scholarly journals Prediction of HF-Related Mortality Risk Using Genetic Risk Score Alone and in Combination With Traditional Risk Factors

2021 ◽  
Vol 8 ◽  
Author(s):  
Dong Hu ◽  
Lei Xiao ◽  
Shiyang Li ◽  
Senlin Hu ◽  
Yang Sun ◽  
...  
Keyword(s):  
Risk Score ◽  
Mortality Risk ◽  
Genetic Risk Score ◽  
Low Risk ◽  
C Statistic ◽  
Whole Exome ◽  
Composite Risk ◽  
Traditional Risk Factors ◽  
Discriminant Power ◽  
Related Mortality

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.

scholarly journals Validation of the Zwolle score for selection of very low-risk STEMI patients treated with primary angioplasty

2020 ◽  
Vol 41 (Supplement_2) ◽  
Author(s):  
A Cordero ◽  
B Cid ◽  
P Monteiro ◽  
J.M Garcia-Acuna ◽  
M Rodriguez-Manero ◽  
...  
Keyword(s):  
Hospital Mortality ◽  
Risk Score ◽  
Mortality Risk ◽  
Area Under The Curve ◽  
Coronary Intervention ◽  
Low Risk ◽  
Individual Risk ◽  
Care Organization ◽  
Grace Score ◽  
Selection Of

Abstract Background The Zwolle risk score was designed to stratify the actual in-hospital mortality risk of ST-elevation myocardial infarction (STEMI) patients treated with primary percutaneous coronary intervention (p-PCI) but, also, for decision-making related to patients location in an intensive care unit or not. Since the GRACE score continues being the gold-standard for individual risk assessment in STEMI in most institutions we assessed the specificity of both scores for in-hospital mortality. Methods We assessed the accuracy of Zwolle risk score for in-hospital mortality estimation as compared to the GRACE score in all patients admitted for STEMI in 3 tertitary hospitals. Patients with Zwolle risk score <3 would qualify as “low risk”, 3–5 as “intermediate risk” and ≥6 as “high risk”. Patients with GRACE score <140 were classified as low-risk. Specificity, sensitivity and classification were assessed by ROC curves and the area under the curve (AUC). Results We included 4,446 patients, mean age 64.7 (13.6) years, 24% women and 39% with diabetes. Mean GRACE score was 157.3 (4.9) and Zwolle was 2.8 (3.3). In-hospital mortality was 10.6% (471 patients). Patients who died had higher GRACE score (218.4±4.9 vs. 149.6±37.5; p<0.001) and Zwolle score (7.6±4.3 vs. 2.3±2.18; p<0.001); a statistically significant increase of in-hospital mortality risk, adjusted adjusted by age, gender and revascularization, was observed with both scores (figure). A total of 1,629 patients (40.0%) were classified as low risk by the GRACE score and 2,962 (66.6%) by the Zwolle score; in-hospital mortality was 1.6% and 2.7%, respectively. Moreover, the was a significant increase of in-hospital mortality rate according to Zwolle categories (2.7%; 13.0%; 41.6%)The AUC of both score was the same (p=0.49) but the specificity of GRACE score <140 was 43.1% as compared to 72.6% obtained by Zwolle score <3; patients accurately classified was also lower with the GRACE score threshold (48.8% vs. 73.7%). Conclusions Selection of low-risk STEMI patients treated with p-PCI based on the Zwolle risk score has higher specificity than the GRACE score and might be useful for the care organization in clinical practice. Funding Acknowledgement Type of funding source: None

scholarly journals Identifying individuals with high risk of Alzheimer’s disease using polygenic risk scores

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Ganna Leonenko ◽  
Emily Baker ◽  
Joshua Stevenson-Hoare ◽  
Annerieke Sierksma ◽  
Mark Fiers ◽  
...  
Keyword(s):  
High Risk ◽  
Risk Score ◽  
Genetic Risk ◽  
Prediction Accuracy ◽  
Genetic Risk Score ◽  
Low Risk ◽  
Risk Scores ◽  
Sample Mean ◽  
Polygenic Risk ◽  
Reliable Identification

AbstractPolygenic Risk Scores (PRS) for AD offer unique possibilities for reliable identification of individuals at high and low risk of AD. However, there is little agreement in the field as to what approach should be used for genetic risk score calculations, how to model the effect of APOE, what the optimal p-value threshold (pT) for SNP selection is and how to compare scores between studies and methods. We show that the best prediction accuracy is achieved with a model with two predictors (APOE and PRS excluding APOE region) with pT<0.1 for SNP selection. Prediction accuracy in a sample across different PRS approaches is similar, but individuals’ scores and their associated ranking differ. We show that standardising PRS against the population mean, as opposed to the sample mean, makes the individuals’ scores comparable between studies. Our work highlights the best strategies for polygenic profiling when assessing individuals for AD risk.

