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Background: The CREST model is a prediction model, quantitating the risk of circulatory-etiology death (CED) after cardiac arrest based on variables available at hospital admission, and intend to guide the triage of comatose patients without ST-segment-elevation myocardial infarction after successful cardiopulmonary resuscitation. This study assessed performance of the CREST model in the Target Temperature Management (TTM) trial cohort. Methods: We retrospectively analyzed data from resuscitated out-of-hospital cardiac arrest (OHCA) patients in the TTM-trial. Demographics, clinical characteristics, and CREST variables (history of coronary artery disease, initial heart rhythm, initial ejection fraction, shock at admission and ischemic time > 25 min) were assessed in univariate and multivariable analysis. The primary outcome was CED. The discriminatory power of the logistic regression model was assessed using the C-statistic and goodness of fit was tested according to Hosmer-Lemeshow. Results: Among 329 patients eligible for final analysis, 71 (22%) had CED. History of ischemic heart disease, previous arrhythmia, older age, initial non-shockable rhythm, shock at admission, ischemic time > 25 min and severe left ventricular dysfunction were variables associated with CED in univariate analysis. CREST variables were entered into a logistic regression model and the area under the curve for the model was 0.73 with adequate calibration according to Hosmer-Lemeshow test (p = 0.602). Conclusions: The CREST model had good validity and a discrimination capability for predicting circulatory-etiology death after resuscitation from cardiac arrest without ST-segment elevation myocardial infarction. Application of this model could help to triage high-risk patients for transfer to specialized cardiac centers.

Background: Sedation and analgesia are recommended during targeted temperature management (TTM) after cardiac arrest, but there are few data to provide guidance on dosing to bedside clinicians. We evaluated differences in patient-level sedation and analgesia dosing in an international multicenter TTM trial to better characterize current practice and clinically important outcomes. Methods: A total 950 patients in the international TTM trial were randomly assigned to a TTM of 33 °C or 36 °C after resuscitation from cardiac arrest in 36 intensive care units. We recorded cumulative doses of sedative and analgesic drugs at 12, 24, and 48 h and normalized to midazolam and fentanyl equivalents. We compared number of medications used, dosing, and titration among centers by using multivariable models, including common severity of illness factors. We also compared dosing with time to awakening, incidence of clinical seizures, and survival. Results: A total of 614 patients at 18 centers were analyzed. Propofol (70%) and fentanyl (51%) were most frequently used. The average dosages of midazolam and fentanyl equivalents were 0.13 (0.07, 0.22) mg/kg/h and 1.16 (0.49, 1.81) µg/kg/h, respectively. There were significant differences in number of medications (p < 0.001), average dosages (p < 0.001), and titration at all time points between centers (p < 0.001), and the outcomes of patients in these centers were associated with all parameters described in the multivariate analysis, except for a difference in the titration of sedatives between 12 and 24 h (p = 0.40). There were associations between higher dosing at 48 h (p = 0.003, odds ratio [OR] 1.75) and increased titration of analgesics between 24 and 48 h (p = 0.005, OR 4.89) with awakening after 5 days, increased titration of sedatives between 24 and 48 h with awakening after 5 days (p < 0.001, OR > 100), and increased titration of sedatives between 24 and 48 h with a higher incidence of clinical seizures in the

