Algorithmic fairness in cardiovascular disease risk prediction: overcoming inequalities

Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › fagfællebedømt

Standard

Algorithmic fairness in cardiovascular disease risk prediction : overcoming inequalities. / Varga, Tibor V.

I: Open Heart, Bind 10, Nr. 2, 2023.

Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › fagfællebedømt

Harvard

Varga, TV 2023, 'Algorithmic fairness in cardiovascular disease risk prediction: overcoming inequalities', Open Heart, bind 10, nr. 2. https://doi.org/10.1136/openhrt-2023-002395

APA

Varga, T. V. (2023). Algorithmic fairness in cardiovascular disease risk prediction: overcoming inequalities. Open Heart, 10(2). https://doi.org/10.1136/openhrt-2023-002395

Vancouver

Varga TV. Algorithmic fairness in cardiovascular disease risk prediction: overcoming inequalities. Open Heart. 2023;10(2). https://doi.org/10.1136/openhrt-2023-002395

Author

Varga, Tibor V. / Algorithmic fairness in cardiovascular disease risk prediction : overcoming inequalities. I: Open Heart. 2023 ; Bind 10, Nr. 2.

Bibtex

@article{ba3130e64512433395f02a12e90d1a6e,
title = "Algorithmic fairness in cardiovascular disease risk prediction: overcoming inequalities",
abstract = "The main purpose of prognostic risk prediction models is to identify individuals who are at risk of disease, to enable early intervention. Current prognostic cardiovascular risk prediction models, such as the Systematic COronary Risk Evaluation (SCORE2) and the SCORE2-Older Persons (SCORE2-OP) models, which represent the clinically used gold standard in assessing patient risk for major cardiovascular events in the European Union (EU), generally overlook socioeconomic determinants, leading to disparities in risk prediction and resource allocation. A central recommendation of this article is the explicit inclusion of individual-level socioeconomic determinants of cardiovascular disease in risk prediction models. The question of whether prognostic risk prediction models can promote health equity remains to be answered through experimental research, potential clinical implementation and public health analysis. This paper introduces four distinct fairness concepts in cardiovascular disease prediction and their potential to narrow existing disparities in cardiometabolic health.",
keywords = "Humans, Aged, Aged, 80 and over, Health Promotion, Cardiovascular Diseases/diagnosis, Socioeconomic Factors, Prognosis",
author = "Varga, {Tibor V}",
note = "{\textcopyright} Author(s) (or their employer(s)) 2023. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.",
year = "2023",
doi = "10.1136/openhrt-2023-002395",
language = "English",
volume = "10",
journal = "Open Heart",
issn = "2398-595X",
publisher = "BMJ",
number = "2",

}

RIS

TY - JOUR

T1 - Algorithmic fairness in cardiovascular disease risk prediction

T2 - overcoming inequalities

AU - Varga, Tibor V

N1 - © Author(s) (or their employer(s)) 2023. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.

PY - 2023

Y1 - 2023

N2 - The main purpose of prognostic risk prediction models is to identify individuals who are at risk of disease, to enable early intervention. Current prognostic cardiovascular risk prediction models, such as the Systematic COronary Risk Evaluation (SCORE2) and the SCORE2-Older Persons (SCORE2-OP) models, which represent the clinically used gold standard in assessing patient risk for major cardiovascular events in the European Union (EU), generally overlook socioeconomic determinants, leading to disparities in risk prediction and resource allocation. A central recommendation of this article is the explicit inclusion of individual-level socioeconomic determinants of cardiovascular disease in risk prediction models. The question of whether prognostic risk prediction models can promote health equity remains to be answered through experimental research, potential clinical implementation and public health analysis. This paper introduces four distinct fairness concepts in cardiovascular disease prediction and their potential to narrow existing disparities in cardiometabolic health.

AB - The main purpose of prognostic risk prediction models is to identify individuals who are at risk of disease, to enable early intervention. Current prognostic cardiovascular risk prediction models, such as the Systematic COronary Risk Evaluation (SCORE2) and the SCORE2-Older Persons (SCORE2-OP) models, which represent the clinically used gold standard in assessing patient risk for major cardiovascular events in the European Union (EU), generally overlook socioeconomic determinants, leading to disparities in risk prediction and resource allocation. A central recommendation of this article is the explicit inclusion of individual-level socioeconomic determinants of cardiovascular disease in risk prediction models. The question of whether prognostic risk prediction models can promote health equity remains to be answered through experimental research, potential clinical implementation and public health analysis. This paper introduces four distinct fairness concepts in cardiovascular disease prediction and their potential to narrow existing disparities in cardiometabolic health.

KW - Humans

KW - Aged

KW - Aged, 80 and over

KW - Health Promotion

KW - Cardiovascular Diseases/diagnosis

KW - Socioeconomic Factors

KW - Prognosis

U2 - 10.1136/openhrt-2023-002395

DO - 10.1136/openhrt-2023-002395

M3 - Journal article

C2 - 37963683

VL - 10

JO - Open Heart

JF - Open Heart

SN - 2398-595X

IS - 2

ER -

ID: 374829877