A practical composite risk score for the development of Haemolytic Uraemic Syndrome from Shiga toxin-producing Escherichia coli

2019 ◽  
Vol 29 (5) ◽  
pp. 861-868 ◽  
Author(s):  
Douglas Hamilton ◽  
John Cullinan
Keyword(s):  
Escherichia Coli ◽  
High Risk ◽  
Risk Score ◽  
Shiga Toxin ◽  
Haemolytic Uraemic Syndrome ◽  
Low Risk ◽  
Medium Risk ◽  
Composite Risk ◽  
Very High ◽  
Estimation Of Risk

Abstract Background Haemolytic Uraemic Syndrome (HUS) is a serious complication of Shiga toxin-producing Escherichia coli (STEC) infection and the key reason why intensive health protection against STEC is required. However, although many potential risk factors have been identified, accurate estimation of risk of HUS from STEC remains challenging. Therefore, we aimed to develop a practical composite score to promptly estimate the risk of developing HUS from STEC. Methods This was a retrospective cohort study where data for all confirmed STEC infections in Ireland during 2013–15 were subjected to statistical analysis with respect to predicting HUS. Multivariable logistic regression was used to develop a composite risk score, segregating risk of HUS into ‘very low risk’ (0–0.4%), ‘low risk’ (0.5–0.9%), ‘medium risk’ (1.0–4.4%), ‘high risk’ (4.5–9.9%) and ‘very high risk’ (10.0% and over). Results There were 1397 STEC notifications with complete information regarding HUS, of whom 5.1% developed HUS. Young age, vomiting, bloody diarrhoea, Shiga toxin 2, infection during April to November, and infection in Eastern and North-Eastern regions of Ireland, were all statistically significant independent predictors of HUS. Demonstration of a risk gradient provided internal validity to the risk score: 0.2% in the cohort with ‘very low risk’ (1/430), 1.1% with ‘low risk’ (2/182), 2.3% with ‘medium risk’ (8/345), 3.1% with ‘high risk’ (3/98) and 22.2% with ‘very high risk’ (43/194) scores, respectively, developed HUS. Conclusion We have developed a composite risk score which may be of practical value, once externally validated, in prompt estimation of risk of HUS from STEC infection.

scholarly journals A genetic risk score predicts recurrent events after myocardial infarction in young patients with a low level of traditional risk factors

2021 ◽  
Vol 42 (Supplement_1) ◽  
Author(s):  
F Mendonca ◽  
M I Mendonca ◽  
M Temtem ◽  
M Santos ◽  
J A Sousa ◽  
...  
Keyword(s):  
Risk Factors ◽  
High Risk ◽  
Genetic Risk ◽  
Risk Group ◽  
Low Risk ◽  
Low Level ◽  
Coronary Syndrome ◽  
C Statistic ◽  
Traditional Risk Factors

