Treating Rhythmic and Periodic EEG Patterns in Comatose Survivors of Cardiac Arrest.

BACKGROUND: Whether the treatment of rhythmic and periodic electroencephalographic (EEG) patterns in comatose survivors of cardiac arrest improves outcomes is uncertain. METHODS: We conducted an open-label trial of suppressing rhythmic and periodic EEG patterns detected on continuous EEG monitoring in comatose survivors of cardiac arrest. Patients were randomly assigned in a 1:1 ratio to a stepwise strategy of antiseizure medications to suppress this activity for at least 48 consecutive hours plus standard care (antiseizure-treatment group) or to standard care alone (control group); standard care included targeted temperature management in both groups. The primary outcome was neurologic outcome according to the score on the Cerebral Performance Category (CPC) scale at 3 months, dichotomized as a good outcome (CPC score indicating no, mild, or moderate disability) or a poor outcome (CPC score indicating severe disability, coma, or death). Secondary outcomes were mortality, length of stay in the intensive care unit (ICU), and duration of mechanical ventilation. RESULTS: We enrolled 172 patients, with 88 assigned to the antiseizure-treatment group and 84 to the control group. Rhythmic or periodic EEG activity was detected a median of 35 hours after cardiac arrest; 98 of 157 patients (62%) with available data had myoclonus. Complete suppression of rhythmic and periodic EEG activity for 48 consecutive hours occurred in 49 of 88 patients (56%) in the antiseizure-treatment group and in 2 of 83 patients (2%) in the control group. At 3 months, 79 of 88 patients (90%) in the antiseizure-treatment group and 77 of 84 patients (92%) in the control group had a poor outcome (difference, 2 percentage points; 95% confidence interval, -7 to 11; P = 0.68). Mortality at 3 months was 80% in the antiseizure-treatment group and 82% in the control group. The mean length of stay in the ICU and mean duration of mechanical ventilation were slightly longer in the antiseizure-treatment group than in the control group. CONCLUSIONS: In comatose survivors of cardiac arrest, the incidence of a poor neurologic outcome at 3 months did not differ significantly between a strategy of suppressing rhythmic and periodic EEG activity with the use of antiseizure medication for at least 48 hours plus standard care and standard care alone. (Funded by the Dutch Epilepsy Foundation; TELSTAR ClinicalTrials.gov number, NCT02056236.).

A randomised trial of anti-GM-CSF otilimab in severe COVID-19 pneumonia (OSCAR).

BACKGROUND: Granulocyte-macrophage colony-stimulating factor (GM-CSF) and dysregulated myeloid cell responses are implicated in the pathophysiology and severity of COVID-19. METHODS: In this randomised, sequential, multicentre, placebo-controlled, double-blind study, adults aged 18-79 years (Part 1) or ≥70 years (Part 2) with severe COVID-19, respiratory failure and systemic inflammation (elevated C-reactive protein/ferritin) received a single intravenous infusion of otilimab 90 mg (human anti-GM-CSF monoclonal antibody) plus standard care (NCT04376684). The primary outcome was the proportion of patients alive and free of respiratory failure at Day 28. RESULTS: In Part 1 (n=806 randomised 1:1 otilimab:placebo), 71% of otilimab-treated patients were alive and free of respiratory failure at Day 28 versus 67% who received placebo; the model-adjusted difference of 5.3% was not statistically significant (95% CI -0.8-11.4%, p=0.09). A nominally significant model-adjusted difference of 19.1% (95% CI 5.2-33.1%, p=0.009) was observed in the predefined 70-79 years subgroup, but this was not confirmed in Part 2 (n=350 randomised) where the model-adjusted difference was 0.9% (95% CI -9.3-11.2%, p=0.86). Compared with placebo, otilimab resulted in lower serum concentrations of key inflammatory markers, including the putative pharmacodynamic biomarker CC chemokine ligand 17, indicative of GM-CSF pathway blockade. Adverse events were comparable between groups and consistent with severe COVID-19. CONCLUSIONS: There was no significant difference in the proportion of patients alive and free of respiratory failure at Day 28. However, despite the lack of clinical benefit, a reduction in inflammatory markers was observed with otilimab, in addition to an acceptable safety profile.

