Clinicians' approach to predicting post-cardiac arrest outcomes for patients enrolled in a United States clinical trial.

PURPOSE: Perceived poor prognosis can lead to withdrawal of life-sustaining therapies (WLST) in patients who might otherwise recover. We characterized clinicians' approach to post-arrest prognostication in a multicenter clinical trial. METHODS: Semi-structured interviews were conducted with clinicians who treated a comatose post-cardiac arrest patient enrolled in the Influence of Cooling Duration on Efficacy in Cardiac Arrest Patients (ICECAP) trial (NCT04217551). Two authors independently analyzed each interview using inductive and deductive coding. The clinician reported how they arrived at a prognosis for the specific patient. We summarized the frequency with which clinicians reported using objective diagnostics to formulate their prognosis, and compared the reported approaches to established guidelines. Each respondent provided demographic information and described local neuroprognostication practices. RESULTS: We interviewed 30 clinicians at 19 US hospitals. Most claimed adherence to local hospital neuroprognostication protocols (n = 19). Prognostication led to WLST for perceived poor neurological prognosis in 15/30 patients, of whom most showed inconsistencies with guidelines or trial recommendations, respectively. In 10/15 WLST cases, clinicians reported relying on multimodal testing. A prevalent theme was the use of "clinical gestalt," defined as prognosticating based on a patient's overall appearance or a subjective impression in the absence of objective data. Many clinicians (21/30) reported using clinical gestalt for initial prognostication, with 9/21 expressing high confidence initially. CONCLUSION: Clinicians in our study state they follow neuroprognostication guidelines in general but often do not do so in actual practice. They reported clinical gestalt frequently informed early, highly confident prognostic judgments, and few objective tests changed initial impressions. Subjective prognostication may undermine well-designed trials.

Brain computed tomography after resuscitation from in-hospital cardiac arrest.

BACKGROUND: Few data characterize the role of brain computed tomography (CT) after resuscitation from in-hospital cardiac arrest (IHCA). We hypothesized that identifying a neurological etiology of arrest or cerebral edema on brain CT are less common after IHCA than after resuscitation from out-of-hospital cardiac arrest (OHCA). METHODS: We included all patients comatose after resuscitation from IHCA or OHCA in this retrospective cohort analysis. We abstracted patient and arrest clinical characteristics, as well as pH and lactate, to estimate systemic illness severity. Brain CT characteristics included quantitative measurement of the grey-to-white ratio (GWR) at the level of the basal ganglia and qualitative assessment of sulcal and cisternal effacement. We compared GWR distribution by stratum (no edema ≥1.30, mild-to-moderate <1.30 and >1.20, severe ≤1.20) and newly identified neurological arrest etiology between IHCA and OHCA groups. RESULTS: We included 2,306 subjects, of whom 420 (18.2%) suffered IHCA. Fewer IHCA subjects underwent post-arrest brain CT versus OHCA subjects (149 (35.5%) vs 1,555 (82.4%), p < 0.001). Cerebral edema for IHCA versus OHCA was more often absent (60.1% vs. 47.5%) or mild-to-moderate (34.3% vs. 27.9%) and less often severe (5.6% vs. 24.6%). A neurological etiology of arrest was identified on brain CT in 0.5% of IHCA versus 3.2% of OHCA. CONCLUSIONS: Although severe edema was less frequent in IHCA relative to OHCA, mild-to-moderate or severe edema occurred in one in three patients after IHCA. Unsuspected neurological etiologies of arrest were rarely discovered by CT scan in IHCA patients.

Emotional distress, social support, and functional dependence predict readiness for hospital discharge in a prospective sample of cognitively intact cardiac arrest survivors.

AIM: To inform screening, referral and treatment initiatives, we tested the hypothesis that emotional distress, social support, functional dependence, and cognitive impairment within 72 hours prior to discharge predict readiness for discharge in awake and alert cardiac arrest (CA) survivors. METHODS: This was a secondary analysis of a prospective single-center cohort of CA survivors enrolled between 4/2021 and 9/2022. We quantified emotional distress using the Posttraumatic Stress Disorder Checklist-5 and PROMIS Emotional Distress - Anxiety and Depression Short Forms 4a; perceived social support using the ENRICHD Social Support Inventory; functional dependence using the modified Rankin Scale; and cognitive impairment using the Telephone Interview for Cognitive Status. Our primary outcome was readiness for discharge, measured using the Readiness for Hospital Discharge Scale. We used multivariable linear regression to test the independent association of each survivorship factor and readiness for discharge. RESULTS: We included 110 patients (64% male, 88% white, mean age 59 [standard deviation ± 13.1 years]). Emotional distress, functional dependence, and social support were independently associated with readiness for discharge (adjusted ß's [absolute value]: 0.25-0.30, all p < 0.05). CONCLUSIONS: Hospital systems should consider implementing routine in-hospital screening for emotional distress, social support, and functional dependence for CA survivors who are awake, alert and approaching hospital discharge, and prioritize brief in hospital treatment or post-discharge referrals.