Background: Sedation and analgesia are recommended during targeted temperature management (TTM) after cardiac arrest, but there are few data to provide guidance on dosing to bedside clinicians. We evaluated differences in patient-level sedation and analgesia dosing in an international multicenter TTM trial to better characterize current practice and clinically important outcomes. Methods: A total 950 patients in the international TTM trial were randomly assigned to a TTM of 33 °C or 36 °C after resuscitation from cardiac arrest in 36 intensive care units. We recorded cumulative doses of sedative and analgesic drugs at 12, 24, and 48 h and normalized to midazolam and fentanyl equivalents. We compared number of medications used, dosing, and titration among centers by using multivariable models, including common severity of illness factors. We also compared dosing with time to awakening, incidence of clinical seizures, and survival. Results: A total of 614 patients at 18 centers were analyzed. Propofol (70%) and fentanyl (51%) were most frequently used. The average dosages of midazolam and fentanyl equivalents were 0.13 (0.07, 0.22) mg/kg/h and 1.16 (0.49, 1.81) µg/kg/h, respectively. There were significant differences in number of medications (p < 0.001), average dosages (p < 0.001), and titration at all time points between centers (p < 0.001), and the outcomes of patients in these centers were associated with all parameters described in the multivariate analysis, except for a difference in the titration of sedatives between 12 and 24 h (p = 0.40). There were associations between higher dosing at 48 h (p = 0.003, odds ratio [OR] 1.75) and increased titration of analgesics between 24 and 48 h (p = 0.005, OR 4.89) with awakening after 5 days, increased titration of sedatives between 24 and 48 h with awakening after 5 days (p < 0.001, OR > 100), and increased titration of sedatives between 24 and 48 h with a higher incidence of clinical seizures in the

Background: The CREST model is a prediction model, quantitating the risk of circulatory-etiology death (CED) after cardiac arrest based on variables available at hospital admission, and intend to guide the triage of comatose patients without ST-segment-elevation myocardial infarction after successful cardiopulmonary resuscitation. This study assessed performance of the CREST model in the Target Temperature Management (TTM) trial cohort. Methods: We retrospectively analyzed data from resuscitated out-of-hospital cardiac arrest (OHCA) patients in the TTM-trial. Demographics, clinical characteristics, and CREST variables (history of coronary artery disease, initial heart rhythm, initial ejection fraction, shock at admission and ischemic time > 25 min) were assessed in univariate and multivariable analysis. The primary outcome was CED. The discriminatory power of the logistic regression model was assessed using the C-statistic and goodness of fit was tested according to Hosmer-Lemeshow. Results: Among 329 patients eligible for final analysis, 71 (22%) had CED. History of ischemic heart disease, previous arrhythmia, older age, initial non-shockable rhythm, shock at admission, ischemic time > 25 min and severe left ventricular dysfunction were variables associated with CED in univariate analysis. CREST variables were entered into a logistic regression model and the area under the curve for the model was 0.73 with adequate calibration according to Hosmer-Lemeshow test (p = 0.602). Conclusions: The CREST model had good validity and a discrimination capability for predicting circulatory-etiology death after resuscitation from cardiac arrest without ST-segment elevation myocardial infarction. Application of this model could help to triage high-risk patients for transfer to specialized cardiac centers.

Background: Out of Hospital Cardiac arrest (OHCA) survivors with ST elevation (STE) with or without shockable rhythms often benefit from coronary angiography (CAG) and, if indicated, percutaneous coronary intervention (PCI). However, the benefits of CAG and PCI in OHCA survivors with nonshockable rhythms (PEA/asystole) and no STE are debated. Methods: Using the International Cardiac Arrest Registry (INTCAR 2.0), representing 44 centers in the US and Europe, comatose OHCA survivors with known presenting rhythms and post resuscitation ECGs were identified. Survival to hospital discharge, neurological recovery on discharge, and impact of CAG with or without PCI on such outcome were assessed and compared with other groups (shockable rhythms with or without STE). Results: Total of 2113 OHCA survivors were identified and described as; nonshockable/no STE (Nsh-NST) (n = 940, 44.5%), shockable/no STE (Sh-NST) (n = 716, 33.9%), nonshockable/STE (Nsh-ST) (n = 110, 5.2%), and shockable/STE (Sh-ST) (n = 347, 16.4%). Of Nsh-NST, 13.7% (129) were previously healthy before CA and only 17.3% (161) underwent CAG; of those, 30.4% (52) underwent PCI. A total of 18.6% (174) Nsh-NST patients survived to hospital discharge, with 57.5% (100) of such survivors having good neurological recovery (cerebral performance category 1 or 2) on discharge. Coronary angiography was associated with improved odds for survival and neurological recovery among all groups, including those with NSh-NST. Conclusions: Nonshockable initial rhythms with no ST elevation post resuscitation was the most common presentation after OHCA. Although most of these patients did not undergo coronary angiography, among those who did, 1 in 4 patients had a culprit lesion and underwent revascularization. Invasive CAG should be at least considered for all OHCA survivors, including those with nonshockable rhythms and no ST elevation post resuscitation.