Abstract Introduction Coronary Heart Disease (CAD) is a multifactorial disease, including environmental and genetic risk factors. Current smoking, dyslipidemia and diabetes have a significant impact in long- term mortality and morbidity. However, several genetic variants associated with CAD but not with traditional risk factors (TRFs) has been reported to improve prediction of events and extended mortality, in younger CAD people. Aim To evaluate the clinical utility of a GRS composed by variants from GWAS associated to CAD but not with TRF to predict life-long residual risk in patients under 55 years old and a low level of TRFs. Methods We conducted a prospective study with 573 consecutive patients aged &lt;55 years presenting with AMI and a low level of TRFs (without diabetes and with LDL cholesterol &gt;150 mg/ml). We analysed several biochemical markers and performed a GRS with variants not associated with TRFs (TCF21 rs12190287, CDKN2B-AS1 rs1333049, CDKN2B rs4977574, PHACTR1 rs1332844, MIA3 rs17465637, ADAMTS7 rs3825807, ZC3HC1 rs11556924, SMAD3 rs17228212 and GJA4 rs618675). We studied the GRS association with a primary composite endpoint of all-cause vascular morbidity and mortality including recurrent acute coronary syndrome (myocardial infarct and unstable angina), coronary revascularization (coronary artery bypass grafting (CABG) and percutaneous coronary intervention (PCI), re-hospitalization for heart failure, ischemic stroke and cardiovascular dead. Results A total of 573 patients were studied and followed up for a mean of 4.7±4.0 years. There were 169 recurrent cardiovascular events. The GRS was sub-divided into terciles, verifying that patients in the third tercile (high risk) had a higher number of risk alleles. Compared with the low-risk GRS tercile, the multivariate-adjusted HR for recurrences was 1.520 (95% CI 1.011–2.286); p=0.044 for the intermediate-risk group and was 2.051 (95% CI 1.382–3.044); p&lt;0.0001 for the high-risk group. Inclusion of the GRS in the model with TRFs alone (low risk) improved the C-statistic analysis (C-statistic = 0.030; p=0.004), cNRI (continuous net reclassification improvement) (30.8%), and the IDI (integrated discrimination improvement index) (0.022). Conclusions A multilocus GRS may identify young coronary disease patients with a low level of TRFs but at significant risk of long-term events recurrence. The genetic information may improve prediction discrimination, and reclassification over the conventional risk factors alone, providing better cost-effective therapeutic strategies. FUNDunding Acknowledgement Type of funding sources: None. Figure 1

P6188Predicting type 2 diabetes mellitus: combining a genetic risk score with traditional risk factors

2018 ◽  
Vol 39 (suppl_1) ◽  
Author(s):  
J Ponte Monteiro ◽  
M I Mendonca ◽  
A Pereira ◽  
A C Sousa ◽  
R Rodrigues ◽  
...  
Keyword(s):  
Diabetes Mellitus ◽  
Risk Factors ◽  
Type 2 Diabetes ◽  
Type 2 Diabetes Mellitus ◽  
Risk Score ◽  
Genetic Risk ◽  
Genetic Risk Score ◽  
Traditional Risk Factors

scholarly journals Predicting two-year mortality from discharge after acute coronary syndrome: An internationally-based risk score

2017 ◽  
Vol 8 (8) ◽  
pp. 727-737 ◽  
Author(s):  
Stuart J Pocock ◽  
Yong Huo ◽  
Frans Van de Werf ◽  
Simon Newsome ◽  
Chee Tang Chin ◽  
...  
Keyword(s):  
Acute Coronary Syndrome ◽  
Confidence Interval ◽  
Risk Score ◽  
Mortality Risk ◽  
Post Discharge ◽  
Coronary Syndrome ◽  
Predictive Variables ◽  
C Statistic ◽  
Risk Scoring

Background: Long-term risk of post-discharge mortality associated with acute coronary syndrome remains a concern. The development of a model to reliably estimate two-year mortality risk from hospital discharge post-acute coronary syndrome will help guide treatment strategies. Methods: EPICOR (long-tErm follow uP of antithrombotic management patterns In acute CORonary syndrome patients, NCT01171404) and EPICOR Asia (EPICOR Asia, NCT01361386) are prospective observational studies of 23,489 patients hospitalized for an acute coronary syndrome event, who survived to discharge and were then followed up for two years. Patients were enrolled from 28 countries across Europe, Latin America and Asia. Risk scoring for two-year all-cause mortality risk was developed using identified predictive variables and forward stepwise Cox regression. Goodness-of-fit and discriminatory power was estimated. Results: Within two years of discharge 5.5% of patients died. We identified 17 independent mortality predictors: age, low ejection fraction, no coronary revascularization/thrombolysis, elevated serum creatinine, poor EQ-5D score, low haemoglobin, previous cardiac or chronic obstructive pulmonary disease, elevated blood glucose, on diuretics or an aldosterone inhibitor at discharge, male sex, low educational level, in-hospital cardiac complications, low body mass index, ST-segment elevation myocardial infarction diagnosis, and Killip class. Geographic variation in mortality risk was seen following adjustment for other predictive variables. The developed risk-scoring system provided excellent discrimination ( c-statistic=0.80, 95% confidence interval=0.79–0.82) with a steep gradient in two-year mortality risk: >25% (top decile) vs. ~1% (bottom quintile). A simplified risk model with 11 predictors gave only slightly weaker discrimination ( c-statistic=0.79, 95% confidence interval =0.78–0.81). Conclusions: This risk score for two-year post-discharge mortality in acute coronary syndrome patients ( www.acsrisk.org ) can facilitate identification of high-risk patients and help guide tailored secondary prevention measures.