A multi-center, randomized, double blinded, placebo-controlled trial of amantadine to stimulate awakening in comatose patients resuscitated from cardiac arrest.

Objectives: We hypothesized that the administration of amantadine would increase awakening of comatose patients resuscitated from cardiac arrest. Methods: We performed a prospective, randomized controlled pilot trial, randomizing subjects to amantadine 100mg twice daily or placebo for up to 7 days. The study drug was administered between 72-120 hours after resuscitation and patients with absent N20 cortical responses, early cerebral edema, or ongoing malignant electroencephalography patterns were excluded. Our primary outcome was awakening, defined as following two-step commands, within 28 days of cardiac arrest. Secondary outcomes included length of stay, awakening, time to awakening, and neurologic outcome measured by Cerebral Performance Category (CPC) at hospital discharge. We compared the proportion of subjects awakening and hospital survival using Fisher's exact tests and time to awakening and hospital length of stay using Wilcoxon rank sum tests. Results: After 2 years, we stopped the study due to slow enrollment and lapse of funding. We enrolled 14 subjects (12% of goal enrollment), 7 in the amantadine arm and 7 in the placebo arm. The proportion of patients who awakened within 28 days after cardiac arrest did not differ between amantadine (n=2, 28.57%) and placebo groups (n=3, 42.86%) (p = 1.00). There were no differences in secondary outcomes. Study medication was stopped in three (21%) subjects. Adverse events included a recurrence of seizures (n=2; 14%), both of which occurred in the placebo arm. Conclusion: We could not determine the effect of amantadine on awakening in comatose survivors of cardiac arrest due to small sample size.

Prehospital Electroencephalography to Detect Traumatic Brain Injury during Helicopter Transport: A Pilot Observational Cohort Study.

OBJECTIVE: Early recognition of traumatic brain injury (TBI) is important to facilitate time-sensitive care. Electroencephalography (EEG) can identify TBI, but feasibility of EEG has not been evaluated in prehospital settings. We tested the feasibility of obtaining single-channel EEG during air medical transport after trauma. We measured association between quantitative EEG features, early blood biomarkers, and abnormalities on head computerized tomography (CT). METHODS: We performed a pilot prospective, observational study enrolling consecutive patients transported by critical care air ambulance from the scene of trauma to a Level I trauma center. During transport, prehospital clinicians placed a sensor on the patient's forehead to record EEG. We reviewed EEG waveforms and selected 90 seconds of recording for quantitative analysis. EEG data processing included fast Fourier transform to summarize component frequency power in the delta (0-4 Hz), theta (4-8 Hz), and alpha (8-13 Hz) ranges. We collected blood samples on day 1 and day 3 post-injury and measured plasma levels of two brain injury biomarkers (ubiquitin C-terminal hydrolase L1 [UCH-L1] and glial fibrillary acidic protein [GFAP]). We compared predictors between individuals with and without CT-positive TBI findings. RESULTS: Forty subjects were enrolled, with EEG recordings successfully obtained in 34 (85%). Reasons for failure included uncharged battery (n = 5) and user error (n = 1). Data were lost in three cases. Of 31 subjects with data, interpretable EEG signal was recorded in 26 (84%). Mean age was 48 (SD 16) years, 79% were male, and 50% suffered motor vehicle crashes. Eight subjects (24%) had CT-positive TBI. Subjects with and without CT-positive TBI had similar median delta power, alpha power, and theta power. UCH-L1 and GFAP plasma levels did not differ across groups. Delta power inversely correlated with UCH-L1 day 1 plasma concentration (r = -0.60, p = 0.03). CONCLUSIONS: Prehospital EEG acquisition is feasible during air transport after trauma.

Metabolic Manipulation and Therapeutic Hypothermia.