Background The American College of Cardiology Interventional Council published consensus-based recommendations to help identify resuscitated cardiac arrest patients with unfavorable clinical features in whom invasive procedures are unlikely to improve survival. Objectives This study sought to identify how many unfavorable features are required before prognosis is significantly worsened and which features are most impactful in predicting prognosis. Methods Using the INTCAR (International Cardiac Arrest Registry), the impact of each proposed “unfavorable feature” on survival to hospital discharge was individually analyzed. Logistic regression was performed to assess the association of such unfavorable features with poor outcomes. Results Seven unfavorable features (of 10 total) were captured in 2,508 patients successfully resuscitated after cardiac arrest (ongoing cardiopulmonary resuscitation and noncardiac etiology were exclusion criteria in our registry). Chronic kidney disease was used in lieu of end-stage renal disease. In total, 39% survived to hospital discharge. The odds ratio (OR) of survival to hospital discharge for each unfavorable feature was as follows: age >85 years OR: 0.30 (95% CI: 0.15 to 0.61), time-to-ROSC >30 min OR: 0.30 (95% CI: 0.23 to 0.39), nonshockable rhythm OR: 0.39 (95% CI: 0.29 to 0.54), no bystander cardiopulmonary resuscitation OR: 0.49 (95% CI: 0.38 to 0.64), lactate >7 mmol/l OR: 0.50 (95% CI: 0.40 to 0.63), unwitnessed arrest OR: 0.58 (95% CI: 0.44 to 0.78), pH <7.2 OR: 0.78 (95% CI: 0.63 to 0.98), and chronic kidney disease OR: 0.96 (95% CI: 0.70 to 1.33). The presence of any 3 or more unfavorable features predicted <40% survival. Presence of the 3 strongest risk factors (age >85 years, time-to-ROSC >30 min, and non-ventricular tachycardia/ventricular fibrillation) together or ≥6 unfavorable features predicted a ≤10% chance of survival to discharge. Conclusions Patients successfully resuscitated from cardiac ar

Purpose Functional outcomes vary between centers after out-of-hospital cardiac arrest (OHCA) and are partially explained by pre-existing health status and arrest characteristics, while the effects of in-hospital treatments on functional outcome are less understood. We examined variation in functional outcomes by center after adjusting for patient- and arrest-specific characteristics and evaluated how in-hospital management differs between high- and low-performing centers. Methods Analysis of observational registry data within the International Cardiac Arrest Registry was used to perform a hierarchical model of center-specific risk standardized rates for good outcome, adjusted for demographics, pre-existing functional status, and arrest-related factors with treatment center as a random effect variable. We described the variability in treatments and diagnostic tests that may influence outcome at centers with adjusted rates significantly above and below registry average. Results A total of 3855 patients were admitted to an ICU following cardiac arrest with return of spontaneous circulation. The overall prevalence of good outcome was 11-63% among centers. After adjustment, center-specific risk standardized rates for good functional outcome ranged from 0.47 (0.37-0.58) to 0.20 (0.12-0.26). High-performing centers had faster time to goal temperature, were more likely to have goal temperature of 33 degrees C, more likely to perform unconscious cardiac catheterization and percutaneous coronary intervention, and had differing prognostication practices than low-performing centers. Conclusions Center-specific differences in outcomes after OHCA after adjusting for patient-specific factors exist. This variation could partially be explained by in-hospital management differences. Future research should address the contribution of these factors to the differences in outcomes after resuscitation., Correction in: INTENSIVE CARE MEDICINE, Volume: 45, Issue: 8, Pages: 1176-1176, DOI: 10.1007/s00134-019-05687-x