Validation of the Pittsburgh Cardiac Arrest Category illness severity score

Etiology and Determinants of In-Hospital Survival in Patients Resuscitated After Out-of-Hospital Cardiac Arrest in an Urban Medical Center

Out-of-hospital cardiac arrest (OHCA) is a leading cause of mortality globally. The goals of this study were to describe common causes of OHCA in an urban US medical center, identify predictive factors for survival, and to assess whether neurological status upon return of spontaneous circulation might be predictive of outcomes: 124 consecutive patients aged 18 years and older with OHCA admitted at Advocate Illinois Masonic Medical Center were studied. All patients resuscitated in the field with return of spontaneous circulation then transferred to the emergency department were included. The Glasgow Coma Score (GCS) was evaluated immediately on hospital arrival. In the total group, 34% (42 of 124) were discharged alive. In patients with coronary artery disease (CAD), 51% (20 of 39) were discharged alive versus 26% (22 of 85) of non-CAD patients (p <0.01). Initial GCS ≥ 9 was highly predictive of survival: 94% (34 of 36) of patients with GCS ≥ 9 survived versus 9% (8 of 88) with GCS ≤ 8 (p <0.0001). Defibrillation in the field was predictive of survival (chi-square = 7.81, p = 0.005). In the CAD group, all 16 patients with GCS ≥ 9 on presentation to the Emergency Department survived whereas all 13 with GCS ≤ 5 died (both p <0.0001). In the non-CAD group, 18 of 20 patients with GCS ≥ 9 survived, whereas only 2 of 52 with GCS ≤ 5 survived (both p <0.0001). Multivariate analysis by logistic regression showed that the strongest predictor of survival in the non-CAD subgroup was GCS (OR 0.27, CI 0.19 to 0.55, p <0.001). In conclusion, the etiology of the OHCA, immediate neurologic status, and defibrillation in the field (suggesting presenting arrhythmia) were predictive of survival. Immediate neurological recovery (GCS ≥ 9) regardless of etiology was a strong predictor of survival to discharge. Additional predictive factors depend on the etiology of the OHCA event. These data suggest that these straightforward factors can be helpful in predicting outcome in patients resuscitated after OHCA.

Rate of intra-arrest epinephrine administration and early post-arrest organ failure after in-hospital cardiac arrest

INTRODUCTION

Data supporting epinephrine administration during resuscitation of in-hospital cardiac arrest (IHCA) are limited. We hypothesized that more frequent epinephrine administration would predict greater early end-organ dysfunction and worse outcomes after IHCA.

METHODS

We performed a retrospective cohort study including patients resuscitated from IHCA at one of 67 hospitals between 2010 and 2019 who were ultimately cared for at a single tertiary care hospital. Our primary exposure of interest was rate of intra-arrest epinephrine bolus administration (mg/min). We considered several outcomes, including severity of early cardiovascular failure (modeled using Sequential Organ Failure Assessment (SOFA) cardiovascular subscore), early neurological and early global illness severity injury (modeled as Pittsburgh Cardiac Arrest Category (PCAC)). We used generalized linear models to test for independent associations between rate of epinephrine administration and outcomes.

RESULTS

We included 695 eligible patients. Mean age was 62 ± 15 years, 416 (60%) were male and 172 (26%) had an initial shockable rhythm. Median arrest duration was 16 [IQR 9-25] min, and median rate of epinephrine administration was 0.2 [IQR 0.1-0.3] mg/min. Higher rate of epinephrine predicted worse PCAC, and lower survival in patients with initial shockable rhythms. There was no association between rate of epinephrine and other outcomes.

CONCLUSION

Higher rates of epinephrine administration during IHCA are associated with more severe early global illness severity.

Clinical Predictive Models of Sudden Cardiac Arrest: A Survey of the Current Science and Analysis of Model Performances

Background

More than 500 000 sudden cardiac arrests (SCAs) occur annually in the United States. Clinical predictive models (CPMs) may be helpful tools to differentiate between patients who are likely to survive or have good neurologic recovery and those who are not. However, which CPMs are most reliable for discriminating between outcomes in SCA is not known.

Methods and Results

We performed a systematic review of the literature using the Tufts PACE (Predictive Analytics and Comparative Effectiveness) CPM Registry through February 1, 2020, and identified 81 unique CPMs of SCA and 62 subsequent external validation studies. Initial cardiac rhythm, age, and duration of cardiopulmonary resuscitation were the 3 most commonly used predictive variables. Only 33 of the 81 novel SCA CPMs (41%) were validated at least once. Of 81 novel SCA CPMs, 56 (69%) and 61 of 62 validation studies (98%) reported discrimination, with median c‐statistics of 0.84 and 0.81, respectively. Calibration was reported in only 29 of 62 validation studies (41.9%). For those novel models that both reported discrimination and were validated (26 models), the median percentage change in discrimination was −1.6%. We identified 3 CPMs that had undergone at least 3 external validation studies: the out‐of‐hospital cardiac arrest score (9 validations; median c‐statistic, 0.79), the cardiac arrest hospital prognosis score (6 validations; median c‐statistic, 0.83), and the good outcome following attempted resuscitation score (6 validations; median c‐statistic, 0.76).

Conclusions

Although only a small number of SCA CPMs have been rigorously validated, the ones that have been demonstrate good discrimination.

Postarrest Interventions that Save Lives

Patients resuscitated from cardiac arrest require complex management. An organized approach to early postarrest care can improve patient outcomes. Priorities include completing a focused diagnostic work-up to identify and reverse the inciting cause of arrest, stabilizing cardiorespiratory instability to prevent rearrest, minimizing secondary brain injury, evaluating the risk and benefits of transfer to a specialty care center, and avoiding early neurologic prognostication.

Data-driven classification of arrest location for emergency department cardiac arrests

BACKGROUND

Resuscitation research is inconsistent in how emergency department (ED) arrests are classified. We tested whether clinical features of ED arrests more closely resembled out-of-hospital cardiac arrest (OHCA) or in-hospital cardiac arrest (IHCA).

METHODS

We performed a retrospective study including all patients resuscitated from cardiac arrest at a single academic medical center from January 2010 to December 2019. We abstracted clinical information from our prospective registry. We used unsupervised learning (k-prototypes) to identify clusters within the OHCA and IHCA cohorts. We determined the number of subgroups using scree plots. We assigned individual ED arrest patients the nearest OHCA or IHCA cluster based on the shortest Gower distance from that patient to the nearest cluster center. In our secondary analysis, we determined the optimal number of clusters in each of the 3 arrest cohorts, and then calculated the mean Gower distances with the standard deviation (SD) between cluster centers (ED-IHCA, ED-OHCA, IHCA-OHCA).

RESULTS

We included 2723 patients: 372 (14%) ED arrests, 1709 (63%) OHCA, and 642 (23%) IHCA. We identified 3 clusters of OHCA patients, and 4 clusters of IHCA patients. Of ED arrest cases, 292 (78%) most closely resembled an IHCA cluster and 80 (22%) most closely resembled an OHCA cluster. Mean (SD) Gower distance between ED arrest and IHCA centers was 0.33 (0.2). Mean Gower distances between ED arrest-OHCA centers and between IHCA-OHCA centers were 0.41 (0.11).

CONCLUSION

Across multiple aggregated measures, ED arrests resemble IHCA more than OHCA.

Association of Initial Illness Severity and Outcomes After Cardiac Arrest With Targeted Temperature Management at 36 °C or 33 °C

IMPORTANCE

It is uncertain what the optimal target temperature is for targeted temperature management (TTM) in patients who are comatose following cardiac arrest.

OBJECTIVE

To examine whether illness severity is associated with changes in the association between target temperature and patient outcome.

DESIGN, SETTING, AND PARTICIPANTS

This cohort study compared outcomes for 1319 patients who were comatose after cardiac arrest at a single center in Pittsburgh, Pennsylvania, from January 2010 to December 2018. Initial illness severity was based on coma and organ failure scores, presence of severe cerebral edema, and presence of highly malignant electroencephalogram (EEG) after resuscitation.

EXPOSURE

TTM at 36 °C or 33 °C.

MAIN OUTCOMES AND MEASURES

Primary outcome was survival to hospital discharge, and secondary outcomes were modified Rankin Scale and cerebral performance category.

