Multimodal Prognostication Research Articles

EEG for good outcome prediction after cardiac arrest: A multicentre cohort study

AimAssess the prognostic ability of a non-highly malignant and reactive EEG to predict good outcome after cardiac arrest (CA). MethodsProspective observational multicentre substudy of the “Targeted Hypothermia versus Targeted Normothermia after Out-of-hospital Cardiac Arrest Trial”, also known as the TTM2-trial. Presence or absence of highly malignant EEG patterns and EEG reactivity to external stimuli were prospectively assessed and reported by the trial sites. Highly malignant patterns were defined as burst-suppression or suppression with or without superimposed periodic discharges. Multimodal prognostication was performed 96 h after CA. Good outcome at 6 months was defined as a modified Rankin Scale score of 0–3. Results873 comatose patients at 59 sites had an EEG assessment during the hospital stay. Of these, 283 (32%) had good outcome. EEG was recorded at a median of 69 h (IQR 47–91) after CA. Absence of highly malignant EEG patterns was seen in 543 patients of whom 255 (29% of the cohort) had preserved EEG reactivity. A non-highly malignant and reactive EEG had 56% (CI 50–61) sensitivity and 83% (CI 80–86) specificity to predict good outcome. Presence of EEG reactivity contributed (p < 0.001) to the specificity of EEG to predict good outcome compared to only assessing background pattern without taking reactivity into account. ConclusionNearly one-third of comatose patients resuscitated after CA had a non-highly malignant and reactive EEG that was associated with a good long-term outcome. Reactivity testing should be routinely performed since preserved EEG reactivity contributed to prognostic performance.

A metabolic profile of xenon and metabolite associations with 6-month mortality after out-of-hospital cardiac arrest: A post-hoc study of the randomised Xe-Hypotheca trial.

Out-of-hospital cardiac arrest (OHCA) carries a relatively poor prognosis and requires multimodal prognostication to guide clinical decisions. Identification of previously unrecognized metabolic routes associated with patient outcome may contribute to future biomarker discovery. In OHCA, inhaled xenon elicits neuro- and cardioprotection. However, the metabolic effects remain unknown. In this post-hoc study of the randomised, 2-group, single-blind, phase 2 Xe-Hypotheca trial, 110 OHCA survivors were randomised 1:1 to receive targeted temperature management (TTM) at 33°C with or without inhaled xenon during 24 h. Blood samples for nuclear magnetic resonance spectroscopy metabolic profiling were drawn upon admission, at 24 and 72 h. At 24 h, increased lactate, adjusted hazard-ratio 2.25, 95% CI [1.53; 3.30], p<0.001, and decreased branched-chain amino acids (BCAA) leucine 0.64 [0.5; 0.82], p = 0.007, and valine 0.37 [0.22; 0.63], p = 0.003, associated with 6-month mortality. At 72 h, increased lactate 2.77 [1.76; 4.36], p<0.001, and alanine 2.43 [1.56; 3.78], p = 0.001, and decreased small HDL cholesterol ester content (S-HDL-CE) 0.36 [0.19; 0.68], p = 0.021, associated with mortality. No difference was observed between xenon and control groups. In OHCA patients receiving TTM with or without xenon, high lactate and alanine and decreased BCAAs and S-HDL-CE associated with increased mortality. It remains to be established whether current observations on BCAAs, and possibly alanine and lactate, could reflect neural damage via their roles in the metabolism of the neurotransmitter glutamate. Xenon did not significantly alter the measured metabolic profile, a potentially beneficial attribute in the context of compromised ICU patients. Trial Registry number: ClinicalTrials.gov Identifier: NCT00879892.

Quantitative pupillometry for neuroprognostication in comatose post-cardiac arrest patients: A protocol for a predefined sub-study of the Blood pressure and Oxygenations Targets after Out-of-Hospital Cardiac Arrest (BOX)-trial

