Development of heart failure risk prediction models based on a multi-marker approach using random forest algorithms

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.

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Evaluating risk prediction models for adults with heart failure: A systematic literature review

PLoS ONE ◽

10.1371/journal.pone.0224135 ◽

2020 ◽

Vol 15 (1) ◽

pp. e0224135 ◽

Cited By ~ 8

Author(s):

Gian Luca Di Tanna ◽

Heidi Wirtz ◽

Karen L. Burrows ◽

Gary Globe

Keyword(s):

Heart Failure ◽

Literature Review ◽

Risk Prediction ◽

Systematic Literature Review ◽

Prediction Models ◽

Risk Prediction Models

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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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