Chest compression quality and patient outcomes with the use of a CPR feedback device: A retrospective study

Feedback devices were developed to guide resuscitations as targets recommended by various guidelines are difficult to achieve. Yet, there is limited evidence to support their use for in-hospital cardiac arrests (IHCA), and they did not correlate with patient outcomes. Therefore, this study has investigated the compression quality and patient outcomes in IHCA with the use of a feedback device via a retrospective study of inpatient code blue activations in a Singapore hospital over one year. The primary outcome was compression quality and secondary outcomes were survival, downtime and neurological status. 64 of 110 (58.2%) cases were included. Most resuscitations (71.9%) met the recommended chest compression fraction (CCF, defined as the proportion of time spent on compressions during resuscitation) despite overall quality being suboptimal. Greater survival to discharge and better neurological status in resuscitated patients respectively correlated with higher median CCF (p = 0.040 and 0.026 respectively) and shorter downtime (p < 0.001 and 0.001 respectively); independently, a higher CCF correlated with a shorter downtime (p = 0.014). Overall, this study demonstrated that reducing interruptions is crucial for good outcomes in IHCA. However, compression quality remained suboptimal despite feedback device implementation, possibly requiring further simulation training and coaching. Future multicentre studies incorporating these measures should be explored.

Prognosis of Cardiac Arrest-Peri-arrest and Post-arrest Considerations

There has been only a small improvement in survival and neurologic outcomes in patients with cardiac arrest in recent decades. Type of arrest, length of total arrest time, and location of arrest alter the trajectory of survival and neurologic outcome. In the post-arrest phase, clinical markers such as blood markers, pupillary light response, corneal reflex, myoclonic jerking, somatosensory evoked potential, and electroencephalography testing can be used to help guide neurological prognostication. Most of the testing should be performed 72 hours post-arrest with special considerations for longer observation periods in patients who underwent TTM or who had prolonged sedation and/or neuromuscular blockade.

Temporarily Removed

BACKGROUND

Basic life support education for schoolchildren has become a key initiative to increase bystander cardiopulmonary resuscitation rates. Our objective was to review the existing literature on teaching schoolchildren basic life support to identify the best practices to provide basic life support training in schoolchildren.

METHODS

After topics and subgroups were defined, a comprehensive literature search was conducted. Systematic reviews and controlled and uncontrolled prospective and retrospective studies containing data on students <20 years of age were included.

RESULTS

Schoolchildren are highly motivated to learn basic life support. The CHECK-CALL-COMPRESS algorithm is recommended for all schoolchildren. Regular training in basic life support regardless of age consolidates long-term skills. Young children from 4 years of age are able to assess the first links in the chain of survival. By 10 to 12 years of age, effective chest compression depths and ventilation volumes can be achieved on training manikins. A combination of theoretical and practical training is recommended. Schoolteachers serve as effective basic life support instructors. Schoolchildren also serve as multipliers by passing on basic life support skills to others. The use of age-appropriate social media tools for teaching is a promising approach for schoolchildren of all ages.

CONCLUSIONS

Schoolchildren basic life support training has the potential to educate whole generations to respond to cardiac arrest and to increase survival after out-of-hospital cardiac arrest. Comprehensive legislation, curricula, and scientific assessment are crucial to further develop the education of schoolchildren in basic life support.

Evaluation of Serum Biomarkers and Electroencephalogram to Determine Survival Outcomes in Pediatric Post-Cardiac-Arrest Patients

Cardiac arrest causes primary and secondary brain injuries. We evaluated the association between neuron-specific enolase (NSE), serum S-100B (S100B), electroencephalogram (EEG) patterns, and post-cardiac arrest outcomes in pediatric patients. A prospective observational study was conducted in the pediatric intensive care unit and included 41 post-cardiac arrest patients who underwent EEG and serum sampling for NSE and S100B. The participants were aged 1 month to 18 years who experienced cardiac arrest and underwent CPR after a sustained return of spontaneous circulation for ≥48 h. Approximately 19.5% (n = 8) of patients survived until ICU discharge. Convulsions and sepsis were significantly associated with higher mortality (relative risk: 1.33 [95% CI = 1.09-1.6] and 1.99 [95% CI = 0.8-4.7], respectively). Serum NSE and S100B levels were not statistically associated with the outcome (p = 0.278 and 0.693, respectively). NSE levels were positively correlated with the duration of CPR. EEG patterns were significantly associated with the outcome (p = 0.01). Non-epileptogenic EEG activity was associated with the highest survival rate. Post-cardiac arrest syndrome is a serious condition with a high mortality rate. Management of sepsis and convulsions affects prognosis. We believe that NSE and S100B may have no benefit in survival evaluation. EEG can be considered for post-cardiac arrest patients.

