P4152Implications of the ACC/AHA risk score for heart failure risk prediction and its comparison with existing heart failure risk prediction models: A prospective population-based cohort study

2019 ◽  
Vol 40 (Supplement_1) ◽  
Author(s):  
B Arshi ◽  
J C Van Den Berge ◽  
B Van Dijk ◽  
J W Deckers ◽  
M A Ikram ◽  
...  

Abstract Background In 2013, the American College of Cardiology (ACC) and the American Heart Association (AHA) developed a score for assessment of cardiovascular risk. Due to between study variability in ascertainment and adjudication of heart failure (HF), incident HF was not included as an endpoint in the ACC/AHA risk score. Purpose To assess the performance of the ACC/AHA risk score for HF risk prediction in a large population-based cohort and to compare its performance with the existing HF risk prediction models including the Atherosclerosis Risk in Communities (ARIC) model and the Health Aging and Body Composition (Health ABC) model. Methods The study included 2743 men and 3646 women from a prospective population-based cohort study. Cox proportional hazards models were fitted using risk factors applied by the ACC/AHA model for cardiovascular risk, the ARIC model and the Health ABC model. Independent relationship of each predictor with 10-year HF incidence was estimated in men and women. Next, N-terminal pro-b-type natriuretic peptide (NT-pro-BNP) was added to the ACC/AHA model. The performance of all fitted models was evaluated and compared in terms of discrimination, calibration and the Akaike Information Criterion (AIC). In addition, area under the receiver operator characteristic curve (AUC), sensitivity and specificity of each model in predicting 10-year incident of HF was assessed. The incremental value of NT-pro-BNP to the ACC/AHA model, was assessed using the continuous net reclassification improvement index (NRI). Results During a median follow-up of 13 years (63127 person-years), 387 HF events in women and 259 in men were recorded. The Optimism-corrected c-statistic for ACC/AHA model was 0.76 (95% confidence interval (CI): 0.73–0.79) for men and 0.76 (95% CI: 0.74–0.79) for women. The ARIC model provided the largest c-statistic for both men [0.82 (95% CI: 0.80–0.84)] and women [95% CI: 0.81 (0.79–0.83)] among the three models. Calibration of the models was reasonable. Addition of NT-pro-BNP to the ACC/AHA model considerably improved model fitness for men and for women. The AIC improved from 3104.62 to 2976.28 among men and from 5161.63 to 4921.51 among women. The c-statistic also improved to 0.81 (0.78–0.84) in men and 0.79 (0.77–0.81) in women. The continuous NRI for the addition of NT-pro-BNP to the base model was 5.3% (95% CI: −12.3–28.6%) for men and 15.9% (95% CI: 2.7–24.7%) for women. Conclusions Compared to HF-specific models, the ACC/AHA model, containing routine clinically available risk factors, had a reasonable performance in prediction of HF risk. Inclusion of NT-pro-BNP in the ACC/AHA model strongly increased the model performance. To achieve a better model performance for 10-year prediction of incident HF, updating the simple ACC/AHA risk score with the addition of NT-pro-BNP is recommended.

2016 ◽  
Vol 60 ◽  
pp. 260-269 ◽  
Author(s):  
Vahid Taslimitehrani ◽  
Guozhu Dong ◽  
Naveen L. Pereira ◽  
Maryam Panahiazar ◽  
Jyotishman Pathak

2014 ◽  
Vol 2 (5) ◽  
pp. 437-439 ◽  
Author(s):  
Wayne C. Levy ◽  
Inder S. Anand

Circulation ◽  
2014 ◽  
Vol 129 (suppl_1) ◽  
Author(s):  
Mary E Lacy ◽  
Gregory Wellenius ◽  
Charles B Eaton ◽  
Eric B Loucks ◽  
Adolfo Correa ◽  
...  