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.

Coronary Angiography after Cardiac Arrest without ST-Segment Elevation.

BACKGROUND: Ischemic heart disease is a major cause of out-of-hospital cardiac arrest. The role of immediate coronary angiography and percutaneous coronary intervention (PCI) in the treatment of patients who have been successfully resuscitated after cardiac arrest in the absence of ST-segment elevation myocardial infarction (STEMI) remains uncertain. METHODS: In this multicenter trial, we randomly assigned 552 patients who had cardiac arrest without signs of STEMI to undergo immediate coronary angiography or coronary angiography that was delayed until after neurologic recovery. All patients underwent PCI if indicated. The primary end point was survival at 90 days. Secondary end points included survival at 90 days with good cerebral performance or mild or moderate disability, myocardial injury, duration of catecholamine support, markers of shock, recurrence of ventricular tachycardia, duration of mechanical ventilation, major bleeding, occurrence of acute kidney injury, need for renal-replacement therapy, time to target temperature, and neurologic status at discharge from the intensive care unit. RESULTS: At 90 days, 176 of 273 patients (64.5%) in the immediate angiography group and 178 of 265 patients (67.2%) in the delayed angiography group were alive (odds ratio, 0.89; 95% confidence interval [CI], 0.62 to 1.27; P = 0.51). The median time to target temperature was 5.4 hours in the immediate angiography group and 4.7 hours in the delayed angiography group (ratio of geometric means, 1.19; 95% CI, 1.04 to 1.36). No significant differences between the groups were found in the remaining secondary end points. CONCLUSIONS: Among patients who had been successfully resuscitated after out-of-hospital cardiac arrest and had no signs of STEMI, a strategy of immediate angiography was not found to be better than a strategy of delayed angiography with respect to overall survival at 90 days. (Funded by the Netherlands Heart Institute and others; COACT Netherlands Trial Register number, NTR4973.).

Targeted Temperature Management in Out-of-Hospital Cardiac Arrest With Shockable Rhythm: A Post Hoc Analysis of the Coronary Angiography After Cardiac Arrest Trial.

OBJECTIVES: The optimal targeted temperature in patients with shockable rhythm is unclear, and current guidelines recommend targeted temperature management with a correspondingly wide range between 32°C and 36°C. Our aim was to study survival and neurologic outcome associated with targeted temperature management strategy in postarrest patients with initial shockable rhythm. DESIGN: Observational substudy of the Coronary Angiography after Cardiac Arrest without ST-segment Elevation trial. SETTING: Nineteen hospitals in The Netherlands. PATIENTS: The Coronary Angiography after Cardiac Arrest trial randomized successfully resuscitated patients with shockable rhythm and absence of ST-segment elevation to a strategy of immediate or delayed coronary angiography. In this substudy, 459 patients treated with mild therapeutic hypothermia (32.0-34.0°C) or targeted normothermia (36.0-37.0°C) were included. Allocation to targeted temperature management strategy was at the discretion of the physician. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: After 90 days, 171 patients (63.6%) in the mild therapeutic hypothermia group and 129 (67.9%) in the targeted normothermia group were alive (hazard ratio, 0.86 [95% CI, 0.62-1.18]; log-rank p = 0.35; adjusted odds ratio, 0.89; 95% CI, 0.45-1.72). Patients in the mild therapeutic hypothermia group had longer ICU stay (4 d [3-7 d] vs 3 d [2-5 d]; ratio of geometric means, 1.32; 95% CI, 1.15-1.51), lower blood pressures, higher lactate levels, and increased need for inotropic support. Cerebral Performance Category scores at ICU discharge and 90-day follow-up and patient-reported Mental and Physical Health Scores at 1 year were similar in the two groups. CONCLUSIONS: In the context of out-of-hospital cardiac arrest with shockable rhythm and no ST-elevation, treatment with mild therapeutic hypothermia was not associated with improved 90-day survival compared with targeted normothermia. Neurologic outcomes at 90 days as well as patient-reported Mental and Physical Health Scores at 1 year did not differ between the groups.