RESULTS

Among 1319 patients, 728 (55.2%) had TTM at 33 °C (451 [62.0%] men; median [interquartile range] age, 61 [50-72] years) and 591 (44.8%) had TTM at 36 °C (353 [59.7%] men; median [interquartile range] age, 59 [48-69] years). Overall, 184 of 187 patients (98.4%) with severe cerebral edema died and 234 of 243 patients (96.3%) with highly malignant EEG died regardless of TTM strategy. Comparing TTM at 33 °C with TTM at 36 °C in 911 patients (69.1%) with neither severe cerebral edema nor highly malignant EEG, survival was lower in patients with mild to moderate coma and no shock (risk difference, -13.8%; 95% CI, -24.4% to -3.2%) but higher in patients with mild to moderate coma and cardiopulmonary failure (risk difference, 21.8%; 95% CI, 5.4% to 38.2%) or with severe coma (risk difference, 9.7%; 95% CI, 4.0% to 15.3%). Interactions were similar for functional outcomes. Most deaths (633 of 968 [65.4%]) resulted after withdrawal of life-sustaining therapies.

CONCLUSIONS AND RELEVANCE

In this study, TTM at 33 °C was associated with better survival than TTM at 36 °C among patients with the most severe post-cardiac arrest illness but without severe cerebral edema or malignant EEG. However, TTM at 36 °C was associated with better survival among patients with mild- to moderate-severity illness.

Are providers overconfident in predicting outcome after cardiac arrest?

AIM

To quantify the accuracy of health care providers' predictions of survival and function at hospital discharge in a prospective cohort of patients resuscitated from cardiac arrest. To test whether self-reported confidence in their predictions was associated with increased accuracy and whether this relationship varied across providers.

METHODOLOGY

We presented critical care and neurology providers with clinical vignettes using real data from post-arrest patients. We asked providers to predict survival, function at discharge, and report their confidence in these predictions. We used mixed effects models to explore predictors of confidence, accuracy, and the relationship between the two.

RESULTS

We completed 470 assessments of 62 patients with 65 providers. Of patients, 49 (78%) died and 9 (15%) had functionally favourable survival. Providers accurately predicted survival in 308/470 (66%) assessments. In most errors (146/162, 90%), providers incorrectly predicted survival. Providers accurately predicted function in 349/470 (74%) assessments. In most errors (114/121, 94%), providers incorrectly predicted favourable functional recovery. Providers were confident (median confidence predicting survival 80 [IQR 60-90]; median confidence predicting function 80 [IQR 60-95]). Confidence explained 9% and 18% of variation in accuracy predicting survival and function, respectively. We observed significant between-provider variability in accuracy (median odds ratio (MOR) for predicting survival 2.93, 95%CI 1.94-5.52; MOR for predicting function 5.42, 95%CI 3.01-13.2).

CONCLUSIONS

Providers varied in accuracy predicting post-arrest outcomes and most errors were optimistic. Self-reported confidence explained little variation in accuracy.

Background Frequency Patterns in Standard Electroencephalography as an Early Prognostic Tool in Out-of-Hospital Cardiac Arrest Survivors Treated with Targeted Temperature Management

We investigated the prognostic value of standard electroencephalography, a 30-min recording using 21 electrodes on the scalp, during the early post-cardiac arrest period, and evaluated the performance of electroencephalography findings combined with other clinical features for predicting favourable outcomes in comatose out-of-hospital cardiac arrest (OHCA) survivors treated with targeted temperature management (TTM). This observational registry-based study was conducted at a tertiary care hospital in Korea using the data of all consecutive adult non-traumatic comatose OHCA survivors who underwent standard electroencephalography during TTM between 2010 and 2018. The primary outcome was a 6-month favourable neurological outcome (Cerebral Performance Category score of 1 or 2). Among 170 comatose OHCA survivors with median electroencephalography time of 22 h, a 6-month favourable neurologic outcome was observed in 34.1% (58/170). After adjusting other clinical characteristics, an electroencephalography background with dominant alpha and theta waves had the highest odds ratio of 13.03 (95% confidence interval, 4.69–36.22) in multivariable logistic analysis. A combination of other clinical features (age < 65 years, initial shockable rhythm, resuscitation duration < 20 min) with an electroencephalography background with dominant alpha and theta waves increased predictive performance for favourable neurologic outcomes with a high specificity of up to 100%. A background with dominant alpha and theta waves in standard electroencephalography during TTM could be a simple and early favourable prognostic finding in comatose OHCA survivors.

Sensitivity of Continuous Electroencephalography to Detect Ictal Activity After Cardiac Arrest

IMPORTANCE

Epileptiform electroencephalographic (EEG) patterns are common after resuscitation from cardiac arrest, are associated with patient outcome, and may require treatment. It is unknown whether continuous EEG monitoring is needed to detect these patterns or if brief intermittent monitoring is sufficient. If continuous monitoring is required, the necessary duration of observation is unknown.

OBJECTIVE

To quantify the time-dependent sensitivity of continuous EEG for epileptiform event detection, and to compare continuous EEG to several alternative EEG-monitoring strategies for post-cardiac arrest outcome prediction.

DESIGN, SETTING, AND PARTICIPANTS

This observational cohort study was conducted in 2 academic medical centers between September 2010 and January 2018. Participants included 759 adults who were comatose after being resuscitated from cardiac arrest and who underwent 24 hours or more of EEG monitoring.

MAIN OUTCOMES AND MEASURES

Epileptiform EEG patterns associated with neurological outcome at hospital discharge, such as seizures likely to cause secondary injury.

RESULTS

Overall, 759 patients were included in the analysis; 281 (37.0%) were female, and the mean (SD) age was 58 (17) years. Epileptiform EEG activity was observed in 414 participants (54.5%), of whom only 26 (3.4%) developed potentially treatable seizures. Brief intermittent EEG had an estimated 66% (95% CI, 62%-69%) to 68% (95% CI, 66%-70%) sensitivity for detection of prognostic epileptiform events. Depending on initial continuity of the EEG background, 0 to 51 hours of monitoring were needed to achieve 95% sensitivity for the detection of prognostic epileptiform events. Brief intermittent EEG had a sensitivity of 7% (95% CI, 4%-12%) to 8% (95% CI, 4%-12%) for the detection of potentially treatable seizures, and 0 to 53 hours of continuous monitoring were needed to achieve 95% sensitivity for the detection of potentially treatable seizures. Brief intermittent EEG results yielded similar information compared with continuous EEG results when added to multivariable models predicting neurological outcome.

CONCLUSIONS AND RELEVANCE

Compared with continuous EEG monitoring, brief intermittent monitoring was insensitive for detection of epileptiform events. Monitoring EEG results significantly improved multimodality prediction of neurological outcome, but continuous monitoring appeared to add little additional information compared with brief intermittent monitoring.

State-of-the-art considerations in post-arrest care

Cardiac arrest has a high rate of morbidity and mortality. Several advances in post-cardiac arrest management can improve outcome, but are time-dependent, placing the emergency physician in a critical role to both recognize the need for and initiate therapy. We present a novel perspective of both the workup and therapeutic interventions geared toward the emergency physician during the first few hours of care. We describe how the immediate care of a post-cardiac arrest patient is resource intensive and requires simultaneous evaluation for the underlying cause and intensive management to prevent further end organ damage, particularly of the central nervous system. The goal of the initial focused assessment is to rapidly determine if any reversible causes of cardiac arrest are present and to intervene when possible. Interventions performed in this acute period are aimed at preventing additional brain injury through optimizing hemodynamics, providing ventilatory support, and by using therapeutic hypothermia when indicated. After the initial phase of care, disposition is guided by available resources and the clinician's judgment. Transfer to a specialized cardiac arrest center is prudent in centers that do not have significant support or experience in the care of these patients.

Comparison of parametric and nonparametric methods for outcome prediction using longitudinal data after cardiac arrest

INTRODUCTION

Predicting outcome after cardiac arrest is challenging. We previously tested group-based trajectory modeling (GBTM) for prognostication based on baseline characteristics and quantitative electroencephalographic (EEG) trajectories. Here, we describe implementation of this method in a freely available software package and test its performance against alternative options.

METHODS

We included comatose patients admitted to a single center after resuscitation from cardiac arrest from April 2010 to April 2019 who underwent ≥6 h of EEG monitoring. We abstracted clinical information from our prospective registry and summarized suppression ratio in 48 hourly epochs. We tested three classes of longitudinal models: frequentist, statistically based GBTMs; non-parametric (i.e. machine learning) k-means models; and Bayesian regression. Our primary outcome of interest was discharge CPC 1-3 (vs unconsciousness or death). We compared sensitivity for detecting poor outcome at a false positive rate (FPR) <1%.

