CHINESE JOURNAL OF MEDICINAL GUIDE >
Research Progress on Symptom Network of Acute Coronary Syndrome Based on Precision Medicine
Received date: 2025-01-09
Revised date: 2025-02-13
Accepted date: 2025-03-14
Online published: 2025-07-23
Acute coronary syndrome (ACS) is one of the leading causes of death globally, with complex symptoms influenced by multiple factors. Rapid identification of ACS is crucial for determining its prognosis. The traditional symptom research of ACS focuses on a single symptom or symptom cluster, easily ignoring the differences in symptom phenotypes, mechanisms, physiological, and psychological factors, which may not be conducive to developing precise intervention strategies. Symptom network is a novel research paradigm based on the perspective of precision medicine, which reveals pathological mechanisms and intrinsic associations by integrating multi-dimensional data such as complex symptoms and biomarkers of ACS. It provides new ideas for precision prevention and treatment of the disease. This study systematically reviews the concepts and principles of symptom network and their application advantages and challenges in the precise diagnosis and treatment of ACS. Symptom networks visualize the complex associations among symptoms through the network structure and specificity indicators constructed by nodes and edges, and effectively identify core and bridge symptoms. Studies have shown that symptom networks can not only accurately reflect the dynamic changes of complex ACS symptoms, but also combine with biomarkers to realize early identification and precise intervention. However, this research paradigm still faces challenges such as data safety, limitations of multi-omics technology and application of results. More empirical studies are needed to improve the model of symptom network and promote the application of precision medicine in ACS symptom management in the future.
Ping WEI
,
Hua ZHANG
.
Research Progress on Symptom Network of Acute
Coronary Syndrome Based on Precision Medicine
[1] Byrne RA, Rossello X, Coughlan JJ, et al. 2023 ESC Guidelines for the management of acute coronary syndromes[J].Eur Heart J,2023,44(38):3720-3826.
[2] 倪烨,赵娅,张必利.依洛尤单抗在非ST段抬高型急性冠状动脉综合征患者PCI术后的应用效果研究[J].中国医药导刊, 2023,25(12):1253-1258.
[3] Heart Federation. World Heart Report 2023[EB/OL].(2023-05-20) [2024-12-16].https://world-heart-federation.org/resource/world-heart-report-2023/.
[4] Sattayaraksa A, Ananchaisarp T, Vichitkunakorn P, et al. Diagnostic performance of a mnemonic for warning symptoms in predicting acute coronary syndrome diagnosis: a retrospective cross-sectional study[J].Int J Public Health,2023,68(15):1606115.
[5] Bergmark BA, Mathenge N, Merlini PA, et al. Acute coronary syndromes[J]. Lancet, 2022,399(10332):1347-1358.
[6] 晋从巧,钱琛玥,龚赟,等.青年急性心肌梗死发病非传统危险因素的研究[J].中国医药导刊,2024,26(8):764-773.
[7] Ge Z, Kan J, Gao X, et al. Ticagrelor alone versus ticagrelor plus aspirin from month 1 to month 12 after percutaneous coronary intervention in patients with acute coronary syndromes (ULTIMATE-DAPT): a randomised, placebo-controlled, double-blind clinical trial[J].Lancet,2024,403(10439):1866-1878.
[8] 惠汝太.强化大数据建设,迈入心血管精准医学新时代——发挥我国制度优势,强化大数据意识,开展心血管精准医疗[J]. 中国医药导刊,2022,24(4):314-318.
[9] Fried EI, Boschloo L, van Borkulo CD, et al. Commentary: "consistent superiority of selective serotonin reuptake inhibitors over placebo in reducing depressed mood in patients with major depression"[J].Front Psychiatry,2015,6:117.
[10] Zhu Z, Xing W, Hu Y, et al. Paradigm shift: moving from symptom clusters to symptom networks[J].Asia Pac J Oncol Nurs, 2022,9(1):5-6.
[11] Williams MO, Buekers J, Castano-Vinyals G, et al. Climate anxiety and its association with health behaviours and generalized anxiety: an intensive longitudinal study[J].Br J Health Psychol,2024,29(4):1080-1095.
[12] Hein K, Zarate D, Burleigh T, et al. Pixels and perception: mapping the association between digital media and psychotic-like experiences in adolescents[J].Compr Psychiatry,2024,134:152509.
[13] 杨中方,朱政,胡雁,等.症状网络在症状管理中的应用进展[J].护理学杂志,2022,37(5):91-94.
[14] 惠汝太.强化大数据建设,迈入心血管精准医学新时代——心血管精准医疗发展及面临的挑战与瓶颈[J].中国医药导刊,2022,24(3):206-210.
[15] 李雅暄,宋沧桑,李兴德,等.伏立康唑基因多态性与血药浓度的研究进展[J].中国药物评价,2024,41(4):307-313.
[16] Lee LY, Pandey AK, Maron BA, et al. Network medicine in cardiovascular research[J].Cardiovasc Res,2021,117(10):2186-2202.
