Machine learning in precision diabetes care and cardiovascular risk prediction

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Abstract

Artificial intelligence and machine learning are driving a paradigm shift in medicine, promising data-driven, personalized solutions for managing diabetes and the excess cardiovascular risk it poses. In this comprehensive review of machine learning applications in the care of patients with diabetes at increased cardiovascular risk, we offer a broad overview of various data-driven methods and how they may be leveraged in developing predictive models for personalized care. We review existing as well as expected artificial intelligence solutions in the context of diagnosis, prognostication, phenotyping, and treatment of diabetes and its cardiovascular complications. In addition to discussing the key properties of such models that enable their successful application in complex risk prediction, we define challenges that arise from their misuse and the role of methodological standards in overcoming these limitations. We also identify key issues in equity and bias mitigation in healthcare and discuss how the current regulatory framework should ensure the efficacy and safety of medical artificial intelligence products in transforming cardiovascular care and outcomes in diabetes.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12933-023-01985-3.

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Most cited references 129

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Canagliflozin and Cardiovascular and Renal Events in Type 2 Diabetes

Bruce Neal, Vlado Perkovic, Kenneth Mahaffey … (2017)

Background Canagliflozin is a sodium-glucose cotransporter 2 inhibitor that reduces glycemia as well as blood pressure, body weight, and albuminuria in people with diabetes. We report the effects of treatment with canagliflozin on cardiovascular, renal, and safety outcomes. Methods The CANVAS Program integrated data from two trials involving a total of 10,142 participants with type 2 diabetes and high cardiovascular risk. Participants in each trial were randomly assigned to receive canagliflozin or placebo and were followed for a mean of 188.2 weeks. The primary outcome was a composite of death from cardiovascular causes, nonfatal myocardial infarction, or nonfatal stroke. Results The mean age of the participants was 63.3 years, 35.8% were women, the mean duration of diabetes was 13.5 years, and 65.6% had a history of cardiovascular disease. The rate of the primary outcome was lower with canagliflozin than with placebo (occurring in 26.9 vs. 31.5 participants per 1000 patient-years; hazard ratio, 0.86; 95% confidence interval [CI], 0.75 to 0.97; P<0.001 for noninferiority; P=0.02 for superiority). Although on the basis of the prespecified hypothesis testing sequence the renal outcomes are not viewed as statistically significant, the results showed a possible benefit of canagliflozin with respect to the progression of albuminuria (hazard ratio, 0.73; 95% CI, 0.67 to 0.79) and the composite outcome of a sustained 40% reduction in the estimated glomerular filtration rate, the need for renal-replacement therapy, or death from renal causes (hazard ratio, 0.60; 95% CI, 0.47 to 0.77). Adverse reactions were consistent with the previously reported risks associated with canagliflozin except for an increased risk of amputation (6.3 vs. 3.4 participants per 1000 patient-years; hazard ratio, 1.97; 95% CI, 1.41 to 2.75); amputations were primarily at the level of the toe or metatarsal. Conclusions In two trials involving patients with type 2 diabetes and an elevated risk of cardiovascular disease, patients treated with canagliflozin had a lower risk of cardiovascular events than those who received placebo but a greater risk of amputation, primarily at the level of the toe or metatarsal. (Funded by Janssen Research and Development; CANVAS and CANVAS-R ClinicalTrials.gov numbers, NCT01032629 and NCT01989754 , respectively.).

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Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs.

Varun Gulshan, Lily Peng, Marc Coram … (2016)

Deep learning is a family of computational methods that allow an algorithm to program itself by learning from a large set of examples that demonstrate the desired behavior, removing the need to specify rules explicitly. Application of these methods to medical imaging requires further assessment and validation.

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High-performance medicine: the convergence of human and artificial intelligence

Eric Topol (2019)

The use of artificial intelligence, and the deep-learning subtype in particular, has been enabled by the use of labeled big data, along with markedly enhanced computing power and cloud storage, across all sectors. In medicine, this is beginning to have an impact at three levels: for clinicians, predominantly via rapid, accurate image interpretation; for health systems, by improving workflow and the potential for reducing medical errors; and for patients, by enabling them to process their own data to promote health. The current limitations, including bias, privacy and security, and lack of transparency, along with the future directions of these applications will be discussed in this article. Over time, marked improvements in accuracy, productivity, and workflow will likely be actualized, but whether that will be used to improve the patient-doctor relationship or facilitate its erosion remains to be seen.

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Author and article information

Contributors

Evangelos K. Oikonomou: evangelos.oikonomou@yale.edu

Rohan Khera: rohan.khera@yale.edu

Journal

Journal ID (nlm-ta): Cardiovasc Diabetol

Journal ID (iso-abbrev): Cardiovasc Diabetol

Title: Cardiovascular Diabetology

Publisher: BioMed Central (London )

ISSN (Electronic): 1475-2840

Publication date (Electronic): 25 September 2023

Publication date PMC-release: 25 September 2023

Publication date Collection: 2023

Volume: 22

Electronic Location Identifier: 259

Affiliations

[1 ]GRID grid.47100.32, ISNI 0000000419368710, Section of Cardiovascular Medicine, Department of Internal Medicine, , Yale School of Medicine, ; New Haven, CT USA

[2 ]GRID grid.47100.32, ISNI 0000000419368710, Section of Health Informatics, Department of Biostatistics, , Yale School of Public Health, ; New Haven, CT USA

[3 ]GRID grid.47100.32, ISNI 0000000419368710, Section of Biomedical Informatics and Data Science, , Yale School of Medicine, ; New Haven, CT USA

[4 ]Center for Outcomes Research and Evaluation, Yale-New Haven Hospital, ( https://ror.org/05tszed37) 195 Church St, 6th floor, New Haven, CT 06510 USA

Article

Publisher ID: 1985

DOI: 10.1186/s12933-023-01985-3

PMC ID: 10521578

PubMed ID: 37749579

SO-VID: d975b4de-52c3-43e2-859d-ea78603ff8fb

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Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

History

Date received : 25 July 2023

Date accepted : 7 September 2023

Funding

Funded by: FundRef http://dx.doi.org/10.13039/100000050, National Heart, Lung, and Blood Institute, United States;

Award ID: F32HL170592

Award ID: K23HL153775

Award Recipient : Evangelos K. Oikonomou Rohan Khera

Funded by: Doris Duke Charitable Foundation,United States

Award ID: 2022060

Award Recipient : Rohan Khera

Custom metadata

ScienceOpen disciplines: Endocrinology & Diabetes

Keywords: machine learning,artificial intelligence,prediction,personalized medicine,digital health,diabetes,cardiovascular disease

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ScienceOpen disciplines: Endocrinology & Diabetes

Keywords: machine learning, artificial intelligence, prediction, personalized medicine, digital health, diabetes, cardiovascular disease

Machine learning in precision diabetes care and cardiovascular risk prediction

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Karger: Digital Health

Most cited references 129

Canagliflozin and Cardiovascular and Renal Events in Type 2 Diabetes

Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs.

High-performance medicine: the convergence of human and artificial intelligence

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