Suboptimal plasma concentrations with posaconazole suspension as prophylaxis in critically ill COVID-19 patients at risk of Covid-associated pulmonary aspergillosis.

WHAT IS KNOWN AND OBJECTIVE: The safety and efficacy of different antifungal agents in the prophylaxis of invasive fungal infection in patients with haematological disorders are known. We comment on the poor bioavailability of posaconazole suspension to suggest that it is not useful in critically ill COVID patients. COMMENT: The increased mortality and high incidence of COVID-associated pulmonary aspergillosis (CAPA) might justify administration of off-label posaconazole for preventing CAPA, being the only drug officially registered for prophylaxis of fungal infections. We decided to initiate off-label posaconazole prophylaxis in COVID-19 patients, who were mechanically ventilated and exposed to high-dose steroids for progressive pulmonary disease or ARDS. We found that posaconazole suspension was inadequate. Very low trough levels were observed after administration, and the dose adjustments necessary for the therapeutic drug monitoring (TDM) of the drug in our critically ill ICU patients were not useful. WHAT IS NEW AND CONCLUSION: Posaconazole suspension should not be used to prevent CAPA in COVID-19 patients on high-dose steroid therapy.

C-Terminal Proarginine Vasopressin is Associated with Disease Outcome and Mortality, but not with Delayed Cerebral Ischemia in Critically Ill Patients with an Aneurysmal Subarachnoid Hemorrhage: A Prospective Cohort Study.

BACKGROUND: Aneurysmal subarachnoid hemorrhage (aSAH) is an important indication for intensive care unit admission and may lead to significant morbidity and mortality. We assessed the ability of C-terminal proarginine vasopressin (CT-proAVP) to predict disease outcome, mortality, and delayed cerebral ischemia (DCI) in critically ill patients with aSAH compared with the World Federation of Neurological Surgeons (WFNS) score and Acute Physiological and Chronic Health Evaluation IV (APACHE IV) model. METHODS: C-terminal proarginine vasopressin was collected on admission in this single-center, prospective, observational cohort study. The primary aim was to investigate the relationship between CT-proAVP and poor functional outcome at 1 year (Glasgow Outcome Scale score 1-3) in a multivariable logistic regression model adjusted for WFNS and APACHE IV scores. Secondary aims were mortality and DCI. The multivariable logistic regression model for DCI was also adjusted for the modified Fisher scale. RESULTS: In 100 patients, the median CT-proAVP level was 24.9 pmol/L (interquartile range 11.5-53.8); 45 patients had a poor 1-year functional outcome, 19 patients died within 30 days, 25 patients died within 1 year, and DCI occurred in 28 patients. Receiver operating characteristics curves revealed high accuracy for CT-proAVP to identify patients with poor 1-year functional outcome (area under the curve [AUC] 0.84, 95% confidence interval [CI] 0.77-0.92, p < 0.001), 30-day mortality (AUC 0.84, 95% CI 0.76-0.93, p < 0.001), and 1-year mortality (AUC 0.79, 95% CI 0.69-0.89, p < 0.001). CT-proAVP had a low AUC for identifying patients with DCI (AUC 0.67, 95% CI 0.55-0.79, p 0.008). CT-proAVP ≥ 24.9 pmo/L proved to be a significant predictor for poor 1-year functional outcome (odds ratio [OR] 8.04, 95% CI 2.97-21.75, p < 0.001), and CT-proAVP ≥ 29.1 pmol/L and ≥ 27.7 pmol/L were significant predictors for 30-day and 1-year mortality (OR 9.31, 95% CI 1.55-56.07, p 0.015 and OR 5.15, 95% CI 1.48-17.93, p 0.010) in multivariable models with WFNS and APACHE IV scores. CT-proAVP ≥ 29.5 pmol/L was not a significant predictor for DCI in a multivariable model adjusted for the modified Fisher scale (p = 0.061). CONCLUSIONS: C-terminal proarginine vasopressin was able to predict poor functional outcome and mortality in critically ill patients with aSAH. Its prognostic ability to predict DCI was low. TRIAL REGISTRATION: Nederlands Trial Register: NTR4118.

