Prediction Of Neurological Outcome Research Articles

Lactate Predicts Neurological Outcomes after Perinatal Asphyxia in Post-Hypothermia Era: A Prospective Cohort Study.

Background: Neonatal hypoxic-ischemic encephalopathy (HIE) is the most common cause of mortality and neurological disability in infancy after perinatal asphyxia. Reliable biomarkers to predict neurological outcomes of neonates after perinatal asphyxia are still not accessible in clinical practice. Methods: A prospective cohort study enrolled neonates with perinatal asphyxia. Biochemical blood tests and cerebral Doppler ultrasound were measured within 6 h of age and at the 4th day old. Neurological outcomes were assessed at 1 year old. Results: Sixty-four neonates with perinatal asphyxia were enrolled. Fifty-eight (90%) had hypoxic-ischemic encephalopathy (HIE) including 20 (34%) Stage I, 21 (36%) Stage II, and 17 (29%) Stage III. In the asphyxiated infants without therapeutic hypothermia, HIE stage, PH, and base excess levels within 6 h of age were the predictors of adverse outcomes. In the asphyxiated infants receiving therapeutic hypothermia, HIE stage failed to predict outcomes. Instead, blood lactate levels and pulsatility index (PI) of medial cerebral arteries (MCA) either in 6 h of age or at the 4th day old independently predicted adverse outcomes. Conclusions: Blood lactate, which is a common accessible test at the hospital and MCA PI on cerebral ultrasound could predict adverse outcomes in asphyxiated infants receiving therapeutic hypothermia.

Predicting neurological outcome in comatose patients after cardiac arrest with multiscale deep neural networks

ObjectiveElectroencephalography (EEG) is an important tool for neurological outcome prediction after cardiac arrest. However, the complexity of continuous EEG data limits timely and accurate interpretation by clinicians. We develop a deep neural network (DNN) model to leverage complex EEG trends for early and accurate assessment of cardiac arrest coma recovery likelihood. MethodsWe developed a multiscale DNN combining convolutional neural networks (CNN) and recurrent neural networks (long short-term memory [LSTM]) using EEG and demographic information (age, gender, shockable rhythm) from a multicenter cohort of 1,038 cardiac arrest patients. The CNN learns EEG feature representations while the multiscale LSTM captures short-term and long-term EEG dynamics on multiple time scales. Poor outcome is defined as a Cerebral Performance Category (CPC) score of 3-5 and good outcome as CPC score 1-2 at 3-6 months after cardiac arrest. Performance is evaluated using area under the receiver operating characteristic curve (AUC) and calibration error. ResultsModel performance increased with EEG duration, with AUC increasing from 0.83 (95% Confidence Interval [CI] 0.79-0.87 at 12h to 0.91 (95%CI 0.88-0.93) at 66h. Sensitivity of good and poor outcome prediction was 77% and 75% at a specificity of 90%, respectively. Sensitivity of poor outcome was 50% at a specificity of 99%. Predicted probability was well matched to the observation frequency of poor outcomes, with a calibration error of 0.11 [0.09-0.14]. ConclusionsThese results demonstrate that incorporating EEG evolution over time improves the accuracy of neurologic outcome prediction for patients with coma after cardiac arrest.

Development and validation of early prediction for neurological outcome at 90 days after return of spontaneous circulation in out-of-hospital cardiac arrest

AimTo develop and validate a model for the early prediction of long-term neurological outcome in patients with non-traumatic out-of-hospital cardiac arrest (OHCA). MethodsWe analysed multicentre OHCA registry data of adult patients with non-traumatic OHCA who experienced return of spontaneous circulation (ROSC) and had been admitted to the intensive care unit between 2013 and 2017. We allocated 1329 (2013–2015) and 1025 patients (2016–2017) to the derivation and validation sets, respectively. The primary outcome was the dichotomized cerebral performance category (CPC) at 90 days, defined as good (CPC 1–2) or poor (CPC 3–5). We developed 2 models: model 1 included variables without laboratory data, and model 2 included variables with laboratory data available immediately after ROSC. Logistic regression with least absolute shrinkage and selection operator regularization was employed for model development. Measures of discrimination, accuracy, and calibration (C-statistics, Brier score, calibration plot, and net benefit) were assessed in the validation set. ResultsThe C-statistic (95% confidence intervals) of models 1 and 2 in the validation set was 0.947 (0.930–0.964) and 0.950 (0.934–0.966), respectively. The Brier score of models 1 and 2 in the validation set was 0.0622 and 0.0606, respectively. The calibration plot showed that both models were well-calibrated to the observed outcome. Decision curve analysis indicated that model 2 was similar to model 1. ConclusionThe prediction tool containing detailed in-hospital information showed good performance for predicting neurological outcome at 90 days immediately after ROSC in patients with OHCA.