BackgroundResuscitation guidelines propose a multimodal prognostication strategy algorithm at ≥72 hours after the return of spontaneous circulation to evaluate neurological outcome for unconscious cardiac arrest survivors. Even though guidelines suggest quantitative pupillometry for assessing pupillary light reflex, threshold values are not yet validated.This study aims to validate pre-specified thresholds of quantitative pupillometry by quantitatively assessing the percentage reduction of pupillary size (qPLR) <4% and Neurological Pupil index (NPi) ≤2 and in predicting unfavorable neurological outcome. Both as an isolated predictor and combined with guideline-suggested neuron-specific enolase (NSE) threshold >60 μg L−1 in the current prognostication strategy algorithm. MethodsWe conduct this pre-planned diagnostic sub-study in the randomized, controlled, multicenter clinical trial “Blood Pressure and Oxygenation Targets after Out-of-Hospital Cardiac Arrest-trial”. Blinded to treating physicians and outcome assessors, measurements of qPLR and NPi are obtained from cardiac arrest survivors at time points (±6 hours) of admission, after 24, 48, and 72 hours, or until the time of awakening or death. DiscussionThis study will be the largest prospective study investigating the predictive performance of automated quantitative pupillometry in unconscious patients resuscitated from cardiac arrest. We will test specific threshold values of NPi ≤2 and qPLR <4% to predict unfavorable outcome following cardiac arrest. The validation of pupillometry alone and combined with NSE with the criteria of the current prognostication strategy algorithm will hopefully increase the level of evidence and support clinical neuroprognostication with automated quantitative pupillometry in unconscious post-cardiac arrest patients. Trial registrationRegistered March 30, 2017, at ClinicalTrials.gov (Identifier: NCT03141099).

Different neuroprognostication thresholds of neuron-specific enolase in shockable and non-shockable out-of-hospital cardiac arrest: a prospective multicenter observational study in Korea (the KORHN-PRO registry)

BackgroundSerum neuron-specific enolase (NSE) is the only recommended biomarker for multimodal prognostication in postcardiac arrest patients, but low sensitivity of absolute NSE threshold limits its utility. This study aimed to evaluate the prognostic performance of serum NSE for poor neurologic outcome in out-of-hospital cardiac arrest (OHCA) survivors based on their initial rhythm and to determine the NSE cutoff values with false positive rate (FPR) < 1% for each group.MethodsThis study included OHCA survivors who received targeted temperature management (TTM) and had serum NSE levels measured at 48 h after return of spontaneous circulation in the Korean Hypothermia Network, a prospective multicenter registry from 22 university-affiliated teaching hospitals in South Korea between October 2015 and December 2018. The primary outcome was poor outcome at 6 month, defined as a cerebral performance category of 3–5.ResultsOf 623 patients who underwent TTM with NSE measured 48 h after the return of spontaneous circulation, 245 had an initial shockable rhythm. Median NSE level was significantly higher in the non-shockable group than in the shockable group (104.6 [40.6–228.4] vs. 25.9 [16.7–53.4] ng/mL, P < 0.001). Prognostic performance of NSE assessed by area under the receiver operating characteristic curve to predict poor outcome was significantly higher in the non-shockable group than in the shockable group (0.92 vs 0.86). NSE cutoff values with an FPR < 1% in the non-shockable and shockable groups were 69.3 (sensitivity of 42.1%) and 102.7 ng/mL (sensitivity of 76%), respectively.ConclusionNSE prognostic performance and its cutoff values with FPR < 1% for predicting poor outcome in OHCA survivors who underwent TTM differed between shockable and non-shockable rhythms, suggesting postcardiac arrest survivor heterogeneity.Trial registration KORHN-PRO, NCT02827422. Registered 11 September 2016—Retrospectively registered, https://clinicaltrials.gov/ct2/show/NCT02827422

State of the Art Post-Cardiac Arrest Care: Evolution and future of post cardiac arrest care.

Out-of-hospital cardiac arrest is a leading cause of mortality. In the prehospital setting, bystander response with cardiopulmonary resuscitation and use of publicly available automated external defibrillators have been associated with improved survival. Early in-hospital treatment still focuses on emergency coronary angiography for selected patients. For patients remaining comatose, temperature control to avoid fever is still recommended, but former hypothermic targets have been abandoned. For patients without spontaneous awakening, the use of a multimodal prognostication model is key. After discharge, follow-up with screening for cognitive and emotional disabilities is recommended. There has been an incredible evolution of research within cardiac arrest. Two decades ago, the largest trials include a few hundred patients. Today, undergoing studies are planning to include 10-20 times as many patients, with improved methodology. This article describes the evolution and perspectives for the future in post-cardiac arrest care.

