Prediction Of Neurological Outcome Research Articles

Critical care EEG monitoring in children with abusive head trauma: a retrospective study of seizure burden and predictors of neurological outcomes

Critical care EEG monitoring in children with abusive head trauma: a retrospective study of seizure burden and predictors of neurological outcomes

Role of Serum Ferritin and Prediction of Neurological Outcome in Acute Ischemic Stroke Patients

Background: Stroke, also known as cerebrovascular accident (CVA), is an abrupt onset of neurological deficiency caused by a specific vascular aetiology. Aim: This study aimed to assess the role of serum ferritin in acute ischaemic stroke patients to correlate serum ferritin and neurological scales on the day of admission and during follow-up and to predict neurological outcomes. Methods: This prospective observational study included 100 patients who presented with acute ischaemic stroke clinically and with radiological imaging in the General Medicine and Neurology departments of Saveetha Medical College and Hospital for 18 months. Functional disability was assessed for all patients after 3 months based on the correlation of serum ferritin levels with the neurological scales, and the outcome was estimated. Results: The mean age of the patients was 59.45 ± 14.39. The serum ferritin level and Canadian Stroke Scale score during admission on day 7th and day 30th, were significantly different (p < 0.01). Serum ferritin levels were positively associated with the Glasgow Coma Scale score at admission and on the 7th and 30th days after admission. Ferritin levels were significantly higher in the moderate category under all circumstances (p < 0.01). Patients with mRS scores of 2, 3, and 4 were compared with serum ferritin levels and found to be significantly associated with serum ferritin levels. Conclusion: This study found that patients with higher serum ferritin levels experienced increased stroke severity and lower GCS and mRS scores, which correlated with increased morbidity.

Optimal Timing of the Neutrophil-to-Lymphocyte Ratio and Platelet-to-Lymphocyte Ratio as Early Predictors of Neurological Outcomes in Postcardiac Arrest Patients

The neutrophil-to-lymphocyte ratio (NLR) and platelet-to-lymphocyte ratio (PLR) have been recognized as predictors of various critical illnesses. Our study aimed to investigate whether the NLR and PLR measured at different timepoints could predict poor neurological outcomes at 6 months. This observational retrospective cohort study included adults who had experienced out-of-hospital cardiac arrest (OHCA) and received targeted temperature management between November 2015 and December 2020. Patients with an active infection, as confirmed by an initial blood culture, were excluded. Multivariate logistic regression models were used to determine the association between the NLR and PLR at 0, 24, and 48 h after return of spontaneous circulation and poor neurological outcomes, defined as a Cerebral Performance Category score of ≥3 at 6 months. The NLR at 24 h, but not the NLR or PLR at other timepoints, was significantly associated with poor neurological outcomes (odds ratio: 1.05; 95% CI: 1.01–1.09; p = 0.018). The NLR at 24 h showed moderate accuracy in predicting poor neurological outcomes, with an AUC of 0.619. A cutoff value of 9.0 achieved 72.5% sensitivity and 47.7% specificity. The NLR measured at 24 h after ROCS could be used for early neuroprognostication given its low cost and widespread availability.

Characterisation of plasma proteins and circulating microRNAs in out-of-hospital cardiac arrest patients - approaches for predicting outcome

