DeltaScan for the Assessment of Acute Encephalopathy and Delirium in ICU and non-ICU Patients, a Prospective Cross-Sectional Multicenter Validation Study.

OBJECTIVES: To measure the diagnostic accuracy of DeltaScan: a portable real-time brain state monitor for identifying delirium, a manifestation of acute encephalopathy (AE) detectable by polymorphic delta activity (PDA) in single-channel electroencephalograms (EEGs). DESIGN: Prospective cross-sectional study. SETTING: Six Intensive Care Units (ICU's) and 17 non-ICU departments, including a psychiatric department across 10 Dutch hospitals. PARTICIPANTS: 494 patients, median age 75 (IQR:64-87), 53% male, 46% in ICUs, 29% delirious. MEASUREMENTS: DeltaScan recorded 4-minute EEGs, using an algorithm to select the first 96 seconds of artifact-free data for PDA detection. This algorithm was trained and calibrated on two independent datasets. METHODS: Initial validation of the algorithm for AE involved comparing its output with an expert EEG panel's visual inspection. The primary objective was to assess DeltaScan's accuracy in identifying delirium against a delirium expert panel's consensus. RESULTS: DeltaScan had a 99% success rate, rejecting 6 of the 494 EEG's due to artifacts. Performance showed and an Area Under the Receiver Operating Characteristic Curve (AUC) of 0.86 (95% CI: 0.83-0.90) for AE (sensitivity: 0.75, 95%CI=0.68-0.81, specificity: 0.87 95%CI=0.83-0.91. The AUC was 0.71 for delirium (95%CI=0.66-0.75, sensitivity: 0.61 95%CI=0.52-0.69, specificity: 72, 95%CI=0.67-0.77). Our validation aim was an NPV for delirium above 0.80 which proved to be 0.82 (95%CI: 0.77-0.86). Among 84 non-delirious psychiatric patients, DeltaScan differentiated delirium from other disorders with a 94% (95%CI: 87-98%) specificity. CONCLUSIONS: DeltaScan can diagnose AE at bedside and shows a clear relationship with clinical delirium. Further research is required to explore its role in predicting delirium-related outcomes.

Caspofungin Weight-Based Dosing Supported by a Population Pharmacokinetic Model in Critically Ill Patients.

The objective of this study was to develop a population pharmacokinetic model and to determine a dosing regimen for caspofungin in critically ill patients. Nine blood samples were drawn per dosing occasion. Fifteen patients with (suspected) invasive candidiasis had one dosing occasion and five had two dosing occasions, measured on day 3 (±1) of treatment. Pmetrics was used for population pharmacokinetic modeling and probability of target attainment (PTA). A target 24-h area under the concentration-time curve (AUC) value of 98 mg·h/liter was used as an efficacy parameter. Secondarily, the AUC/MIC targets of 450, 865, and 1,185 were used to calculate PTAs for Candida glabrata, C. albicans, and C. parapsilosis, respectively. The final 2-compartment model included weight as a covariate on volume of distribution (V). The mean V of the central compartment was 7.71 (standard deviation [SD], 2.70) liters/kg of body weight, the mean elimination constant (Ke ) was 0.09 (SD, 0.04) h-1, the rate constant for the caspofungin distribution from the central to the peripheral compartment was 0.44 (SD, 0.39) h-1, and the rate constant for the caspofungin distribution from the peripheral to the central compartment was 0.46 (SD, 0.35) h-1 A loading dose of 2 mg/kg on the first day, followed by 1.25 mg/kg as a maintenance dose, was chosen. With this dose, 98% of the patients were expected to reach the AUC target on the first day and 100% of the patients on the third day. The registered caspofungin dose might not be suitable for critically ill patients who were all overweight (≥120 kg), over 80% of median weight (78 kg), and around 25% of lower weight (≤50 kg). A weight-based dose regimen might be appropriate for achieving adequate exposure of caspofungin in intensive care unit patients.