Deep learning-assisted radiomics facilitates multimodal prognostication for personalized treatment strategies in low-grade glioma

Determining the optimal course of treatment for low grade glioma (LGG) patients is challenging and frequently reliant on subjective judgment and limited scientific evidence. Our objective was to develop a comprehensive deep learning assisted radiomics model for assessing not only overall survival in LGG, but also the likelihood of future malignancy and glioma growth velocity. Thus, we retrospectively included 349 LGG patients to develop a prediction model using clinical, anatomical, and preoperative MRI data. Before performing radiomics analysis, a U2-model for glioma segmentation was utilized to prevent bias, yielding a mean whole tumor Dice score of 0.837. Overall survival and time to malignancy were estimated using Cox proportional hazard models. In a postoperative model, we derived a C-index of 0.82 (CI 0.79–0.86) for the training cohort over 10 years and 0.74 (Cl 0.64–0.84) for the test cohort. Preoperative models showed a C-index of 0.77 (Cl 0.73–0.82) for training and 0.67 (Cl 0.57–0.80) test sets. Our findings suggest that we can reliably predict the survival of a heterogeneous population of glioma patients in both preoperative and postoperative scenarios. Further, we demonstrate the utility of radiomics in predicting biological tumor activity, such as the time to malignancy and the LGG growth rate.

Specific thresholds of quantitative pupillometry parameters predict unfavorable outcome in comatose survivors early after cardiac arrest

AimQuantitative pupillometry is the guideline-recommended method for assessing pupillary light reflex for multimodal prognostication in comatose patients resuscitated from out-of-hospital cardiac arrest (OHCA). However, threshold values predicting an unfavorable outcome have been inconsistent across studies; therefore, we aimed to identify specific thresholds for all quantitative pupillometry parameters. MethodsComatose post-OHCA patients were consecutively admitted to the cardiac arrest center at Copenhagen University Hospital Rigshospitalet from April 2015 to June 2017. The parameters of quantitatively assessed pupillary light reflex (qPLR), Neurological Pupil index (NPi), average/max constriction velocity (CV/MCV), dilation velocity (DV), and latency of constriction (Lat) were recorded on the first three days after admission. We evaluated the prognostic performance and identified thresholds achieving zero percent false positive rate (0% PFR) for an unfavorable outcome of 90-day Cerebral Performance Category (CPC) 3–5. Treating physicians were blinded for pupillometry results. ResultsOf the 135 post-OHCA patients, the primary outcome occurred for 53 (39%) patients.On any day during hospitalization, a qPLR < 4%, NPi < 2.45, CV < 0.1 mm/s, and an MCV < 0.335 mm/s predicted 90-day unfavorable neurological outcome with 0% FPR (95%CI: 0–0%), with sensitivities of 28% (17–40%), 9% (2–19%), 13% (6–23%), and 17% (8–26%), respectively on day 1. ConclusionWe found that specific thresholds of all quantitative pupillometry parameters, measured at any time following hospital admission until day 3, predicted a 90-day unfavorable outcome with 0% FPR in comatose patients resuscitated from OHCA. However, at 0% FPR, thresholds resulted in low sensitivity. These findings should be further validated in larger multicenter clinical trials.

Quantitative pupillometry thresholds for early neuroprognostication after cardiac arrest: A BOX-trial substudy