Neurologic Outcome Prediction in the Intensive Care Unit.

The burden of severe and disabling neurologic injury on survivors, families, and society can be profound. Neurologic outcome prediction, or neuroprognostication, is a complex undertaking with many important ramifications. It allows patients with good prognoses to be supported aggressively, survive, and recover; conversely, it avoids inappropriate prolonged and costly care in those with devastating injuries. Striving to maintain a high prediction performance during prognostic assessments encompasses acknowledging the shortcomings of this task and the challenges created by advances in medicine, which constantly shift the natural history of neurologic conditions. Embracing the unknowns of outcome prediction and the boundaries of knowledge surrounding neurologic recovery and plasticity is a necessary step toward refining neuroprognostication practices and improving the accuracy of prognostic impressions. The pillars of modern neuroprognostication include comprehensive characterization of neurologic injury burden (primary and secondary injuries), gauging cerebral resilience and estimated neurologic reserve, and tying it all together with individual values surrounding the acceptable extent of disability and the difficulties of an arduous convalescence journey. Comprehensive multimodal frameworks of neuroprognostication using different prognostic tools to portray the burden of neurologic injury coupled with the characterization of individual values and the degree of cerebral reserve and resilience are the cornerstone of modern outcome prediction.

A randomized control trial on early open patient department-based assessment tool for prediction of neurological outcomes at the age of 1 month of life in asphyxiated neonates who underwent whole body cooling at tertiary care hospital in Southern Rajasthan

Background: Therapeutic hypothermia (TH) is standard-of-care for infants with moderate to severe hypoxic ischemic encephalopathy (HIE) in developed countries. It should be implemented within 6 hours after delivery, and it has shown to significantly decrease the risk of brain injury in newborns exposed to perinatal hypoxemic ischemic insult with improved neurological outcomes. Objectives of the study were to assess early neurological outcome of neonates with moderate to severe birth asphyxia treated with TH.Methods: A randomized control trial was done in NICU of Balchikitsalaya, RNT medical college, Udaipur, on neonates with evidence of grade II/III HIE. We used phase changing material-FS 21, FS 29 to provide whole body hypothermia for 72 hours within 6 hours of birth and observed the babies for early neurological outcome. Results: A total of 60 neonates were enrolled in the study, 30 as cases, treated with therapeutic hypothermia and 30 as controls in normothermic environment. Neurological assessment was made on the basis of Thompson scoring and Ameil-Tison neurological assessment at term (ATNAT) at one month of age. We observed a better ATNAT score in TH group as compared to controls (p<0.05). Thompson score was significantly less in surviving neonates suggestive of better early neonatal outcome. Conclusions: Statistically significant data suggestive of better early neurological outcomes were seen in the cases treated with 72 hours of hypothermia. These babies not only had lesser Thompson scores, but also showed better tone, and Improved ATNAT scores at 1 month of age, making this study important as an early marker of neurological injury/morbidity in later life.

Machine Learning Models for Survival and Neurological Outcome Prediction of Out-of-Hospital Cardiac Arrest Patients.