RESULTS

Of 1,010 included subjects, 250 (25%) were awake and alive at hospital discharge. GBTM and k-means derived trajectories, group sizes and group-specific outcomes were comparable. Conditional on an FPR < 1%, GBTMs yielded optimal sensitivity (38%) over 48 h. More sensitive methods had 2-3 % FPRs.

CONCLUSION

We explored fundamentally different tools for patient-level predictions based on longitudinal and time-invariant patient data. Of the evaluated methods, GBTM resulted in optimal sensitivity while maintaining a false positive rate <1%. The provided code and software of this method provides an easy-to-use implementation for outcome prediction based on GBTMs.

The current significance of the FOUR score: A systematic review and critical analysis of the literature

BACKGROUND

The Full Outline of Un-Responsiveness Score (FOURs) is a scale for clinical assessment of consciousness that was introduced to overcome disadvantages of the widely accepted Glasgow Coma Scale (GCS).

OBJECTIVE

To carry out a systematic review and critical analysis of the available literature on the clinical application of FOURs and perform a comparison to GCS, in terms of reliability and predictive value.

METHODS

Initial search retrieved a total of 147 papers. After applying strict inclusion criteria and further article selection to overcome data heterogeneity, a statistical comparison of inter-rater reliability, in-hospital mortality and long-term outcome prediction between the two scales in the adult and pediatric population was done.

RESULTS

Even though FOURs is more complicated than GCS, its application remains quite simple. Its reliability, validity and predictive value have been supported by an increasing number of studies, especially in critical care. A statistically significant difference (p = .034) in predicting in-hospital mortality in adults, in favor of FOURs when compared to GCS, was found. However, whether it poses a clinically significant advantage in detecting patients' deterioration and outcome prediction, compared to other scaling systems, remains unclear.

CONCLUSIONS

Further studies are needed to discern the FOURs' clinical usefulness, especially in patients in non-critical condition, with milder disorders of consciousness.

Phenotyping Cardiac Arrest: Bench and Bedside Characterization of Brain and Heart Injury Based on Etiology

OBJECTIVES

Cardiac arrest etiology may be an important source of between-patient heterogeneity, but the impact of etiology on organ injury is unknown. We tested the hypothesis that asphyxial cardiac arrest results in greater neurologic injury than cardiac etiology cardiac arrest (ventricular fibrillation cardiac arrest), whereas ventricular fibrillation cardiac arrest results in greater cardiovascular dysfunction after return of spontaneous circulation.

DESIGN

Prospective observational human and randomized animal study.

SETTING

University laboratory and ICUs.

PATIENTS

Five-hundred forty-three cardiac arrest patients admitted to ICU.

SUBJECTS

Seventy-five male Sprague-Dawley rats.

INTERVENTIONS

We examined neurologic and cardiovascular injury in Isoflurane-anesthetized rat cardiac arrest models matched by ischemic time. Hemodynamic and neurologic outcomes were assessed after 5 minutes no flow asphyxial cardiac arrest or ventricular fibrillation cardiac arrest. Comparison was made to injury patterns observed after human asphyxial cardiac arrest or ventricular fibrillation cardiac arrest.

MEASUREMENTS AND MAIN RESULTS

In rats, cardiac output (20 ± 10 vs 45 ± 9 mL/min) and pH were lower and lactate higher (9.5 ± 1.0 vs 6.4 ± 1.3 mmol/L) after return of spontaneous circulation from ventricular fibrillation cardiac arrest versus asphyxial cardiac arrest (all p < 0.01). Asphyxial cardiac arrest resulted in greater early neurologic deficits, 7-day neuronal loss, and reduced freezing time (memory) after conditioned fear (all p < 0.05). Brain antioxidant reserves were more depleted following asphyxial cardiac arrest. In adjusted analyses, human ventricular fibrillation cardiac arrest was associated with greater cardiovascular injury based on peak troponin (7.8 ng/mL [0.8-57 ng/mL] vs 0.3 ng/mL [0.0-1.5 ng/mL]) and ejection fraction by echocardiography (20% vs 55%; all p < 0.0001), whereas asphyxial cardiac arrest was associated with worse early neurologic injury and poor functional outcome at hospital discharge (n = 46 [18%] vs 102 [44%]; p < 0.0001). Most ventricular fibrillation cardiac arrest deaths (54%) were the result of cardiovascular instability, whereas most asphyxial cardiac arrest deaths (75%) resulted from neurologic injury (p < 0.0001).

CONCLUSIONS

In transcending rat and human studies, we find a consistent phenotype of heart and brain injury after cardiac arrest based on etiology: ventricular fibrillation cardiac arrest produces worse cardiovascular dysfunction, whereas asphyxial cardiac arrest produces worsened neurologic injury associated with greater oxidative stress.

Association of antiepileptic drugs with resolution of epileptiform activity after cardiac arrest

INTRODUCTION

We tested the impact of antiepileptic drug (AED) administration on post-cardiac arrest epileptiform electroencephalographic (EEG) activity.

METHODS

We studied an observational cohort of comatose subjects treated at a single academic medical center after cardiac arrest from September 2010 to January 2018. We aggregated the observed EEG patterns into 5 categories: suppressed; discontinuous background with superimposed epileptiform activity; discontinuous background without epileptiform features; continuous background with epileptiform activity; and continuous background without epileptiform activity. We calculated overall probabilities of transitions between EEG states in a multistate model, then used Aalen's additive regression to test if AEDs or hypothermia are associated with a change in these probabilities.

RESULTS

Overall, 828 subjects had EEG-monitoring for 42,840 h with a median of 40 [IQR 23-64] h per subject. Among patients with epileptiform findings on initial monitoring, 50% transitioned at least once to a non-epileptiform, non-suppressed state. By contrast, 19% with non-epileptiform initial activity transitioned to an epileptiform state at least once. Overall, 568 (78%) patients received at least one AED. Among patients with continuous EEG background activity, valproate, levetiracetam and lower body temperature were each associated with an increased probability of transition from epileptiform states to non-epileptiform states, where patients with discontinuous EEG background activity no agent linked to an increased probability of transitioning from epileptiform states.

CONCLUSION

After cardiac arrest, the impact of AEDs may depend on the presence of continuous cortical background activity. These data serve to inform experimental work to better define the opportunities to improve neurologic care post-cardiac arrest.

External validation of a risk classification at the emergency department of post-cardiac arrest syndrome patients undergoing targeted temperature management

INTRODUCTION

There are no established risk classification for post-cardiac arrest syndrome (PCAS) patients at the Emergency Department (ED) undergoing targeted temperature management (TTM). The aim of this study was to externally validate a simplified version of our prognostic score, the "post-Cardiac Arrest Syndrome for Therapeutic hypothermia score" (revised CAST [rCAST]) and estimate the predictive accuracy of the risk classification based on it.

METHODS

For the external validation, we used data from an out-of-hospital cardiac arrest (OHCA) registry of the Japanese Association for Acute Medicine (JAAM), which is a multicenter, prospective registry of OHCA patients across Japan. Eligible patients were PCAS patients treated with TTM at 33-36 °C between June 2014 and December 2015. We validated the accuracy of rCAST for predicting the neurological outcomes at 30 and 90 days.

RESULTS

Among the 12,024 OHCA patients, the data of 460 PCAS patients treated by TTM were eligible for the validation. The areas under the curve of rCAST for predicting the neurological outcomes at 30 and 90 days were 0.892 and 0.895, respectively. The estimated sensitivity and specificity of the risk categories for the outcomes were as follows: 0.95 (95% CI: 0.92-0.98) and 0.47 (0.40-0.55) for the low (rCAST: ≤5.5), 0.62 (0.56-0.68) and 0.48 (0.40-0.55) for the moderate (rCAST: 6.0-14.0), and 0.57 (0.51-0.63) and 0.95 (0.91-0.98) for the high severity category (rCAST: ≥14.5).

CONCLUSIONS

The rCAST was useful for predicting the neurological outcomes with high accuracy in PCAS patients, and the three grades was developed for a risk classification based on the rCAST.

Evoked potentials improve multimodal prognostication after cardiac arrest

AIM

Predicting recovery in comatose post-cardiac arrest patients requires multiple modalities of prognostic assessment. In isolation, absent N20 cortical responses in somatosensory evoked potentials (SSEPs) are a specific predictor of poor outcome. It is unknown whether SSEP results, when assessed in the context of prior knowledge (demographic and clinical information), change the pretest predicted probability of recovery.

