Saqib Muhammad, Perswani Prinka, Muneem Abraar, Mumtaz Hassan, Neha Fnu, Ali Saiyad, Tabassum Shehroze
Khyber Medical College, Peshawar.
University of California Riverside, Riverside.
Ann Med Surg (Lond). 2024 May 6;86(6):3615-3623. doi: 10.1097/MS9.0000000000002138. eCollection 2024 Jun.
Globally, cardiovascular diseases take the lives of over 17 million people each year, mostly through myocardial infarction, or MI, and heart failure (HF). This comprehensive literature review examines various aspects related to the diagnosis, prediction, and prognosis of HF in the context of machine learning (ML). The review covers an array of topics, including the diagnosis of HF with preserved ejection fraction (HFpEF) and the identification of high-risk patients with HF with reduced ejection fraction (HFrEF). The prediction of mortality in different HF populations using different ML approaches is explored, encompassing patients in the ICU, and HFpEF patients using biomarkers and gene expression. The review also delves into the prediction of mortality and hospitalization rates in HF patients with mid-range ejection fraction (HFmrEF) using ML methods. The findings highlight the significance of a multidimensional approach that encompasses clinical evaluation, laboratory assessments, and comprehensive research to improve our understanding and management of HF. Promising predictive models incorporating biomarkers, gene expression, and consideration of epigenetics demonstrate potential in estimating mortality and identifying high-risk HFpEF patients. This literature review serves as a valuable resource for researchers, clinicians, and healthcare professionals seeking a comprehensive and updated understanding of the role of ML diagnosis, prediction, and prognosis of HF across different subtypes and patient populations.
在全球范围内，心血管疾病每年导致超过1700万人死亡，主要死因是心肌梗死（MI）和心力衰竭（HF）。这篇综合性文献综述探讨了机器学习（ML）背景下与HF诊断、预测和预后相关的各个方面。该综述涵盖了一系列主题，包括射血分数保留的心力衰竭（HFpEF）的诊断以及射血分数降低的心力衰竭（HFrEF）高危患者的识别。探讨了使用不同ML方法预测不同HF人群的死亡率，包括重症监护病房（ICU）患者以及使用生物标志物和基因表达的HFpEF患者。该综述还深入研究了使用ML方法预测射血分数中等范围的心力衰竭（HFmrEF）患者的死亡率和住院率。研究结果强调了多维方法的重要性，该方法包括临床评估、实验室评估和全面研究，以增进我们对HF的理解和管理。纳入生物标志物、基因表达并考虑表观遗传学的有前景的预测模型在估计死亡率和识别高危HFpEF患者方面显示出潜力。这篇文献综述为寻求全面、最新了解ML在不同亚型和患者群体的HF诊断、预测和预后中作用的研究人员、临床医生和医疗保健专业人员提供了宝贵资源。
