Raina M. Merchant

Part 9: Post–Cardiac Arrest Care 2010 American Heart Association Guidelines for Cardiopulmonary Resuscitation and Emergency Cardiovascular Care

The goal of immediate post-cardiac arrest care is to optimize systemic perfusion, restore metabolic homeostasis, and support organ system function to increase the likelihood of intact neurological survival. The post-cardiac arrest period is often marked by hemodynamic instability as well as metabolic abnormalities. Support and treatment of acute myocardial dysfunction and acute myocardial ischemia can increase the probability of survival. Interventions to reduce secondary brain injury, such as therapeutic hypothermia, can improve survival and neurological recovery. Every organ system is at risk during this period, and patients are at risk of developing multiorgan dysfunction. The comprehensive treatment of diverse problems after cardiac arrest involves multidisciplinary aspects of critical care, cardiology, and neurology. For this reason, it is important to admit patients to appropriate critical-care units with a prospective plan of care to anticipate, monitor, and treat each of these diverse problems. It is also important to appreciate the relative strengths and weaknesses of different tools for estimating the prognosis of patients after cardiac arrest.

Early goal-directed hemodynamic optimization combined with therapeutic hypothermia in comatose survivors of out-of-hospital cardiac arrest

BACKGROUND Comatose survivors of out-of-hospital cardiac arrest (OHCA) have high in-hospital mortality due to a complex pathophysiology that includes cardiovascular dysfunction, inflammation, coagulopathy, brain injury and persistence of the precipitating pathology. Therapeutic hypothermia (TH) is the only intervention that has been shown to improve outcomes in this patient population. Due to the similarities between the post-cardiac arrest state and severe sepsis, it has been postulated that early goal-directed hemodyamic optimization (EGDHO) combined with TH would improve outcome of comatose cardiac arrest survivors. OBJECTIVE We examined the feasibility of establishing an integrated post-cardiac arrest resuscitation (PCAR) algorithm combining TH and EGDHO within 6h of emergency department (ED) presentation. METHODS In May, 2005 we began prospectively identifying comatose (Glasgow Motor Score<6) survivors of OHCA treated with our PCAR protocol. The PCAR patients were compared to matched historic controls from a cardiac arrest database maintained at our institution. RESULTS Between May, 2005 and January, 2008, 18/20 (90%) eligible patients were enrolled in the PCAR protocol. They were compared to historic controls from 2001 to 2005, during which time 18 patients met inclusion criteria for the PCAR protocol. Mean time from initiation of TH to target temperature (33 degrees C) was 2.8h (range 0.8-23.2; SD=h); 78% (14/18) had interventions based upon EGDHO parameters; 72% (13/18) of patients achieved their EGDHO goals within 6h of return of spontaneous circulation (ROSC). Mortality for historic controls who qualified for the PCAR protocol was 78% (14/18); mortality for those treated with the PCAR protocol was 50% (9/18) (p=0.15). CONCLUSIONS In patients with ROSC after OHCA, EGDHO and TH can be implemented simultaneously.

Incidence of treated cardiac arrest in hospitalized patients in the United States.

Objective:The incidence and incidence over time of cardiac arrest in hospitalized patients is unknown. We sought to estimate the event rate and temporal trends of adult inhospital cardiac arrest treated with a resuscitation response. Design:Three approaches were used to estimate the inhospital cardiac arrest event rate. First approach: calculate the inhospital cardiac arrest event rate at hospitals (n = 433) in the Get With The Guidelines-Resuscitation registry, years 2003–2007, and multiply this by U.S. annual bed days. Second approach: use the Get With The Guidelines-Resuscitation inhospital cardiac arrest event rate to develop a regression model (including hospital demographic, geographic, and organizational factors), and use the model coefficients to calculate predicted event rates for acute care hospitals (n = 5445) responding to the American Hospital Association survey. Third approach: classify acute care hospitals into groups based on academic, urban, and bed size characteristics, and determine the average event rate for Get With The Guidelines-Resuscitation hospitals in each group, and use weighted averages to calculate the national inhospital cardiac arrest rate. Annual event rates were calculated to estimate temporal trends. Setting:Get With The Guidelines-Resuscitation registry. Patients:Adult inhospital cardiac arrest with a resuscitation response. Measurements and Main Results:The mean adult treated inhospital cardiac arrest event rate at Get With The Guidelines-Resuscitation hospitals was 0.92/1000 bed days (interquartile range 0.58 to 1.2/1000). In hospitals (n = 150) contributing data for all years of the study period, the event rate increased from 2003 to 2007. With 2.09 million annual U.S. bed days, we estimated 192,000 inhospital cardiac arrests throughout the United States annually. Based on the regression model, extrapolating Get With The Guidelines-Resuscitation hospitals to hospitals participating in the American Hospital Association survey projected 211,000 annual inhospital cardiac arrests. Using weighted averages projected 209,000 annual U.S. inhospital cardiac arrests. Conclusions:There are approximately 200,000 treated cardiac arrests among U.S. hospitalized patients annually, and this rate may be increasing. This is important for understanding the burden of inhospital cardiac arrest and developing strategies to improve care for hospitalized patients.

