Ward Rebecca A, Aghaeepour Nima, Bhattacharyya Roby P, Clish Clary B, Gaudillière Brice, Hacohen Nir, Mansour Michael K, Mudd Philip A, Pasupneti Shravani, Presti Rachel M, Rhee Eugene P, Sen Pritha, Spec Andrej, Tam Jenny M, Villani Alexandra-Chloé, Woolley Ann E, Hsu Joe L, Vyas Jatin M
Division of Infectious Disease, Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA.
Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, California, USA.
Open Forum Infect Dis. 2021 Sep 25;8(11):ofab483. doi: 10.1093/ofid/ofab483. eCollection 2021 Nov.
The field of infectious diseases currently takes a reactive approach and treats infections as they present in patients. Although certain populations are known to be at greater risk of developing infection (eg, immunocompromised), we lack a systems approach to define the true risk of future infection for a patient. Guided by impressive gains in "omics" technologies, future strategies to infectious diseases should take a precision approach to infection through identification of patients at intermediate and high-risk of infection and deploy targeted preventative measures (ie, prophylaxis). The advances of high-throughput immune profiling by multiomics approaches (ie, transcriptomics, epigenomics, metabolomics, proteomics) hold the promise to identify patients at increased risk of infection and enable risk-stratifying approaches to be applied in the clinic. Integration of patient-specific data using machine learning improves the effectiveness of prediction, providing the necessary technologies needed to propel the field of infectious diseases medicine into the era of personalized medicine.
传染病领域目前采取的是一种被动应对的方法,即根据患者出现的感染情况进行治疗。尽管已知某些人群感染风险更高(例如免疫功能低下者),但我们缺乏一种系统方法来确定患者未来感染的真正风险。在“组学”技术取得显著进展的引领下,未来传染病防治策略应通过识别感染中高风险患者,采取精准的感染防治方法,并部署针对性的预防措施(即预防性治疗)。多组学方法(即转录组学、表观基因组学、代谢组学、蛋白质组学)在高通量免疫分析方面的进展有望识别出感染风险增加的患者,并使风险分层方法能够应用于临床。利用机器学习整合患者特定数据可提高预测的有效性,为推动传染病医学领域进入个性化医疗时代提供所需的必要技术。