Abstract Funding Acknowledgements Type of funding sources: Private grant(s) and/or Sponsorship. Main funding source(s): Novo Nordisk Foundation Background Quantitative pupillometry as part of multimodal prognostication in comatose out-of-hospital cardiac arrest (OHCA) patients is promising, but the lack of validated thresholds for outcome presents a challenge for clinical use. Studies have suggested that a percentage reduction of pupillary size (qPLR, %) ≤3.8% and a neurological pupil index (NPi, 0-5) ≤2 is 100% specific for poor neurological outcome. However, this remains to be validated in a large consecutive population. Purpose To assess the proposed thresholds for qPLR and NPi for early identification of poor neurological outcome, defined as Cerebral Performance Category (CPC) 3–5 at 3-months follow-up after OHCA. Methods This predefined, prospective substudy from a large multicenter, randomized trial, obtained quantitative pupillometry measurements from consecutively enrolled patients at 0, 24, 48, 72, 96, and 120 hours after OHCA (t0-t120). However, pupillometry was not used to guide clinical decision-making. Specificity and sensitivity were calculated for the proposed thresholds of qPLR and NPi. Overall prognostic performances were assessed with logistic regression and area under the receiver operating characteristic (ROC) curves (AUC). Models were further adjusted for potential confounders. Results Of the 789 patients comprising the BOX-trial population, serial measurements of quantitative pupillometry were performed in a total of 710 (90%) patients. Patients had a mean age of 63±14 (range 18-93) years, predominantly of male sex (82%), with primary outcome occurring in 266 (37%) patients. For all time points, both median values of qPLR and NPi were significantly lower in patients with poor neurological outcome. The highest AUC was found at t48 for both qPLR and NPi. In the unadjusted model, AUC was 0.80 [95%CI 0.76-0.84] for qPLR and 0.79 [95%CI 0.75-0.83] for NPi (Fig 1). In the adjusted model AUC was 0.92 [95%CI: 0.87-0.96] for qPLR and 0.92 [95%CI: 0.88-0.97] for NPi (Fig 2). Both parameters had a significant contribution to the AUC in the adjusted model, with no significant difference in performance between qPLR and NPi in either model. At t48, specificity for CPC 3-5 was 99% [95%CI 97-100] with sensitivity at 20% [95%CI 15-26] for qPLR≤3.8% and 100% [95%CI 99-100] with sensitivity at 10% [95%CI 6-14] for NPi ≤2. At time points t0, t24, t72, t96, and t120 specificity was 91%, 96%, 99%, 100%, and 100%, respectively for qPLR≤3.8%, and 100% for all time points for NPi ≤2. Conclusion The predictive performance of qPLR and NPi was equally good with optimal timing of measurements at 48 hours after admission. The BOX-trial validates thresholds of NPi ≤2 with 100% specificity for outcome at all time points and of qPLR≤3.8% with 99-100% specificity at t48-t120. These findings support the clinical usefulness of quantitative pupillometry for multimodal neuroprognostication of comatose OHCA patients.

Early NSE measurements at the ICU bear prognostic value in patients with and without CPR

Abstract Funding Acknowledgements Type of funding sources: None. Background Neuron-specific enolase (NSE) represents a diagnostic tool for identifying poor neurological outcomes in patients following CPR. As part of multimodal prognostication, NSE values above 60 µg/L after 48h and/or 72h were associated with a worse neurologic outcome (European Resuscitation Council Guidelines 2021). Despite the prognostic value of NSE 48h to 72h after CPR, measurements at admission to the ICU remain the topic of discussion as divergent findings are present in the literature (primarily in small collectives). Purpose Within the proposed study, we aimed to illuminate the prognostic value of NSE measurements on the day of admission to the ICU. Furthermore, we conducted a comparison of patients with and without CPR prior to ICU admission. Due to hemolysis sensitivity, we adjusted NSE values according to a previously published protocol to reveal potential changes in predictive potential through the occurrence of hemolysis. Methods This was a prospective, observational cohort study. 232 consecutive patients admitted to a cardiac ICU were screened over the course of one year. Patients with active malignancies or traumatic brain injuries were excluded. Furthermore, samples with hemolysis index &amp;gt;30 units were excluded to guarantee stable measurements. NSE values were measured on the day of admission to the ICU in a total of 202 patients (154 without previous CPR, 48 with CPR) using specific ELISA kits. According to an earlier publication, hemolysis was considered to account for an artificial increase of 0.29µg/L per unit (range 5-30 units) and was subsequently subtracted. Results Median age was 66.96 (IQR: 55.7-76.9), and 41.1% of the included patients were female. The total 30-day-mortality was calculated to be 27.2%, and 48 patients (23.8%) were admitted following CPR. Interestingly NSE at admission was not higher in patients after CPR (3.23 ± 3.88 µg/L vs 5.43 ± 5.78 µg/L, p = 0.072). However, NSE measurements on the day of admission were significantly elevated in 30-day none-survivors (3.23 ± 3.84 µg/L vs 6.91 ± 6.55 µg/L, p &amp;lt; 0.001). The same findings were made when excluding all patients after CPR (30-day survivors: 3.24 ± 3.99 µg/L vs 30-day none-survivors: 6.15 ± 6.17µg/L, p = 0.001). No statistically significant differences in survival were found when stratifying for the primary cause of admission. Comparing hemolysis-corrected NSE values to raw data showed a slight increase in positive predictive value for 30-day mortality in the total cohort (ROC-AUC: 0.732 vs 0.721) as well as in patients without prior CPR (ROC-AUC: 0.722 vs 0.703). Conclusion We provided evidence for the prognostic value of early NSE measurements at the ICU. In both the total cohort as well as a sub-cohort without patients admitted after CPR, 30-day none-survivors displayed markedly elevated levels of NSE. Moreover, the positive predictive value of these measurements was slightly increased when correcting for hemolysis &amp;gt;5 units.

