Increasing Participation of Women in Cardiovascular Trials

Cho, Leslie; Vest, Amanda R.; O’Donoghue, Michelle L.; Ogunniyi, Modele O.; Sarma, Amy; Denby, Kara; Lau, Emily S.; Poole, Jeanne E.; Lindley, Kathryn J.; Mehran, Roxana

doi:10.1016/j.jacc.2021.06.022

Cited by 68 publications

(32 citation statements)

References 82 publications

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“…Interventions to improve the participation of females in EP clinical trials should aim for increasing sex, race, and ethnic diversity among trial leadership and investigators and adopting innovative approaches to participant recruitment and retention. 207 This includes intentional and proactive strategies to enroll more females by a priori setting a goal for enrollment and considering adopting stratified enrollment by sex, for example. An initiative that was embedded in a couple of EP clinical trials to augment female participation was the Women Opt-In for Heart Research (WIN-Her) program, but the success of this initiative remains uncertain.…”

Section: Females and Clinical Trialsmentioning

confidence: 99%

Arrhythmias in Female Patients: Incidence, Presentation and Management

Zeitler

Poole

Chen

et al. 2022

Circulation Research

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There is a growing appreciation for differences in epidemiology, treatment, and outcomes of cardiovascular conditions by sex. Historically, cardiovascular clinical trials have under-represented females, but findings have nonetheless been applied to clinical care in a sex-agnostic manner. Thus, much of the collective knowledge about sex-specific cardiovascular outcomes result from post hoc and secondary analyses. In some cases, these investigations have revealed important sex-based differences with implications for optimizing care for female patients with arrhythmias. This review explores the available evidence related to cardiac arrhythmia care among females, with emphasis on areas in which important sex differences are known or suggested. Considerations related to improving female enrollment in clinical trials as a way to establish more robust clinical evidence for the treatment of females are discussed. Areas of remaining evidence gaps are provided, and recommendations for areas of future research and specific action items are suggested. The overarching goal is to improve appreciation for sex-based differences in cardiac arrhythmia care as 1 component of a comprehensive plan to optimize arrhythmia care for all patients.

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Section: Females and Clinical Trialsmentioning

confidence: 99%

Arrhythmias in Female Patients: Incidence, Presentation and Management

Zeitler

Poole

Chen

et al. 2022

Circulation Research

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“…Recently, cardiovascular societies, councils, and leaders within cardiovascular research have published documents detailing recommendations for improving sex, racial, and ethnic diversity in cardiovascular research. 20 , 21 , 22 , 23 , 24 These recommendations detail interventions that can be done not only at the patient or research participant level, but also at research team and institutional levels. Similarly, it will be critical to apply these same multilevel interventions to improve diversity, equity, and inclusion at the highest levels of cardiology leadership because changes cannot occur by intervening at the leadership level alone.…”

Section: A Path Forward: Diversity In Leadership Deepening the Pipeli...mentioning

confidence: 99%

A Look Back, A Path Forward

Han

Belardo²,

Ibrahim

et al. 2022

JACC: Case Reports

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“…Women, racial/ethnic minorities, and other under-served groups have been historically under-represented in CTs [169]. Disparities have been identified in the study of cardiovascular disease [49,110,203], oncology [47,74], and acquired immunodeficiency syndrome (AIDS) [99,226]; these particularly affect women of racial/ethnic minorities [99,160]. While most CTs have been implemented in relatively wealthy regions such as North America and Western Europe [240], the pharmaceutical industry has driven globalization of clinical research, particularly to developing countries [61,224].…”

Section: Subject Participationmentioning

confidence: 99%

“…ML model outputs are dependent on their training datasets, which may reflect historical biases [202], thus perpetuating existent disparities in society. Women and racial/ethnic minorities are systematically underrepresented in clinical research, contributing to disproportionately higher incidence of and mortality from cancer [67] and cardiovascular diseases [49], and public health misconceptions [33]. Healthcare algorithms used in practice as well as those considered state-of-the-art for certain clinical prediction tasks have been discovered to exhibit racial [186], gender, and socioeconomic biases [45].…”

Section: Introductionmentioning

confidence: 99%

Multi-disciplinary fairness considerations in machine learning for clinical trials

Chien¹,

Deliu²,

Turner³

et al. 2022

Preprint

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While interest in the application of machine learning to improve healthcare has grown tremendously in recent years, a number of barriers prevent deployment in medical practice. A notable concern is the potential to exacerbate entrenched biases and existing health disparities in society. The area of fairness in machine learning seeks to address these issues of equity; however, appropriate approaches are context-dependent, necessitating domain-specific consideration. We focus on clinical trials, i.e., research studies conducted on humans to evaluate medical treatments. Clinical trials are a relatively under-explored application in machine learning for healthcare, in part due to complex ethical, legal, and regulatory requirements and high costs. Our aim is to provide a multi-disciplinary assessment of how fairness for machine learning fits into the context of clinical trials research and practice. We start by reviewing the current ethical considerations and guidelines for clinical trials and examine their relationship with common definitions of fairness in machine learning. We examine potential sources of unfairness in clinical trials, providing concrete examples, and discuss the role machine learning might play in either mitigating potential biases or exacerbating them when applied without care. Particular focus is given to adaptive clinical trials, which may employ machine learning. Finally, we highlight concepts that require further investigation and development, and emphasize new approaches to fairness that may be relevant to the design of clinical trials.

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Increasing Participation of Women in Cardiovascular Trials

Cited by 68 publications

References 82 publications

Arrhythmias in Female Patients: Incidence, Presentation and Management

Arrhythmias in Female Patients: Incidence, Presentation and Management

A Look Back, A Path Forward

Multi-disciplinary fairness considerations in machine learning for clinical trials

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