Risk Prediction Models and Risk Factors for Early Heart Failure after Acute Myocardial Infarction

Abstract Background Risk prediction models incorporating single nucleotide polymorphisms (SNPs) could lead to individualized prevention of colorectal cancer (CRC). However, the added value of incorporating SNPs into models with only traditional risk factors is still not clear. Hence, our primary aim was to summarize literature on risk prediction models including genetic variants for CRC, while our secondary aim was to evaluate the improvement of discriminatory accuracy when adding SNPs to a prediction model with only traditional risk factors. Methods We conducted a systematic review on prediction models incorporating multiple SNPs for CRC risk prediction. We tested whether a significant trend in the increase of Area Under Curve (AUC) according to the number of SNPs could be observed, and estimated the correlation between AUC improvement and number of SNPs. We estimated pooled AUC improvement for SNP-enhanced models compared with non-SNP-enhanced models using random effects meta-analysis, and conducted meta-regression to investigate the association of specific factors with AUC improvement. Results We included 33 studies, 78.79% using genetic risk scores to combine genetic data. We found no significant trend in AUC improvement according to the number of SNPs (p for trend = 0.774), and no correlation between the number of SNPs and AUC improvement (p = 0.695). Pooled AUC improvement was 0.040 (95% CI: 0.035, 0.045), and the number of cases in the study and the AUC of the starting model were inversely associated with AUC improvement obtained when adding SNPs to a prediction model. In addition, models constructed in Asian individuals achieved better AUC improvement with the incorporation of SNPs compared with those developed among individuals of European ancestry. Conclusions Though not conclusive, our results provide insights on factors influencing discriminatory accuracy of SNP-enhanced models. Genetic variants might be useful to inform stratified CRC screening in the future, but further research is needed.

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Risk Prediction Models for Patients With Acute Heart Failure: A Protocol for Systematic Review, Critical Appraisal, and Meta-Analysis

10.21203/rs.3.rs-948336/v1 ◽

2021 ◽

Author(s):

Xuecheng Zhang ◽

Kehua Zhou ◽

Jingjing Zhang ◽

Ying Chen ◽

Hengheng Dai ◽

...

Keyword(s):

Heart Failure ◽

Systematic Review ◽

Prediction Model ◽

Acute Heart Failure ◽

Risk Prediction ◽

Methodological Quality ◽

Critical Appraisal ◽

Prediction Models ◽

Cochrane Library ◽

Risk Prediction Models

Abstract Background Nearly a third of patients with acute heart failure (AHF) die or are readmitted within three months after discharge, accounting for the majority of costs associated with heart failure-related care. A considerable number of risk prediction models, which predict outcomes for mortality and readmission rates, have been developed and validated for patients with AHF. These models could help clinicians stratify patients by risk level and improve decision making, and provide specialist care and resources directed to high-risk patients. However, clinicians sometimes reluctant to utilize these models, possibly due to their poor reliability, the variety of models, and/or the complexity of statistical methodologies. Here, we describe a protocol to systematically review extant risk prediction models. We will describe characteristics, compare performance, and critically appraise the reporting transparency and methodological quality of risk prediction models for AHF patients. Method Embase, Pubmed, Web of Science, and the Cochrane Library will be searched from their inception onwards. A back word will be searched on derivation studies to find relevant external validation studies. Multivariable prognostic models used for AHF and mortality and/or readmission rate will be eligible for review. Two reviewers will conduct title and abstract screening, full-text review, and data extraction independently. Included models will be summarized qualitatively and quantitatively. We will also provide an overview of critical appraisal of the methodological quality and reporting transparency of included studies using the Prediction model Risk of Bias Assessment Tool(PROBAST tool) and the Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis(TRIPOD statement). Discussion The result of the systematic review could help clinicians better understand and use the prediction models for AHF patients, as well as make standardized decisions about more precise, risk-adjusted management. Systematic review registration : PROSPERO registration number CRD42021256416.

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Abstract 196: Development and Validation of Machine Learning-based Race-specific Models to Predict 10-year Risk of Heart Failure: A Multi-cohort Analysis

Circulation ◽

10.1161/circ.142.suppl_3.196 ◽

2020 ◽

Vol 142 (Suppl_3) ◽

Author(s):

Matthew W Segar ◽

Byron Jaeger ◽

Kershaw V Patel ◽

Vijay Nambi ◽

Chiadi E Ndumele ◽

...