Outcome Prediction of Postanoxic Coma: A Comparison of Automated Electroencephalography Analysis Methods.

BACKGROUND: To compare three computer-assisted quantitative electroencephalography (EEG) prediction models for the outcome prediction of comatose patients after cardiac arrest regarding predictive performance and robustness to artifacts. METHODS: A total of 871 continuous EEGs recorded up to 3 days after cardiac arrest in intensive care units of five teaching hospitals in the Netherlands were retrospectively analyzed. Outcome at 6 months was dichotomized as "good" (Cerebral Performance Category 1-2) or "poor" (Cerebral Performance Category 3-5). Three prediction models were implemented: a logistic regression model using two quantitative features, a random forest model with nine features, and a deep learning model based on a convolutional neural network. Data from two centers were used for training and fivefold cross-validation (n = 663), and data from three other centers were used for external validation (n = 208). Model output was the probability of good outcome. Predictive performances were evaluated by using receiver operating characteristic analysis and the calculation of predictive values. Robustness to artifacts was evaluated by using an artifact rejection algorithm, manually added noise, and randomly flattened channels in the EEG. RESULTS: The deep learning network showed the best overall predictive performance. On the external test set, poor outcome could be predicted by the deep learning network at 24 h with a sensitivity of 54% (95% confidence interval [CI] 44-64%) at a false positive rate (FPR) of 0% (95% CI 0-2%), significantly higher than the logistic regression (sensitivity 33%, FPR 0%) and random forest models (sensitivity 13%, FPR, 0%) (p < 0.05). Good outcome at 12 h could be predicted by the deep learning network with a sensitivity of 78% (95% CI 52-100%) at a FPR of 12% (95% CI 0-24%) and by the logistic regression model with a sensitivity of 83% (95% CI 83-83%) at a FPR of 3% (95% CI 3-3%), both significantly higher than the random forest model (sensitivity 1%, FPR 0%) (p < 0.05). The results of the deep learning network were the least affected by the presence of artifacts, added white noise, and flat EEG channels. CONCLUSIONS: A deep learning model outperformed logistic regression and random forest models for reliable, robust, EEG-based outcome prediction of comatose patients after cardiac arrest.

How are rapid diagnostic tests for infectious diseases used in clinical practice: a global survey by the International Society of Antimicrobial Chemotherapy (ISAC).

Novel rapid diagnostic tests (RDTs) offer huge potential to optimise clinical care and improve patient outcomes. In this study, we aim to assess the current patterns of use around the world, identify issues for successful implementation and suggest best practice advice on how to introduce new tests. An electronic survey was devised by the International Society of Antimicrobial Chemotherapy (ISAC) Rapid Diagnostics and Biomarkers working group focussing on the availability, structure and impact of RDTs around the world. It was circulated to ISAC members in December 2019. Results were collated according to the UN human development index (HDI). 81 responses were gathered from 31 different countries. 84% of institutions reported the availability of any test 24/7. In more developed countries, this was more for respiratory viruses, whereas in high and medium/low developed countries, it was for HIV and viral hepatitis. Only 37% of those carrying out rapid tests measured the impact. There is no 'one-size fits all' solution to RDTs: the requirements must be tailored to the healthcare setting in which they are deployed and there are many factors that should be considered prior to this.

Monitoring circulating dipeptidyl peptidase 3 (DPP3) predicts improvement of organ failure and survival in sepsis: a prospective observational multinational study.

