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Impact of Clinical Sepsis Phenotypes on Mortality and Fluid Status in Critically Ill Patients.

医学 败血症 病危 感染性休克 重症监护医学 重症监护室 重症监护 多器官功能障碍综合征 器官功能障碍 沙发评分 严重败血症 机械通风 危重病 全身炎症反应综合征 内科学
作者
Elizabeth A Shald,Michael Erdman,Jason Ferreira
出处
期刊:Shock [Lippincott Williams & Wilkins]
标识
DOI:10.1097/shk.0000000000001864
摘要

BACKGROUND Sepsis is associated with high rates of in-hospital mortality, despite being the focus of medical research and public health initiatives for several years. The primary objective of this study was to determine the influence of phenotypes on rates of in-hospital mortality throughout ICU admission. MATERIALS AND METHODS Retrospective, single center cohort study. Medical ICU of an academic medical center. Medical ICU patients admitted between January 2016 and August 2019 with a alert were screened for admitting diagnosis of or septic shock. Patients were classified into one of four clinical sepsis phenotypes: multi-organ failure (MOF), respiratory dysfunction (RD), neurologic dysfunction (ND), or other patients (OP). RESULTS An analysis of 320 patients was completed. In-hospital mortality was different between groups (p < 0.001). Patients with the MOF phenotype had the highest rate of mortality (48.4%), followed by the ND phenotype (39.7%), RD phenotype (20.8%), and OP phenotype (13.7%). There were differences in volume balances between phenotypes at 48 hours (p = 0.001), 72 hours (p < 0.001), and 96 hours (p < 0.001) after hospital presentation, with the MOF and ND phenotypes having the largest volume balances at these time points. Ventilator-free days (p < 0.001) and ICU length of stay (LOS) (p = 0.030) were different between groups. There was no difference in hospital LOS (p = 0.479). CONCLUSIONS This data supports the presence of marked intra-disease differences in patient presentation and correlation with clinical outcomes including mortality. Additionally, significantly more positive fluid balances were observed between survivors and non-survivors in some patient subsets. Using pragmatic clinical variables readily available to providers to classify patients into phenotypes has the propensity to guide treatment strategies in the future.

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