Hypothermia has multiple physiological effects, including decreasing metabolic rate and oxygen consumption (VO2). There are few human data about the magnitude of change in VO2 with decreases in core temperature. We aimed to quantify to magnitude of reduction in resting VO2 as we reduced core temperature in lightly sedated healthy individuals. After informed consent and physical screening, we cooled participants by rapidly infusing 20 mL/kg of cold (4°C) saline intravenously and placing surface cooling pads on the torso. We attempted to suppress shivering using a 1 mcg/kg intravenous bolus of dexmedetomidine followed by titrated infusion at 1.0 to 1.5 µg/(kg·h). We measured resting metabolic rate VO2 through indirect calorimetry at baseline (37°C) and at 36°C, 35°C, 34°C, and 33°C. Nine participants had mean age 30 (standard deviation 10) years and 7 (78%) were male. Baseline VO2 was 3.36 mL/(kg·min) (interquartile range 2.98-3.76) mL/(kg·min). VO2 was associated with core temperature and declined with each degree decrease in core temperature, unless shivering occurred. Over the entire range from 37°C to 33°C, median VO2 declined 0.7 mL/(kg·min) (20.8%) in the absence of shivering. The largest average decrease in VO2 per degree Celsius was by 0.46 mL/(kg·min) (13.7%) and occurred between 37°C and 36°C in the absence of shivering. After a participant developed shivering, core body temperature did not decrease further, and VO2 increased. In lightly sedated humans, metabolic rate decreases around 5.2% for each 1°C decrease in core temperature from 37°C to 33°C. Because the largest decrease in metabolic rate occurs between 37°C and 36°C, subclinical shivering or other homeostatic reflexes may be present at lower temperatures.

Simple approach to quantify hypoxic-ischemic brain injury severity from computed tomography imaging files after cardiac arrest.

BACKGROUND: Grey-white ratio (GWR) can estimate severity of cytotoxic cerebral edema secondary to hypoxic-ischemic brain injury after cardiac arrest and predict progression to death by neurologic criteria (DNC). Current approaches to calculating GWR are not standardized and have variable interrater reliability. We tested if measures of variance of brain density on early computed tomographic (CT) imaging after cardiac arrest could predict DNC. METHODS: We performed a retrospective cohort study, identifying post-arrest patients treated between 2011 and 2020 at our single center. We extracted demographic data from our registry and Digital Imaging and Communication in Medicine (DICOM) files for each patient's first brain CT. We analyzed slices 15-20 of each DICOM, corresponding to the level of the basal ganglia while accommodating differences in patient anatomy. We extracted pixel arrays and converted the radiodensities to Hounsfield units (HU). To focus on brain tissue densities, we excluded HU > 60 and < 10. We calculated the variance of each patient's HU distribution and the difference between the means of a two-group Gaussian finite mixture model. We compared these novel metrics to existing measures of cerebral edema, then randomly divided our data into 80% training and 20% test sets and used logistic regression to predict DNC. RESULTS: Of 1,133 included subjects, 457 (40%) were female, mean (standard deviation) age was 58 (16) years, and 115 (10%) progressed to DNC. CTs were obtained a median [interquartile range] of 4.2 [2.8-5.7] hours post-arrest. Our novel measures correlated weakly with GWR. HU variance, but not difference between mixture model means, differed significantly between subjects with and without sulcal or cistern effacement. GWR outperformed our novel measures in predicting progression to DNC with an area under the receiver operating characteristic curve (AUC) of 0.82, compared to HU variance (AUC = 0.73) and the difference between mixture model means (AUC = 0.56). CONCLUSION: There are differences in the distribution of HU on post-arrest CT in patients with qualitative measures of cerebral edema. Current methods to quantify cerebral edema outperform simple measures of attenuation variance on early brain CT. Further analyses could investigate if these measures of variance, or other distributional characteristics of brain density, have improved predictive performance on brain CTs obtained later in the clinical course or derived from discrete regions of anatomical interest.

Impact of Preexisting Depression and Anxiety on Hospital Readmission and Long-Term Survival After Cardiac Arrest.