scholarly journals Predicting Risk of Venous Thromboembolism in Multiple Myeloma: The Impede VTE Score

Blood ◽  
2018 ◽  
Vol 132 (Supplement 1) ◽  
pp. 141-141
Author(s):  
Kristen M. Sanfilippo ◽  
Suhong Luo ◽  
Tzu-Fei Wang ◽  
Tanya Wildes ◽  
Joseph Mikhael ◽  
...  
Keyword(s):  
Multiple Myeloma ◽  
Venous Thromboembolism ◽  
High Risk ◽  
Prediction Model ◽  
Risk Prediction ◽  
Risk Score ◽  
Research Funding ◽  
Risk Prediction Model ◽  
Low Risk ◽  
C Statistic

Abstract Introduction: Venous thromboembolism (VTE) is a common cause of morbidity and mortality among patients with multiple myeloma (MM). Thromboprophylaxis is a safe and effective way to decrease VTE in other high-risk populations. Current guidelines recommend use of thromboprophylaxis in MM patients at high-risk of VTE, but no validated model predicts VTE in MM. A risk prediction model for VTE in MM would allow for use of thromboprophylaxis in MM patients at high-risk of VTE while sparing those at low risk. Therefore, we sought to develop a risk prediction model for VTE in MM. Patients and Methods: Using a nationwide cohort of Veterans, we identified 4,448 patients diagnosed with MM between 1999 and 2014. We retrospectively followed patients for 180 days after start of MM chemotherapy. We identified candidate risk factors through literature review for inclusion into the time-to-event models. We used the methods of Fine and Gray to model time to VTE while accounting for the competing risk of (non-VTE) death. To minimize immortal time bias, all treatment variables were entered as time-varying variables. Using a backward, step-wise approach, we retained variables in the model with a p ≤ 0.05, or with a p < 0.50 with findings consistent with prior literature. Using beta coefficients, we developed a risk score by multiplying by a common factor and rounding to the nearest integer. The risk score for each patient was the sum of all scores for each predictor variable. We assessed model performance with Harrell's c-statistic and with the inverse probability of censoring weighting approach. Through bootstrap analysis, we validated the model internally. We carried out all statistical analyses using SAS version 9.4 (SAS Institute, Cary, NC). Results: The median time from MM diagnosis to start of treatment was 37 days. A total of 53 patients (5.7%) developed VTE within 6 months after start of MM-specific therapy. The mean time from chemotherapy start to VTE was 69 days, with 69% of VTE events occurring in the first 3 months of chemotherapy. The factors associated with VTE were combined to develop the IMPEDE VTE score (IMID 3 points, BMI 1 point, Pathologic fracture pelvis/femur 2 points, ESA 1 point, Dexamethasone (High-dose 4 points, Low-Dose 2 points)/Doxorubicin 2 points, Ethnicity/Race= Asian -3 points, history of VTE 3 points, Tunneled line/CVC 2 points) (Table 1). In addition, use of therapeutic anticoagulation (-5 points) with warfarin or low molecular weight heparin (LWMH) and use of prophylactic LMWH or aspirin (-2 points) were associated with a decreased risk of VTE. The model showed satisfactory discrimination in both the derivation cohort (Harrell's c-statistic = 0.66) and in the bootstrap validation, c-statistic = 0.65 (95% CI: 0.62 - 0.69). Using three risk groups, the incident rate of VTE with the first 6-months of starting chemotherapy was 3.1% for scores ≤ 3 (low-risk), 7.5% for a score of 4-6 (intermediate-risk), and 13.3% for patients with a score of ≥ 7 (high-risk). The risk of developing VTE within 6 months after starting chemotherapy was significantly higher for patients with intermediate- and high-risk scores compared to low-risk (Table 2). Conclusions and Relevance: We developed a risk prediction rule, IMPEDE VTE, which can identify patients with MM at high-risk of developing VTE after starting chemotherapy. IMPEDE VTE could guide clinicians in selecting patients for thromboprophylaxis in MM. Disclosures Sanfilippo: BMS/Pfizer: Speakers Bureau. Wang:Daiichi Sankyo: Consultancy, Other: Travel. Wildes:Janssen: Research Funding. Mikhael:Onyx, Celgene, Sanofi, AbbVie: Research Funding. Carson:Flatiron Health: Employment; Washington University in St. Louis: Employment; Roche: Consultancy.

