Feasibility of bispectral index monitoring to guide early post-resuscitation cardiac arrest triage

External validation of the CREST model to predict early circulatory-etiology death after out-of-hospital cardiac arrest without initial ST-segment elevation myocardial infarction

BACKGROUND

The CREST model is a prediction model, quantitating the risk of circulatory-etiology death (CED) after cardiac arrest based on variables available at hospital admission, and intend to guide the triage of comatose patients without ST-segment-elevation myocardial infarction after successful cardiopulmonary resuscitation. This study assessed performance of the CREST model in the Target Temperature Management (TTM) trial cohort.

METHODS

We retrospectively analyzed data from resuscitated out-of-hospital cardiac arrest (OHCA) patients in the TTM-trial. Demographics, clinical characteristics, and CREST variables (history of coronary artery disease, initial heart rhythm, initial ejection fraction, shock at admission and ischemic time > 25 min) were assessed in univariate and multivariable analysis. The primary outcome was CED. The discriminatory power of the logistic regression model was assessed using the C-statistic and goodness of fit was tested according to Hosmer-Lemeshow.

RESULTS

Among 329 patients eligible for final analysis, 71 (22%) had CED. History of ischemic heart disease, previous arrhythmia, older age, initial non-shockable rhythm, shock at admission, ischemic time > 25 min and severe left ventricular dysfunction were variables associated with CED in univariate analysis. CREST variables were entered into a logistic regression model and the area under the curve for the model was 0.73 with adequate calibration according to Hosmer-Lemeshow test (p = 0.602).

CONCLUSIONS

The CREST model had good validity and a discrimination capability for predicting circulatory-etiology death after resuscitation from cardiac arrest without ST-segment elevation myocardial infarction. Application of this model could help to triage high-risk patients for transfer to specialized cardiac centers.

Bispectral index and suppression ratio after cardiac arrest: are they useful as bedside tools for rational treatment escalation plans?

INTRODUCTION AND OBJECTIVES

Myocardial dysfunction contributes to early mortality (24-72 hours) among survivors of a cardiac arrest (CA). The benefits of mechanical support in refractory shock should be balanced against the patient's potential for neurological recovery. To date, these early treatment decisions have been taken based on limited information leading mainly to undertreatment. Therefore, there is a need for early, reliable, accessible, and simple tools that offer information on the possibilities of neurological improvement.

METHODS

We collected data from bispectral index (BIS) and suppression ratio (SR) monitoring of adult comatose survivors of CA managed with targeted temperature management (TTM). Neurological status was assessed according to the Cerebral Performance Category (CPC) scale.

RESULTS

We included 340 patients. At the first full neurological evaluation, 211 patients (62.1%) achieved good outcome or CPC 1-2. Mean BIS values were significantly higher and median SR lower in patients with CPC 1-2. An average BIS> 26 during first 12 hours of TTM predicted good outcome with 89.5% sensitivity and 75.8% specificity (AUC of 0.869), while average SR values> 24 during the first 12 hours of TTM predicted poor outcome (CPC 3-5) with 91.5% sensitivity and 81.8% specificity (AUC, 0.906). Hourly BIS and SR values exhibited good predictive performance (AUC> 0.85), as soon as hour 2 for SR and hour 4 for BIS.

CONCLUSIONS

BIS/SR are associated with patients' potential for neurological recovery after CA. This finding could help to create awareness of the possibility of a better outcome in patients who might otherwise be wrongly considered as nonviable and to establish personalized treatment escalation plans.

Depth of Anesthesia Monitoring

The electroencephalogram (EEG) can be analyzed in its raw form for characteristic drug-induced patterns of change or summarized using mathematical parameters as a processed electroencephalogram (pEEG). In this article we aim to summarize the contemporary literature pertaining to the commonly available pEEG monitors including the effects of commonly used anesthetic drugs on the EEG and pEEG parameters, pEEG monitor pitfalls, and the clinical implications of pEEG monitoring for anesthesia, pediatrics, and intensive care.

Management of Comatose Survivors of Cardiac Arrest

PURPOSE OF REVIEW

Because the whole-body ischemia-reperfusion insult associated with cardiac arrest often results in brain injury, neurologists perform an important role in postresuscitation cardiac arrest care. This article provides guidance for the assessment and management of brain injury following cardiac arrest.

RECENT FINDINGS

Neurologists have many roles in postresuscitation cardiac arrest care: (1) early assessment of brain injury severity to help inform triage for invasive circulatory support or revascularization; (2) advocacy for the maintenance of a neuroprotective thermal, hemodynamic, biochemical, and metabolic milieu; (3) detection and management of seizures; (4) development of an accurate, multimodal, and conservative approach to prognostication; (5) application of shared decision-making paradigms around the likely outcomes of therapy and the goals of care; and (6) facilitation of the neurocognitive assessment of survivors. Therefore, optimal management requires early neurologist involvement in patient care, a detailed knowledge of postresuscitation syndrome and its complex interactions with prognosis, expertise in bringing difficult cases to their optimal conclusions, and a support system for survivors with cognitive deficits.

