Artificial intelligence systems for predicting chronic ischemic heart disease outcomes in cardiac surgery patients based on presence of anemia: a literature review

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Abstract

BACKGROUND: In Russia, the number of people undergoing heart surgery exceeds 600 thousand annually. These include anemia in 30–70% of patients with a 4-fold increased risk of one-year death, a 5-fold increased risk of stent thrombosis, a 1.3-fold increased risk of recurrent acute coronary events, and a 2-fold increased risk of bleeding. However, among the prognostic systems developed using artificial intelligence (AI) technologies, few take the presence of anemia into account. Existing digital platforms are not designed to support clinical decision making.

AIM: The review aimed to evaluate existing AI platforms for predicting the course of ischemic heart disease (IHD) and systems that take into account the presence of anemia.

MATERIALS AND METHOD: The PubMed and Russian Science Citation Index databases from 2000 to January 2024 were analyzed. Using Keywords of “artificial intelligence”, “anemia”, “coronary heart disease”, “hemoglobin”, and “cardiac surgery”, 906 articles were found, of which 38 met the inclusion criteria for analysis.

RESULTS: In some countries, AI platforms have been created to predict the course of IHD. This review analyzes published data on the development and use of AI-based digital products for the management of patients with IHD, including those that take into account key hemodynamic parameters.

CONCLUSION: Analysis of existing developments revealed a focus on solving prognostic problems. However, in our opinion, the range of parameters analyzed is not wide enough. For example, anemia, which plays a key role in modifying the risk of adverse outcomes in IHD, has not been considered as a factor.

About the authors

Tatyana Y. Kalyuta

Saratov State Medical University named after V.I. Razumovsky

Author for correspondence.
Email: tatianakaluta@yandex.ru
ORCID iD: 0000-0003-3172-0804
SPIN-code: 4982-7861

MD, Cand. Sci. (Medicine)

Russian Federation, Saratov

Irina P. Emelyanova

Saratov State Medical University named after V.I. Razumovsky

Email: irisha-9966@mail.ru
ORCID iD: 0000-0002-4178-9437
SPIN-code: 1766-8528
Russian Federation, Saratov

Valeriy V. Suvorov

Saratov State Medical University named after V.I. Razumovsky

Email: valeriy_s@inbox.ru
ORCID iD: 0000-0002-4181-9034
SPIN-code: 4757-5250

Cand. Sci. (History)

Russian Federation, Saratov

Alexander S. Fedonnikov

Saratov State Medical University named after V.I. Razumovsky

Email: fedonnikov@mail.ru
ORCID iD: 0000-0003-0344-4419
SPIN-code: 2248-5246

MD, Dr. Sci. (Medicine)

Russian Federation, Saratov

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Supplementary files

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2. Appendix 1. Analysis of interpreted and predicted parameters in publications on developed artificial intelligence systems.
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3. Appendix 2. The frequency of use of indicators included in the data interpreted by the authors of the articles
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