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Background: Accurate evaluation of level of disorder of consciousness (DOC) is clinically challenging. Objective: This study aimed to establish a distinctive DOC-related pattern (DOCRP) for assessing disease severity and distinguishing unresponsive wakefulness syndrome (UWS) from minimally conscious state (MCS). Methods: Fifteen patients with DOC and eighteen health subjects with F-18-fluorodeoxyglucose (F-18-FDG) positron emission tomography (PET) were enrolled in this study. All patients were assessed by Coma Recovery Scale-Revised (CRS-R) and all individuals were randomly divided into two cohorts (Cohort A and B). DOCRP was identified in Cohort A and subsequently validated in Cohort B and A+B. We also assessed the discriminatory power of DOCRP between MCS and UWS. Results: The DOCRP was characterized bilaterally by relatively decreased metabolism in the medial and lateral frontal lobes, parieto-temporal lobes, cingulate gyrus and caudate, associated with relatively increased metabolism in the cerebellum and brainstem. DOCRP expression exhibited high accuracy in differentiating DOC patients from controls (P

ABSTRACT: Objectives: Effective antifungal therapy is important to reduce mortality in patients with invasive fungal infections (IFIs). Numerous factors affect pharmacokinetic/pharmacodynamic (PK/PD) parameters in critically-ill patients. To guide individualised administration in critically-ill patients, it is of great significance to determine the population pharmacokinetics of caspofungin. Methods: A prospective study in 42 ICU patients with IFIs was conducted in China. A population pharmacokinetic model of caspofungin was established using a non-linear mixed-effects model, which was utilised to investigate the effects of demographic indices, liver function and kidney function on pharmacokinetics. Additionally, appropriate dosages of caspofungin under various scenarios were determined based on MICs and probability of target attainment (PTA) at specific dosages. Results: In critically-ill Chinese patients, clearance (CL), volume of distribution (V) and area under the curve at steady-state (AUCss) of caspofungin were 0.32 L/h, 6.77 L and 135.47 mg•h/L, respectively. Blood albumin and total bilirubin levels were factors affecting CL, while body weight was the only factor affecting V among Chinese people with relatively low weight compared with other populations. A maintenance dose of 50 mg caspofungin achieved a high PTA for treating IFIs caused by Candida albicans (MIC ≤ 0.06 mg/L) and Candida glabrata (MIC ≤ 0.125 mg/L). The maintenance dose of caspofungin should be adjusted to 70–200 mg for IFIs caused by C. albicans (MIC, 0.06–0.125 mg/L). For IFIs caused by Candida parapsilosis, an MIC > 0.03 mg/L is associated with a very low PTA, but higher doses of caspofungin or alternative antifungals need to be further studied. Conclusion: The population pharmacokinetic model established here described well the PK/PD characteristics of caspofungin in critically-ill Chinese patients. These results could guide the formulation of individualised caspofungin dosing regimens for critically-ill patients.

Purpose: Developing a risk prediction model for invasive fungal disease based on an analysis of the disease-related risk factors in critically ill patients in the intensive care unit (ICU) to diagnose the invasive fungal disease in the early stages and determine the time of initiating early antifungal treatment. Methods: Data were collected retrospectively from 141 critically ill adult patients with at least 4 days of general ICU stay at Sun Yat-sen Memorial Hospital, Sun Yat-sen University during the period from February 2015 to February 2016. Logistic regression was used to develop the risk prediction model. Discriminative power was evaluated by the area under the receiver operating characteristics (ROC) curve (AUC). Results: Sequential organ failure assessment (SOFA) score, antibiotic treatment period, and positive culture of Candida albicans other than normally sterile sites are the three predictors of invasive fungal disease in critically ill patients in the ICU. The model performs well with an ROC-AUC of .73. Conclusion: The risk prediction model performs well to discriminate between critically ill patients with or without invasive fungal disease. Physicians could use this prediction model for early diagnosis of invasive fungal disease and determination of the time to start early antifungal treatment of critically ill patients in the ICU. Keywords: critical illness, invasive fungal infections, logistic models, risk, risk factors