Abstract Rationale: Early prognostication after out-of-hospital cardiac arrest (OHCA) is an unmet clinical need. There is limited data on serial release kinetics of cardiac, brain and systemic biomarkers such as Neuron Specific Enolase (NSE) and S-100B. miRNAs are sensitive biomarker candidates in acute myocardial infarction (MI). Objective The objective was to characterise circulating biomolecules in the early release kinetics after OHCA, compared with a STEMI control group and assessed with clinical outcome. Methods and Results Serial blood samples of patients with OHCA with STEMI (n=20) and STEMI control (n=20) were collected at baseline and at 6, 12, 24 and 48h after admission. Untargeted plasma proteomics were performed using data-independent acquisition–mass spectrometry (DIA–MS). Candidate proteins and miRNAs were quantified. Proteomics returned n=156 proteins with significantly different kinetics between OHCA and STEMI. Six clusters, depicting distinct biological pathways, with characteristic differences between OHCA and STEMI were identified. The most distinct and significant differences between OHCA and STEMI patients were seen for acute phase reactants LBP, AACT, CRP and the lipid metabolite A2GL as well as endothelial and platelet miR-126 and cardiomyocyte-apoptosis/ischemia-reperfusion related miR-320a. Cardiogenic shock was best characterised by differential kinetics of coagulation and complement related proteins FA9, HRG, FHR1 and inflammation related miR-146. The primary endpoint was poor neurological recovery at 30-days. Individuals with good outcome showed distinct differences in adaptive immune response, complement and coagulation cascades and hemostasis. Circulating plasma levels of hypoxia-associated miR-101, miR-210 and S-100B protein were significantly elevated at baseline in OHCA patients. These molecules further showed higher baseline values in patients with poor outcome. For current prognosis marker NSE, this difference only develops after 48h. Cardiac/muscle miRNAs followed an early release-pattern similar to high-sensitivity cardiac troponin-T (hs-cTnT). Machine learning models were used to calculate the AUC for the prediction of poor neurological outcome at baseline. This was highest for hypoxia-induced miR-210 (AUC: 0.854), which performed significantly better than the protein biomarkers NSE (AUC: 0.646) and S-100B (AUC:0.656). Conclusions Untargeted mass spectrometry reveals distinct biological pathways and candidate biomolecules affected in patients suffering from OHCA compared with STEMI controls, in patients developing cardiogenic shock and in those with adverse versus good neurological outcome. Platelet-, inflammation and hypoxia associated miRNAs are associated with OHCA, cardiogenic shock and prognostication of adverse neurological outcome. Pronounced differences in early release kinetics of RNA and protein biomarkers may improve identification of OHCA patients at risk of poor outcome.

Radiographic predictors of neurologic outcomes in patients with basilar artery occlusion: A single-center experience.

Randomized controlled trials indicate functional and mortality benefits in endovascular therapy for basilar artery occlusion (BAO). However, these studies only include patients who meet specific prognostic scores. This study investigates radiographic predictors of posterior circulation territory infarcts that may predict neurologic outcome at 90-day post-intervention. This is a retrospective cohort study of a prospectively maintained thrombectomy database of all patients who underwent mechanical thrombectomy for BAO. Baseline demographics, comorbidities, baseline functional status (mRS), and severity of presenting neurologic deficits (NIHSS) were collected. Pc-ASPECTS, posterior circulation collateral score (PCCS), and basilar artery on computed tomography angiography (BATMAN) measured radiographic characteristics. Core infarct territory was identified. Primary outcomes were good neurologic outcome (mRS 0-3) and poor neurologic outcome (mRS 4-6) at 90-day post-thrombectomy. 90-day mortality was a secondary outcome. About 21.5% of patients achieved a good neurologic outcome. About 32.3% of patients were deceased at 90 days. Receiver operating characteristic analysis shows PCCS collateral scores (AUC = 0.74, SE = 0.03, CI = 0.62-0.74) and BATMAN (AUC = 0.72, SE = 0.04, CI = 0.35-0.49) have potential to differentiate between those with good neurologic outcome from those with poor neurologic outcome. Although there was no statistically significant difference in AUC between the three curves, pc-ASPECTS score trended toward being weaker predictor of neurologic outcome (AUC = 0.49, SE = 0.04, CI = 0.35-0.49). There were significant associations between 90-day poor neurologic outcome and established infarcts within the pons (p = 0.01), left cerebellum (p = 0.01), and left occipital lobe (p = 0.03) on pre-thrombectomy CT. Low BATMAN and PCCS collateral scores can be predictors of poor neurologic outcomes at 90-day post-thrombectomy for BAO while pc-ASPECTS score may be a weaker predictor of outcome.

Predictors of Neurological Outcomes in Patients with Poor Glasgow Coma Scale Scores 1 Week After Aneurysmal Subarachnoid Hemorrhage