SUMMARY

Neurologists have a critical role in postresuscitation cardiac arrest care and are key participants in the treatment team from the time of first restoration of a perfusing heart rhythm through the establishment of rehabilitation services for survivors.

Neurologic outcome after out-of-hospital cardiac arrest could be predicted with the help of bispectral-index during early targeted temperature management

Background

Outcome prediction is crucial for out-of-hospital cardiac arrest (OHCA) survivors. Several attempts have been made to use the bispectral index (BIS) for this purpose. We aimed to investigate the prognostic power of the BIS during the early stage of targeted temperature management (TTM) after OHCA.

Methods

From Jan 2014 to Feb 2017, the BIS was determined in OHCA patients as soon as possible after the start of TTM. We injected a neuro-muscular blocking agent and recoded the BIS value and the time when the electromyographic (EMG) factor reached zero. The primary outcome was the cerebral performance category scale (CPC) score at 6 months, and a poor outcome was defined as a CPC score of 3, 4, or 5. The exclusion criteria were age under 18 years, traumatic cardiac arrest, and BIS data with a non-zero EMG factor.

Results

Sixty-five patients were included in this study. Good outcomes were observed for 16 patients (24.6%), and poor outcomes were observed for 49 patients (75.4%). The mean time of BIS recording was 2.3 ± 1.0 h after return of spontaneous circulation (ROSC). The mean BIS values of the good outcome and poor outcome groups were 35.6 ± 13.1 and 5.5 ± 9.2, respectively (p < 0.001). The area under the curve was 0.961. Use of a cut-off value of 20.5 to predict a good outcome yielded a sensitivity of 87.5% and specificity of 93.9%. Use of a cut-off value of 10.5 to predict a poor outcome yielded a sensitivity of 87.8% and specificity of 100%.

Conclusion

With the help of BIS, physicians could predict that a patient who has BIS value over 20.5 after ROSC could have a big chance to get good neurological outcome in less than three hours.

The prognostic value of bispectral index and suppression ratio monitoring after out-of-hospital cardiac arrest: a prospective observational study

Background

We investigated the ability of bispectral index (BIS) monitoring to predict poor neurological outcome in out-of-hospital cardiac arrest (OHCA) patients fully treated according to guidelines.

Results

In this prospective, observational study, 77 successfully resuscitated OHCA patients were enrolled in whom BIS, suppression ratio (SR) and electromyographic (EMG) values were continuously monitored during the first 36 h after the initiation of targeted temperature management at 33 °C. The Cerebral Performance Category (CPC) scale was used to define patients’ outcome at 180 days after OHCA (CPC 1–2: good–CPC 3–5: poor neurological outcome). Using mean BIS and SR values calculated per hour, receiver operator characteristics curves were constructed to determine the optimal time point and threshold to predict poor neurological outcome. At 180 days post-cardiac arrest, 39 patients (51%) had a poor neurological outcome. A mean BIS value ≤ 25 at hour 12 predicted poor neurological outcome with a sensitivity of 49% (95% CI 30–65%), a specificity of 97% (95% CI 85–100%) and false positive rate (FPR) of 6% (95% CI 0–29%) [AUC: 0.722 (0.570–0.875); p = 0.006]. A mean SR value ≥ 3 at hour 23 predicted poor neurological with a sensitivity of 74% (95% CI 56–87%), a specificity of 92% (95% CI 78–98%) and FPR of 11% (95% CI 3–29%) [AUC: 0.836 (0.717–0.955); p < 0.001]. No relationship was found between mean EMG and BIS < 25 (R² = 0.004; p = 0.209).

Conclusion

This study found that mean BIS ≤ 25 at hour 12 and mean SR ≥ 3 at hour 23 might be used to predict poor neurological outcome in an OHCA population with a presumed cardiac cause. Since no correlation was observed between EMG and BIS < 25, our calculated BIS threshold might assist with poor outcome prognostication following OHCA.

Prediction of conscious awareness recovery after severe acute ischemic stroke

BACKGROUND

Ischemic stroke that is sufficiently severe to cause loss of consciousness has a high rate of morbidity and mortality. Many individuals who suffer from these disorders never recover conscious awareness. In this study, we aimed to identify an accurate and simple bedside method to predict awareness recovery after severe acute ischemic stroke.

METHODS

Between 2013 and 2017, all consecutive patients with consciousness disorders after acute ischemic stroke were recruited to the study. The demographic data, Full Outline of UnResponsiveness (FOUR) score, Glasgow Coma Scale (GCS) and Bispectral Index (BIS) were recorded at study entry and each day of Days 3-8 during the stroke period. We followed patients for 90days to assess whether the subjects recovered conscious awareness. Logistic regression and receiver operating characteristic (ROC) curve analyses were used to evaluate the predictors' abilities for outcome prognosis.