[17] Mirzaei S, Burke L, Rosenfeld AG, et al. Protein cytokines, cytokine gene polymorphisms, and potential acute coronary syndrome symptoms[J].Biol Res Nurs,2019,21(5):552-563.
[18] Rosenfeld AG, Knight EP, Steffen A, et al. Symptom clusters in patients presenting to the emergency department with possible acute coronary syndrome differ by sex, age, and discharge diagnosis[J].Heart Lung,2015,44(5):368-375.
[19] Mao Y, Shi Y, Qiao W, et al. Symptom clusters and unplanned hospital readmission in Chinese patients with acute myocardial infarction on admission[J].Front Cardiovasc Med,2024,11(5):1388648.
[20] 李东枝.基于症状管理概念修正模型对急性冠脉综合征症状群及其影响因素研究[D].山西中医药大学,2021.
[21] Wei Y, Cheng W, Lu Y, et al. Features and differences in core symptom clusters in home-based hospice patients with advanced cancer: a network analysis[J].Cancer Med,2024,13(21):e70370.
[22] Zhu Z, Sun Y, Kuang Y, et al. Contemporaneous symptom networks of multidimensional symptom experiences in cancer survivors: a network analysis[J].Cancer Med,2023,12(1):663-673.
[23] 曾凯,陈小芳.急性心肌梗死患者PTSD症状网络特点分析[J]. 重庆医学,2021,50(3):495-499.
[24] 李月,陈务贤,李高叶,等.经皮冠状动脉介入术后病人多维症状网络分析[J].护理研究,2024,38(7):1129-1138.
[25] Fernández-de-Las-Peñas C, Palacios-Ceña M, Valera-Calero JA, et al. Understanding the interaction between clinical, emotional and psychophysical outcomes underlying tension-type headache: a network analysis approach[J].J Neurol,2022,269(8):4525-4534.
[26] Kalantari E, Kouchaki S, Miaskowski C, et al. Network analysis to identify symptoms clusters and temporal interconnections in oncology patients[J].Sci Rep,2022,12(1):17052.
[27] 余骏雯,朱政,胡天天,等.症状网络的特异性指标[J].护士进修杂志, 2023, 38(24):2229-2234.
[28] Zhang Y, Liu L, Chen L, et al. Investigation of core symptoms and symptom clusters in maintenance hemodialysis patients: a network analysis[J].J Nurs Scholarsh,2024,56(5):628-637.
[29] 吴俊慧,周伟娇,王伟轩,等.老年慢病精准护理重点领域研究进展[J].四川大学学报(医学版),2023,54(4):731-735.
[30] Shanthamallu US, Kilpatrick C, Jones A, et al. A network-based framework to discover treatment-response-predicting biomarkers for complex diseases[J].J Mol Diagn,2024,26(10):917-930.
[31] Wang Y, Miao L, Tao L, et al. Weighted gene coexpression network analysis identifies the key role associated with acute coronary syndrome[J].Aging,2020,12(19):19440-19454.
[32] Vignoli A, Tenori L, Giusti B, et al. Differential network analysis reveals metabolic determinants associated with mortality in acute myocardial infarction patients and suggests potential mechanisms underlying different clinical scores used to predict death[J].J Proteome Res,2020,19(2):949-961.
[33] 陈思琦,郑洋滨,沈震亚,等.基于网络药理学和分子对接技术探讨枳实治疗肝内胆汁淤积的作用机制[J].中国药物评价,2023,40(3):231-239.
[34] 赵晨旭,钟方元,董建勋,等.机器学习在急性冠脉综合征风险评估中的应用[J].中华心血管病杂志,2024,52(3):311-315.
[35] 惠汝太.强化大数据建设,迈入心血管精准医学新时代——数字科学时代基因组与后基因组组学研究计划[J]. 中国医药导刊,2022,24(5):429-432.
[36] Hao M, Zhang H, Jiang S, et al. Metrics of physiological network topology are novel biomarkers to capture functional disability and health[J].J Gerontol A Biol Sci Med Sci,2024,80(1):glae268.
[37] Saarinen SL, Borregaard B, Ekholm O, et al. Self-reported mental and physical health is associated with not returning to work in patients with ischemic heart disease[J].Int J Cardiol,2024,409:132180.
[38] Khera R, Oikonomou EK, Nadkarni GN, et al. Transforming cardiovascular care with artificial intelligence: from discovery to practice: jacc state-of-the-art review[J].J Am Coll Cardiol,2024,84(1):97-114.
[39] 吴倩倩,龚桂姿,李春梅,等.数字疗法在慢性阻塞性肺疾病患者自我管理中的研究进展[J].中国医药导刊,2024,26(9):868-873.
[40] Antiperovitch P, Mortara D, Barrios J, et al. Continuous atrial fibrillation monitoring from photoplethysmography: comparison between supervised deep learning and heuristic signal processing[J].JACC Clin Electrophysiol,2024,10(2):334-345.
[41] Loscalzo J, Barabási AL, Silverman EK. Network medicine: complex systems in human disease and therapeutics[M].Cambridge, Massachusetts:Harvard University Press,2017:177-197.
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