This study assessed neurological outcomes and variables associated with favorable outcomes in aSAH patients with low functional status (Glasgow Coma Scale [GCS] score ≤8) on postbleed day 7 (PBD7). A retrospective analysis was conducted of all patients in the Barrow Ruptured Aneurysm Trial (January 1, 2014-July 31, 2019) treated for a ruptured aneurysm and who had a GCS score ≤8 on PBD7. The primary outcome was a favorable neurological outcome (modified Rankin Scale score ≤2) at last follow-up. Of 312 patients, 63 had low GCS scores at PBD7. These patients had a significantly greater proportion of poor Hunt and Hess scale grades (≥4) (44/63 [70%] vs 49/249 [19.7%], P < 0.001) and poor Fisher grades (grade=4) (58/63 [92%] vs 174/249 [69.9%], P < 0.001) compared to patients who did not have low GCS scores on PBD7, but no differences were found in age, sex, anterior location, aneurysm size, or type of treatment. Of the 63 patients, 7 (11%) experienced a favorable neurological outcome. On univariate analysis, none of the physical examination reflexes predicted a favorable neurological outcome. The middle cerebral artery aneurysm territory was the only significant predictor of a favorable neurological outcome by multivariate analysis (odds ratio, 10.8; 95% confidence interval, 1.16-100], P = 0.04). This study yielded no significant physical examination findings that predict a favorable outcome in patients with GCS score ≤8 on PBD7. This finding may inform the decision of whether to prolong hospital management or arrange for end-of-life care.

Prediction of neurologic outcome after out-of-hospital cardiac arrest: An interpretable approach with machine learning

Out-of-hospital cardiac arrest (OHCA) is a critical condition with low survival rates. In patients with a return of spontaneous circulation, brain injury is a leading cause of death.In this study, we propose an interpretable machine learning approach for predicting neurologic outcome after OHCA, using information available at the time of hospital admission. MethodsThe study population were 55 615 OHCA cases registered in the Swedish Cardiopulmonary Resuscitation Registry between 2010 and 2020. The dataset was split to training and validation sets (for model development) and test set (for evaluation of the final model). We used an XGBoost algorithm with stratified, repeated 10-fold cross-validation along with Optuna framework for hyperparameters tuning. The final model was trained on 10 features selected based on the importance scores and evaluated on the test set in terms of discrimination, calibration and bias-variance tradeoff. We used SHapley Additive exPlanations to address the ‘black-box’ model and align with eXplainable artificial intelligence. ResultsThe final model achieved: area under the receiver operating characteristic value 0.964 (95% confidence interval (CI) [0.960–0.968]), sensitivity 0.606 (95% CI [0.573–0.634]), specificity 0.975 (95% CI [0.972–0.978]), positive predictive value (PPV) 0.664 (95% CI [0.625–0.696]), negative predictive value (NPV) 0.969 (95% CI [0.966–0.972]), macro F1 0.803 (95% CI [0.788–0.816]), and showed a very good calibration. SHAP features with the highest impact on the model’s output were:’ROSC on arrival to hospital’, ‘Initial rhythm asystole’ and ‘Conscious on arrival to hospital’. ConclusionsThe XGBoost machine learning model with 10 features available at the time of hospital admission showed good performance for predicting neurologic outcome after OHCA, with no apparent signs of overfitting.

Prediction of Neurological Outcomes in Elderly Patients With Head Trauma Using the Geriatric Trauma Outcome Score: A Retrospective Observational Study.

Introduction Head trauma in elderly people is a problem in today's aging society. Elderly people are susceptible to head trauma because of their declining physical function; this tends to be severe. Outcome prediction is important in decision-making regarding treatment strategies; however, there is no unified method for predicting neurological outcomes in elderly patients with head trauma. Methods Elderly patients with head trauma admitted to the Japan Red Cross Narita Hospital between January 2019 and August 2023 were enrolled in this single-center, retrospective observational study. A favorable neurological outcome was defined as a cerebral performance category scale of 1 or 2. Multivariate logistic regression analysis and receiver operating characteristic curve analysis were performed to investigate the association between geriatric trauma outcome scores and outcomes and to evaluate the predictive value of geriatric trauma outcome scores. The primary outcome was a favorable neurological outcome at discharge, and the secondary outcome was in-hospital mortality. Results A total of 313 elderly patients with head trauma were eligible for analysis. Multivariate logistic regression analysis revealed that the geriatric trauma outcome score was significantly associated with a favorable neurological outcome at discharge (odds ratio 0.94, P <0.0001). In the receiver operating characteristic curve analysis, the geriatric trauma outcome score had a good predictive value for favorable neurological outcomes at discharge (area under the receiver operating characteristic curve 0.83). Conclusions The geriatric trauma outcome score had good predictive value for favorable neurological outcomes at discharge in elderly patients with head trauma and has the potential to aid in decision-making regarding treatment strategies for elderly patients with head trauma.