Keyword(s):

Machine Learning ◽

Risk Factors ◽

Heart Failure ◽

Risk Prediction ◽

Prediction Models ◽

External Validation ◽

Superior Performance ◽

Risk Models ◽

Black And White ◽

White Adults

Introduction: Heart failure (HF) risk and the underlying biological risk factors vary by race. Machine learning (ML) may improve race-specific HF risk prediction but this has not been fully evaluated. Methods: The study included participants from 4 cohorts (ARIC, DHS, JHS, and MESA) aged > 40 years, free of baseline HF, and with adjudicated HF event follow-up. Black adults from JHS and white adults from ARIC were used to derive race-specific ML models to predict 10-year HF risk. The ML models were externally validated in subgroups of black and white adults from ARIC (excluding JHS participants) and pooled MESA/DHS cohorts and compared to prior established HF risk scores developed in ARIC and MESA. Harrell’s C-index and Greenwood-Nam-D’Agostino chi-square were used to assess discrimination and calibration, respectively. Results: In the derivation cohorts, 288 of 4141 (7.0%) black and 391 of 8242 (4.7%) white adults developed HF over 10 years. The ML models had excellent discrimination in both black and white participants (C-indices = 0.88 and 0.89). In the external validation cohorts for black participants from ARIC (excluding JHS, N = 1072) and MESA/DHS pooled cohorts (N = 2821), 131 (12.2%) and 115 (4.1%) developed HF. The ML model had adequate calibration and demonstrated superior discrimination compared to established HF risk models (Fig A). A consistent pattern was also observed in the external validation cohorts of white participants from the MESA/DHS pooled cohorts (N=3236; 100 [3.1%] HF events) (Fig A). The most important predictors of HF in both races were NP levels. Cardiac biomarkers and glycemic parameters were most important among blacks while LV hypertrophy and prevalent CVD and traditional CV risk factors were the strongest predictors among whites (Fig B). Conclusions: Race-specific and ML-based HF risk models that integrate clinical, laboratory, and biomarker data demonstrated superior performance when compared to traditional risk prediction models.

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Risk Factors and Trends in Incidence of Heart Failure Following Acute Myocardial Infarction

The American Journal of Cardiology ◽

10.1016/j.amjcard.2018.03.005 ◽

2018 ◽

Vol 122 (1) ◽

pp. 1-5 ◽

Cited By ~ 9

Author(s):

Jennifer Wellings ◽

John B. Kostis ◽

Davit Sargsyan ◽

Javier Cabrera ◽

William J. Kostis

Keyword(s):

Risk Factors ◽

Myocardial Infarction ◽

Heart Failure ◽

Acute Myocardial Infarction

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Risk prediction models for heart failure admissions in adults with congenital heart disease

International Journal of Cardiology ◽

10.1016/j.ijcard.2020.08.039 ◽

2021 ◽

Vol 322 ◽

pp. 149-157 ◽

Cited By ~ 3

Author(s):

Sarah Cohen ◽

Aihua Liu ◽

Fei Wang ◽

Liming Guo ◽

James M. Brophy ◽

...

Keyword(s):

Heart Failure ◽

Congenital Heart Disease ◽

Heart Disease ◽

Risk Prediction ◽

Congenital Heart ◽

Prediction Models ◽

Risk Prediction Models

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Assessing the Predictive Validity of Simple Dementia Risk Models in Harmonized Stroke Cohorts

Stroke ◽

10.1161/strokeaha.120.027473 ◽

2020 ◽

Vol 51 (7) ◽

pp. 2095-2102

Author(s):

Eugene Y.H. Tang ◽

Christopher I. Price ◽

Louise Robinson ◽

Catherine Exley ◽

David W. Desmond ◽

...

Keyword(s):

Risk Factors ◽

General Population ◽

Risk Prediction ◽

Prediction Models ◽

Disease Risk ◽

Risk Index ◽

Area Under The Curve ◽

Risk Prediction Models ◽

C Statistic ◽

Dementia Risk

Background and Purpose: Stroke is associated with an increased risk of dementia. To assist in the early identification of individuals at high risk of future dementia, numerous prediction models have been developed for use in the general population. However, it is not known whether such models also provide accurate predictions among stroke patients. Therefore, the aim of this study was to determine whether existing dementia risk prediction models that were developed for use in the general population can also be applied to individuals with a history of stroke to predict poststroke dementia with equivalent predictive validity. Methods: Data were harmonized from 4 stroke studies (follow-up range, ≈12–18 months poststroke) from Hong Kong, the United States, the Netherlands, and France. Regression analysis was used to test 3 risk prediction models: the Cardiovascular Risk Factors, Aging and Dementia score, the Australian National University Alzheimer Disease Risk Index, and the Brief Dementia Screening Indicator. Model performance or discrimination accuracy was assessed using the C statistic or area under the curve. Calibration was tested using the Grønnesby and Borgan and the goodness-of-fit tests. Results: The predictive accuracy of the models varied but was generally low compared with the original development cohorts, with the Australian National University Alzheimer Disease Risk Index (C-statistic, 0.66) and the Brief Dementia Screening Indicator (C-statistic, 0.61) both performing better than the Cardiovascular Risk Factors, Aging and Dementia score (area under the curve, 0.53). Conclusions: Dementia risk prediction models developed for the general population do not perform well in individuals with stroke. Their poor performance could have been due to the need for additional or different predictors related to stroke and vascular risk factors or methodological differences across studies (eg, length of follow-up, age distribution). Future work is needed to develop simple and cost-effective risk prediction models specific to poststroke dementia.

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