Background Out-of-hospital cardiac arrest (OHCA) is a major health problem worldwide, and neurologic injury remains the leading cause of morbidity and mortality among survivors of OHCA. The purpose of this study was to investigate whether a machine learning algorithm could detect complex dependencies between clinical variables in emergency departments in OHCA survivors and perform reliable predictions of favorable neurologic outcomes. Methods This study included adults (≥18 years of age) with a sustained return of spontaneous circulation after successful resuscitation from OHCA between 1 January 2004 and 31 December 2014. We applied three machine learning algorithms, including logistic regression (LR), support vector machine (SVM), and extreme gradient boosting (XGB). The primary outcome was a favorable neurological outcome at hospital discharge, defined as a Glasgow-Pittsburgh cerebral performance category of 1 to 2. The secondary outcome was a 30-day survival rate and survival-to-discharge rate. Results The final analysis included 1071 participants from the study period. For neurologic outcome prediction, the area under the receiver operating curve (AUC) was 0.819, 0.771, and 0.956 in LR, SVM, and XGB, respectively. The sensitivity and specificity were 0.875 and 0.751 in LR, 0.687 and 0.793 in SVM, and 0.875 and 0.904 in XGB. The AUC was 0.766 and 0.732 in LR, 0.749 and 0.725 in SVM, and 0.866 and 0.831 in XGB, for survival-to-discharge and 30-day survival, respectively. Conclusions Prognostic models trained with ML technique showed appropriate calibration and high discrimination for survival and neurologic outcome of OHCA without using prehospital data, with XGB exhibiting the best performance.

Initial Blood pH, Lactate and Base Deficit Add No Value to Peri-Arrest Factors in Prognostication of Neurological Outcome After Out-of-Hospital Cardiac Arrest.

Background: In cardiac arrest survivors, metabolic parameters [pH value, lactate concentration, and base deficit (BD)] are routinely added to peri-arrest factors (including age, sex, bystander cardiopulmonary resuscitation, shockable first rhythm, resuscitation duration, adrenaline dose) to enhance early outcome prediction. However, the additional value of this strategy remains unclear.Methods: We used our resuscitation database to screen all patients ≥18 years who had suffered in- or out-of-hospital cardiac arrest (IHCA, OHCA) between January 1st, 2005 and May 1st, 2019. Patients with incomplete data, without return of spontaneous circulation or treatment with sodium bicarbonate were excluded. To analyse the added value of metabolic parameters to prognosticate neurological function, we built three models using logistic regression. These models included: (1) Peri-arrest factors only, (2) peri-arrest factors plus metabolic parameters and (3) metabolic parameters only. Receiver operating characteristics curves regarding 30-day good neurological function (Cerebral Performance Category 1-2) were analysed.Results: A total of 2,317 patients (OHCA: n = 1842) were included. In patients with OHCA, model 1 and 2 had comparable predictive value. Model 3 was inferior compared to model 1. In IHCA patients, model 2 performed best, whereas both metabolic (model 3) and peri-arrest factors (model 1) demonstrated similar power. PH, lactate and BD had interchangeable areas under the curve in both IHCA and OHCA.Conclusion: Although metabolic parameters may play a role in IHCA, no additional value in the prediction of good neurological outcome could be found in patients with OHCA. This highlights the importance of accurate anamnesis especially in patients with OHCA.

Neuron-specific enolase and long-term neurological outcome after OHCA – A validation study

AimsTo investigate what NSE levels predict long-term neurological prognosis at 24, 48 and 72 hours after ROSC in a cohort of out-of-hospital cardiac arrest and to validate previously suggested NSE cut-offs, including the latest ERC guidelines (2021). MethodsPatients admitted to intensive care units in four hospitals in Southern Sweden between 2014–2018 were included. Blood samples were handled by a single local laboratory. The primary outcome was neurological outcome according to the Cerebral Performance Category (CPC) scale at 2–6 months after cardiac arrest. Results368 patients were included for analysis. A ≤2% false positive rate for the prediction of poor neurological outcome was achieved with an NSE cut-off value of >101 μg/L at 48 hours and >80 μg/L at 72 hours. The cut-off suggested by the recent ERC guidelines of >60 μg/L at 48 and/or 72 hours generated a false positive rate of 4.3% (95 %CI 0.9–7.4%). ConclusionA local validation study of the ability of serum levels of neuron-specific enolase to predict long-term poor neurological outcome after out-of-hospital cardiac arrest generated higher cut-offs than suggested by previous publications.

Diffuse Axonal Injury Grade on Early MRI is Associated with Worse Outcome in Children with Moderate-Severe Traumatic Brain Injury.

