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Traditional and Genetic Risk Score and Stroke Risk Prediction in Korea.

ABSTRACT: BACKGROUND AND OBJECTIVES:Whether using both traditional risk factors and genetic variants for stroke as opposed to using either of the 2 alone improves the prediction of stroke risk remains unclear. The purpose of this study was to compare the predictability of stroke risk between models using traditional risk score (TRS) and genetic risk score (GRS). METHODS:We used a case-cohort study from the Korean Cancer Prevention Study-II (KCPS-II) Biobank (n=156,701). We genotyped 72 single nucleotide polymorphisms (SNPs) identified in genome-wide association study (GWAS) on the KCPS-II sub-cohort members and stroke cases. We calculated GRS by summing the number of risk alleles. Prediction models with or without GRS were evaluated in terms of the area under the receiver operating characteristic curve (AUROC). RESULTS:Sixteen out of 72 SNPs identified in GWAS showed significant associations with stroke, with an odds ratio greater than 2.0. For participants aged <40 years, AUROCs for incident stroke were 0.58, 0.65, and 0.67 in models using modifiable TRS only, GRS only, and TRS plus GRS, respectively, showing that GRS only model had better prediction than TRS only. For participants aged ?40 years, however, TRS only model had better prediction than GRS only model. Favorable levels of traditional risk were associated with significantly lower stroke risks within each genetic risk category. CONCLUSIONS:TRS and GRS were both independently associated with stroke risk. Using genetic variants in addition to traditional risk factors may be the most accurate way of predicting stroke risk, particularly in relatively younger individuals.

SUBMITTER: Jung KJ

PROVIDER: S-EPMC6072664 | biostudies-literature | 2018 Aug

REPOSITORIES: biostudies-literature

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Traditional and Genetic Risk Score and Stroke Risk Prediction in Korea.

Jung Keum Ji KJ Hwang Semi S Lee Sunmi S Kim Hyeon Chang HC Kim Hyeon Chang HC Jee Sun Ha SH

Korean circulation journal 20180801 8

<h4>Background and objectives</h4>Whether using both traditional risk factors and genetic variants for stroke as opposed to using either of the 2 alone improves the prediction of stroke risk remains unclear. The purpose of this study was to compare the predictability of stroke risk between models using traditional risk score (TRS) and genetic risk score (GRS).<h4>Methods</h4>We used a case-cohort study from the Korean Cancer Prevention Study-II (KCPS-II) Biobank (n=156,701). We genotyped 72 sing ...[more]

PMID: 30073812

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Project description:Background: Common variants may contribute to the variation of prognosis of heart failure (HF) among individual patients, but no systematical analysis was conducted using transcriptomic and whole exome sequencing (WES) data. We aimed to construct a genetic risk score (GRS) and estimate its potential as a predictive tool for HF-related mortality risk alone and in combination with traditional risk factors (TRFs). Methods and Results: We reanalyzed the transcriptomic data of 177 failing hearts and 136 healthy donors. Differentially expressed genes (fold change >1.5 or <0.68 and adjusted P < 0.05) were selected for prognosis analysis using our whole exome sequencing and follow-up data with 998 HF patients. Statistically significant variants in these genes were prepared for GRS construction. Traditional risk variables were in combination with GRS for the construct of the composite risk score. Kaplan-Meier curves and receiver operating characteristic (ROC) analysis were used to assess the effect of GRS and the composite risk score on the prognosis of HF and discriminant power, respectively. We found 157 upregulated and 173 downregulated genes. In these genes, 31 variants that were associated with the prognosis of HF were finally identified to develop GRS. Compared with individuals with low risk score, patients with medium- and high-risk score showed 2.78 (95%CI = 1.82-4.24, P = 2 × 10-6) and 6.54 (95%CI = 4.42-9.71, P = 6 × 10-21) -fold mortality risk, respectively. The composite risk score combining GRS and TRF predicted mortality risk with an HR = 5.41 (95% CI = 2.72-10.64, P = 1 × 10-6) for medium vs. low risk and HR = 22.72 (95% CI = 11.9-43.48, P = 5 × 10-21) for high vs. low risk. The discriminant power of GRS is excellent with a C statistic of 0.739, which is comparable to that of TRF (C statistic = 0.791). The combination of GRS and TRF could significantly increase the predictive ability (C statistic = 0.853). Conclusions: The 31-SNP GRS could well distinguish those HF patients with poor prognosis from those with better prognosis and provide clinician with reference for the intensive therapy, especially when combined with TRF. Clinical Trial Registration: https://www.clinicaltrials.gov/, identifier: NCT03461107.

Project description:ObjectiveWe compared the accuracy of clinicians and a risk score (iScore) to predict observed outcomes following an acute ischemic stroke.MethodsThe JURaSSiC (Clinician JUdgment vs Risk Score to predict Stroke outComes) study assigned 111 clinicians with expertise in acute stroke care to predict the probability of outcomes of 5 ischemic stroke case scenarios. Cases (n = 1,415) were selected as being representative of the 10 most common clinical presentations from a pool of more than 12,000 stroke patients admitted to 12 stroke centers. The primary outcome was prediction of death or disability (modified Rankin Scale [mRS] ≥3) at discharge within the 95% confidence interval (CI) of observed outcomes. Secondary outcomes included 30-day mortality and death or institutionalization at discharge.ResultsClinicians made 1,661 predictions with overall accuracy of 16.9% for death or disability at discharge, 46.9% for 30-day mortality, and 33.1% for death or institutionalization at discharge. In contrast, 90% of the iScore-based estimates were within the 95% CI of observed outcomes. Nearly half (n = 53 of 111; 48%) of participants were unable to accurately predict the probability of the primary outcome in any of the 5 rated cases. Less than 1% (n = 1) provided accurate predictions in 4 of the 5 cases and none accurately predicted all 5 case outcomes. In multivariable analyses, the presence of patient characteristics associated with poor outcomes (mRS ≥3 or death) in previous studies (older age, high NIH Stroke Scale score, and nonlacunar subtype) were associated with more accurate clinician predictions of death at 30 days (odds ratio [OR] 2.40, 95% CI 1.57-3.67) and with a trend for more accurate predictions of death or disability at discharge (OR 1.85, 95% CI 0.99-3.46).ConclusionsClinicians with expertise in stroke performed poorly compared to a validated tool in predicting the outcomes of patients with an acute ischemic stroke. Use of the risk stroke outcome tool may be superior for decision-making following an acute ischemic stroke.

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Traditional and Genetic Risk Score and Stroke Risk Prediction in Korea.

Publications

Traditional and Genetic Risk Score and Stroke Risk Prediction in Korea.

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