RESULTS

We assessed a total of 102 patients. 23 (23%) patients died without awareness recovery, whereas 61 (60%) patients regained conscious awareness. The earliest time with the greatest ability to predict conscious awareness recovery occurred at Day 3 after stroke, and the area under the ROC curve (AUC) of the multivariate model was 0.948 (95% confidence interval (CI) 0.885 to 0.982). Age, BIS, motor response and brainstem responses were independent predictors for conscious awareness recovery. We subsequently created a 20-point score termed "ABMB" based on their coefficients in the Day 3 model. The AUC of ABMB at Day 3 was 0.931 (95% CI 0.882 to 0.980). The ABMB also showed good predictive ability at Days 4 and 5.

CONCLUSIONS

The ABMB score accurately identified patients who will recover conscious awareness within 90days after acute severe ischemic stroke and thus provides a useful outcome message for clinicians and relatives.

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.

Recorded time periods of bispectral index values equal to zero predict neurological outcome after out-of-hospital cardiac arrest

Background

Prognostication in out-of-hospital cardiac arrest (OHCA) survivors is often difficult. Recent studies have shown the predictive ability of bispectral index (BIS) monitoring to assist with early neuroprognostication. The aim of this study was to investigate whether characteristics of BIS values equal to zero (BIS 0) (i.e. duration and/or uni- versus bilateral presence) instead of simply their occurrence are better indicators for poor neurological outcome after OHCA by aiming at a specificity of 100%.

Methods

Between 2011 and 2015, all successfully resuscitated OHCA patients were treated with targeted temperature management (TTM) at 33 °C for 24 hours followed by rewarming over 12 hours (0.3 °C/h). In total, BIS values were registered in 77 OHCA patients. The occurrence of unilateral (BIS 0 at one hemisphere) and bilateral (BIS 0 at both hemispheres) BIS 0 values as well as their total duration were calculated. Receiver operating characteristic (ROC) curves were constructed using the total duration with BIS 0 values calculated from the initiation of TTM onwards to determine poor neurological outcome.

Results

In 30 of 77 OHCA patients (39%), at least one BIS 0 value occurred during the first 48 hours after admission. Of these 30 patients, six (20%) had a good (cerebral performance category (CPC) 1–2) and 24 (80%) a poor neurological outcome (CPC3–5) at 180 days post-CA. Within these 30 patients, the incidence of bilateral BIS 0 values was higher in patients with poor neurological outcome (CPC1–2: 2 (33%) vs. CPC3–5: 19 (79%); p = 0.028). The presence of a BIS 0 value predicted poor neurological outcome with a sensitivity of 62% and specificity of 84% (AUC: 0.729; p = 0.001). With a ROC analysis, a total duration of 30,3 minutes with BIS 0 values calculated over the first 48 hours predicted poor neurological outcome with a sensitivity of 63% and specificity of 100% (AUC: 0.861; p = 0.007).

Conclusions

This study shows that a prolonged duration with (bilateral) BIS 0 values serves as a better outcome predictor after OHCA as compared to a single observation.

Increasing or fluctuating bispectral index values during post-resuscitation targeted temperature management can predict clinical seizures after rewarming

AIM

To investigate whether an increasing bispectral index (BIS) value during targeted temperature management (TTM) correlates with increased clinical seizures after TTM or worse neurological prognoses after TTM.

METHODS

We performed a retrospective prognostication study of patients who were treated with TTM after recovery of spontaneous circulation from cardiac arrest at a tertiary care hospital. We recorded the BIS regularly during TTM and calculated the correlations of the mean BIS values, standard deviations of the BIS values, and linear regression coefficient of the trend of the BIS values over time as index tests. Study outcomes included the occurrence of clinical seizures after TTM and unfavourable neurological outcomes (defined as a Cerebral Performance Scale score of 3-5). Receiver operating characteristics (ROC) analyses evaluated the predictability of the index tests for the study outcomes.

RESULTS

Of 534 patients with post-cardiac arrest who were admitted to the intensive care unit, 103 were enrolled in this study. Thirty-one patients (30.1%) experienced sequelae in the form of clinical seizures, and 52 (50.5%) had unfavourable neurological outcomes at 30days post-resuscitation. The standard deviation (area under the ROC curve [AUC]=0.763) and the regression coefficient (AUC=0.763) had higher predictability of clinical seizures than the mean BIS value (AUC=0.657); in contrast, the low mean BIS value best predicted unfavourable neurological outcomes (AUC=0.861) compared to the standard deviation (AUC=0.532) and regression coefficient (AUC=0.501).

CONCLUSION

An increase of, or greater fluctuation in, BIS during hypothermia may predict clinical seizures after TTM.