BackgroundTraumatic brain injury (TBI) is the leading cause of death and disability in children, but effective tools for predicting outcome remain elusive. Although many pediatric patients receive early magnetic resonance imaging (MRI), data on its utility in prognostication are lacking. Diffuse axonal injury (DAI) is a hallmark of TBI detected on early MRI and was shown previously to improve prognostication in adult patients with TBI. In this exploratory study, we investigated whether DAI grade correlates with functional outcome and improves prognostic accuracy when combined with core clinical variables and computed tomography (CT) biomarkers in pediatric patients with moderate-severe TBI (msTBI).MethodsPediatric patients (≤ 19 years) who were admitted to two regional level one trauma centers with a diagnosis of msTBI (Glasgow Coma Scale [GCS] score < 13) between 2011 and 2019 were identified through retrospective chart review. Patients who underwent brain MRI within 30 days of injury and had documented clinical follow-up after discharge were included. Age, pupil reactivity, and initial motor GCS score were collected as part of the International Mission for Prognosis and Analysis of Clinical Trials in TBI (IMPACT) model. Imaging was reviewed to calculate the Rotterdam score (CT) and DAI grade (MRI) and to evaluate for presence of hypoxic-ischemic injury (MRI). The primary outcome measure was the Pediatric Cerebral Performance Category Scale (PCPCS) score at 6 months after TBI, with favorable outcome defined as PCPCS scores 1–3 and unfavorable outcome defined as PCPCS scores 4–6. The secondary outcome measure was discharge disposition to home versus to an inpatient rehabilitation facility.ResultOf 55 patients included in the study, 45 (82%) had severe TBI. The most common mechanism of injury was motor vehicle collision (71%). Initial head CT scans showed acute hemorrhage in 84% of patients. MRI was acquired a median of 5 days after injury, and hemorrhagic DAI lesions were detected in 87% of patients. Each 1-point increase in DAI grade increased the odds of unfavorable functional outcome by 2.4-fold. When controlling for core IMPACT clinical variables, neither the DAI grade nor the Rotterdam score was independently correlated with outcome and neither significantly improved outcome prediction over the IMPACT model alone.ConclusionsA higher DAI grade on early MRI is associated with worse 6-month functional outcome and with discharge to inpatient rehabilitation in children with acute msTBI in a univariate analysis but does not independently correlate with outcome when controlling for the GCS score. Addition of the DAI grade to the core IMPACT model does not significantly improve prediction of poor neurological outcome. Further study is needed to elucidate the utility of early MRI in children with msTBI.Supplementary InformationThe online version contains supplementary material available at 10.1007/s12028-021-01336-8.

Clinical value of miR-191-5p in predicting the neurological outcome after out-of-hospital cardiac arrest.

The diagnostic and prognostic value of microRNAs (miRNA) in human disease has been confirmed in a number of clinical studies. The purpose of this study was to investigate the predictive value of miR-191-5p in the neurological outcome of patients recovering from out-of-hospital cardiac arrest (OHCA). A total of 260 patients undergoing the target temperature management trial were analyzed. The expression level of serum miR-191-5p was detected by qRT-PCR at 48h after return of spontaneous circulation (ROSC). ROC curve was established to evaluate the ability of miR-191-5p as a biomarker for predicting adverse neurological outcomes after OHCA. Kaplan-Meier curve and Cox regression analysis were used for survival analysis. One hundred eighteen patients (45%) had poor neurological outcomes at 6months. The expression level of serum miR-191-5p in patients with poor neurological outcomes was significantly lower than that in patients with good neurological prognosis (P < 0.001) and was not associated with TTM trial. The AUC, sensitivity, and specificity of the ROC curve were 0.899, 84.7%, and 82.4%, respectively, suggesting that the level of miR-191-5p had the ability to predict neurological outcome. By the end of the experiment, 88 patients (34%) were dead. Results of survival analysis showed that lower miR-191-5p expression level was significantly associated with lower survival rate (HR: 0.344, 95% CI = 0.208-0.567, P < 0.001). The level of miR-191-5p was down-regulated in patients with poor neurological outcomes, and it could be used as a promising novel biomarker for prediction of neurological outcome and survival after OHCA.

