Ressler Adam, Wang Joyce, Rao Krishna
Division of Infectious Diseases, Department of Internal Medicine, University of Michigan Medical School, Ann Arbor, MI, USA.
Department of Microbiology and Immunology, University of Michigan Medical School, Ann Arbor, MI, USA.
Therap Adv Gastroenterol. 2021 Oct 8;14:17562848211048127. doi: 10.1177/17562848211048127. eCollection 2021.
In the United States, infection (CDI) is the leading cause of healthcare-associated infection, affecting nearly half a million people and resulting in more than 20,000 in-hospital deaths every year. It is therefore imperative to better characterize the intricate interplay between microbial factors, host immunologic signatures, and clinical features that are associated with adverse outcomes of severe CDI. In this narrative review, we discuss the implications of genetics and virulence factors in the molecular epidemiology of CDI, and the utility of early biomarkers in predicting the clinical trajectory of patients at risk of developing severe CDI. Furthermore, we identify associations between host immune factors and CDI outcomes in both animal models and human studies. Next, we highlight clinical factors including renal dysfunction, aging, blood biomarkers, level of care, and chronic illnesses that can affect severe CDI diagnosis and outcome. Finally, we present our perspectives on two specific treatments pertinent to patient outcomes: metronidazole administration and surgery. Together, this review explores the various venues of CDI research and highlights the importance of integrating microbial, host, and clinical data to help clinicians make optimal treatment decisions based on accurate prediction of disease progression.
在美国，艰难梭菌感染（CDI）是医疗保健相关感染的主要原因，每年影响近50万人，并导致超过20000人在医院死亡。因此，必须更好地描述与严重CDI不良后果相关的微生物因素、宿主免疫特征和临床特征之间的复杂相互作用。在这篇叙述性综述中，我们讨论了遗传学和毒力因子在CDI分子流行病学中的意义，以及早期生物标志物在预测有发展为严重CDI风险患者临床病程中的作用。此外，我们在动物模型和人体研究中确定了宿主免疫因素与CDI结局之间的关联。接下来，我们强调包括肾功能不全、衰老、血液生物标志物、护理水平和慢性疾病等可能影响严重CDI诊断和结局的临床因素。最后，我们阐述了与患者结局相关的两种特定治疗方法的观点：甲硝唑给药和手术。总之，本综述探讨了CDI研究的各个领域，并强调了整合微生物、宿主和临床数据以帮助临床医生基于对疾病进展的准确预测做出最佳治疗决策的重要性。