External Validation of Two Models to Predict Delirium in Critically Ill Adults Using Either the Confusion Assessment Method-ICU or the Intensive Care Delirium Screening Checklist for Delirium Assessment.

OBJECTIVES: To externally validate two delirium prediction models (early prediction model for ICU delirium and recalibrated prediction model for ICU delirium) using either the Confusion Assessment Method-ICU or the Intensive Care Delirium Screening Checklist for delirium assessment. DESIGN: Prospective, multinational cohort study. SETTING: Eleven ICUs from seven countries in three continents. PATIENTS: Consecutive, delirium-free adults admitted to the ICU for greater than or equal to 6 hours in whom delirium could be reliably assessed. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: The predictors included in each model were collected at the time of ICU admission (early prediction model for ICU delirium) or within 24 hours of ICU admission (recalibrated prediction model for ICU delirium). Delirium was assessed using the Confusion Assessment Method-ICU or the Intensive Care Delirium Screening Checklist. Discrimination was determined using the area under the receiver operating characteristic curve. The predictive performance was determined for the Confusion Assessment Method-ICU and Intensive Care Delirium Screening Checklist cohort, and compared with both prediction models' original reported performance. A total of 1,286 Confusion Assessment Method-ICU-assessed patients and 892 Intensive Care Delirium Screening Checklist-assessed patients were included. Compared with the area under the receiver operating characteristic curve of 0.75 (95% CI, 0.71-0.79) in the original study, the area under the receiver operating characteristic curve of the early prediction model for ICU delirium was 0.67 (95% CI, 0.64-0.71) for delirium as assessed using the Confusion Assessment Method-ICU and 0.70 (95% CI, 0.66-0.74) using the Intensive Care Delirium Screening Checklist. Compared with the original area under the receiver operating characteristic curve of 0.77 (95% CI, 0.74-0.79), the area under the receiver operating characteristic curve of the recalibrated prediction model for ICU delirium was 0.75 (95% CI, 0.72-0.78) for assessing delirium using the Confusion Assessment Method-ICU and 0.71 (95% CI, 0.67-0.75) using the Intensive Care Delirium Screening Checklist. CONCLUSIONS: Both the early prediction model for ICU delirium and recalibrated prediction model for ICU delirium are externally validated using either the Confusion Assessment Method-ICU or the Intensive Care Delirium Screening Checklist for delirium assessment. Per delirium prediction model, both assessment tools showed a similar moderate-to-good statistical performance. These results support the use of either the early prediction model for ICU delirium or recalibrated prediction model for ICU delirium in ICUs around the world regardless of whether delirium is evaluated with the Confusion Assessment Method-ICU or Intensive Care Delirium Screening Checklist.

Cost-effectiveness of procalcitonin testing to guide antibiotic treatment duration in critically ill patients: results from a randomised controlled multicentre trial in the Netherlands.

