Rhee Chanu, Klompas Michael
Department of Population Medicine, Harvard Medical School/Harvard Pilgrim Health Care Institute, Boston, Massachusetts.
Division of Infectious Diseases, Department of Medicine, Brigham and Women's Hospital, Boston, Massachusetts.
Antimicrob Steward Healthc Epidemiol. 2022 Feb 28;2(1):e32. doi: 10.1017/ash.2022.16. eCollection 2022.
Hospital-onset sepsis accounts for 10%-15% of all sepsis cases and is associated with very high mortality rates, yet to date most hospitals have paid little attention to tracking its incidence and outcomes. This contrasts sharply with the substantial effort that hospitals and regulatory agencies spend tracking and reporting a limited subset of healthcare-associated infections. The recent development of the Center for Disease Control and Prevention's hospital-onset Adult Sepsis Event (ASE) definition, however, provides a validated and standardized mechanism for facilities to identify patients with nosocomial sepsis using routinely available electronic health record data. Recent data have demonstrated that hospital-onset ASE surveillance identifies many infections that are largely missed by current reportable healthcare-associated infections and that are associated with much higher mortality rates. Expanding the breadth of surveillance to include these highly consequential infections could help identify new targets for prevention and quality improvement and ultimately catalyze better outcomes for hospitalized patients. More work is needed, however, to characterize the preventability of hospital-onset ASE, develop and validate robust case-mix adjustment tools, and facilitate widespread uptake in hospitals with limited resources.
医院获得性脓毒症占所有脓毒症病例的10%-15%，且与极高的死亡率相关，但迄今为止，大多数医院很少关注追踪其发病率和转归情况。这与医院和监管机构花费大量精力追踪和报告有限的一部分医疗相关感染形成了鲜明对比。然而，美国疾病控制与预防中心近期制定的医院获得性成人脓毒症事件（ASE）定义，为医疗机构提供了一种经过验证的标准化机制，可利用常规可得的电子健康记录数据识别医院感染性脓毒症患者。近期数据表明，医院获得性ASE监测能识别出许多当前可报告的医疗相关感染很大程度上遗漏的感染，且这些感染与高得多的死亡率相关。将监测范围扩大到包括这些后果严重的感染，有助于确定预防和质量改进的新目标，并最终为住院患者带来更好的转归。然而，还需要开展更多工作来明确医院获得性ASE的可预防性，开发并验证强大的病例组合调整工具，并推动资源有限的医院广泛采用。