BACKGROUND: While sudden cardiac arrest (CA) survivors are at risk for developing psychiatric disorders, little is known about the impact of preexisting mental health conditions on long-term survival or postacute healthcare utilization. We examined the prevalence of preexisting psychiatric conditions in CA patients who survived hospital discharge, characterized incidence and reason for inpatient psychiatry consultation during these patients' acute hospitalizations, and determined the association of pre-CA depression and anxiety with hospital readmission rates and long-term survival. We hypothesized that prior depression or anxiety would be associated with higher hospital readmission rates and lower long-term survival. METHODS: We conducted a retrospective cohort study including patients resuscitated from in- and out-of-hospital CA who survived both admission and discharge from a single hospital between January 1, 2010, and December 31, 2017. We identified patients from our prospective registry, then performed a structured chart review to abstract past psychiatric history, prescription medications for psychiatric conditions, and identify inpatient psychiatric consultations. We used administrative data to identify readmissions within 1 year and vital status through December 31, 2020. We used multivariable Cox regressions controlling for patient demographics, medical comorbidities, discharge Cerebral Performance Category and disposition, depression, and anxiety history to predict long-term survival and hospital readmission. RESULTS: We included 684 subjects. Past depression or anxiety was noted in 24% (n = 162) and 19% (n = 129) of subjects. A minority of subjects (n = 139, 20%) received a psychiatry consultation during the index hospitalization. Overall, 262 (39%) subjects had at least 1 readmission within 1 year. Past depression was associated with an increased hazard of hospital readmission (hazard ratio 1.50, 95% CI 1.11-2.04), while past anxiety was not associated with readmission. Neither depression nor anxiety were independently associated with long-term survival. CONCLUSIONS: Depression is an independent risk factor for hospital readmission in CA survivors.

Diagnostic yield of computed tomography after non-traumatic out-of-hospital cardiac arrest.

AIM: Determine the frequency with which computed tomography (CT) after out-of-hospital cardiac arrest (OHCA) identifies clinically important findings. METHODS: We included non-traumatic OHCA patients treated at a single center from February 2019 to February 2021. Clinical practice was to obtain CT head in comatose patients. Additionally, CT of the cervical spine, chest, abdomen, and pelvis were obtained if clinically indicated. We identified CT imaging obtained within 24 hours of emergency department (ED) arrival and summarized radiology findings. We used descriptive statistics to summarize population characteristics and imaging results, report their frequencies and, post hoc, compared time from ED arrival to catheterization between patients who did and did not undergo CT. RESULTS: We included 597 subjects, of which 491 (82.2%) had a CT obtained. Time to CT was 4.1 hours [2.8-5.7]. Most (n = 480, 80.4%) underwent CT head, of which 36 (7.5%) had intracranial hemorrhage and 161 (33.5%) had cerebral edema. Fewer subjects (230, 38.5%) underwent a cervical spine CT, and 4 (1.7%) had acute vertebral fractures. Most subjects (410, 68.7%) underwent a chest CT, and abdomen and pelvis CT (363, 60.8%). Chest CT abnormalities included rib or sternal fractures (227, 55.4%), pneumothorax (27, 6.6%), aspiration or pneumonia (309, 75.4%), mediastinal hematoma (18, 4.4%) and pulmonary embolism (6, 3.7%). Significant abdomen and pelvis findings were bowel ischemia (24, 6.6%) and solid organ laceration (7, 1.9%). Most subjects that had CT imaging deferred were awake and had shorter time to catheterization. CONCLUSIONS: CT identifies clinically important pathology after OHCA.

Protective positive psychology factors and emotional distress after cardiac arrest.

BACKGROUND: There is a critical need to identify factors that can prevent emotional distress post-cardiac arrest (CA). CA survivors have previously described benefitting from utilizing positive psychology constructs (mindfulness, existential well-being, resilient coping, social support) to cope with distress. Here, we explored associations between positive psychology factors and emotional distress post-CA. METHODS: We recruited CA survivors treated from 4/2021-9/2022 at a single academic medical center. We assessed positive psychology factors (mindfulness [Cognitive and Affective Mindfulness Scale-Revised], existential well-being [Meaning in Life Questionnaire Presence of Meaning subscale], resilient coping [Brief Resilient Coping Scale], perceived social support [ENRICHD Social Support Inventory]) and emotional distress (posttraumatic stress [Posttraumatic Stress Checklist-5], anxiety and depression symptoms [PROMIS Emotional Distress - Anxiety and Depression Short Forms 4a]) just before discharge from the index hospitalization. We selected covariates for inclusion in our multivariable models based on an association with any emotional distress factor (p < 0.10). For our final, multivariable regression models, we individually tested the independent association of each positive psychology factor and emotional distress factor. RESULTS: We included 110 survivors (mean age 59 years, 64% male, 88% non-Hispanic White, 48% low income); 36.4% of survivors scored above the cut-off for at least one measure of emotional distress. In separate adjusted models, each positive psychology factor was independently associated with emotional distress (ß: -0.20 to -0.42, all p < 0.05). CONCLUSIONS: Higher levels of mindfulness, existential well-being, resilient coping, and perceived social support were each associated with less emotional distress. Future intervention development studies should consider these factors as potential treatment targets.