Influence of blood-pressure and oxygen levels on the prognostic value of quantitative pupillometry for comatose cardiac arrest patients - a BOX-trial substudy

Abstract Funding Acknowledgements Type of funding sources: Private grant(s) and/or Sponsorship. Main funding source(s): Novo Nordisk Foundation Background Quantitative pupillometry has been implemented in resuscitation guidelines as part of multimodal prognostication in comatose out-of-hospital cardiac arrest (OHCA) patients. Post-resuscitation intensive care allows for different blood-pressure and oxygen targets, but evidence on the implication of these targets on prognostication is limited. Purpose To assess the impact of different blood-pressure and oxygen targets in resuscitated OHCA patients on the prognostic ability of the quantitatively assessed percentage reduction of pupillary size (qPLR, %) and the neurological pupil index (NPi, 0-5). Methods The BOX-trial is a multicenter, double-blind, randomized trial, comparing low (63 mm Hg) versus high (77 mm Hg) mean arterial blood-pressure targets and restrictive (9 to 10 kPa) versus liberal (13 to 14 kPa) partial pressure of arterial oxygen targets in comatose survivors of OHCA. In this predefined, prospective substudy, quantitative pupillometry measurements were obtained from consecutively enrolled patients at 0, 24, 48, 72, 96, and 120 hours after OHCA (t0-t120). The prognostic performance of qPLR and NPi for high and low blood-pressure groups, and liberal and restrictive oxygen target groups were illustrated through the area under the receiver operating characteristic (ROC) curves (AUC). The time point achieving the highest AUC was used for further analyses. AUCs were compared with DeLong’s test. Results The highest AUC for qPLR and NPi were achieved for measurements made 48 hours after OHCA. qPLR produced AUC at 0.79 [95%CI 0.73-0.85] and 0.81 [95%CI 0.75-0.86] for high and low blood-pressure groups, and 0.79 [95%CI 0.73-0.85] and 0.80 [95%CI 0.74-0.86] for liberal and restrictive oxygen target groups, respectively (Fig 1). For NPi AUC were 0.79 [95%CI 0.73-0.85] and 0.78 [95%CI 0.72-0.84] in high and low blood-pressure groups, and 0.77 [95%CI 0.71-0.83] and 0.80 [95%CI 0.75-0.86] in liberal and restrictive oxygen target groups, respectively (Fig 2). No significant difference between any groups, for either blood-pressure or oxygen targets, was found. Conclusion Prognostic value of quantitative pupillometry for predicting outcome in resuscitated OHCA patients was high and consistent across different blood-pressure and oxygen level targets.

Multicentric validation of a prognostic tool for predicting brain death following out-of-hospital cardiac arrest in children

AimOut-of-hospital cardiac arrest (OHCA) in pediatric patients is associated with high rates of mortality and neurologic injury, with no definitive evidence-based method to predict outcomes available. A prognostic scoring tool for adults, The Brain Death After Cardiac Arrest (BDCA) score, was recently developed and validated. We aimed to validate this score in pediatric patients. MethodsRetrospective cohort study of pediatric patients admitted to 5 PICUs after OHCA between 2011 and 2021. We extracted BDCA score elements for those who survived at least 24 hours but died as a result of their OHCA. We assessed score discrimination for the definitive outcome of brain death. Subgroup analysis was performed for infants < 12mo versus children ≥ 12mo, those who likely had brain death but had withdrawal of life sustaining therapy (WLST) prior to declaration, and by etiology and duration of arrest. Results389 subjects were identified across 5 institutions, with 282 meeting inclusion criteria. 169 (59.9%) were formally declared brain dead; 58 (20.6%) had findings consistent with brain death but had withdrawal of life sustaining therapies prior to completion of formal declaration. Area under the receiver operating characteristic curve for the age ≥ 12mo cohort was 0.82 [95% CI 0.75, 0.90], which mirrored the adult subject AUCs of 0.82 [0.77, 0.86] and 0.81 [0.76, 0.86] in the development and validation cohorts. Scores demonstrated worse discrimination in the infant cohort (AUC = 0.61). ConclusionsThe BDCA score shows promise in children ≥ 12mo following OHCA and may be considered in conjunction with existing multimodal prognostication approaches.

Deep Learning in Ischemic Stroke Imaging Analysis: A Comprehensive Review.