BACKGROUND: Dipeptidyl peptidase 3 (DPP3) is a cytosolic enzyme involved in the degradation of various cardiovascular and endorphin mediators. High levels of circulating DPP3 (cDPP3) indicate a high risk of organ dysfunction and mortality in cardiogenic shock patients. METHODS: The aim was to assess relationships between cDPP3 during the initial intensive care unit (ICU) stay and short-term outcome in the AdrenOSS-1, a prospective observational multinational study in twenty-four ICU centers in five countries. AdrenOSS-1 included 585 patients admitted to the ICU with severe sepsis or septic shock. The primary outcome was 28-day mortality. Secondary outcomes included organ failure as defined by the Sequential Organ Failure Assessment (SOFA) score, organ support with focus on vasopressor/inotropic use and need for renal replacement therapy. cDPP3 levels were measured upon admission and 24 h later. RESULTS: Median [IQR] cDPP3 concentration upon admission was 26.5 [16.2-40.4] ng/mL. Initial SOFA score was 7 [5-10], and 28-day mortality was 22%. We found marked associations between cDPP3 upon ICU admission and 28-day mortality (unadjusted standardized HR 1.8 [CI 1.6-2.1]; adjusted HR 1.5 [CI 1.3-1.8]) and between cDPP3 levels and change in renal and liver SOFA score (p = 0.0077 and 0.0009, respectively). The higher the initial cDPP3 was, the greater the need for organ support and vasopressors upon admission; the longer the need for vasopressor(s), mechanical ventilation or RRT and the higher the need for fluid load (all p < 0.005). In patients with cDPP3 > 40.4 ng/mL upon admission, a decrease in cDPP3 below 40.4 ng/mL after 24 h was associated with an improvement of organ function at 48 h and better 28-day outcome. By contrast, persistently elevated cDPP3 at 24 h was associated with worsening organ function and high 28-day mortality. CONCLUSIONS: Admission levels and rapid changes in cDPP3 predict outcome during sepsis. Trial Registration ClinicalTrials.gov, NCT02393781. Registered on March 19, 2015.

Duration of antibiotic treatment using procalcitonin-guided treatment algorithms in older patients: a patient-level meta-analysis from randomized controlled trials.

BACKGROUND: Older patients have a less pronounced immune response to infection, which may also influence infection biomarkers. There is currently insufficient data regarding clinical effects of procalcitonin (PCT) to guide antibiotic treatment in older patients. OBJECTIVE AND DESIGN: We performed an individual patient data meta-analysis to investigate the association of age on effects of PCT-guided antibiotic stewardship regarding antibiotic use and outcome. SUBJECTS AND METHODS: We had access to 9,421 individual infection patients from 28 randomized controlled trials comparing PCT-guided antibiotic therapy (intervention group) or standard care. We stratified patients according to age in four groups (<75 years [n = 7,079], 75-80 years [n = 1,034], 81-85 years [n = 803] and >85 years [n = 505]). The primary endpoint was the duration of antibiotic treatment and the secondary endpoints were 30-day mortality and length of stay. RESULTS: Compared to control patients, mean duration of antibiotic therapy in PCT-guided patients was significantly reduced by 24, 22, 26 and 24% in the four age groups corresponding to adjusted differences in antibiotic days of -1.99 (95% confidence interval [CI] -2.36 to -1.62), -1.98 (95% CI -2.94 to -1.02), -2.20 (95% CI -3.15 to -1.25) and - 2.10 (95% CI -3.29 to -0.91) with no differences among age groups. There was no increase in the risk for mortality in any of the age groups. Effects were similar in subgroups by infection type, blood culture result and clinical setting (P interaction >0.05). CONCLUSIONS: This large individual patient data meta-analysis confirms that, similar to younger patients, PCT-guided antibiotic treatment in older patients is associated with significantly reduced antibiotic exposures and no increase in mortality.

Elevated methaemoglobin in a critically ill patient as a result of hydrogen peroxide exposure: A case study.