METHODS

In a single center retrospective study, a cohort of 323 patients admitted to post-cardiac arrest service at a tertiary care center were classified into a group based on SSEP testing. We built adjusted logistic regression models including clinical examination findings on the day SSEPs were recorded to generate a pre-test outcome probability for awakening, withdrawal of life-sustaining therapy (WLST) and survival to discharge. We then added the upper extremity N20 cortical response results to the model to obtain updated outcome probabilities. ROC curve was used to determine the additive effect of using SSEPs to the model. Survival to discharge, awakening, and WLST due to neurological reasons were designated as primary, secondary and tertiary outcomes, respectively.

RESULTS

Analyses showed that evoked potentials are ordered in sicker patients. Adding SSEP to the model increased the proportion of patients with less than 1% and 5% chance of survival, as well as the proportion of patients with over 95% chance of WLST. AUC for survival increased from 0.85 to 0.93 when SSEP was included (p = 0.006).

CONCLUSION

Adding the N20 SSEP response results to prior knowledge changed the predicted probability of WLST and survival to discharge in comatose post-arrest patients.

Lipidomics Detection of Brain Cardiolipins in Plasma Is Associated With Outcome After Cardiac Arrest

OBJECTIVES

Brain mitochondrial dysfunction limits neurologic recovery after cardiac arrest. Brain polyunsaturated cardiolipins, mitochondria-unique and functionally essential phospholipids, have unprecedented diversification. Since brain cardiolipins are not present in plasma normally, we hypothesized their appearance would correlate with brain injury severity early after cardiac arrest and return of spontaneous circulation.

DESIGN

Observational case-control study.

SETTING

Two medical centers within one city.

PARTICIPANTS (SUBJECTS)

We enrolled 41 adult cardiac arrest patients in whom blood could be obtained within 6 hours of resuscitation. Two subjects were excluded following outlier analysis. Ten healthy subjects were controls. Sprague-Dawley rats were used in asphyxial cardiac arrest studies.

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

We developed a high-resolution liquid chromatography/mass spectrometry method and determined cardiolipins speciation in human brain, heart, and plasma within 6 hours of (return of spontaneous circulation) from 39 patients with cardiac arrest, 5 with myocardial infarction, and 10 healthy controls. Cerebral score was derived from brain-specific cardiolipins identified in plasma of patients with varying neurologic injury and outcome. Using a rat model of cardiac arrest, cardiolipins were quantified in plasma, brain, and heart. Human brain exhibited a highly diverse cardiolipinome compared with heart that allowed the identification of brain-specific cardiolipins. Nine of 26 brain-specific cardiolipins were detected in plasma and correlated with brain injury. The cerebral score correlated with early neurologic injury and predicted discharge neurologic/functional outcome. Cardiolipin (70:5) emerged as a potential point-of-care marker predicting injury severity and outcome. In rat cardiac arrest, a significant reduction in hippocampal cardiolipins corresponded to their release from the brain into systemic circulation. Cerebral score was significantly increased in 10 minutes versus 5 minutes no-flow cardiac arrest and naïve controls.

CONCLUSIONS

Brain-specific cardiolipins accumulate in plasma early after return of spontaneous circulation and proportional to neurologic injury representing a promising novel biomarker.

In-Hospital Cardiac Arrest: A Review

IMPORTANCE

In-hospital cardiac arrest is common and associated with a high mortality rate. Despite this, in-hospital cardiac arrest has received little attention compared with other high-risk cardiovascular conditions, such as stroke, myocardial infarction, and out-of-hospital cardiac arrest.

OBSERVATIONS

In-hospital cardiac arrest occurs in over 290 000 adults each year in the United States. Cohort data from the United States indicate that the mean age of patients with in-hospital cardiac arrest is 66 years, 58% are men, and the presenting rhythm is most often (81%) nonshockable (ie, asystole or pulseless electrical activity). The cause of the cardiac arrest is most often cardiac (50%-60%), followed by respiratory insufficiency (15%-40%). Efforts to prevent in-hospital cardiac arrest require both a system for identifying deteriorating patients and an appropriate interventional response (eg, rapid response teams). The key elements of treatment during cardiac arrest include chest compressions, ventilation, early defibrillation, when applicable, and immediate attention to potentially reversible causes, such as hyperkalemia or hypoxia. There is limited evidence to support more advanced treatments. Post-cardiac arrest care is focused on identification and treatment of the underlying cause, hemodynamic and respiratory support, and potentially employing neuroprotective strategies (eg, targeted temperature management). Although multiple individual factors are associated with outcomes (eg, age, initial rhythm, duration of the cardiac arrest), a multifaceted approach considering both potential for neurological recovery and ongoing multiorgan failure is warranted for prognostication and clinical decision-making in the post-cardiac arrest period. Withdrawal of care in the absence of definite prognostic signs both during and after cardiac arrest should be avoided. Hospitals are encouraged to participate in national quality-improvement initiatives.

CONCLUSIONS AND RELEVANCE

An estimated 290 000 in-hospital cardiac arrests occur each year in the United States. However, there is limited evidence to support clinical decision making. An increased awareness with regard to optimizing clinical care and new research might improve outcomes.

Prognostication after cardiac arrest: Results of an international, multi-professional survey

AIM

We explored preferences for prognostic test performance characteristics and error tolerance in decisions regarding withdrawal or continuation of life-sustaining therapy (LST) after cardiac arrest in a diverse cohort of medical providers.

METHODOLOGY

We distributed a survey through professional societies and research networks. We asked demographic characteristics, preferences for prognostic test performance characteristics and views on acceptable false positive rates for decisions about LST after cardiac arrest.

RESULTS

Overall, 640 respondents participated in our survey. Most respondents were attending physicians (74%) with >10 years of experience (59%) and practiced at academic centers (77%). Common specialties were neurology (22%), neuro- or general critical care (24%) and palliative care (31%). The majority (56%) felt an acceptable FPR for withdrawal of LST from patients who might otherwise have recovered was ≤0.1%. Acceptable FPRs for continuing LST in patients with unrecognized irrecoverable injury was higher, with 59% choosing a threshold ≤1%. Compared to providers with >10 years of experience, those with <5 years thought lower FPRs were acceptable (P < 0.001 for both). Palliative care providers accepted significantly higher FPRs for withdrawal of LST (P < 0.0001), and critical care providers accepted significantly higher FPRs for provision of long-term LST (P = 0.02). With regard to test performance characteristics, providers favored accuracy over timeliness, and prefer tests be optimized to predictrather than favorable outcomes.

CONCLUSION

Medical providers are comfortable with low acceptable FPR for withdrawal (≤0.1%) and continuation (≤1%) of LST after cardiac arrest. These FPRs may be lower than can be achieved with current prognostic modalities.

Differential association of subtypes of epileptiform activity with outcome after cardiac arrest

BACKGROUND

Epileptiform activity is common after cardiac arrest, although intensity of electroencephalographic (EEG) monitoring may affect detection rates. Prior work has grouped these patterns together as "malignant," without considering discrete subtypes. We describe the incidence of distinct patterns in the ictal-interictal spectrum at two centers and their association with outcomes.

METHODS

We analyzed a retrospective cohort of comatose post-arrest patients admitted at two academic centers from January 2011 to October 2014. One center uses routine continuous EEG, the other acquires "spot" EEG at the treating physicians' discretion. We reviewed all available EEG data and classified epileptiform patterns. We abstracted antiepileptic drugs (AEDs) administrations from the electronic medical record. We compared apparent incidence of each pattern between centers, and compared outcomes (awakening from coma, survival to discharge, discharge modified Rankin Scale (mRS) 0-2) across EEG patterns and number of AEDs administered.

RESULTS

We included 818 patients. Routine continuous EEG was associated with a higher apparent incidence of polyspike burst-suppression (25% vs 13% P < 0.001). Frequency of other epileptiform findings did not differ. Among patients with any epileptiform pattern, only 2/258 (1%, 95%CI 0-3%) were discharged with mRS 0-2, although 24/258 (9%, 95%CI 6-14%) awakened and 36/258 (14%, 95%CI 10-19%) survived. The proportions that awakened and survived decreased in a stepwise manner with progressively worse EEG patterns (range 38% to 2% and 32% to 7%, respectively). Among patients receiving ≥3 AEDs, only 5/80 (6%, 95%CI 2-14%) awakened and 1/80 (1%, 95%CI 0-7%) had a mRS 0-2.