LDIAED: A lightweight deep learning algorithm implementable on automated external defibrillators

Differentiating between shockable and non-shockable Electrocardiogram (ECG) signals would increase the success of resuscitation by the Automated External Defibrillators (AED). In this study, a Deep Neural Network (DNN) algorithm is used to distinguish 1.4-second segment shockable signals from non-shockable signals promptly. The proposed technique is frequency-independent and is trained with signals from diverse patients extracted from MIT-BIH, MIT-BIH Malignant Ventricular Ectopy Database (VFDB), and a database for ventricular tachyarrhythmia signals from Creighton University (CUDB) resulting, in an accuracy of 99.1%. Finally, the raspberry pi minicomputer is used to load the optimized version of the model on it. Testing the implemented model on the processor by unseen ECG signals resulted in an average latency of 0.845 seconds meeting the IEC 60601-2-4 requirements. According to the evaluated results, the proposed technique could be used by AED’s.

Head CT for the intensivist: 10 tips and pearls

Head imaging is an essential diagnostic tool for the management of patients with most acute neurological emergencies involving the brain. While numerous modalities including magnetic resonance imaging and catheter angiography play a role, computed tomography (CT) of the brain is far and away the most widely utilized technique because of its widespread availability and the fact that it is usually easier to implement in critically ill and potentially unstable patients. CT is particularly useful in identifying acute intracranial hemorrhage and this makes it often indispensable in the management of patients with traumatic brain injury and hemorrhagic stroke. However, shortcomings in identifying early ischemia on non-contrast CT mean that care must be taken in considering findings early after symptom onset, with newer CT sequences such as CT angiography and CT perfusion adding value. The critical role played by intensivist in managing neurocritical care patients necessitates familiarity and ability with viewing and understanding the advantages and shortcomings of head CT imaging and under which circumstances other modalities may be appropriate to obtain. This manuscript provides ten different circumstances commonly encountered in neurocritical care and how intensivists can use CT for the benefit of their patients.

EEG Patterns and Outcomes After Hypoxic Brain Injury: A Systematic Review and Meta-analysis

Electroencephalography (EEG) is used to prognosticate recovery in comatose patients with hypoxic ischemic brain injury (HIBI) secondary to cardiac arrest. We sought to determine the prognostic use of specific EEG patterns for predicting disability and death following HIBI secondary to cardiac arrest. This systematic review searched Medline, Embase, and Cochrane Central up to January 2020. We included original research involving prospective and retrospective cohort studies relating specific EEG patterns to disability and death in comatose adult patients suffering HIBI post cardiac arrest requiring admission to an intensive care setting. We evaluated study quality using the Quality of Diagnostic Accuracy Studies 2 tool. Descriptive statistics were used to summarize study, patient, and EEG characteristics. We pooled study-level estimates of sensitivity and specificity for EEG patterns defined a priori using a random effect bivariate and univariate meta-analysis when appropriate. Funnel plots were used to assess publication bias. Of 5191 abstracts, 333 were reviewed in full text, of which 57 were included in the systematic review and 32 in meta-analyses. No reported EEG pattern was found to be invariably associated with death or disability across all studies. Pooled specificities of status epilepticus, burst suppression, and electrocerebral silence were high (92-99%), but sensitivities were low (6-39%) when predicting a composite outcome of disability and death. Study quality varied depending on domain; patient flow and timing performed was well conducted in all, whereas EEG interpretation was retrospective in 17 of 39 studies. Accounting for variable study quality, EEG demonstrates high specificity with a low risk of false negative outcome attribution for disability and death when status epilepticus, burst suppression, or electrocerebral silence is detected. Increased use of standardized cross-study protocols and definitions of EEG patterns are required to better evaluate the prognostic use of EEG for comatose patients with HIBI following cardiac arrest.

Prognostic value of automated pupillometry: an unselected cohort from a cardiac intensive care unit

Background:

Patients admitted to a cardiac intensive care unit are often unconscious with uncertain prognosis. Automated infrared pupillometry for neurological assessment in the intensive care unit may provide early prognostic information. This study aimed to determine the prognostic value of automated pupillometry in different subgroups of patients in a cardiac intensive care unit with 30-day mortality as the primary endpoint and neurological outcome as the secondary endpoint.

Methods:

A total of 221 comatose patients were divided into three groups: out-of-hospital cardiac arrest, in-hospital cardiac arrest and others (i.e. patients with cardiac diagnoses other than cardiac arrest). Automated pupillometry was serially performed until discharge or death and pupil measurements were analysed using the neurological pupil index algorithm. We applied receiver operating characteristic curves in univariable and multivariable logistic regression models and a calculated Youden index identified neurological pupil index cut-off values at different specificities.