BACKGROUND: Procalcitonin (PCT) testing can help in safely reducing antibiotic treatment duration in intensive care patients with sepsis. However, the cost-effectiveness of such PCT guidance is not yet known. METHODS: A trial-based analysis was performed to estimate the cost-effectiveness of PCT guidance compared with standard of care (without PCT guidance). Patient-level data were used from the SAPS trial in which 1546 patients were randomised. This trial was performed in the Netherlands, which is a country with, on average, low antibiotic use and a short duration of hospital stay. As quality of life among sepsis survivors was not measured during the SAPS, this was derived from a Dutch follow-up study. Outcome measures were (1) incremental direct hospital cost and (2) incremental cost per quality-adjusted life year (QALY) gained from a healthcare perspective over a one-year time horizon. Uncertainty in outcomes was assessed with bootstrapping. RESULTS: Mean in-hospital costs were €46,081/patient in the PCT group compared with €46,146/patient with standard of care (i.e. - €65 (95% CI - €6314 to €6107); - 0.1%). The duration of the first course of antibiotic treatment was lower in the PCT group with 6.9 vs. 8.2 days (i.e. - 1.2 days (95% CI - 1.9 to - 0.4), - 14.8%). This was accompanied by lower in-hospital mortality of 21.8% vs. 29.8% (absolute decrease 7.9% (95% CI - 13.9% to - 1.8%), relative decrease 26.6%), resulting in an increase in mean QALYs/patient from 0.47 to 0.52 (i.e. + 0.05 (95% CI 0.00 to 0.10); + 10.1%). However, owing to high costs among sepsis survivors, healthcare costs over a one-year time horizon were €73,665/patient in the PCT group compared with €70,961/patient with standard of care (i.e. + €2704 (95% CI - €4495 to €10,005), + 3.8%), resulting in an incremental cost-effectiveness ratio of €57,402/QALY gained. Within this time frame, the probability of PCT guidance being cost-effective was 64% at a willingness-to-pay threshold of €80,000/QALY. CONCLUSIONS: Although the impact of PCT guidance on total healthcare-related costs during the initial hospitalisation episode is likely negligible, the lower in-hospital mortality may lead to a non-significant increase in costs over a one-year time horizon. However, since uncertainty remains, it is recommended to investigate the long-term cost-effectiveness of PCT guidance, from a societal perspective, in different countries and settings.

Early EEG for outcome prediction of postanoxic coma: prospective cohort study with cost-minimization analysis.

BACKGROUND: We recently showed that electroencephalography (EEG) patterns within the first 24 hours robustly contribute to multimodal prediction of poor or good neurological outcome of comatose patients after cardiac arrest. Here, we confirm these results and present a cost-minimization analysis. Early prognosis contributes to communication between doctors and family, and may prevent inappropriate treatment. METHODS: A prospective cohort study including 430 subsequent comatose patients after cardiac arrest was conducted at intensive care units of two teaching hospitals. Continuous EEG was started within 12 hours after cardiac arrest and continued up to 3 days. EEG patterns were visually classified as unfavorable (isoelectric, low-voltage, or burst suppression with identical bursts) or favorable (continuous patterns) at 12 and 24 hours after cardiac arrest. Outcome at 6 months was classified as good (cerebral performance category (CPC) 1 or 2) or poor (CPC 3, 4, or 5). Predictive values of EEG measures and cost-consequences from a hospital perspective were investigated, assuming EEG-based decision- making about withdrawal of life-sustaining treatment in the case of a poor predicted outcome. RESULTS: Poor outcome occurred in 197 patients (51% of those included in the analyses). Unfavorable EEG patterns at 24 hours predicted a poor outcome with specificity of 100% (95% CI 98-100%) and sensitivity of 29% (95% CI 22-36%). Favorable patterns at 12 hours predicted good outcome with specificity of 88% (95% CI 81-93%) and sensitivity of 51% (95% CI 42-60%). Treatment withdrawal based on an unfavorable EEG pattern at 24 hours resulted in a reduced mean ICU length of stay without increased mortality in the long term. This gave small cost reductions, depending on the timing of withdrawal. CONCLUSIONS: Early EEG contributes to reliable prediction of good or poor outcome of postanoxic coma and may lead to reduced length of ICU stay. In turn, this may bring small cost reductions.

[Insulin therapy on intensive care should not be too intensive]. / Insulinetherapie op de ic: niet te intensief.

Intensive insulin therapy for critically ill patients is implemented as standard therapy in many ICUs, even though the evidence supporting this approach comes from just two studies at a single centre. Moreover, the results could not be repeated in other multicenter trials and there is increasing evidence of a risk of hypoglycaemia. However, it appears that many intensive care specialists have been too zealous in their attempts to attain euglycemia during critical care. A recent RCT with over 6000 patients demonstrated that intensive glucose control increases mortality by 2.4%.