Duration of cardiopulmonary resuscitation and phenotype of post-cardiac arrest brain injury.

BACKGROUND: Patients resuscitated from cardiac arrest have variable severity of primary hypoxic ischemic brain injury (HIBI). Signatures of primary HIBI on brain imaging and electroencephalography (EEG) include diffuse cerebral edema and burst suppression with identical bursts (BSIB). We hypothesize distinct phenotypes of primary HIBI are associated with increasing cardiopulmonary resuscitation (CPR) duration. METHODS: We identified from our prospective registry of both in-and out-of-hospital CA patients treated between January 2010 to January 2020 for this cohort study. We abstracted CPR duration, neurological examination, initial brain computed tomography gray to white ratio (GWR), and initial EEG pattern. We considered four phenotypes on presentation: awake; comatose with neither BSIB nor cerebral edema (non-malignant coma); BSIB; and cerebral edema (GWR ≤ 1.20). BSIB and cerebral edema were considered as non-mutually exclusive outcomes. We generated predicted probabilities of brain injury phenotype using localized regression. RESULTS: We included 2,440 patients, of whom 545 (23%) were awake, 1,065 (44%) had non-malignant coma, 548 (23%) had BSIB and 438 (18%) had cerebral edema. Only 92 (4%) had both BSIB and edema. Median CPR duration was 16 [IQR 8-28] minutes. Median CPR duration increased in a stepwise manner across groups: awake 6 [3-13] minutes; non-malignant coma 15 [8-25] minutes; BSIB 21 [13-31] minutes; cerebral edema 32 [22-46] minutes. Predicted probability of phenotype changes over time. CONCLUSIONS: Brain injury phenotype is related to CPR duration, which is a surrogate for severity of HIBI. The sequence of most likely primary HIBI phenotype with progressively longer CPR duration is awake, coma without BSIB or edema, BSIB, and finally cerebral edema.

Time to Awakening and Self-Fulfilling Prophecies After Cardiac Arrest.

OBJECTIVES: Withdrawal of life-sustaining therapies for perceived poor neurologic prognosis (WLST-N) is common after resuscitation from cardiac arrest and may bias outcome estimates from models trained using observational data. We compared several approaches to outcome prediction with the goal of identifying strategies to quantify and reduce this bias. DESIGN: Retrospective observational cohort study. SETTING: Two academic medical centers ("UPMC" and "University of Alabama Birmingham" [UAB]). PATIENTS: Comatose adults resuscitated from cardiac arrest. INTERVENTION: None. MEASUREMENTS AND MAIN RESULTS: As potential predictors, we considered clinical, laboratory, imaging, and quantitative electroencephalography data available early after hospital arrival. We followed patients until death, discharge, or awakening from coma. We used penalized Cox regression with a least absolute shrinkage and selection operator penalty and five-fold cross-validation to predict time to awakening in UPMC patients and then externally validated the model in UAB patients. This model censored patients after WLST-N, considering subsequent potential for awakening to be unknown. Next, we developed a penalized logistic model predicting awakening, which treated failure to awaken after WLST-N as a true observed outcome, and a separate logistic model predicting WLST-N. We scaled and centered individual patients' Cox and logistic predictions for awakening to allow direct comparison and then explored the difference in predictions across probabilities of WLST-N. Overall, 1,254 patients were included, and 29% awakened. Cox models performed well (mean area under the curve was 0.93 in the UPMC test sets and 0.83 in external validation). Logistic predictions of awakening were systematically more pessimistic than Cox-based predictions for patients at higher risk of WLST-N, suggesting potential for self-fulfilling prophecies to arise when failure to awaken after WLST-N is considered as the ground truth outcome. CONCLUSIONS: Compared with traditional binary outcome prediction, censoring outcomes after WLST-N may reduce potential for bias and self-fulfilling prophecies.

Status Epilepticus: A Neurologic Emergency.

In this review, we discuss treatment and considerations for status epilepticus in general intensive care unit patients, acquired brain injury, autoimmune conditions, toxidromes, pediatrics, and pregnancy.

Early risk stratification for progression to death by neurological criteria following out-of-hospital cardiac arrest.