Neuron-Specific Enolase as a Predictor of Neurologic Outcomes in Extracorporeal Cardiopulmonary Resuscitation Patients.

Background: Neuron-specific enolase (NSE) has traditionally been used as a biomarker to predict neurologic outcomes after cardiac arrest. This study aimed to evaluate the utility of NSE in predicting neurologic outcomes in patients undergoing extracorporeal cardiopulmonary resuscitation (ECPR). Methods: This observational cohort study included 47 consecutive adult ECPR patients (median age, 59.0 years; 74.5% males) treated between January 2018 and December 2021 at a tertiary extracorporeal life support center. The primary outcome was a poor neurologic outcome, defined as a Cerebral Performance Category score of 3-5 at hospital discharge. Results: Twelve (25.5%) patients had abnormal findings on computed tomography of the brain. A poor neurologic outcome was demonstrated in 22 (46.8%) patients. The NSE level at 72 h after ECPR showed the best prediction power for a poor neurologic outcome compared with NSE at 24 and 48 h. A cutoff value exceeding 61.9 μg/L for NSE at 72 h yielded an area under the curve (AUC) of 0.791 for predicting poor neurologic outcomes and exceeding 62.1 μg/L with an AUC of 0.838 for 30-day mortality. Conclusions: NSE levels at 72 h after ECPR appear to be a reliable biomarker for predicting poor neurologic outcomes and 30-day mortality in ECPR patients.

Predictors of Neurological Disability at Hospital Discharge for Acyclovir-treated Pediatric Herpes Simplex Virus Encephalitis.

Herpes simplex virus encephalitis (HSE) is a rare but serious neurological infection that causes neurological dysfunction. Research is lacking on the clinical predictors of neurological outcomes and the optimal duration of therapy for pediatric HSE patients. In this study of pediatric HSE patients, we identified factors predicting neurological disability at hospital discharge and examined associations of acyclovir therapy duration with neurological outcomes. This was a retrospective cohort study on 37 children diagnosed with HSE confirmed by polymerase chain reaction at age 1 month to 16 years from 2015 to 2021 in Ho Chi Minh City's Children's Hospital No. 2, Vietnam. For the acyclovir duration analysis, we examined 22 children with negative polymerase chain reaction on day 14 of treatment. Poisson regression was used to obtain the risk ratio and 95% confidence interval. The study population consisted of 73% males, with a median age of 14 months (interquartile range: 9-35). Coma at acyclovir treatment, hypotension and the need for mechanical ventilation ≥48 hours significantly predicted neurological disability in the bivariate analysis. There were no significant associations between acyclovir duration (14 vs. 21 days) and neurological outcomes, adjusting for age at diagnosis and pediatric Glasgow Coma Scale score at acyclovir initiation. We identified significant predictors of neurological disability unaffected by postacyclovir treatment factors. Among patients with negative HSE polymerase chain reaction on day 14, 14 days of acyclovir treatment may be as effective as 21 days. Additional studies on the effects of acyclovir duration are needed.

Quantitative EEG reactivity induced by electrical stimulation predicts good outcome in comatose patients after cardiac arrest

BackgroundEEG reactivity is a predictor for neurological outcome in comatose patients after cardiac arrest (CA); however, its application is limited by variability in stimulus types and visual assessment. We aimed to evaluate the prognostic value of the quantitative analysis of EEG reactivity induced by standardized electrical stimulation and for early prognostication in this population.MethodsThis prospective observational study recruited post-CA comatose patients in Xuanwu Hospital, Capital Medical University (Beijing, China) between January 2016 and June 2023. EEG reactivity to electrical or traditional pain stimulation was randomly performed via visual and quantitative analysis. Neurological outcome within 6 months was dichotomized as good (Cerebral Performance Categories, CPC 1–2) or poor (CPC 3–5).ResultsFifty-eight post-CA comatose patients were admitted, and 52 patients were included in the final analysis, of which 19 (36.5%) had good outcomes. EEG reactivity induced with the electrical stimulation had superior performance to the traditional pain stimulation for good outcome prediction (quantitative analysis: AUC 0.932 vs. 0.849, p = 0.048). When using the electrical stimulation, the AUC of EEG reactivity to predict good outcome by visual analysis was 0.838, increasing to 0.932 by quantitative analysis (p = 0.039). Comparing to the traditional pain stimulation by visual analysis, the AUC of EEG reactivity for good prognostication by the electrical stimulation with quantitative analysis was significantly improved (0.932 vs. 0.770, p = 0.004).ConclusionsEEG reactivity induced by the standardized electrical stimulation in combination with quantitative analysis is a promising formula for post-CA comatose patients, with increased predictive accuracy.

