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Machine learning and statistical methods for predicting mortality in heart failure

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Abstract

Heart failure is a debilitating clinical syndrome associated with increased morbidity, mortality, and frequent hospitalization, leading to increased healthcare budget utilization. Despite the exponential growth in the introduction of pharmacological agents and medical devices that improve survival, many heart failure patients, particularly those with a left ventricular ejection fraction less than 40%, still experience persistent clinical symptoms that lead to an overall decreased quality of life. Clinical risk prediction is one of the strategies that has been implemented for the selection of high-risk patients and for guiding therapy. However, most risk predictive models have not been well-integrated into the clinical setting. This is partly due to inherent limitations, such as creating risk predicting models using static clinical data that does not consider the dynamic nature of heart failure. Another limiting factor preventing clinicians from utilizing risk prediction models is the lack of insight into how predictive models are built. This review article focuses on describing how predictive models for risk-stratification of patients with heart failure are built.

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Abbreviations

ANN:

Artificial neural networks

AUC:

Area under the curve

CI:

Confidence interval

INTER-CHF:

International Congestive Heart Failure

KNN:

K-nearest neighbors

LR:

Linear regression

LVEF:

Left ventricular ejection fraction

NB:

Naïve Bayes

NYHA:

New York Heart Association

PRAISE:

Prospective Randomized Amlodipine Survival Evaluation

SHF:

Seattle Heart Failure

SVM:

Support vector machine

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Funding

Dr. Dineo Mpanya is a full-time PhD Clinical Research fellow in the Division of Cardiology at the University of the Witwatersrand. Her PhD is funded by the Professor Bongani Mayosi Netcare Clinical Scholarship, the Discovery Academic Fellowship [Grant No. 03902], the Carnegie Corporation of New York [Grant No. b8749], and the South African Heart Association.

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Authors and Affiliations

  1. Department of Internal Medicine, Division of Cardiology, School of Clinical Medicine, Faculty of Health Sciences, University of the Witwatersrand and the Charlotte Maxeke Johannesburg Academic Hospital, 17 Jubilee Road, Parktown, Johannesburg, Gauteng, 2193, South Africa

    Dineo Mpanya

  2. Faculty of Engineering and Built Environment, School of Electrical and Information Engineering, University of the Witwatersrand, Johannesburg, South Africa

    Turgay Celik

  3. Netcare Sunninghill, Sunward Park Hospitals and Division of Cardiology, Department of Internal Medicine, School of Clinical Medicine, Faculty of Health Sciences, University of the Witwatersrand and the Charlotte Maxeke Johannesburg Academic Hospital, Johannesburg, South Africa

    Eric Klug

  4. Department of Paediatrics and Child Health, School of Clinical Medicine, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa

    Hopewell Ntsinjana

  5. Institute of Data Science , University of the Witwatersrand , Johannesburg, South Africa

    Dineo Mpanya & Turgay Celik

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  1. Dineo Mpanya
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  2. Turgay Celik
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Contributions

HN, TC, and DM contributed to the study conception and design. DM conducted the literature search and wrote the first draft of the manuscript. All authors (HN, TC, EK and DM) commented on previous versions of the manuscript. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Dineo Mpanya.

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Conflict of interest

EK has received consulting fees from Novartis Pharmaceuticals, Pfizer, Servier, Takeda, and AstraZeneca. All other authors declare that they have no conflict of interest.

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Not requested as the manuscript is a narrative literature review.

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Mpanya, D., Celik, T., Klug, E. et al. Machine learning and statistical methods for predicting mortality in heart failure. Heart Fail Rev 26, 545–552 (2021). https://doi.org/10.1007/s10741-020-10052-y

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