Integrating social media into emergency-preparedness efforts.

Social media are changing the way people communicate both in their day-to-day lives and during disasters that threaten public health. Engaging with and using such media may help the emergency-management community to respond to disasters.

Inter-hospital variability in post-cardiac arrest mortality

AIM A growing body of evidence suggests that variability in post-cardiac arrest care contributes to differential outcomes of patients with initial return of spontaneous circulation after cardiac arrest. We examined hospital-level variation in mortality of patients admitted to United States intensive care units (ICUs) with a diagnosis of cardiac arrest. METHODS Patients with a primary ICU admission diagnosis of cardiac arrest were identified in the 2002--2005 Acute Physiology and Chronic Health Evaluation (APACHE) IV dataset, a multicenter clinical registry of ICU patients. RESULTS We identified 4674 patients from 39 hospitals. The median number of annual patients was 33 per hospital (range: 12-116). Mean APACHE score was 94 (+/-38), and overall mortality was 56.8%. Age, severity of illness (acute physiology score), and admission Glasgow Coma Scale were all associated with increased mortality (p<0.001). There was no survival difference for patients admitted from the emergency department vs. the inpatient floor. Among institutions, unadjusted in-hospital mortality ranged from 41% to 81%. After adjusting for age and severity of illness, institutional mortality ranged from 46% to 68%. Patients treated at higher volume centers were significantly less likely to die in the hospital. CONCLUSIONS We demonstrate hospital-level variation in severity adjusted mortality among patients admitted to the ICU after cardiac arrest. We identify a volume-outcome relationship showing lower mortality among patients admitted to ICUs that treat a high volume of post-cardiac arrest patients. Prospective studies should identify hospital-level and patient care factors that contribute to post-cardiac arrest survival.

Primary Outcomes for Resuscitation Science Studies A Consensus Statement From the American Heart Association

Background and Purpose— The guidelines presented in this consensus statement are intended to serve researchers, clinicians, reviewers, and regulators in the selection of the most appropriate primary outcome for a clinical trial of cardiac arrest therapies. The American Heart Association guidelines for the treatment of cardiac arrest depend on high-quality clinical trials, which depend on the selection of a meaningful primary outcome. Because this selection process has been the subject of much controversy, a consensus conference was convened with national and international experts, the National Institutes of Health, and the US Food and Drug Administration. Methods— The Research Working Group of the American Heart Association Emergency Cardiovascular Care Committee nominated subject leaders, conference attendees, and writing group members on the basis of their expertise in clinical trials and a diverse perspective of cardiovascular and neurological outcomes (see the online-only Data Supplement). Approval was obtained from the Emergency Cardiovascular Care Committee and the American Heart Association Manuscript Oversight Committee. Preconference position papers were circulated for review; the conference was held; and postconference consensus documents were circulated for review and comments were invited from experts, conference attendees, and writing group members. Discussions focused on (1) when after cardiac arrest the measurement time point should occur; (2) what cardiovascular, neurological, and other physiology should be assessed; and (3) the costs associated with various end points. The final document underwent extensive revision and peer review by the Emergency Cardiovascular Care Committee, the American Heart Association Science Advisory and Coordinating Committee, and oversight committees. Results— There was consensus that no single primary outcome is appropriate for all studies of cardiac arrest. The best outcome measure is the pairing of a time point and physiological condition that will best answer the question under study. Conference participants were asked to assign an outcome to each of 4 hypothetical cases; however, there was not complete agreement on an ideal outcome measure even after extensive discussion and debate. There was general consensus that it is appropriate for earlier studies to enroll fewer patients and to use earlier time points such as return of spontaneous circulation, simple “alive versus dead,” hospital mortality, or a hemodynamic parameter. For larger studies, a longer time point after arrest should be considered because neurological assessments fluctuate for at least 90 days after arrest. For large trials designed to have a major impact on public health policy, longer-term end points such as 90 days coupled with neurocognitive and quality-of-life assessments should be considered, as should the additional costs of this approach. For studies that will require regulatory oversight, early discussions with regulatory agencies are strongly advised. For neurological assessment of post–cardiac arrest patients, researchers may wish to use the Cerebral Performance Categories or modified Rankin Scale for global outcomes. Conclusions— Although there is no single recommended outcome measure for trials of cardiac arrest care, the simple Cerebral Performance Categories or modified Rankin Scale after 90 days provides a reasonable outcome parameter for many trials. The lack of an easy-to-administer neurological functional outcome measure that is well validated in post–cardiac arrest patients is a major limitation to the field and should be a high priority for future development.