Explainable machine learning for predicting neurological outcome in hemorrhagic and ischemic stroke patients in critical care.

The objective of this study is to develop accurate machine learning (ML) models for predicting the neurological status at hospital discharge of critically ill patients with hemorrhagic and ischemic stroke and identify the risk factors associated with the neurological outcome of stroke, thereby providing healthcare professionals with enhanced clinical decision-making guidance. Data of stroke patients were extracted from the eICU Collaborative Research Database (eICU-CRD) for training and testing sets and the Medical Information Mart for Intensive Care IV (MIMIC IV) database for external validation. Four machine learning models, namely gradient boosting classifier (GBC), logistic regression (LR), multi-layer perceptron (MLP), and random forest (RF), were used for prediction of neurological outcome. Furthermore, shapley additive explanations (SHAP) algorithm was applied to explain models visually. A total of 1,216 hemorrhagic stroke patients and 954 ischemic stroke patients from eICU-CRD and 921 hemorrhagic stroke patients 902 ischemic stroke patients from MIMIC IV were included in this study. In the hemorrhagic stroke cohort, the LR model achieved the highest area under curve (AUC) of 0.887 in the test cohort, while in the ischemic stroke cohort, the RF model demonstrated the best performance with an AUC of 0.867 in the test cohort. Further analysis of risk factors was conducted using SHAP analysis and the results of this study were converted into an online prediction tool. ML models are reliable tools for predicting hemorrhagic and ischemic stroke neurological outcome and have the potential to improve critical care of stroke patients. The summarized risk factors obtained from SHAP enable a more nuanced understanding of the reasoning behind prediction outcomes and the optimization of the treatment strategy.

External Validation of Updated Prediction Models for Neurological Outcomes at 90 Days in Patients With Out-of-Hospital Cardiac Arrest.

Few prediction models for individuals with early-stage out-of-hospital cardiac arrest (OHCA) have undergone external validation. This study aimed to externally validate updated prediction models for OHCA outcomes using a large nationwide dataset. We performed a secondary analysis of the JAAM-OHCA (Comprehensive Registry of In-Hospital Intensive Care for Out-of-Hospital Cardiac Arrest Survival and the Japanese Association for Acute Medicine Out-of-Hospital Cardiac Arrest) registry. Previously developed prediction models for patients with cardiac arrest who achieved the return of spontaneous circulation were updated. External validation was conducted using data from 56 institutions from the JAAM-OHCA registry. The primary outcome was a dichotomized 90-day cerebral performance category score. Two models were updated using the derivation set (n=3337). Model 1 included patient demographics, prehospital information, and the initial rhythm upon hospital admission; Model 2 included information obtained in the hospital immediately after the return of spontaneous circulation. In the validation set (n=4250), Models 1 and 2 exhibited a C-statistic of 0.945 (95% CI, 0.935-0.955) and 0.958 (95% CI, 0.951-0.960), respectively. Both models were well-calibrated to the observed outcomes. The decision curve analysis showed that Model 2 demonstrated higher net benefits at all risk thresholds than Model 1. A web-based calculator was developed to estimate the probability of poor outcomes (https://pcas-prediction.shinyapps.io/90d_lasso/). The updated models offer valuable information to medical professionals in the prediction of long-term neurological outcomes for patients with OHCA, potentially playing a vital role in clinical decision-making processes.

Intra-operative Predictors of Neurological Outcomes After Cardiac Surgery

Abstract Background: Despite the advancement in surgical techniques, neurological complications encountered post-cardiac surgery remain significant. Stroke, seizure, and deep coma are major postoperative neurological complications reported in numerous patients. This study aims to identify perioperative predictors for neurological complications and focus on the relationship between these predictors and major neurological complications, particularly stroke, seizure, and deep coma. Materials and Methods: This retrospective cohort study was conducted among patients who underwent cardiac surgery between January 2014 and December 2020 at King Fahd Hospital of University, Khobar, Saudi Arabia. Out of 432 patients who had surgery, only 331 patients met the inclusion criteria. A comparison was made between patients who developed postoperative neurological complications and those who did not. The collected data was analyzed using the Statistical Package of Social Sciences program version 29 (Armonk, NY: IBM Corp. USA). Results: There were 50 patients with postoperative neurological complications compared to 281 patients with no complications. The rate of perioperative haemodynamic instability was higher in the complication group (14/48, 29.2%) compared to the non-complication group (47/238, 19.7%). Stroke was the highest neurological complication encountered (14/50, 28%). Conclusions: Perioperative haemodynamic instability is more common in patients with postoperative neurological complications compared to patients with no complications. Additionally, stroke is the most common major neurological complication. Aortic surgeries, combined surgeries, and haemodynamic instability are the most common perioperative predictors of stroke.