WHAT IS KNOWN AND OBJECTIVE: Formation of methaemoglobinaemia (MetHb) decreases oxygen capacity in the blood, leading to tissue hypoxia. This condition may be acquired following exposure to certain drugs. CASE SUMMARY: A critically ill patient with necrotizing fasciitis unexpectedly developed marked and unexplained MetHb (6.7%). Her digital medication list did not reveal the causative factor. However, deeper exploration showed the use of other compounds (acetone, hydrogen peroxide) not routinely visible on the medication list. WHAT IS NEW AND CONCLUSION: Elevated MetHb likely resulted from high-volume hydrogen peroxide 3% exposure. Clinicians should be cautious rinsing large open wounds with hydrogen peroxide. When MetHb is diagnosed, less familiar compounds, usually not on the medication list, should be considered in the differential diagnosis and extensive hetero-anamnesis is mandatory.

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.

Early electroencephalography for outcome prediction of postanoxic coma: A prospective cohort study.

OBJECTIVE: To provide evidence that early electroencephalography (EEG) allows for reliable prediction of poor or good outcome after cardiac arrest. METHODS: In a 5-center prospective cohort study, we included consecutive, comatose survivors of cardiac arrest. Continuous EEG recordings were started as soon as possible and continued up to 5 days. Five-minute EEG epochs were assessed by 2 reviewers, independently, at 8 predefined time points from 6 hours to 5 days after cardiac arrest, blinded for patients' actual condition, treatment, and outcome. EEG patterns were categorized as generalized suppression (<10 µV), synchronous patterns with ≥50% suppression, continuous, or other. Outcome at 6 months was categorized as good (Cerebral Performance Category [CPC] = 1-2) or poor (CPC = 3-5). RESULTS: We included 850 patients, of whom 46% had a good outcome. Generalized suppression and synchronous patterns with ≥50% suppression predicted poor outcome without false positives at ≥6 hours after cardiac arrest. Their summed sensitivity was 0.47 (95% confidence interval [CI] = 0.42-0.51) at 12 hours and 0.30 (95% CI = 0.26-0.33) at 24 hours after cardiac arrest, with specificity of 1.00 (95% CI = 0.99-1.00) at both time points. At 36 hours or later, sensitivity for poor outcome was ≤0.22. Continuous EEG patterns at 12 hours predicted good outcome, with sensitivity of 0.50 (95% CI = 0.46-0.55) and specificity of 0.91 (95% CI = 0.88-0.93); at 24 hours or later, specificity for the prediction of good outcome was <0.90. INTERPRETATION: EEG allows for reliable prediction of poor outcome after cardiac arrest, with maximum sensitivity in the first 24 hours. Continuous EEG patterns at 12 hours after cardiac arrest are associated with good recovery. ANN NEUROL 2019;86:203-214.

Association of kidney function with effectiveness of procalcitonin-guided antibiotic treatment: a patient-level meta-analysis from randomized controlled trials.

Objectives: Patients with impaired kidney function have a significantly slower decrease of procalcitonin (PCT) levels during infection. Our aim was to study PCT-guided antibiotic stewardship and clinical outcomes in patients with impairments of kidney function as assessed by creatinine levels measured upon hospital admission. Methods: We pooled and analyzed individual data from 15 randomized controlled trials who were randomly assigned to receive antibiotic therapy based on a PCT-algorithms or based on standard of care. We stratified patients on the initial glomerular filtration rate (GFR, ml/min/1.73 m2) in three groups (GFR >90 [chronic kidney disease; CKD 1], GFR 15-89 [CKD 2-4] and GFR<15 [CKD 5]). The main efficacy and safety endpoints were duration of antibiotic treatment and 30-day mortality. Results: Mean duration of antibiotic treatment was significantly shorter in PCT-guided (n=2,492) compared to control patients (n=2,510) (9.5-7.6 days; adjusted difference in days -2.01 [95% CI, -2.45 to -1.58]). CKD 5 patients had overall longer treatment durations, but a 2.5-day reduction in treatment duration was still found in patients receiving in PCT-guided care (11.3 vs. 8.6 days [95% CI -3.59 to -1.40]). There were 397 deaths in 2,492 PCT-group patients (15.9%) compared to 460 deaths in 2,510 control patients (18.3%) (adjusted odds ratio, 0.88 [95% CI 0.78 to 0.98)]. Effects of PCT-guidance on antibiotic treatment duration and mortality were similar in subgroups stratified by infection type and clinical setting (p interaction >0.05). Conclusions: This individual patient data meta-analysis confirms that the use of PCT in patients with impaired kidney function, as assessed by admission creatinine levels, is associated with shorter antibiotic courses and lower mortality rates.