Predictors of Neurological Outcome Following Infant Cardiac Surgery Without Deep Hypothermic Circulatory Arrest

The aim of this study is to describe the clinical characteristics, perioperative course and neuroimaging abnormalities of infants with congenital heart disease (CHD) undergoing heart surgery without deep hypothermic circulatory arrest (DHCA) and identify variables associated with neurological outcome. Infants with CHD undergoing open-heart surgery without DHCA between 2009 and 2017 were identified from a cardiac surgery database. Full-term infants < 10weeks of age at the time of surgery who had both a pre- and postoperative brain magnetic resonance imaging exam (MRI) were included. Clinical characteristics and perioperative variables were collected from the electronic medical record. Brain Injury Scores (BIS) were assigned to pre- and postoperative brain MRIs. Variables were examined for association with neurological outcome at 12months of age or greater. Forty-two infants were enrolled in the study, of whom 69% (n = 29) had a neurological assessment ≥ to 12months of age. Adverse neurological outcome was associated with longer intensive care unit (ICU) stay (P = 0.003), lengthier mechanical ventilation (P = 0.031), modified Blalock-Taussig (MBT) shunt procedure (P = 0.005) and postoperative seizures (P = 0.005). Total BIS scores did not predict outcome but postoperative infarction and/or intraparenchymal hemorrhage (IPH) was associated with worse outcome by multivariable analysis (P = 0.018). Infants with CHD undergoing open-heart surgery without DHCA are at increased risk of worse neurological outcome when their ICU stay is prolonged, mechanical ventilation is extended, MBT shunt is performed or when postoperative seizures are present. Cerebral infarctions and IPH on postoperative MRI are also associated with worse outcome.

Combination of neuron-specific enolase measurement and initial neurological examination for the prediction of neurological outcomes after cardiac arrest

This study aimed to investigate the efficacy of the combination of neuron-specific enolase (NSE) measurement and initial neurological examination in predicting the neurological outcomes of patients with cardiac arrest (CA) by retrospectively analyzing data from the Korean Hypothermia Network prospective registry. NSE levels were recorded at 48 and 72 h after CA. The initial Full Outline of UnResponsiveness (FOUR) and Glasgow Coma Scale (GCS) scores were recorded. These variables were categorized using the scorecard method. The primary endpoint was poor neurological outcomes at 6 months. Of the 475 patients, 171 (36%) had good neurological outcomes at 6 months. The areas under the curve (AUCs) of the categorized NSE levels at 72 h, GCS score, and FOUR score were 0.889, 0.722, and 0.779, respectively. The AUCs of the combinations of categorized NSE levels at 72 h with categorized GCS scores and FOUR score were 0.910 and 0.912, respectively. Each combination was significantly higher than the AUC value of the categorized NSE level at 72 h alone (with GCS: p = 0.015; with FOUR: p = 0.026). Combining NSE measurement and initial neurological examination improved the prediction of neurological outcomes.

Automated Assessment of Brain CT After Cardiac Arrest-An Observational Derivation/Validation Cohort Study.

Prognostication of outcome is an essential step in defining therapeutic goals after cardiac arrest. Gray-white-matter ratio obtained from brain CT can predict poor outcome. However, manual placement of regions of interest is a potential source of error and interrater variability. Our objective was to assess the performance of poor outcome prediction by automated quantification of changes in brain CTs after cardiac arrest. Observational, derivation/validation cohort study design. Outcome was determined using the Cerebral Performance Category upon hospital discharge. Poor outcome was defined as death or unresponsive wakefulness syndrome/coma. CTs were automatically decomposed using coregistration with a brain atlas. ICUs at a large, academic hospital with circulatory arrest center. We identified 433 cardiac arrest patients from a large previously established database with brain CTs within 10 days after cardiac arrest. None. Five hundred sixteen brain CTs were evaluated (derivation cohort n = 309, validation cohort n = 207). Patients with poor outcome had significantly lower radiodensities in gray matter regions. Automated GWR_si (putamen/posterior limb of internal capsule) was performed with an area under the curve of 0.86 (95%-CI: 0.80-0.93) for CTs taken later than 24 hours after cardiac arrest (similar performance in the validation cohort). Poor outcome (Cerebral Performance Category 4-5) was predicted with a specificity of 100% (95% CI, 87-100%, derivation; 88-100%, validation) at a threshold of less than 1.10 and a sensitivity of 49% (95% CI, 36-58%, derivation) and 38% (95% CI, 27-50%, validation) for CTs later than 24 hours after cardiac arrest. Sensitivity and area under the curve were lower for CTs performed within 24 hours after cardiac arrest. Automated gray-white-matter ratio from brain CT is a promising tool for prediction of poor neurologic outcome after cardiac arrest with high specificity and low-to-moderate sensitivity. Prediction by gray-white-matter ratio at the basal ganglia level performed best. Sensitivity increased considerably for CTs performed later than 24 hours after cardiac arrest.