CONCLUSION

We found high rates of epileptiform EEG findings, regardless of intensity of EEG monitoring. The association of distinct ictal-interictal EEG findings with outcome was variable.

A novel methodological framework for multimodality, trajectory model-based prognostication

INTRODUCTION

Prognostic tools typically combine several time-invariant clinical predictors using regression models that yield a single, time-invariant outcome prediction. This results in considerable information loss as repeatedly or continuously sampled data are aggregated into single summary measures. We describe a method for real-time multivariate outcome prediction that accommodates both longitudinal data and time-invariant clinical characteristics.

METHODS

We included comatose patients treated after resuscitation from cardiac arrest who underwent ≥6 h of electroencephalographic (EEG) monitoring. We used Persyst v13 (Persyst Development Corp, Prescott AZ) to generate quantitative EEG (qEEG) features and calculated hourly summaries of whole brain suppression ratio and amplitude-integrated EEG. We randomly selected half of subjects as a training sample and used the other half as a test sample. We applied group-based trajectory modeling (GBTM) to the training sample to group patients based on qEEG evolution, then estimated the relationship of group membership and clinical covariates with awakening from coma and surviving to hospital discharge using logistic regression. We used these parameters to calculate posterior probabilities of group membership (PPGMs) in the test sample, and built three prognostic models: adjusted logistic regression (no GBTM), unadjusted GBTM (no clinical covariates) and adjusted GBTM (all data). We compared these models performance characteristics.

RESULTS

We included 723 patients. Group-specific outcome estimates from a 7-group GBTM ranged from 0 to 75%. Compared to unadjusted GBTM, adjusted GBTM calibration was significantly improved at 6 and 12 h, and time to an outcome estimate <10% and <5% were significantly shortened. Compared to simple logistic regression, adjusted GBTM identified a substantially larger proportion of subjects with outcome probability <1%.

CONCLUSIONS

We describe a novel methodology for combining GBTM output and clinical covariates to estimate patient-specific prognosis over time. Refinement of such methods should form the basis for new avenues of prognostication research that minimize loss of clinically important information.

Predicting neurological recovery with Canonical Autocorrelation Embeddings

Early prediction of the potential for neurological recovery after resuscitation from cardiac arrest is difficult but important. Currently, no clinical finding or combination of findings are sufficient to accurately predict or preclude favorable recovery of comatose patients in the first 24 to 48 hours after resuscitation. Thus, life-sustaining therapy is often continued for several days in patients whose irrecoverable injury is not yet recognized. Conversely, early withdrawal of life-sustaining therapy increases mortality among patients who otherwise might have gone on to recover. In this work, we present Canonical Autocorrelation Analysis (CAA) and Canonical Autocorrelation Embeddings (CAE), novel methods suitable for identifying complex patterns in high-resolution multivariate data often collected in highly monitored clinical environments such as intensive care units. CAE embeds sets of datapoints onto a space that characterizes their latent correlation structures and allows direct comparison of these structures through the use of a distance metric. The methodology may be particularly suitable when the unit of analysis is not just an individual datapoint but a dataset, as for instance in patients for whom physiological measures are recorded over time, and where changes of correlation patterns in these datasets are informative for the task at hand. We present a proof of concept to illustrate the potential utility of CAE by applying it to characterize electroencephalographic recordings from 80 comatose survivors of cardiac arrest, aiming to identify patients who will survive to hospital discharge with favorable functional recovery. Our results show that with very low probability of making a Type 1 error, we are able to identify 32.5% of patients who are likely to have a good neurological outcome, some of whom have otherwise unfavorable clinical characteristics. Importantly, some of these had 5% predicted chance of favorable recovery based on initial illness severity measures alone. Providing this information to support clinical decision-making could motivate the continuation of life-sustaining therapies for these patients.

The severity of psychiatric disorders

The issue of the severity of psychiatric disorders has great clinical importance. For example, severity influences decisions about level of care, and affects decisions to seek government assistance due to psychiatric disability. Controversy exists as to the efficacy of antidepressants across the spectrum of depression severity, and whether patients with severe depression should be preferentially treated with medication rather than psychotherapy. Measures of severity are used to evaluate outcome in treatment studies and may be used as meaningful endpoints in clinical practice. But, what does it mean to say that someone has a severe illness? Does severity refer to the number of symptoms a patient is experiencing? To the intensity of the symptoms? To symptom frequency or persistence? To the impact of symptoms on functioning or on quality of life? To the likelihood of the illness resulting in permanent disability or death? Putting aside the issue of how severity should be operationalized, another consideration is whether severity should be conceptualized similarly for all illnesses or be disorder specific. In this paper, we examine how severity is characterized in research and contemporary psychiatric diagnostic systems, with a special focus on depression and personality disorders. Our review shows that the DSM-5 has defined the severity of various disorders in different ways, and that researchers have adopted a myriad of ways of defining severity for both depression and personality disorders, although the severity of the former was predominantly defined according to scores on symptom rating scales, whereas the severity of the latter was often linked with impairments in functioning. Because the functional impact of symptom-defined disorders depends on factors extrinsic to those disorders, such as self-efficacy, resilience, coping ability, social support, cultural and social expectations, as well as the responsibilities related to one's primary role function and the availability of others to assume those responsibilities, we argue that the severity of such disorders should be defined independently from functional impairment.

Arrest etiology among patients resuscitated from cardiac arrest

INTRODUCTION

Cardiac arrest etiology is often assigned according to the Utstein template, which differentiates medical (formerly "presumed cardiac") from other causes. These categories are poorly defined, contain within them many clinically distinct etiologies, and are rarely based on diagnostic testing. Optimal clinical care and research require more rigorous characterization of arrest etiology.

METHODS

We developed a novel system to classify arrest etiology using a structured chart review of consecutive patients treated at a single center after in- or out-of-hospital cardiac arrest over four years. Two reviewers independently reviewed a random subset of 20% of cases to calculate inter-rater reliability. We used X2 and Kruskal-Wallis tests to compare baseline clinical characteristics and outcomes across etiologies.

RESULTS

We identified 14 principal arrest etiologies, and developed objective diagnostic criteria for each. Inter-rater reliability was high (kappa = 0.80). Median age of 986 included patients was 60 years, 43% were female and 71% arrested out-of-hospital. The most common etiology was respiratory failure (148 (15%)). A minority (255 (26%)) arrested due to cardiac causes. Only nine (1%) underwent a diagnostic workup that was unrevealing of etiology. Rates of awakening and survival to hospital discharge both differed across arrest etiologies, with survival ranging from 6% to 60% (both P < 0.001), and rates of favorable outcome ranging from 0% to 40% (P < 0.001). Timing and mechanism of death (e.g. multisystem organ failure or brain death) also differed significantly across etiologies.

CONCLUSIONS

Arrest etiology was identifiable in the majority cases via systematic chart review. "Cardiac" etiologies may be less common than previously thought. Substantial clinical heterogeneity exists across etiologies, suggesting previous classification systems may be insufficient.

Performance on the APACHE II, SAPS II, SOFA and the OHCA score of post-cardiac arrest patients treated with therapeutic hypothermia

Objective

This study assessed the ability of the Acute Physiologic and Chronic Health Evaluation (APACHE) II score, Simplified Acute Physiology Score (SAPS) II, Sequential Organ Failure Assessment (SOFA) score, and out-of-hospital cardiac arrest (OHCA) score to predict the outcome of OHCA patients who underwent therapeutic hypothermia (TH).

Methods

This study included OHCA patients treated with TH between January 2010 and December 2013. The APACHE II score, SAPS II, and SOFA score were calculated at the time of admission and 24 h and 48 h after intensive care unit admission. The OHCA score was calculated at the time of admission. The area under the curve (AUC) of the receiver operating characteristic curve and logistic regression analysis were used to evaluate outcome predictability.

Results

Data from a total of 173 patients were included in the analysis. The APACHE II score at 0 h and 48 h, SAPS II at 48 h, and OHCA score had moderate discrimination for mortality (AUC: 0.715, 0.750, 0.720, 0.740). For neurologic outcomes, the APACHE II score at 0 h and 48 h, SAPS II at 0 h and 48 h, and OHCA score showed moderate discrimination (AUC: 0.752, 0.738, 0.771, 0.771, 0.764). The APACHE II score, SAPS II and SOFA score at various time points, in addition to the OHCA score, were independent predictors of mortality and a poor neurologic outcome.