Ischemic stroke is a cerebrovascular disease with a high morbidity and mortality rate, which poses a serious challenge to human health and life. Meanwhile, the management of ischemic stroke remains highly dependent on manual visual analysis of noncontrast computed tomography (CT) or magnetic resonance imaging (MRI). However, artifacts and noise of the equipment as well as the radiologist experience play a significant role on diagnostic accuracy. To overcome these defects, the number of computer-aided diagnostic (CAD) methods for ischemic stroke is increasing substantially during the past decade. Particularly, deep learning models with massive data learning capabilities are recognized as powerful auxiliary tools for the acute intervention and guiding prognosis of ischemic stroke. To select appropriate interventions, facilitate clinical practice, and improve the clinical outcomes of patients, this review firstly surveys the current state-of-the-art deep learning technology. Then, we summarized the major applications in acute ischemic stroke imaging, particularly in exploring the potential function of stroke diagnosis and multimodal prognostication. Finally, we sketched out the current problems and prospects.

EEG Pattern With Spectral Analysis Can Prognosticate Good and Poor Neurologic Outcomes After Cardiac Arrest.

To investigate the prognostic value of a simple stratification system of electroencephalographical (EEG) patterns and spectral types for patients after cardiac arrest. In this prospectively enrolled cohort, using manually selected EEG segments, patients after cardiac arrest were stratified into five independent EEG patterns (based on background continuity and burden of highly epileptiform discharges) and four independent power spectral types (based on the presence of frequency components). The primary outcome is cerebral performance category (CPC) at discharge. Results from multimodal prognostication testing were included for comparison. Of a total of 72 patients, 6 had CPC 1-2 by discharge, all of whom had mostly continuous EEG background without highly epileptiform activity at day 3. However, for the same EEG background pattern at day 3, 19 patients were discharged at CPC 3 and 15 patients at CPC 4-5. After adding spectral analysis, overall sensitivity for predicting good outcomes (CPC 1-2) was 83.3% (95% confidence interval 35.9% to 99.6%) and specificity was 97.0% (89.5% to 99.6%). In this cohort, standard prognostication testing all yielded 100% specificity but low sensitivity, with imaging being the most sensitive at 54.1% (36.9% to 70.5%). Adding spectral analysis to qualitative EEG analysis may further improve the diagnostic accuracy of EEG and may aid developing novel measures linked to good outcomes in postcardiac arrest coma.

Prognostic value of brainstem auditory and visual evoked potentials in cardiac arrest patients with targeted temperature management

PurposeWe analysed the prognostic value of somatosensory, brainstem auditory and visual evoked potentials (SSEPs, BAEPs and VEPs, respectively) for outcome prediction in cardiac arrest patients with targeted temperature management (TTM) and assessed whether BAEP and VEP measurements conferred added value to SSEP measurements. MethodsCases with SSEPs and VEPs or BAEPs were reviewed in a TTM registry. We focused on whether the following responses were clearly discernible: N20 for SSEPs, V for BAEPs, and P100 for VEPs. Each type of evoked potential was classified as absent, present or indeterminable. Neurological outcomes after 6 months were dichotomized as good (Cerebral Performance Category [CPC] 1–2) or poor (CPC 3–5). ResultsFrom 185 patients, 185 SSEPs, 172 BAEPs and 178 VEPs were included. None of the patients with a good outcome had absent SSEP, BAEP or VEP responses. Absent SSEP, BAEP and VEP responses yielded sensitivities of 42.3% (95% confidence interval [CI], 33.7–51.3%), 9.4% (95% CI, 4.6–16.7%) and 54.4% (95% CI, 46.0–62.5%) for poor outcomes, respectively. For the overall cohort, the addition of VEP measurements improved the sensitivities of single SSEP measurements (65.8% [95% CI, 57.7–73.3%] versus 36.2% [95% CI, 28.6–44.4%] and multimodal prognostication using SSEPs, brainstem reflex and brain computed tomography (75.7% [95% CI, 68.0–82.3%] versus 60.5% [95% CI, 52.3–68.4%]). ConclusionsThe prognostic value of VEPs was comparable to that of SSEPs, but the use of BAEPs was limited due to their low sensitivity. Additional VEP measurements can reduce prognostic uncertainty.