Background: In 2010, the American Diabetes Association (ADA) updated diagnostic criteria for diabetes to include hemoglobin A1c (A1c). However, the appropriateness of these criteria in African Americans (AAs) is unclear as A1c may not reflect glycemic control as accurately in AAs as in whites. Moreover, existing diabetes risk prediction models have been developed in populations composed primarily of whites. Objectives were to (1) examine the predictive power of existing diabetes risk prediction models in the Jackson Heart Study (JHS), a prospective cohort of 5,301 AA adults and (2) explore the impact of incorporating A1c into these models. Methods: We selected 3 widely-used diabetes risk prediction models and examined their ability to predict 5-year diabetes risk among 3,185 JHS participants free of diabetes at baseline and who returned for the 5 year follow-up visit. Incident diabetes was identified at follow-up based on current antidiabetic medications, fasting glucose ≥126 mg/dl or A1c ≥6.5%. We evaluated model performance using model discrimination (C-statistic) and reclassification (net reclassification index (NRI) and integrated discrimination improvement (IDI)). For each of the 3 models, model performance in JHS was evaluated using (1) covariates identified in the original published model and (2) published covariates plus A1c. Results: Of 3,185 participants (mean age 53.7; 64.0% female), 9.8% (n=311) developed diabetes over 5 years of follow-up. Each diabetes prediction model suffered a drop in predictive power when applied to JHS using ADA 2010 criteria (Table 1). The performance of all 3 models improved significantly with the addition of A1c, as evidenced by the increase in C-statistic and improvement in reclassification. Conclusion: Despite evidence that A1c may not accurately reflect glycemic control in AAs as well as in whites, adding A1c to existing diabetes risk prediction models developed in primarily white populations significantly improved 5-year predictive power of all 3 models among AAs in the JHS.


2019 ◽  
Vol 132 (7) ◽  
pp. 819-826 ◽  
Author(s):  
Hui Yuan ◽  
Xue-Song Fan ◽  
Yang Jin ◽  
Jian-Xun He ◽  
Yuan Gui ◽  
...  

PLoS ONE ◽  
2020 ◽  
Vol 15 (1) ◽  
pp. e0224135 ◽  
Author(s):  
Gian Luca Di Tanna ◽  
Heidi Wirtz ◽  
Karen L. Burrows ◽  
Gary Globe

2021 ◽  
Author(s):  
Xuecheng Zhang ◽  
Kehua Zhou ◽  
Jingjing Zhang ◽  
Ying Chen ◽  
Hengheng Dai ◽  
...  

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.


Author(s):  
Byron C. Jaeger ◽  
Ryan Cantor ◽  
Venkata Sthanam ◽  
Rongbing Xie ◽  
James K. Kirklin ◽  
...  

Background: Risk prediction models play an important role in clinical decision making. When developing risk prediction models, practitioners often impute missing values to the mean. We evaluated the impact of applying other strategies to impute missing values on the prognostic accuracy of downstream risk prediction models, that is, models fitted to the imputed data. A secondary objective was to compare the accuracy of imputation methods based on artificially induced missing values. To complete these objectives, we used data from the Interagency Registry for Mechanically Assisted Circulatory Support. Methods: We applied 12 imputation strategies in combination with 2 different modeling strategies for mortality and transplant risk prediction following surgery to receive mechanical circulatory support. Model performance was evaluated using Monte-Carlo cross-validation and measured based on outcomes 6 months following surgery using the scaled Brier score, concordance index, and calibration error. We used Bayesian hierarchical models to compare model performance. Results: Multiple imputation with random forests emerged as a robust strategy to impute missing values, increasing model concordance by 0.0030 (25th–75th percentile: 0.0008–0.0052) compared with imputation to the mean for mortality risk prediction using a downstream proportional hazards model. The posterior probability that single and multiple imputation using random forests would improve concordance versus mean imputation was 0.464 and >0.999, respectively. Conclusions: Selecting an optimal strategy to impute missing values such as random forests and applying multiple imputation can improve the prognostic accuracy of downstream risk prediction models.


2021 ◽  
Vol 322 ◽  
pp. 149-157 ◽  
Author(s):  
Sarah Cohen ◽  
Aihua Liu ◽  
Fei Wang ◽  
Liming Guo ◽  
James M. Brophy ◽  
...  

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