Relationship between cerebral hemodynamics, tissue oxygen saturation, and delirium in patients with septic shock: a pilot observational trial

Abstract

Background Patients with septic shock are prone to have impaired cerebral autoregulation and to have an imbalance in cerebral oxygen metabolism. Transcranial doppler (TCD) and tissue oxygen saturation monitoring were performed to observe the changes in cerebral hemodynamic indices of the middle cerebral artery, and in cerebral and peripheral tissue oxygen saturation (StO 2 ) to identify risk factors for sepsis-associated delirium (SAD). Methods Patients with septic shock that were admitted to the Department of Critical Care Medicine of Xiangya Hospital of Central South University from May 2018 to March 2019 were prospectively enrolled, which were divided into an SAD group and a non-SAD group according to the Confusion Assessment Method for the Intensive Care Unit (CAM-ICU). Both groups were compared with respect to patient characteristics, blood gas analysis indexes, organ function indicators, cerebral hemodynamic index, cerebrovascular automatic regulation function (transient hyperemia response rate, THRR index), the changes in regional cerebral oxygen saturation, and peripheral tissue oxygen saturation. Results The incidence of SAD was 39% (20/51). The overall 28-day mortality rate was 43% (22/51). Compared with the non-SAD group, patients in the SAD group required a longer mechanical ventilation time (5 days [95% confidence interval (CI) 2, 6] vs 1day [95% CI 1, 4], p=0.015) and ICU stay (9 days [95% CI 5, 20] vs 5 days [95% CI 3, 9], p=0.042) and has a higher 28-day mortality rate(65% vs 29%, p=0.011).Multivariate regression analysis showed that the THRR index (OR=5.770, 95% CI:1.222-27.255; p=0.027)and mean value of regional cerebral oxygen saturation (rSO 2 ) <55% (OR=3.864, 95% CI:1.026-14.550;p=0.046) were independent risk factors for SAD. Conclusions SAD has a high incidence in septic patients and is associated with poor prognosis. Our results provide a clinical basis for improving early detection and treatment of SAD.

Global research trends in artificial intelligence for critical care with a focus on chord network charts: Bibliometric analysis

“…AI utilization in critical care primarily encompasses disease diagnosis, prediction of disease progression (clinical deterioration), and characterization of specific disease phenotypes or endotypes, [3] such as those found in sepsis, [14] septic shock, [15,16] and acute respiratory distress syndrome. [17] For example, in a large multicenter database, authors [18] compared various techniques for detecting clinical deterioration on wards and found that an AI expert algorithm was developed [19] to predict sepsis early.…”