The capacity of neurological pupil index to predict absence of somatosensory evoked potentials after cardiac arrest—A study protocol

Anoxic-ischemic brain injury is the most common cause of death after cardiac arrest (CA). Robust methods to detect severe injury with a low false positive rate (FPR) for poor neurological outcome include the pupillary light reflex (PLR) and somatosensory evoked potentials (SSEP). The PLR can be assessed manually or with automated pupillometry which provides the neurological pupil index (NPi). We aim to describe the interrelation between NPi values and the absence of SSEP cortical response and to evaluate the capacity of NPi to predict the absence of cortical SSEP response in comatose patients after CA. A total of 50 patients will be included in an explorative, prospective, observational study of adult (>18years) comatose survivors of CA admitted to intensive care in a university hospital. NPi assessed with a hand-held pupillometer will be compared to SSEP signals recorded >48hours after CA. Primary outcomes are sensitivity, specificity, and odds ratio for NPi to predict bilateral absence of the SSEP N20 signal, with NPi values corresponding to <5% FPRs of SSEP absence. Secondary outcomes are the PLR and SSEP sensitivity, specificity, and odds ratio for poor neurological outcome at hospital discharge and death at 30days. The PLR and SSEP may have a systematic interrelation, and a certain NPi threshold could potentially predict the absence of cortical SSEP response. If this can be concluded from the present study, SSEP testing could be excluded in certain patients to save resources in the multimodal prognostication after CA. Editorial comment The interrelation between loss of the pupillary light reflex (PLR) and the loss of cortical response to a somatosensory evoked potential (SSEP) in comatose cardiac arrest patients is not known. This exploratory prospective study is designed to evaluate whether a specific degree of attenuated PLR, as measured by semiautomated pupillometry, can predict the bilateral loss of cortical SSEP response in severe anoxic/ischemic brain injury. Such an interrelation between the two methods would enable the use of pupillometry rather than the more resource demanding SSEP for neurologic prognostication in post cardiac arrest patients. ClinicalTrials.gov, NCT04720482, Registered 21 January 2021, retrospectively registered.

Update on the management of status epilepticus.

Randomized controlled trials investigating the initial pharmacological treatment of status epilepticus have been recently published. Furthermore, status epilepticus arising in comatose survivors after cardiac arrest has received increasing attention in the last years. This review offers an updated assessment of status epilepticus treatment in these different scenarios. Initial benzodiazepines underdosing is common and correlates with development of status epilepticus refractoriness. The recently published ESETT trial provides high-level evidence regarding the equivalence of fosphenytoin, valproate, and levetiracetam as a second-line option. Myoclonus or epileptiform transients on electroencephalography occur in up to 1/3 of patients surviving a cardiac arrest. Contrary to previous assumptions regarding an almost invariable association with death, at least 1/10 of them may awaken with reasonably good prognosis, if treated. Multimodal prognostication including clinical examination, EEG, somatosensory evoked potentials, biochemical markers, and neuroimaging help identifying patients with a chance to recover consciousness, in whom a trial with antimyoclonic compounds and at times general anesthetics is indicated. There is a continuous, albeit relatively slow progress in knowledge regarding different aspect of status epilepticus; recent findings refine some treatment strategies and help improving patients' outcomes. Further high-quality studies are clearly needed to further improve the management of these patients, especially those with severe, refractory status epilepticus forms.

Part 1: Executive Summary: 2020 American Heart Association Guidelines for Cardiopulmonary Resuscitation and Emergency Cardiovascular Care.

Part 1: Executive Summary: 2020 American Heart Association Guidelines for Cardiopulmonary Resuscitation and Emergency Cardiovascular Care.

Cost-effectiveness analysis of multimodal prognostication in cardiac arrest with EEG monitoring.

To determine cost-effectiveness parameters for EEG monitoring in cardiac arrest prognostication. We conducted a cost-effectiveness analysis to estimate the cost per quality-adjusted life-year (QALY) gained by adding continuous EEG monitoring to standard cardiac arrest prognostication using the American Academy of Neurology Practice Parameter (AANPP) decision algorithm: neurologic examination, somatosensory evoked potentials, and neuron-specific enolase. We explored lifetime cost-effectiveness in a closed system that incorporates revenue back into the medical system (return) from payers who survive a cardiac arrest with good outcome and contribute to the health system during the remaining years of life. Good outcome was defined as a Cerebral Performance Category (CPC) score of 1-2 and poor outcome as CPC of 3-5. An improvement in specificity for poor outcome prediction of 4.2% would be sufficient to make continuous EEG monitoring cost-effective (baseline AANPP specificity = 83.9%). In sensitivity analysis, the effect of increased sensitivity on the cost-effectiveness of EEG depends on the utility (u) assigned to a poor outcome. For patients who regard surviving with a poor outcome (CPC 3-4) worse than death (u = -0.34), an increased sensitivity for poor outcome prediction of 13.8% would make AANPP + EEG monitoring cost-effective (baseline AANPP sensitivity = 76.3%). In the closed system, an improvement in sensitivity of 1.8% together with an improvement in specificity of 3% was sufficient to make AANPP + EEG monitoring cost-effective, assuming lifetime return of 50% (USD $70,687). Incorporating continuous EEG monitoring into cardiac arrest prognostication is cost-effective if relatively small improvements in sensitivity and specificity are achieved.

