroke Study

Overview

Journal Stroke

Specialties Cardiology & Vascular Diseases
Neurology

Date 2017 May 21

PMID 28526763

Citations 9

Authors

George Howard

Leslie A McClure

Claudia S Moy

Virginia J Howard

Suzanne E Judd

Ya Yuan

D Leann Long

Paul Muntner

Monika M Safford

Dawn O Kleindorfer

Affiliations

Soon will be listed here.

Abstract

Background And Purpose: The standard for stroke risk stratification is the Framingham Stroke Risk Function (FSRF), an equation requiring an examination for blood pressure assessment, venipuncture for glucose assessment, and ECG to determine atrial fibrillation and heart disease. We assess a self-reported stroke risk function (SRSRF) to stratify stroke risk in comparison to the FSRF.

Methods: Participants from the REGARDS study (Reasons for Geographic and Racial Differences in Stroke) were evaluated at baseline and followed for incident stroke. The FSRF was calculated using directly assessed stroke risk factors. The SRSRF was calculated from 13 self-reported questions to exclude those with prevalent stroke and assess stroke risk. Proportional hazards analysis was used to assess incident stroke risk using the FSRF and SRSRF.

Results: Over an average 8.2-year follow-up, 939 of 23 983 participants had a stroke. The FSRF and SRSRF produced highly correlated risk scores (=0.852; 95% confidence interval, 0.849-0.856); however, the SRSRF had higher discrimination of stroke risk than the FSRF (c=0.7266; 95% confidence interval, 0.7076-0.7457; c=0.7075; 95% confidence interval, 0.6877-0.7273; =0.0038). The 10-year stroke risk in the highest decile of predicted risk was 11.1% for the FSRF and 13.4% for the SRSRF.

Conclusions: A simple self-reported questionnaire can be used to identify those at high risk for stroke better than the gold standard FSRF. This instrument can be used clinically to easily identify individuals at high risk for stroke and also scientifically to identify a subpopulation enriched for stroke risk.

Citing Articles

Facilitating Harmonization of Variables in Framingham, MESA, ARIC, and REGARDS Studies Through a Metadata Repository.

Mallya P, Stevens L, Zhao J, Hong C, Henao R, Economou-Zavlanos N Circ Cardiovasc Qual Outcomes. 2023; 16(11):e009938.

PMID: 37850400 PMC: 10841164. DOI: 10.1161/CIRCOUTCOMES.123.009938.

Predictive Accuracy of Stroke Risk Prediction Models Across Black and White Race, Sex, and Age Groups.

Hong C, Pencina M, Wojdyla D, Hall J, Judd S, Cary M JAMA. 2023; 329(4):306-317.

PMID: 36692561 PMC: 10408266. DOI: 10.1001/jama.2022.24683.

Stroke prevention in rural residents: development of a simplified risk assessment tool with artificial intelligence.

Ding Z, Zhang L, Niu M, Zhao B, Liu X, Huo W Neurol Sci. 2023; 44(5):1687-1694.

PMID: 36653543 DOI: 10.1007/s10072-023-06610-5.

A systematic review of the status and methodological considerations for estimating risk of first ever stroke in the general population.

Xu W, Huang J, Yu Q, Yu H, Pu Y, Shi Q Neurol Sci. 2021; 42(6):2235-2247.

PMID: 33783660 DOI: 10.1007/s10072-021-05219-w.

Atrial fibrillation prevalence, awareness and management in a nationwide survey of adults in China.

Du X, Guo L, Xia S, Du J, Anderson C, Arima H Heart. 2021; .

PMID: 33509976 PMC: 7958113. DOI: 10.1136/heartjnl-2020-317915.

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