Conclusions

The APACHE II score, SAPS II, SOFA score, and OHCA score have different capabilities in discriminating and estimating hospital mortality and neurologic outcomes. The OHCA score, APACHE II score and SAPS II at time zero and 48 h offer moderate predictive accuracy. Other scores at 0 h and 48 h, except for the SOFA score, are independently associated with 30-day mortality and poor cerebral performance.

Demographic, social, economic and geographic factors associated with long-term outcomes in a cohort of cardiac arrest survivors

BACKGROUND

Demographic, social, economic and geographic factors are associated with increased short-term mortality after cardiac arrest. We sought to determine if these factors are additionally associated with long-term outcome differences using a detailed clinical database linked to state-wide administrative data.

METHODS

We included cardiac arrest patients surviving to hospital discharge from five hospitals in the United States from 2005 to 2013, with follow-up through 2015. We obtained information on sex, race, arrest location, initial rhythm, median ZIP code income, post-arrest illness severity, cardiac catheterization, internal cardioverter-defibrillator insertion, rural residence and drive time from residence to the nearest acute care hospital. We used Cox proportional hazard models identify predictors of mortality.

RESULTS

We included 891 patients followed for 2081 patient-years. There were 340 deaths with median survival 6 years. In adjusted models we identified an interaction effect between median ZIP code income and cardiac catheterization. Among patients who had cardiac catheterization there was an attenuated benefit from cardiac catheterization at progressively lower neighborhood incomes (adjusted HR: 0.21 to 0.46 to 0.56). Residence more than 20 min from the nearest acute care hospital was associated with increased hazard of death (adjusted HR: 1.48; 95%CI: 1.35-1.62), after controlling for rural residence and residence in a Medically Underserved Area/Population. Female patients showed less benefit following ICD placement (male adjusted HR: 0.49; female adjusted HR: 0.66).

CONCLUSIONS

There are persistent long-term outcome differences in cardiac arrest survival based on sex, income, and geographic access acute care.

Selection bias, interventions and outcomes for survivors of cardiac arrest

OBJECTIVE

Cardiac catheterisation and implantable cardioverter defibrillator (ICD) insertion are increasingly common following cardiac arrest survival. However, much of the evidence for the benefit is observational, leaving open the possibility that biased patient selection confounds the association between these invasive procedures and improved outcome. We evaluated the likelihood of selection bias in the association between cardiac catheterisation or ICD placement and outcome by measuring long-term outcomes overall and in a cause-specific approach that separated cardiac mortality from non-cardiac mortality.

METHODS

We performed a multivariable survival analysis of a clinical cohort between 2005 and 2013, with follow-up through 2015. We included patients who had out-of-hospital or inhospital cardiac arrest that survived to discharge, and evaluated the association between cardiac catheterisation or ICD insertion and all-cause, cardiovascular and non-cardiovascular mortality.

RESULTS

Among 678 patients who survived cardiac arrest, we observed lower all-cause mortality among patients who underwent cardiac catheterisation (adjusted HR (aHR) 0.40; P<0.01) or ICD insertion (aHR 0.55; P<0.01). However, cause-specific analysis showed that the benefits of cardiac catheterisation and ICD insertion resulted from reduced non-cardiac causes of death (cardiac catheterisation: aHR 0.24, P<0.01; ICD: aHR 0.58, P<0.01), while reduced cardiac cause of death was not associated with cardiac catheterisation (cardiac catheterisation: aHR 0.75, P=0.33).

CONCLUSIONS

There is evidence of selection bias in the secondary prevention survival benefit attributable to cardiac catheterisation for patients who survive cardiac arrest. Observational studies that consider its effects on all-cause mortality likely overestimate the potential benefit of this procedure.

Hemodynamic Resuscitation Characteristics Associated with Improved Survival and Shock Resolution After Cardiac Arrest

PURPOSE

To determine which strategy of early post-cardiac arrest hemodynamic resuscitation was associated with best clinical outcomes. We hypothesized that higher mean arterial pressure (MAP) achieved using IV fluids over vasopressors would yield better outcomes.

METHODS

Retrospective cohort study of post-cardiac arrest patients between March 2011 and June 2012. Patients successfully resuscitated from cardiac arrest, admitted to an intensive care unit and surviving at least 24 h, were included. Patients missing data for >2 h after return of spontaneous circulation were excluded. The institutional standard for post-resuscitation MAP was ≥65 mm Hg with no guidelines on how MAP was supported. We examined the association between early (6 h) average MAP, vasopressor use summarized as cumulative vasopressor index and fluid intake with outcomes including survival to discharge, favorable neurologic outcome based on Cerebral Performance Category 1 or 2, and the surrogate outcome measure of lactate clearance using Pearson correlation and multivariable regression.

RESULTS

Of 118 patients, 55 (46%) survived to hospital discharge, 21 (18%) with favorable neurologic outcome. Higher 6-h mean cumulative vasopressor index was independently associated with worsened survival (OR 0.67; 95% CI 0.53, 0.85; P = 0.001). Resuscitation subgroups receiving higher than median vasopressors had worsened survival to hospital discharge regardless of fluid intake. In addition, higher MAP-6h correlated with increased lactate clearance (r = 0.29; P = 0.011).

CONCLUSIONS

Early post-return of spontaneous circulation hemodynamic resuscitation achieving higher MAP using fluid preferentially over vasopressors is associated with improved survival to hospital discharge as well as better lactate clearance.

Early coronary angiography and percutaneous coronary intervention are associated with improved outcomes after out of hospital cardiac arrest

AIM

Early coronary angiography (CAG) and percutaneous coronary intervention (PCI) are associated with better outcomes in subjects resuscitated from out-of-hospital cardiac arrest (OHCA). We sought to determine the relative contributions of early CAG and PCI to outcomes and adverse events after OHCA.

METHODS

We analyzed 599 OHCA subjects from a prospective two-center registry. Hospital survival, functional outcomes and adverse events were compared between subjects undergoing early CAG (within 24h) with or without PCI and subjects not undergoing early CAG. We adjusted for propensity to perform early CAG and PCI and for post-resuscitation illness severity and care.

RESULTS

Early CAG subjects had improved rates of hospital survival (56.2% versus 31.0%, OR 2.85 [95% CI 2.04-4.00]; p<0.0001) and better functional outcomes compared to no early CAG. Early PCI was associated with improved survival compared to early CAG without PCI (65.6% versus 45.5%, OR 2.29 [95% CI 1.41-3.69]; p<0.001). After multivariate adjustment and propensity matching, early PCI remained significantly associated with improved survival compared with early CAG without PCI and no early CAG, but early CAG without PCI was no longer significantly associated with improved outcome compared with no early CAG. Early CAG and early PCI were not associated with an increase in transfusions or acute kidney injury.

CONCLUSIONS

Early CAG and PCI are associated with improved survival and functional outcomes after OHCA, but only early PCI was associated with a significant benefit after statistical adjustment. Our analysis supports the performance of immediate CAG to determine the need for PCI in selected patients following resuscitation from OHCA.

Neurostimulant use is associated with improved survival in comatose patients after cardiac arrest regardless of electroencephalographic substrate

AIM

Identify EEG patterns that predict or preclude favorable response in comatose post-arrest patients receiving neurostimulants.

METHODS

We examined a retrospective cohort of consecutive electroencephalography (EEG)-monitored comatose post-arrest patients. We classified the last day of EEG recording before neurostimulant administration based on continuity (continuous/discontinuous), reactivity (yes/no) and malignant patterns (periodic discharges, suppression burst, myoclonic status epilepticus or seizures; yes/no). In subjects who did not receive neurostimulants, we examined the last 24h of available recording. For our primary analysis, we used logistic regression to identify EEG predictors of favorable response to treatment (awakening).

RESULTS

In 585 subjects, mean (SD) age was 57 (17) years and 227 (39%) were female. Forty-seven patients (8%) received a neurostimulant. Neurostimulant administration independently predicted improved survival to hospital discharge in the overall cohort (adjusted odds ratio (aOR) 4.00, 95% CI 1.68-9.52) although functionally favorable survival did not differ. No EEG characteristic predicted favorable response to neurostimulants. In each subgroup of unfavorable EEG characteristics, neurostimulants were associated with increased survival to hospital discharge (discontinuous background: 44% vs 7%, P=0.004; non-reactive background: 56% vs 6%, P<0.001; malignant patterns: 63% vs 5%, P<0.001).

CONCLUSION

EEG patterns described as ominous after cardiac arrest did not preclude survival or awakening after neurostimulant administration. These data are limited by their observational nature and potential for selection bias, but suggest that EEG patterns alone should not affect consideration of neurostimulant use.