Resting-State Brain Activity for Early Prediction Outcome in Postanoxic Patients in a Coma with Indeterminate Clinical Prognosis.

Early outcome prediction of postanoxic patients in a coma after cardiac arrest proves challenging. Current prognostication relies on multimodal testing, using clinical examination, electrophysiologic testing, biomarkers, and structural MR imaging. While this multimodal prognostication is accurate for predicting poor outcome (ie, death), it is not sensitive enough to identify good outcome (ie, consciousness recovery), thus leaving many patients with indeterminate prognosis. We specifically assessed whether resting-state fMRI provides prognostic information, notably in postanoxic patients in a coma with indeterminate prognosis early after cardiac arrest, specifically for good outcome. We used resting-state fMRI in a prospective study to compare whole-brain functional connectivity between patients with good and poor outcomes, implementing support vector machine learning. Then, we automatically predicted coma outcome using resting-state fMRI and also compared the prediction based on resting-state fMRI with the outcome prediction based on DWI. Of 17 eligible patients who completed the study procedure (among 351 patients screened), 9 regained consciousness and 8 remained comatose. We found higher functional connectivity in patients recovering consciousness, with greater changes occurring within and between the occipitoparietal and temporofrontal regions. Coma outcome prognostication based on resting-state fMRI machine learning was very accurate, notably for identifying patients with good outcome (accuracy, 94.4%; area under the receiver operating curve, 0.94). Outcome predictors using resting-state fMRI performed significantly better (P < .05) than DWI (accuracy, 60.0%; area under the receiver operating curve, 0.63). Indeterminate prognosis might lead to major clinical uncertainty and significant variations in life-sustaining treatments. Resting-state fMRI might bridge the gap left in early prognostication of postanoxic patients in a coma by identifying those with both good and poor outcomes.

Postresuscitation Care after Out-of-hospital Cardiac Arrest: Clinical Update and Focus on Targeted Temperature Management.

Out-of-hospital cardiac arrest is a major cause of mortality and morbidity worldwide. With the introduction of targeted temperature management more than a decade ago, postresuscitation care has attracted increased attention. In the present review, we discuss best practice hospital management of unconscious out-of-hospital cardiac arrest patients with a special focus on targeted temperature management. What is termed post-cardiac arrest syndrome strikes all organs and mandates access to specialized intensive care. All patients need a secured airway, and most patients need hemodynamic support with fluids and/or vasopressors. Furthermore, immediate coronary angiography and percutaneous coronary intervention, when indicated, has become an essential part of the postresuscitation treatment. Targeted temperature management with controlled sedation and mechanical ventilation is the most important neuroprotective strategy to take. Targeted temperature management should be initiated as quickly as possible, and according to international guidelines, it should be maintained at 32° to 36°C for at least 24 h, whereas rewarming should not increase more than 0.5°C per hour. However, uncertainty remains regarding targeted temperature management components, warranting further research into the optimal cooling rate, target temperature, duration of cooling, and the rewarming rate. Moreover, targeted temperature management is linked to some adverse effects. The risk of infection and bleeding is moderately increased, as is the risk of hypokalemia and magnesemia. Circulation needs to be monitored invasively and any deviances corrected in a timely fashion. Outcome prediction in the individual patient is challenging, and a self-fulfilling prophecy poses a real threat to early prognostication based on clinical assessment alone. Therefore, delayed and multimodal prognostication is now considered a key element of postresuscitation care. Finally, modern postresuscitation care can produce good outcomes in the majority of patients but requires major diagnostic and therapeutic resources and specific training. Hence, recent international guidelines strongly recommend the implementation of regional prehospital resuscitation systems with integrated and specialized cardiac arrest centers.