scholarly journals IgA Nephropathy Susceptibility Loci and Disease Progression

2018 ◽  
Vol 13 (9) ◽  
pp. 1330-1338 ◽  
Author(s):  
Manman Shi ◽  
Yan Ouyang ◽  
Mingxin Yang ◽  
Meng Yang ◽  
Xiaoyan Zhang ◽  
...  
Keyword(s):  
Disease Progression ◽  
Iga Nephropathy ◽  
Risk Score ◽  
Genetic Risk ◽  
Genetic Risk Score ◽  
Susceptibility Loci ◽  
Clinical Model ◽  
C Statistic ◽  
Progression Risk ◽  
Model C

Background and objectivesAt least 20 susceptibility loci of IgA nephropathy have been identified by genome-wide association studies to date. Whether these loci were associated with disease progression is unclear.Design, setting, participants, & measurementsWe enrolled 613 adult patients with IgA nephropathy for a follow-up of ≥12 months. All 20 IgA nephropathy susceptibility loci were selected and their tag single nucleotide polymorphisms (SNPs) were genotyped. After strict quality control, 16 SNPs and 517 patients with IgA nephropathy were eligible for subsequent analysis. Progression was defined as ESKD or 50% decrease in eGFR. A stepwise Cox regression analysis of all SNPs on Akaike information criterion was performed to select the best model.ResultsA four-SNP model, rs11150612 (ITGAM-ITGAX), rs7634389 (ST6GAL1), rs2412971 (HORMAD2), and rs2856717 (HLA-DQ/DR), was selected as the best predictive model. The genetic risk score calculated on the basis of the four SNPs was independently associated with disease progression before (hazard ratio [HR], 1.65; 95% confidence interval [95% CI], 1.29 to 2.12) and after adjustment by a recently reported clinical model (HR, 1.29; 95% CI, 1.03 to 1.62) or clinical–pathologic model (HR, 1.35; 95% CI, 1.03 to 1.77). Compared with low genetic risk, patients with middle genetic risk had a 2.12-fold (95% CI, 1.33 to 3.40) increase of progression risk, whereas patients with high genetic risk had 3.61-fold (95% CI, 2.00 to 6.52) progression risk increase. In addition, incorporation of genetic risk score could potentially increase discrimination of the clinical model (c-statistic increase from 0.83 to 0.86) or the clinical–pathologic model (c-statistic increase from 0.82 to 0.85) in predicting 5-year progression risk.ConclusionsThe four-SNP genetic risk score was independently associated with IgA nephropathy progression and could enhance the performance of clinical and clinical–pathologic risk models.

scholarly journals Association between a Genetic Risk Score Based on Single Nucleotide Polymorphisms of Coronary Artery Disease-Related Genes and Left Main Coronary Artery Disease

2018 ◽  
Vol 2018 ◽  
pp. 1-7 ◽  
Author(s):  
Zhengxi Xu ◽  
Hanning Liu ◽  
Cheng Sun ◽  
Ke Si ◽  
Yan Zhao ◽  
...  
Keyword(s):  
Coronary Artery Disease ◽  
Coronary Artery ◽  
High Risk ◽  
Risk Score ◽  
Genetic Risk ◽  
Risk Group ◽  
Genetic Risk Score ◽  
High Risk Group ◽  
Low Risk ◽  
Artery Disease

Coronary artery disease (CAD) is the leading cause of mortality and morbidity worldwide. Left main coronary artery disease (LMCAD) is a severe phenotype of CAD and has a genetic component. Previous studies identified 3 inflammation-related single nucleotide polymorphisms (SNPs) contributing to the development of LMCAD. We integrated these SNPs into a genetic risk score for the prediction of LMCAD. We enrolled 1544 patients with CAD between 2007 and 2011. The individual associations of the 3 SNPs with LMCAD were assessed. We then calculated the genetic risk score for each patient and stratified patients into low-risk, intermediate-risk, and high-risk categories of genetic risk. In univariable logistic regression analysis, the odds of LMCAD for the high-risk group were 2.81 (95% confidence interval [CI]: 1.72-4.60; P = 0.02) times those of the low-risk group. After adjustment for CAD-related clinical variables, the high-risk group (adjusted OR: 2.78; 95% CI: 1.69-4.58; P = 0.02) had increased odds of LMCAD when compared with the low-risk group. Comparison of model c-statistics showed greater predictive value with regard to LMCAD for the genetic risk score model than the models including single SNPs.

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