ABSTRACT
BACKGROUND: Immune related adverse events impacting the liver are common from immune checkpoint inhibitor (ICI) therapy; however, there is little data regarding the subclinical impact of ICIs on liver inflammation. The study aims to determine whether ICI therapy affects liver attenuation and liver enzymes in melanoma patients with and without hepatic steatosis. METHODS: A retrospective, cohort study was conducted of patients with advanced melanoma treated with ICI therapy who received serial PET-CT scans at the Vanderbilt University Medical Center (VUMC). Primary outcomes included: liver attenuation measured by PET-CT/non-contrast CT and liver enzymes. Hepatic steatosis was diagnosed by radiologists on clinical imaging. RESULTS: Among 839 patients with advanced melanoma treated with ICIs, 81 had serial PET-CT scans approximately 12 months apart and long-term survival; of these 11 patients had pre-existing steatosis/steatohepatitis. Overall, ICI was not associated with significant increases in liver enzymes in all patients; modest decreases in liver enzymes were observed in patients with pre-existing steatosis/steatohepatitis. Similarly, liver attenuation did not change from baseline to post-treatment (58.44 vs 60.60 HU, + 2.17, p = 0.055). CONCLUSIONS: ICIs may not chronically affect liver enzymes or liver attenuation, a non-invasive measure of liver fat content and inflammation, in the general population or in those with pre-existing steatosis/steatohepatitis.
Subject(s)
Fatty Liver , Melanoma , Cohort Studies , Fatty Liver/complications , Humans , Immune Checkpoint Inhibitors/adverse effects , Inflammation/complications , Melanoma/drug therapy , Positron Emission Tomography Computed Tomography , Retrospective Studies , Tomography, X-Ray Computed/methodsABSTRACT
OBJECTIVE: To identify quantitative electroencephalography (EEG)-based indicators of delirium or coma in mechanically ventilated patients. METHODS: We prospectively enrolled 28 mechanically ventilated intensive care unit (ICU) patients to undergo 24-hour continuous EEG, 25 of whom completed the study. We assessed patients twice daily using the Richmond Agitation-Sedation Scale (RASS) and Confusion Assessment Method for the ICU (CAM-ICU). We evaluated the spectral profile, regional connectivity and complexity of 5-minute EEG segments after each assessment. We used penalized regression to select EEG metrics associated with delirium or coma, and compared mixed-effects models predicting delirium with and without the selected EEG metrics. RESULTS: Delta variability, high-beta variability, relative theta power, and relative alpha power contributed independently to EEG-based identification of delirium or coma. A model with these metrics achieved better prediction of delirium or coma than a model with clinical variables alone (Akaike Information Criterion: 36 vs 43, p = 0.006 by likelihood ratio test). The area under the receiver operating characteristic curve for an ad hoc hypothetical delirium score using these metrics was 0.94 (95%CI 0.83-0.99). CONCLUSIONS: We identified four EEG metrics that, in combination, provided excellent discrimination between delirious/comatose and non-delirious mechanically ventilated ICU patients. SIGNIFICANCE: Our findings give insight to neurophysiologic changes underlying delirium and provide a basis for pragmatic, EEG-based delirium monitoring technology.