Association of antiplatelet therapy with patient outcomes after out-of-hospital cardiac arrest

BACKGROUND

Cessation of blood flow during out-of-hospital cardiac arrest (OHCA) results in microvascular thrombosis, protracted hypoperfusion after return of spontaneous circulation and damage to vital organs. We tested the hypothesis that pre-arrest antiplatelet and anticoagulant medication use would be associated with less post-arrest organ dysfunction and better outcomes.

METHODS

We included OHCA patients treated from January 2005 to October 2014 at a single academic medical center. We combined our prospective OHCA registry of clinical and demographic data with a structured chart review to abstract home antiplatelet and anticoagulant medications. We fit unadjusted and adjusted regression models to test the association of antiplatelet and anticoagulant medication use with early post-arrest illness severity, survival and functionally favorable recovery.

RESULTS

Of 1054 subjects, 295 (28%) were prescribed an antiplatelet agent and 147 (14%) were prescribed an anticoagulant prior to arrest. In adjusted models, antiplatelet agents were associated with lower post-arrest illness severity (adjusted OR 0.50 95% CI 0.33-0.77), greater odds of survival to discharge (adjusted OR 1.74 95% CI 1.08-2.80) and greater odds favorable functional outcome (adjusted OR 2.11 95% CI 1.17-3.79). By contrast, anticoagulation via any agent was not associated with illness severity, survival to discharge or favorable outcome.

CONCLUSION

Preventing intra-arrest and post-arrest microvascular thrombosis via antiplatelet agents could represent a novel therapeutic target to improve outcomes after OHCA.

Temperature management for out-of-hospital cardiac arrest

More than 300,000 Americans suffer a cardiac arrest outside of the hospital each year and even among those who are successfully resuscitated and survive to hospital admission, outcomes remain poor. Temperature management (previously known as therapeutic hypothermia) is the only intervention that has been reproducibly demonstrated to ameliorate the neurologic injury that follows cardiac arrest. The results of a recent large randomized controlled trial have highlighted the uncertainty about temperature management strategies following cardiac arrest. This article reviews the issues and recommendations.

Group-Based Trajectory Modeling of Suppression Ratio After Cardiac Arrest

BACKGROUND

Existing studies of quantitative electroencephalography (qEEG) as a prognostic tool after cardiac arrest (CA) use methods that ignore the longitudinal pattern of qEEG data, resulting in significant information loss and precluding analysis of clinically important temporal trends. We tested the utility of group-based trajectory modeling (GBTM) for qEEG classification, focusing on the specific example of suppression ratio (SR).

METHODS

We included comatose CA patients hospitalized from April 2010 to October 2014, excluding CA from trauma or neurological catastrophe. We used Persyst^®v12 to generate SR trends and used semi-quantitative methods to choose appropriate sampling and averaging strategies. We used GBTM to partition SR data into different trajectories and regression associate trajectories with outcome. We derived a multivariate logistic model using clinical variables without qEEG to predict survival, then added trajectories and/or non-longitudinal SR estimates, and assessed changes in model performance.

RESULTS

Overall, 289 CA patients had ≥36 h of EEG yielding 10,404 h of data (mean age 57 years, 81 % arrested out-of-hospital, 33 % shockable rhythms, 31 % overall survival, 17 % discharged to home or acute rehabilitation). We identified 4 distinct SR trajectories associated with survival (62, 26, 12, and 0 %, P < 0.0001 across groups) and CPC (35, 10, 4, and 0 %, P < 0.0001 across groups). Adding trajectories significantly improved model performance compared to adding non-longitudinal data.

CONCLUSIONS

Longitudinal analysis of continuous qEEG data using GBTM provides more predictive information than analysis of qEEG at single time-points after CA.

Derivation and Validation of the CREST Model for Very Early Prediction of Circulatory Etiology Death in Patients Without ST-Segment-Elevation Myocardial Infarction After Cardiac Arrest

BACKGROUND

No practical tool quantitates the risk of circulatory-etiology death (CED) immediately after successful cardiopulmonary resuscitation in patients without ST-segment-elevation myocardial infarction. We developed and validated a prediction model to rapidly determine that risk and facilitate triage to individualized treatment pathways.

METHODS

With the use of INTCAR (International Cardiac Arrest Registry), an 87-question data set representing 44 centers in the United States and Europe, patients were classified as having had CED or a combined end point of neurological-etiology death or survival. Demographics and clinical factors were modeled in a derivation cohort, and backward stepwise logistic regression was used to identify factors independently associated with CED. We demonstrated model performance using area under the curve and the Hosmer-Lemeshow test in the derivation and validation cohorts, and assigned a simplified point-scoring system.

RESULTS

Among 638 patients in the derivation cohort, 121 (18.9%) had CED. The final model included preexisting coronary artery disease (odds ratio [OR], 2.86; confidence interval [CI], 1.83-4.49; P≤0.001), nonshockable rhythm (OR, 1.75; CI, 1.10-2.77; P=0.017), initial ejection fraction<30% (OR, 2.11; CI, 1.32-3.37; P=0.002), shock at presentation (OR, 2.27; CI, 1.42-3.62; P<0.001), and ischemic time >25 minutes (OR, 1.42; CI, 0.90-2.23; P=0.13). The derivation model area under the curve was 0.73, and Hosmer-Lemeshow test P=0.47. Outcomes were similar in the 318-patient validation cohort (area under the curve 0.68, Hosmer-Lemeshow test P=0.41). When assigned a point for each associated factor in the derivation model, the average predicted versus observed probability of CED with a CREST score (coronary artery disease, initial heart rhythm, low ejection fraction, shock at the time of admission, and ischemic time >25 minutes) of 0 to 5 was: 7.1% versus 10.2%, 9.5% versus 11%, 22.5% versus 19.6%, 32.4% versus 29.6%, 38.5% versus 30%, and 55.7% versus 50%.

CONCLUSIONS

The CREST model stratified patients immediately after resuscitation according to risk of a circulatory-etiology death. The tool may allow for estimation of circulatory risk and improve the triage of survivors of cardiac arrest without ST-segment-elevation myocardial infarction at the point of care.

Neurologic Recovery After Cardiac Arrest: a Multifaceted Puzzle Requiring Comprehensive Coordinated Care

OPINION STATEMENT

Surviving cardiac arrest (CA) requires a longitudinal approach with multiple levels of responsibility, including fostering a culture of action by increasing public awareness and training, optimization of resuscitation measures including frequent updates of guidelines and their timely implementation into practice, and optimization of post-CA care. This clearly goes beyond resuscitation and targeted temperature management. Brain-directed physiologic goals should dictate the post-CA management, as accumulating evidence suggests that the degree of hypoxic brain injury is the main determinant of survival, regardless of the etiology of arrest. Early assessment of the need for further hemodynamic and electrophysiologic cardiac interventions, adjusting ventilator settings to avoid hyperoxia/hypoxia while targeting high-normal to mildly elevated PaCO₂, maintaining mean arterial blood pressures >65 mmHg, evaluating for and treating seizures, maintaining euglycemia, and aggressively pursuing normothermia are key steps in reducing the bioenergetic failure that underlies secondary brain injury. Accurate neuroprognostication requires a multimodal approach with standardized assessments accounting for confounders while recognizing the importance of a delayed prognostication when there is any uncertainty regarding outcome. The concept of a highly specialized post-CA team with expertise in the management of post-CA syndrome (mindful of the brain-directed physiologic goals during the early post-resuscitation phase), TTM, and neuroprognostication, guiding the comprehensive care to the CA survivor, is likely cost-effective and should be explored by institutions that frequently care for these patients. Finally, providing tailored rehabilitation care with systematic reassessment of the needs and overall goals is key for increasing independence and improving quality-of-life in survivors, thereby also alleviating the burden on families. Emerging evidence from multicenter collaborations advances the field of resuscitation at an incredible pace, challenging previously well-established paradigms. There is no more room for "conventional wisdom" in saving the survivors of cardiac arrest.