Psychological Language on Twitter Predicts County-Level Heart Disease Mortality:

Hostility and chronic stress are known risk factors for heart disease, but they are costly to assess on a large scale. We used language expressed on Twitter to characterize community-level psychological correlates of age-adjusted mortality from atherosclerotic heart disease (AHD). Language patterns reflecting negative social relationships, disengagement, and negative emotions—especially anger—emerged as risk factors; positive emotions and psychological engagement emerged as protective factors. Most correlations remained significant after controlling for income and education. A cross-sectional regression model based only on Twitter language predicted AHD mortality significantly better than did a model that combined 10 common demographic, socioeconomic, and health risk factors, including smoking, diabetes, hypertension, and obesity. Capturing community psychological characteristics through social media is feasible, and these characteristics are strong markers of cardiovascular mortality at the community level.

Timing of neuroprognostication in postcardiac arrest therapeutic hypothermia

Objective:Early assessment of neurologic recovery is often challenging in survivors of cardiac arrest. Further, little is known about when to assess neurologic status in comatose, postarrest patients receiving therapeutic hypothermia. We sought to evaluate timing of prognostication in cardiac arrest survivors who received therapeutic hypothermia. Design:A retrospective chart review of consecutive postarrest patients receiving therapeutic hypothermia (protocol: 24-hr maintenance at target temperature followed by rewarming over 8 hrs). Data were abstracted from the medical chart, including documentation during the first 96 hrs post arrest of “poor” prognosis, diagnostic tests for neuroprognostication, consultations used for determination of prognosis, and outcome at discharge. Setting:Two academic urban emergency departments. Patients:A total of 55 consecutive patients who underwent therapeutic hypothermia were reviewed between September 2005 and April 2009. Intervention:None. Results:Of our cohort of comatose postarrest patients, 59% (29 of 49) were male, and the mean age was 56 ± 16 yrs. Chart documentation of “poor” or “grave” prognosis occurred “early”: during induction, maintenance of cooling, rewarming, or within 15 hrs after normothermia in 57% (28 of 49) of cases. Of patients with early documentation of poor prognosis, 25% (seven of 28) had care withdrawn within 72 hrs post arrest, and 21% (six of 28) survived to discharge with favorable neurologic recovery. In the first 96 hrs post arrest: 88% (43 of 49) of patients received a head computed tomography, 90% (44 of 49) received electroencephalography, 2% (one of 49) received somatosensory evoked potential testing, and 71% (35 of 49) received neurology consultation. Conclusions:Documentation of “poor prognosis” occurred during therapeutic hypothermia in more than half of patients in our cohort. Premature documentation of poor prognosis may contribute to early decisions to withdraw care. Future guidelines should address when to best prognosticate in postarrest patients receiving therapeutic hypothermia.

Cost-Effectiveness of Therapeutic Hypothermia After Cardiac Arrest

Background—Therapeutic hypothermia can improve survival and neurological outcomes in cardiac arrest survivors, but its cost-effectiveness is uncertain. We sought to evaluate the cost-effectiveness of treating comatose cardiac arrest survivors with therapeutic hypothermia. Methods and Results—A decision model was developed to capture costs and outcomes for patients with witnessed out-of-hospital ventricular fibrillation arrest who received conventional care or therapeutic hypothermia. The Hypothermia After Cardiac Arrest (HACA) trial inclusion criteria were assumed. Model inputs were determined from published data, cooling device companies, and consultation with resuscitation experts. Sensitivity analyses and Monte Carlo simulations were performed to identify influential variables and uncertainty in cost-effectiveness estimates. The main outcome measures were quality-adjusted survival after cardiac arrest, cost of hypothermia implementation, cost of posthospital discharge care, and incremental cost-effectiveness ratios. In our model, postarrest patients receiving therapeutic hypothermia gained an average of 0.66 quality-adjusted life years compared with conventional care, at an incremental cost of

Decoding twitter: Surveillance and trends for cardiac arrest and resuscitation communication

31 254. This yielded an incremental cost-effectiveness ratio of