Results:

In out-of-hospital cardiac arrest patients higher neurological pupil index values were independently associated with lower 30-day mortality. The univariable model for 30-day mortality had an area under the curve of 0.87 and the multivariable model achieved an area under the curve of 0.94. The Youden index identified a neurological pupil index cut-off in out-of-hospital cardiac arrest patients of 2.40 for a specificity of 100%. For patients with in-hospital cardiac arrest and other cardiac diagnoses, we found no association between neurological pupil index values and 30-day mortality, and the univariable models showed poor predictive values.

Conclusion:

Automated infrared pupillometry has promising predictive value after out-of-hospital cardiac arrest, but poor predictive value in patients with in-hospital cardiac arrest or cardiac diagnoses unrelated to cardiac arrest. Our data suggest a possible neurological pupil index cut-off of 2.40 for poor outcome in out-of-hospital cardiac arrest patients.

Prognostic value of 18F-FDG brain PET as an early indicator of neurological outcomes in a rat model of post-cardiac arrest syndrome

Predicting neurological outcomes in patients with post-cardiac arrest syndrome (PCAS) is crucial for identifying those who will benefit from intensive care. We evaluated the predictive value of 18F-FDG PET. PCAS was induced in Sprague Dawley rats. Baseline and post-3-hour images were acquired. Standardized uptake value (SUV) changes before and after PCAS induction (SUV_delta) and SUV ratios (SUVR) of regional SUV normalized to the whole brain SUV were obtained. The Morris water maze (MWM) test was performed after 2 weeks to evaluate neurological outcomes and rats were classified into two groups based on the result. Of 18 PCAS rats, 8 were classified into the good outcome group. The SUV_delta of forebrain regions were significantly decreased in good outcome group (p < 0.05), while the SUV_delta of hindbrain regions were not significantly different according to outcomes. The SUVR of forebrain regions were significantly higher and the SUVR of hindbrain regions were significantly lower in good outcome group (p < 0.05). Forebrain-to-hindbrain ratio predicted a good neurological outcome with a sensitivity of 90% and specificity of 100% using an optimal cutoff value of 1.22 (AUC 0.969, p < 0.05). These results suggest the potential utility of 18F-FDG PET in the early prediction of neurological outcomes in PCAS.

Quantitative EEG predicts outcomes in children after cardiac arrest

OBJECTIVE

To determine whether quantitative EEG (QEEG) features predict neurologic outcomes in children after cardiac arrest.

METHODS

We performed a single-center prospective observational study of 87 consecutive children resuscitated and admitted to the pediatric intensive care unit after cardiac arrest. Full-array conventional EEG data were obtained as part of clinical management. We computed 8 QEEG features from 5-minute epochs every hour after return of circulation. We developed predictive models utilizing random forest classifiers trained on patient age and 8 QEEG features to predict outcome. The features included SD of each EEG channel, normalized band power in alpha, beta, theta, delta, and gamma wave frequencies, line length, and regularity function scores. We measured outcomes using Pediatric Cerebral Performance Category (PCPC) scores. We evaluated the models using 5-fold cross-validation and 1,000 bootstrap samples.

RESULTS

The best performing model had a 5-fold cross-validation accuracy of 0.8 (0.88 area under the receiver operating characteristic curve). It had a positive predictive value of 0.79 and a sensitivity of 0.84 in predicting patients with favorable outcomes (PCPC score of 1-3). It had a negative predictive value of 0.8 and a specificity of 0.75 in predicting patients with unfavorable outcomes (PCPC score of 4-6). The model also identified the relative importance of each feature. Analyses using only frontal electrodes did not differ in prediction performance compared to analyses using all electrodes.

CONCLUSIONS

QEEG features can standardize EEG interpretation and predict neurologic outcomes in children after cardiac arrest.

Therapeutic hypothermia to reduce intracranial pressure after traumatic brain injury: the Eurotherm3235 RCT

BACKGROUND

Traumatic brain injury (TBI) is a major cause of disability and death in young adults worldwide. It results in around 1 million hospital admissions annually in the European Union (EU), causes a majority of the 50,000 deaths from road traffic accidents and leaves a further ≈10,000 people severely disabled.

OBJECTIVE

The Eurotherm3235 Trial was a pragmatic trial examining the effectiveness of hypothermia (32-35 °C) to reduce raised intracranial pressure (ICP) following severe TBI and reduce morbidity and mortality 6 months after TBI.

DESIGN

An international, multicentre, randomised controlled trial.

SETTING

Specialist neurological critical care units.