Recent developments in the management of patients resuscitated from cardiac arrest

Cardiac arrest is the leading cause of death in Europe and the United States. Many patients who are initially resuscitated die in the hospital, and hospital survivors often have substantial neurologic dysfunction. Most cardiac arrests are caused by coronary artery disease; patients with coronary artery disease likely benefit from early coronary angiography and intervention. After resuscitation, cardiac arrest patients remain critically ill and frequently suffer cardiogenic shock and multiorgan failure. Early cardiopulmonary stabilization is important to prevent worsening organ injury. To achieve best patient outcomes, comprehensive critical care management is needed, with primary goals of stabilizing hemodynamics and preventing progressive brain injury. Targeted temperature management is frequently recommended for comatose survivors of cardiac arrest to mitigate the neurologic injury that drives outcomes. Accurate neurologic assessment is central to managing care of cardiac arrest survivors and should combine physical examination with objective neurologic testing, with the caveat that delaying neurologic prognosis is essential to avoid premature withdrawal of supportive care. A combination of clinical findings and diagnostic results should be used to estimate the likelihood of functional recovery. This review focuses on recent advances in care and specific cardiac intensive care strategies that may improve morbidity and mortality for patients after cardiac arrest.

The Brain after Cardiac Arrest

Cardiac arrest is common and deadly. Most patients who are treated in the hospital after achieving return of spontaneous circulation still go on to die from the sequelae of anoxic brain injury. In this review, the authors provide an overview of the mechanisms and consequences of postarrest brain injury. Special attention is paid to potentially modifiable mechanisms of secondary brain injury including seizures, hyperpyrexia, cerebral hypoxia and hypoperfusion, oxidative injury, and the development of cerebral edema. Finally, the authors discuss the outcomes of cardiac arrest survivors with a focus on commonly observed patterns of injury as well as the scales used to measure patient outcome and their limitations.

Continuous EEG monitoring enhances multimodal outcome prediction in hypoxic-ischemic brain injury

OBJECTIVE

Hypoxic brain injury is the largest contributor to disability and mortality after cardiac arrest. We aim to identify electroencephalogram (EEG) characteristics that can predict outcome on cardiac arrest patients treated with targeted temperature management (TTM).

METHODS

We retrospectively examined clinical, EEG, functional outcome at discharge, and in-hospital mortality for 373 adult subjects with return of spontaneous circulation after cardiac arrest. Poor outcome was defined as a Cerebral Performance Category score of 3-5. Pure suppression-burst (SB) was defined as SB not associated with status epilepticus (SE), seizures, or generalized periodic discharges.

RESULTS

In-hospital mortality was 68.6% (N=256). Presence of both unreactive EEG background and SE was associated with a positive predictive value (PPV) of 100% (95% confidence interval: 0.96-1) and a false-positive rate (FPR) of 0% (95% CI: 0-0.11) for poor functional outcome. A prediction model including demographics data, admission exam, presence of status epilepticus, pure SB, and lack of EEG reactivity had an area under the curve of 0.92 (95% CI: 0.87-0.95) for poor functional outcome prediction, and 0.96 (95% CI: 0.94-0.98) for in-hospital mortality. Presence of pure SB (N=87) was confounded by anesthetics use in 83.9% of the cases, and was not an independent predictor of poor functional outcome, having a FPR of 23% (95% CI: 0.19-0.28).

CONCLUSIONS

An unreactive EEG background and SE predicted poor functional outcome and in-hospital mortality in cardiac arrest patients undergoing TTM. Prognostic value of pure SB is confounded by use of sedative agents, and its use on prognostication decisions should be made with caution.

Time to Awakening Is Often Delayed in Patients Who Receive Targeted Temperature Management After Cardiac Arrest

Post cardiac arrest, neuroprognostication remains a complex and clinically challenging issue for critical care providers. For this reason, our primary objective in this study was to determine the frequency of survival and favorable neurological outcomes in post-cardiac arrest patients with delayed time to awakening. To assess whether early withdrawal of care may adversely impact survival, we also sought to describe the time to withdrawal of care of non-surviving patients. We performed a retrospective study of patients resuscitated after cardiac arrest in two large academic community hospitals. We performed a structured chart review of patients treated with therapeutic hypothermia (TH) at one hospital from 2009 to 2015 and at a second hospital from 2013 to 2015. Demographics and Utstein style variables were recorded on all patients, as well as temporal variables to characterize the time interval from Return of Spontaneous Circulation (ROSC) to awakening as recorded by ICU nurses and defined as Glasgow Coma Scale (GCS) of >8. Descriptive data were also captured regarding time to withdrawal of care. We pre-hoc defined delayed awakening as >72 hours post ROSC or >72 hours post rewarming. Our primary outcome was survival to hospital discharge with a secondary outcome of a favorable cerebral performance category of 1 or 2. During this study period, 321 patients received TH, with 111 (34.6%) discharged alive and, of these, 67 (68.5%) experienced a good neurological outcome. Awakening more than 72 hours after return of circulation was common with 31 patients surviving to discharge. Of these, 16 of 31 (51.6%) were found to have a good neurological outcome on hospital discharge. Of the patients who died before discharge, 54 (29.5%) had care withdrawn less than 72 hours after ROSC. A delayed time to awakening is not infrequently associated with a good neurological outcome after TH in patients resuscitated from cardiac arrest.

Combination of initial neurologic examination, quantitative brain imaging and electroencephalography to predict outcome after cardiac arrest

BACKGROUND

Prognosticating outcome following cardiac arrest is challenging and requires a multimodal approach. We tested the hypothesis that the combination of initial neurologic examination, quantitative analysis of head computed tomography (CT) and continuous EEG (cEEG) improve outcome prediction after cardiac arrest.

METHODS

Review of consecutive patients receiving head CT within 24h and cEEG monitoring between April 2010 and May 2013. Initial neurologic examination (Full Outline of UnResponsiveness_Brainstem reflexes (FOUR_B) score and initial Pittsburgh Post-Cardiac Arrest Category (PCAC)), gray matter to white matter attenuation ratio (GWR) on head CT and cEEG patterns were evaluated. The primary outcome was in-hospital mortality.

RESULTS

Of 240 subjects, 70 (29%) survived and 22 (9%) had a good neurologic outcome at hospital discharge. Combined determination of GW ratio and malignant cEEG had an incremental predictive value (AUC: 0.776 for mortality and 0.792 for poor neurologic outcome), with 0% false positive rate when compared with either test alone (AUC of GW ratio: 0.683 for mortality and 0.726 for poor outcome, AUC of malignant cEEG: 0.650 for mortality and 0.647 for poor outcome). Addition of FOUR_B or PCAC to this model improved prediction of mortality (p=0.014 for FOUR_B and 0.001 for PCAC) but not of poor outcome (p=0.786 for FOUR_B and 0.099 for PCAC).

CONCLUSIONS

Combining GWR with cEEG was superior to any individual test for predicting mortality and neurologic outcome. Addition of clinical variables further improved prognostication for mortality but not neurologic outcome. These preliminary data support a multi-modal prognostic workup in this population.

Mechanistic characterization of nitrite-mediated neuroprotection after experimental cardiac arrest

Nitrite acts as an ischemic reservoir of nitric oxide (NO) and a potent S-nitrosating agent which reduced histologic brain injury after rat asphyxial cardiac arrest (ACA). The mechanism(s) of nitrite-mediated neuroprotection remain to be defined. We hypothesized that nitrite-mediated brain mitochondrial S-nitrosation accounts for neuroprotection by reducing reperfusion reactive oxygen species (ROS) generation. Nitrite (4 μmol) or placebo was infused IV after normothermic (37°C) ACA in randomized, blinded fashion with evaluation of neurologic function, survival, brain mitochondrial function, and ROS. Blood and CSF nitrite were quantified using reductive chemiluminescence and S-nitrosation by biotin switch. Direct neuroprotection was verified in vitro after 1 and 4 h neuronal oxygen glucose deprivation measuring neuronal death with inhibition studies to examine mechanism. Mitochondrial ROS generation was quantified by live neuronal imaging using mitoSOX. Nitrite significantly reduced neurologic disability after ACA. ROS generation was reduced in brain mitochondria from nitrite- versus placebo-treated rats after ACA with congruent preservation of brain ascorbate and reduction of ROS in brain sections using immuno-spin trapping. ATP generation was maintained with nitrite up to 24 h after ACA. Nitrite rapidly entered CSF and increased brain mitochondrial S-nitrosation. Nitrite reduced in vitro mitochondrial superoxide generation and improved survival of neurons after oxygen glucose deprivation. Protection was maintained with inhibition of soluble guanylate cyclase but lost with NO scavenging and ultraviolet irradiation. Nitrite therapy results in direct neuroprotection from ACA mediated by reductions in brain mitochondrial ROS in association with protein S-nitrosation. Neuroprotection is dependent on NO and S-nitrosothiol generation, not soluble guanylate cyclase.