Influence of sedation on delirium recognition in critically ill patients: A multinational cohort study.

BACKGROUND: Guidelines advocate intensive care unit (ICU) patients be regularly assessed for delirium using either the Confusion Assessment Method for the ICU (CAM-ICU) or the Intensive Care Delirium Screening Checklist (ICDSC). Single-centre studies, primarily with the CAM-ICU, suggest level of sedation may influence delirium screening results. OBJECTIVE: The objective of this study was to determine the association between level of sedation and delirium occurrence in critically ill patients assessed with either the CAM-ICU or the ICDSC. METHODS: This was a secondary analysis of a multinational, prospective cohort study performed in nine ICUs from seven countries. Consecutive ICU patients with a Richmond Agitation-Sedation Scale (RASS) of -3 to 0 at the time of delirium assessment where a RASS ≤ 0 was secondary to a sedating medication. Patients were assessed with either the CAM-ICU or the ICDSC. Logistic regression analysis was used to account for factors with the potential to influence level of sedation or delirium occurrence. RESULTS: Among 1660 patients, 1203 patients underwent 5741 CAM-ICU assessments [9.6% were delirium positive; at RASS = 0 (3.3% were delirium positive), RASS = -1 (19.3%), RASS = -2 (35.1%); RASS = -3 (39.0%)]. The other 457 patients underwent 3210 ICDSC assessments [11.6% delirium positive; at RASS = 0 (4.9% were delirium positive), RASS = -1 (15.8%), RASS = -2 (26.6%); RASS = -3 (20.6%)]. A RASS of -3 was associated with more positive delirium evaluations (odds ratio: 2.31; 95% confidence interval: 1.34-3.98) in the CAM-ICU-assessed patients (vs. the ICDSC-assessed patients). At a RASS of 0, assessment with the CAM-ICU (vs. the ICDSC) was associated with fewer positive delirium evaluations (odds ratio: 0.58; 95% confidence interval: 0.43-0.78). At a RASS of -1 or -2, no association was found between the delirium assessment method used (i.e., CAM-ICU or ICDSC) and a positive delirium evaluation. CONCLUSIONS: The influence of level of sedation on a delirium assessment result depends on whether the CAM-ICU or ICDSC is used. Bedside ICU nurses should consider these results when evaluating their sedated patients for delirium. Future research is necessary to compare the CAM-ICU and the ICDSC simultaneously in sedated and nonsedated ICU patients. TRIAL REGISTRATION: ClinicalTrials.gov; NCT02518646.

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.

Outcome Prediction in Postanoxic Coma With Deep Learning.

OBJECTIVES: Visual assessment of the electroencephalogram by experienced clinical neurophysiologists allows reliable outcome prediction of approximately half of all comatose patients after cardiac arrest. Deep neural networks hold promise to achieve similar or even better performance, being more objective and consistent. DESIGN: Prospective cohort study. SETTING: Medical ICU of five teaching hospitals in the Netherlands. PATIENTS: Eight-hundred ninety-five consecutive comatose patients after cardiac arrest. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Continuous electroencephalogram was recorded during the first 3 days after cardiac arrest. Functional outcome at 6 months was classified as good (Cerebral Performance Category 1-2) or poor (Cerebral Performance Category 3-5). We trained a convolutional neural network, with a VGG architecture (introduced by the Oxford Visual Geometry Group), to predict neurologic outcome at 12 and 24 hours after cardiac arrest using electroencephalogram epochs and outcome labels as inputs. Output of the network was the probability of good outcome. Data from two hospitals were used for training and internal validation (n = 661). Eighty percent of these data was used for training and cross-validation, the remaining 20% for independent internal validation. Data from the other three hospitals were used for external validation (n = 234). Prediction of poor outcome was most accurate at 12 hours, with a sensitivity in the external validation set of 58% (95% CI, 51-65%) at false positive rate of 0% (CI, 0-7%). Good outcome could be predicted at 12 hours with a sensitivity of 48% (CI, 45-51%) at a false positive rate of 5% (CI, 0-15%) in the external validation set. CONCLUSIONS: Deep learning of electroencephalogram signals outperforms any previously reported outcome predictor of coma after cardiac arrest, including visual electroencephalogram assessment by trained electroencephalogram experts. Our approach offers the potential for objective and real time, bedside insight in the neurologic prognosis of comatose patients after cardiac arrest.