PARTICIPANTS

We included adult participants following TBI. Eligible patients had ICP monitoring in place with an ICP of > 20 mmHg despite first-line treatments. Participants were randomised to receive standard care with the addition of hypothermia (32-35 °C) or standard care alone. Online randomisation and the use of an electronic case report form (CRF) ensured concealment of random treatment allocation. It was not possible to blind local investigators to allocation as it was obvious which participants were receiving hypothermia. We collected information on how well the participant had recovered 6 months after injury. This information was provided either by the participant themself (if they were able) and/or a person close to them by completing the Glasgow Outcome Scale - Extended (GOSE) questionnaire. Telephone follow-up was carried out by a blinded independent clinician.

INTERVENTIONS

The primary intervention to reduce ICP in the hypothermia group after randomisation was induction of hypothermia. Core temperature was initially reduced to 35 °C and decreased incrementally to a lower limit of 32 °C if necessary to maintain ICP at < 20 mmHg. Rewarming began after 48 hours if ICP remained controlled. Participants in the standard-care group received usual care at that centre, but without hypothermia.

MAIN OUTCOME MEASURES

The primary outcome measure was the GOSE [range 1 (dead) to 8 (upper good recovery)] at 6 months after the injury as assessed by an independent collaborator, blind to the intervention. A priori subgroup analysis tested the relationship between minimisation factors including being aged < 45 years, having a post-resuscitation Glasgow Coma Scale (GCS) motor score of < 2 on admission, having a time from injury of < 12 hours and patient outcome.

RESULTS

We enrolled 387 patients from 47 centres in 18 countries. The trial was closed to recruitment following concerns raised by the Data and Safety Monitoring Committee in October 2014. On an intention-to-treat basis, 195 participants were randomised to hypothermia treatment and 192 to standard care. Regarding participant outcome, there was a higher mortality rate and poorer functional recovery at 6 months in the hypothermia group. The adjusted common odds ratio (OR) for the primary statistical analysis of the GOSE was 1.54 [95% confidence interval (CI) 1.03 to 2.31]; when the GOSE was dichotomised the OR was 1.74 (95% CI 1.09 to 2.77). Both results favoured standard care alone. In this pragmatic study, we did not collect data on adverse events. Data on serious adverse events (SAEs) were collected but were subject to reporting bias, with most SAEs being reported in the hypothermia group.

CONCLUSIONS

In participants following TBI and with an ICP of > 20 mmHg, titrated therapeutic hypothermia successfully reduced ICP but led to a higher mortality rate and worse functional outcome.

LIMITATIONS

Inability to blind treatment allocation as it was obvious which participants were randomised to the hypothermia group; there was biased recording of SAEs in the hypothermia group. We now believe that more adequately powered clinical trials of common therapies used to reduce ICP, such as hypertonic therapy, barbiturates and hyperventilation, are required to assess their potential benefits and risks to patients.

TRIAL REGISTRATION

Current Controlled Trials ISRCTN34555414.

FUNDING

This project was funded by the National Institute for Health Research (NIHR) Health Technology Assessment programme and will be published in full in Health Technology Assessment; Vol. 22, No. 45. See the NIHR Journals Library website for further project information. The European Society of Intensive Care Medicine supported the pilot phase of this trial.

Outcome prediction with serial neuron-specific enolase and machine learning in anoxic-ischaemic disorders of consciousness

BACKGROUND

The continuation of life-sustaining therapy in critical care patients with anoxic-ischemic disorders of consciousness (AI-DOC) depends on prognostic tests such as serum neuron-specific enolase (NSE) concentration levels.

OBJECTIVES

To apply predictive models using machine learning methods to examine, one year after onset, the prognostic power of serial measurements of NSE in patients with AI-DOC. To compare the discriminative accuracy of this method to both standard single-day, absolute, and difference-between-days, relative NSE levels.

METHODS

Classification algorithms were implemented and K-nearest neighbours (KNN) imputation was used to avoid complete case elimination of patients with missing NSE values. Non-imputed measurements from Day 0 to Day 6 were used for single day and difference-between-days.

RESULTS

The naive Bayes classifier on imputed serial NSE measurements returned an AUC of (0.81±0.07) for n=126 patients (100 poor outcome). This was greater than logistic regression (0.73±0.08) and all other classifiers. Naive Bayes gave a specificity and sensitivity of 96% and 49%, respectively, for an (uncalibrated) probability decision threshold of 90%. The maximum AUC for a single day was Day 3 (0.75) for a subset of n=79 (61 poor outcome) patients, and for differences between Day 1 and Day 4 (0.81) for a subset of n=46 (39 poor outcome) patients.

CONCLUSION

Imputation avoided the elimination of patients with missing data and naive Bayes outperformed all other classifiers. Machine learning algorithms could detect automatically discriminatory features and the overall predictive power increased from standard methods due to the larger data set.

CODE AVAILABILITY

Data analysis code is available under GNU at: https://github.com/emilymuller1991/outcome_prediction_nse.