Prediction of Neurological Outcomes in Patients with Post-Cardiac Arrest Syndrome

Post-cardiac arrest syndrome is an extremely complex nosology, characterized by high mortality and the development of severe neurological disorders. Predicting the neurological outcome in this pathology is an urgent problem, since it allows determining the tactics of patient management and optimizing the scope of medical care, as well as preparing the patient's family members for expected results of treatment. Currently, clinical, laboratory and instrumental data are used as predictors of an unfavorable neurological outcome (e.g., pupillary responses, neuron-specific enolase levels, electroencephalography). There is no single criterion with high sensitivity and specificity for predicting neurological disorders; therefore, a multimodal approach is required. This article discusses several factors, the combination of which allows predicting the outcome of post-cardiac arrest syndrome with the greatest degree of reliability.

Multimodal Approach to Predict Neurological Outcome after Cardiac Arrest: A Single-Center Experience.

Introduction: The aims of this study were to assess the concordance of different tools and to describe the accuracy of a multimodal approach to predict unfavorable neurological outcome (UO) in cardiac arrest patients. Methods: Retrospective study of adult (>18 years) cardiac arrest patients who underwent multimodal monitoring; UO was defined as cerebral performance category 3–5 at 3 months. Predictors of UO were neurological pupillary index (NPi) ≤ 2 at 24 h; highly malignant patterns on EEG (HMp) within 48 h; bilateral absence of N20 waves on somato-sensory evoked potentials; and neuron-specific enolase (NSE) > 75 μg/L. Time-dependent decisional tree (i.e., NPi on day 1; HMp on day 1–2; absent N20 on day 2–3; highest NSE) and classification and regression tree (CART) analysis were used to assess the prediction of UO. Results: Of 137 patients, 104 (73%) had UO. Abnormal NPi, HMp on day 1 or 2, the bilateral absence of N20 or NSE >75 mcg/L had a specificity of 100% to predict UO. The presence of abnormal NPi was highly concordant with HMp and high NSE, and absence of N20 or high NSE with HMp. However, HMp had weak to moderate concordance with other predictors. The time-dependent decisional tree approach identified 73/103 patients (70%) with UO, showing a sensitivity of 71% and a specificity of 100%. Using the CART approach, HMp on EEG was the only variable significantly associated with UO. Conclusions: This study suggests that patients with UO had often at least two predictors of UO, except for HMp. A multimodal time-dependent approach may be helpful in the prediction of UO after CA. EEG should be included in all multimodal prognostic models.

The Association Between Neurological Prognosis and the Degree of Blood-Brain Barrier Disruption in Cardiac Arrest Survivors Who Underwent Target Temperature Management.

This study aimed to compare day-specific associations of blood-brain barrier (BBB) disruption with neurological outcome in survivors of out-of-hospital cardiac arrest (OHCA) treated with target temperature management (TTM) and lumbar drainage. This retrospective single-center study included 68 survivors of OHCA who underwent TTM between April 2018 and December 2019. The albumin quotient (QA) was calculated as QA = albumincerebrospinal fluid/albuminserum immediately (day 1) and 24 (day 2), 48 (day 3), and 72h (day 4) after the return of spontaneous circulation. The degree of BBB disruption was weighted using the following scoring system: QA value of 0.007 or less (normal), QA value greater than 0.007-0.01 (mild), QA value greater than 0.01-0.02 (moderate), and QA value greater than 0.02 (severe). Points were assigned as follows: 0 (normal), 1 (mild), 4 (moderate), and 9 (severe). Neurological outcome was determined at 6months after the return of spontaneous circulation, as well as cerebral performance category (CPC), dichotomized as good (CPC score 1-2) and poor (CPC score 3-5) outcome. We enrolled 68 patients (48 men, 71%); 37 (54%) patients had a poor neurological outcome. The distributions of poor versus good outcomes at 6months in patients with moderate and severe BBB disruption were 19 of 22 (80%) vs. 18 of 46 (50%) on day 1, 31 of 37 (79%) vs. 6 of 31 (32%) on day 2, 32 of 37 (81%) vs. 5 of 31 (30%) on day 3, and 32 of 39 (85%) vs. 5 of 29 (30%) on day 4 (P < 0.001), respectively. Using receiver operating characteristic analyses, optimal cutoff values (sensitivity, specificity) of QA levels for the prediction of neurological outcome were as follows: day 1, greater than 0.009 (56.8%, 87.1%); day 2, greater than 0.012 (81.1%, 87.1%); day 3, greater than 0.013 (83.8%, 87.1%); day 4, greater than 0.013 (86.5%, 87.1%); the sum of all time points, greater than 0.039 (89.5%, 79.4%); and scoring system, greater than 9 (91.9%, 87.1%). In this proof of concept study, QA was associated with poor neurological outcome in survivors of OHCA treated with TTM with no contraindication to lumbar drainage. A large multicenter prospective study is needed to validate the utility of BBB disruption as a prognosticator of neurological outcome.