Procalcitonin (PCT)-guided antibiotic stewardship: an international experts consensus on optimized clinical use.

Background Procalcitonin (PCT)-guided antibiotic stewardship (ABS) has been shown to reduce antibiotics (ABxs), with lower side-effects and an improvement in clinical outcomes. The aim of this experts workshop was to derive a PCT algorithm ABS for easier implementation into clinical routine across different clinical settings. Methods Clinical evidence and practical experience with PCT-guided ABS was analyzed and discussed, with a focus on optimal PCT use in the clinical context and increased adherence to PCT protocols. Using a Delphi process, the experts group reached consensus on different PCT algorithms based on clinical severity of the patient and probability of bacterial infection. Results The group agreed that there is strong evidence that PCT-guided ABS supports individual decisions on initiation and duration of ABx treatment in patients with acute respiratory infections and sepsis from any source, thereby reducing overall ABx exposure and associated side effects, and improving clinical outcomes. To simplify practical application, the expert group refined the established PCT algorithms by incorporating severity of illness and probability of bacterial infection and reducing the fixed cut-offs to only one for mild to moderate and one for severe disease (0.25 µg/L and 0.5 µg/L, respectively). Further, guidance on interpretation of PCT results to initiate, withhold or discontinue ABx treatment was included. Conclusions A combination of clinical patient assessment with PCT levels in well-defined ABS algorithms, in context with continuous education and regular feedback to all ABS stakeholders, has the potential to improve the diagnostic and therapeutic management of patients suspected of bacterial infection, thereby improving ABS effectiveness.

Predicting outcome in patients with moderate to severe traumatic brain injury using electroencephalography.

BACKGROUND: Better outcome prediction could assist in reliable quantification and classification of traumatic brain injury (TBI) severity to support clinical decision-making. We developed a multifactorial model combining quantitative electroencephalography (qEEG) measurements and clinically relevant parameters as proof of concept for outcome prediction of patients with moderate to severe TBI. METHODS: Continuous EEG measurements were performed during the first 7 days of ICU admission. Patient outcome at 12 months was dichotomized based on the Extended Glasgow Outcome Score (GOSE) as poor (GOSE 1-2) or good (GOSE 3-8). Twenty-three qEEG features were extracted. Prediction models were created using a Random Forest classifier based on qEEG features, age, and mean arterial blood pressure (MAP) at 24, 48, 72, and 96 h after TBI and combinations of two time intervals. After optimization of the models, we added parameters from the International Mission for Prognosis And Clinical Trial Design (IMPACT) predictor, existing of clinical, CT, and laboratory parameters at admission. Furthermore, we compared our best models to the online IMPACT predictor. RESULTS: Fifty-seven patients with moderate to severe TBI were included and divided into a training set (n = 38) and a validation set (n = 19). Our best model included eight qEEG parameters and MAP at 72 and 96 h after TBI, age, and nine other IMPACT parameters. This model had high predictive ability for poor outcome on both the training set using leave-one-out (area under the receiver operating characteristic curve (AUC) = 0.94, specificity 100%, sensitivity 75%) and validation set (AUC = 0.81, specificity 75%, sensitivity 100%). The IMPACT predictor independently predicted both groups with an AUC of 0.74 (specificity 81%, sensitivity 65%) and 0.84 (sensitivity 88%, specificity 73%), respectively. CONCLUSIONS: Our study shows the potential of multifactorial Random Forest models using qEEG parameters to predict outcome in patients with moderate to severe TBI.