BACKGROUND: Some patients resuscitated from out-of-hospital cardiac arrest (OHCA) progress to death by neurological criteria (DNC). We hypothesized that initial brain imaging, electroencephalography (EEG), and arrest characteristics predict progression to DNC. METHODS: We identified comatose OHCA patients from January 2010 to February 2020 treated at a single quaternary care facility in Western Pennsylvania. We abstracted demographics and arrest characteristics; Pittsburgh Cardiac Arrest Category, initial motor exam and pupillary light reflex; initial brain computed tomography (CT) grey-to-white ratio (GWR), sulcal or basal cistern effacement; initial EEG background and suppression ratio. We used two modeling approaches: fast and frugal tree (FFT) analysis to create an interpretable clinical risk stratification tool and ridge regression for comparison. We used bootstrapping to randomly partition cases into 80% training and 20% test sets and evaluated test set sensitivity and specificity. RESULTS: We included 1,569 patients, of whom 147 (9%) had diagnosed DNC. Across bootstrap samples, >99% of FFTs included three predictors: sulcal effacement, and in cases without sulcal effacement, the combination of EEG background suppression and GWR ≤ 1.23. This tree had mean sensitivity and specificity of 87% and 81%. Ridge regression with all available predictors had mean sensitivity 91 % and mean specificity 83%. Subjects falsely predicted as likely to progress to DNC generally died of rearrest or withdrawal of life sustaining therapies due to poor neurological prognosis. Two of these cases awakened from coma during the index hospitalization. CONCLUSIONS: Sulcal effacement on presenting brain CT or EEG suppression with GWR ≤ 1.23 predict progression to DNC after OHCA.

The immunology of the post-cardiac arrest syndrome.

Patients successfully resuscitated from cardiac arrest often have brain injury, myocardial dysfunction, and systemic ischemia-reperfusion injury, collectively termed the post-cardiac arrest syndrome (PCAS). To improve outcomes, potential therapies must be able to be administered early in the post-arrest course and provide broad cytoprotection, as ischemia-reperfusion injury affects all organ systems. Our understanding of the immune system contributions to the PCAS has expanded, with animal models detailing biologically plausible mechanisms of secondary injury, the protective effects of available immunomodulatory drugs, and how immune dysregulation underlies infection susceptibility after arrest. In this narrative review, we discuss the dysregulated immune response in PCAS, human trials of targeted immunomodulation therapies, and future directions for immunomodulation following cardiac arrest.

Bayesian Outcome Prediction After Resuscitation From Cardiac Arrest.

BACKGROUND AND OBJECTIVES: Postarrest prognostication research does not typically account for the sequential nature of real-life data acquisition and interpretation and reports nonintuitive estimates of uncertainty. Bayesian approaches offer advantages well suited to prognostication. We used Bayesian regression to explore the usefulness of sequential prognostic indicators in the context of prior knowledge and compared this with a guideline-concordant algorithm. METHODS: We included patients hospitalized at a single center after cardiac arrest. We extracted prospective data and assumed these data accrued over time as in routine practice. We considered predictors demographic and arrest characteristics, initial and daily neurologic examination, laboratory results, therapeutic interventions, brain imaging, and EEG. We fit Bayesian hierarchical generalized linear multivariate models predicting discharge Cerebral Performance Category (CPC) 4 or 5 (poor outcomes) vs 1-3 including sequential clinical and prognostic data. We explored outcome posterior probability distributions (PPDs) for individual patients and overall. As a comparator, we applied the 2021 European Resuscitation Council and European Society of Intensive Care Medicine (ERC/ESICM) guidelines. RESULTS: We included 2,692 patients of whom 864 (35%) were discharged with a CPC 1-3. Patients' outcome PPDs became narrow and shifted toward 0 or 1 as sequentially acquired information was added to models. These changes were largest after arrest characteristics and initial neurologic examination were included. Using information typically available at or before intensive care unit admission, sensitivity predicting poor outcome was 51% with a 0.6% false-positive rate. In our most comprehensive model, sensitivity for poor outcome prediction was 76% with 0.6% false-positive rate (FPR). The ERC/ESICM algorithm applied to 547 of 2,692 patients and yielded 36% sensitivity with 0% FPR. DISCUSSION: Bayesian models offer advantages well suited to prognostication research. On balance, our findings support the view that in expert hands, accurate neurologic prognostication is possible in many cases before 72 hours postarrest. Although we caution against early withdrawal of life-sustaining therapies, rapid outcome prediction can inform clinical decision making and future clinical trials.

Amiodarone induced movement disorder after cardiac arrest - A case report.

We describe a case of new onset movement disorder in a patient with ventricular tachycardia storm supported with peripheral VA ECMO. The differential diagnosis of abnormal movements in a post cardiac arrest patient requiring temporary mechanical circulatory support for cardiogenic shock is explored.