Amplitude integrated EEG: how much it helps in prognostication in neonatal encephalopathy?

Introduction Neonatal encephalopathy (NE) is associated with a high risk of adverse neurological outcomes. Several neurodiagnostic tests have been evaluated to predict the prognosis. Amplitude integrated Electroencephalogram (aEEG) is now being commonly used for bedside evaluation of cerebral function. There is limited data on the role of aEEG for prognostication in NE, from resource-limited settings. Objective To evaluate the predictive ability of aEEG for abnormal neurological outcomes in neonatal encephalopathy or neonates with encephalopathy. Methods Neonates above 35 weeks of gestation admitted to NICU in a tertiary care hospital with a diagnosis of encephalopathy were enrolled. Clinical characteristics severity of encephalopathy and seizures were recorded. Amplitude integrated recording was started at admission and continued till recovery of trace to normal or for 10 days. The primary outcome was death or abnormal neurological status at 3–6 months of age. The study was registered in the Clinical Trial Registry of India (CTRI/2013/05/003612) Results The incidence of NE was 6% of total admission. Moderate and severe encephalopathy occurred in 58 and 39% of babies respectively. Hypoxic-ischemic encephalopathy was the most common cause. Clinical seizures occurred in 91% of cases. An abnormal aEEG trace was observed in 51 (76.1%) infants with NE. For adverse neurological outcomes at an age average of 4.5 months of age, aEEG had a sensitivity, specificity, NPV, and PPV of 100, 54.2, 100, and 77.5, respectively. Conclusions Clinical staging and aEEG has good predictive ability to detect an adverse neurological outcome. aEEG improves the ability to predict abnormal outcome in babies with moderate encephalopathy. Early recovery of aEEG abnormality correlates with better neurodevelopmental outcomes. KEY MESSAGES What’s known: aEEG is a useful modality to assess neurodevelopmental outcomes however data from developing countries is lacking. What’s new: aEEG monitoring in babies in neonatal encephalopathy may improve the prediction of abnormal neurological outcomes in babies with moderate encephalopathy.

Characteristics and Outcomes of Left Ventricular-Assist Device-Associated Cerebrovascular Events in Setting of Infectious Intracranial Aneurysms.

Background and purpose: The study aims to identify the characteristics and neurological outcomes of the left ventricular-assist device (LVAD)-associated cerebrovascular events (CVE) and infections, particularly in the setting of infectious intracranial aneurysms (IIA).Methods: A single-center retrospective review of patients having undergone LVAD implantation between 2011 and 2017 was conducted using institutional registries and screened for CVE. Patients with CVE were assessed for concurrent bacteremia; neurovascular imaging was then used to isolate patients with IIA. A review of comorbidities, imaging characteristics, and management were performed to determine predictors of neurological outcomes, as defined by the 90-day modified Rankin scale (mRS) scores.Results: Of the 383 HeartMate II LVAD implantations performed, 43 all-cause stroke events were identified across 35 (9%) patients. The majority of the events were hemorrhagic CVE (n=28) with 21 events complicated by bacteremia. Of patients with hemorrhagic CVE and bacteremia, Staphylococcus aureus (n=10) and Pseudomonas aeruginosa (n= 8) infection were the most frequently associated organisms. Severe disability or death (90-day mRS > 4) was observed in 15 patients (63%). Seven patients had confirmed findings of IIA on diagnostic cerebral angiogram and were associated with distal middle cerebral artery (MCA) territory involvement (n=6; 86%) with concurrent Staphylococcus (n=5, 71%) and/or Pseudomonas (n=4, 57%) infections. Overall, a higher incidence of acute and chronic bacteremia was observed in the hemorrhagic CVE subgroup compared to the ischemic CVE subgroup (74% vs 36% & 71% vs 29%, respectively; p <0.05). Despite endovascular and/or surgical intervention in patients with IIA, four patients failed management and elected for comfort measures.Conclusion: Our results indicate that P. aeruginosa and S. aureus bacteremia are associated with a greater incidence of intracranial hemorrhage and worse neurological outcomes. Future management considerations may include pre-implantation cerebrovascular imaging to assess vascular pathology including prior aneurysms and intracranial atherosclerotic disease burden as a screen for higher-risk patients, as well as more aggressive antibiotic therapy at bacteremia onset.

The Lymphocyte Depletion in Patients with Acute Ischemic Stroke Associated with Poor Neurologic Outcome.

PurposeInflammation plays an important role and is involved in all stages of acute ischemic stroke. One of these stages involves the recruitment of leukocytes from the peripheral circulation into the ischemic tissue. Lymphocytes as a subtype of leukocytes are important mediators and can become a predictor of neurological outcome. Several studies have been conducted regarding the correlation between differential lymphocyte counts and acute ischemic stroke. Most of these studies analyzed lymphocyte ratio to other leukocyte subtypes such as neutrophils and monocytes. This study specifically observed the role of lymphocytes as an indicator of the inflammatory response in patients with acute ischemic stroke. This study aimed to observe the correlation among risk factors, infarct location, leukocyte counts, lymphocyte value and neurologic output in acute ischemic stroke patients.Patients and MethodsWe observed and analyzed 193 patients’ data from medical record which met the inclusion and exclusion criteria with a diagnosis of acute ischemic stroke at the Department of Neurology of Dr. Hasan Sadikin General Bandung. Data were then analysed using appropriate statistical tests.ResultsMost patients have more than one risk factor with a leukocyte count of less than 10,000 cell/mm3. Infarct was mostly located in subcortical area (basal ganglia), with moderate average NIHSS values at admission and at discharge. The number of lymphocytes decreased in the subject group with more than 10,000 cell/mm3 leukocytes. Subsequently, data were analyzed using Spearman’s test and there was a correlation between NIHSS on admission and lymphocyte depletion.ConclusionThe lymphocyte depletion in patients with leukocytosis is a predictor of poor NIHSS.

Predictors of neurologic outcome after surgery for cervical ossification of the posterior longitudinal ligament differ based on myelopathy severity: a multicenter study.

The purpose of this retrospective multicenter study was to compare prognostic factors for neurological recovery in patients undergoing surgery for cervical ossification of the posterior longitudinal ligament (OPLL) based on their presenting mild, moderate, or severe myelopathy. The study included 372 consecutive patients with OPLL who underwent surgery for cervical myelopathy between 2006 and 2016 in East Asian countries with a high OPLL prevalence. Baseline and postoperative clinical outcomes were assessed using the Japanese Orthopaedic Association (JOA) myelopathy score and recovery ratio. Radiographic assessment included occupying ratio, cervical range of motion, and sagittal alignment parameters. Patient myelopathy was classified as mild, moderate, or severe based on the preoperative JOA score. Linear and multivariate regression analyses were performed to identify patient and surgical factors associated with neurological recovery stratified by baseline myelopathy severity. The mean follow-up period was 45.4 months (range 25-140 months). The mean preoperative and postoperative JOA scores and recovery ratios for the total cohort were 11.7 ± 3.0, 14.5 ± 2.7, and 55.2% ± 39.3%, respectively. In patients with mild myelopathy, only age and diabetes correlated with recovery. In patients with moderate to severe myelopathy, older age and preoperative increased signal intensity on T2-weighted imaging were significantly correlated with a lower likelihood of recovery, while female sex and anterior decompression with fusion (ADF) were associated with better recovery. Various patient and surgical factors are correlated with likelihood of neurological recovery after surgical treatment for cervical OPLL, depending on the severity of presenting myelopathy. Older age, male sex, intramedullary high signal intensity, and posterior decompression are associated with less myelopathy improvement in patients with worse baseline function. Therefore, myelopathy-specific preoperative counseling regarding prognosis for postoperative long-term neurological improvement should include consideration of these individual and surgical factors.