Plasma glial fibrillary acidic protein and tau: predictors of neurological outcome after cardiac arrest

BackgroundThe purpose was to evaluate glial fibrillary acidic protein (GFAP) and total-tau in plasma as predictors of poor neurological outcome after out-of-hospital (OHCA) and in-hospital cardiac arrest (IHCA), including comparisons with neurofilament light (NFL) and neuron-specific enolase (NSE).MethodsRetrospective multicentre observational study of patients admitted to an intensive care unit (ICU) in three hospitals in Sweden 2014–2018. Blood samples were collected at ICU admission, 12 h, and 48 h post-cardiac arrest. Poor neurological outcome was defined as Cerebral Performance Category 3–5 at 2–6 months after cardiac arrest. Plasma samples were retrospectively analysed for GFAP, tau, and NFL. Serum NSE was analysed in clinical care. Prognostic performances were tested with the area under the receiver operating characteristics curve (AUC).ResultsOf the 428 included patients, 328 were OHCA, and 100 were IHCA. At ICU admission, 12 h and 48 h post-cardiac arrest, GFAP predicted neurological outcome after OHCA with AUC (95% CI) 0.76 (0.70–0.82), 0.86 (0.81–0.90) and 0.91 (0.87–0.96), and after IHCA with AUC (95% CI) 0.77 (0.66–0.87), 0.83 (0.74–0.92) and 0.83 (0.71–0.95). At the same time points, tau predicted outcome after OHCA with AUC (95% CI) 0.72 (0.66–0.79), 0.75 (0.69–0.81), and 0.93 (0.89–0.96) and after IHCA with AUC (95% CI) 0.61 (0.49–0.74), 0.68 (0.56–0.79), and 0.77 (0.65–0.90). Adding the change in biomarker levels between time points did not improve predictive accuracy compared to the last time point. In a subset of patients, GFAP at 12 h and 48 h, as well as tau at 48 h, offered similar predictive value as NSE at 48 h (the earliest time point NSE is recommended in guidelines) after both OHCA and IHCA. The predictive performance of NFL was similar or superior to GFAP and tau at all time points after OHCA and IHCA.ConclusionGFAP and tau are promising biomarkers for neuroprognostication, with the highest predictive performance at 48 h after OHCA, but not superior to NFL. The predictive ability of GFAP may be sufficiently high for clinical use at 12 h after cardiac arrest.

Verbal Glasgow Coma Scale as predictor of persistent disorder of consciousness: Insights for improving accuracy and reliability in clinical practice

This brief report discusses the relationship between verbal function, disorders of consciousness, and neurological follow-up after acute brain injury. It provides valuable insights for improving the accuracy and reliability of Verbal Glasgow Coma Scale scoring in clinical practice. The report addresses the need for standardized training and underlines the importance of physiological stabilization before assessment. Clarity in communication, recognition of non-verbal cues, and serial assessments are emphasized as critical factors to reduce the Verbal Glasgow Coma Scale inconsistencies. It also promotes interdisciplinary collaboration and cultural sensitivity to refine the Verbal Glasgow Coma Scale evaluation, improving the prediction of long-term neurological outcomes after acute brain injury and optimizing effective rehabilitation programs. Possible strategies to implement in the routine clinical practice the provided tips are discussed.

Association between early lactate-related variables and 6-month neurological outcome in out-of-hospital cardiac arrest patients

IntroductionThe role of lactate measurement in out-of-hospital cardiac arrest (OHCA) survivors remains controversial. We assessed the association between early lactate-related variables, OHCA characteristics, and long-term neurological outcome. MethodsIn OHCA patients who received targeted temperature management, lactate levels were measured at 0, 12, and 24 h after the return of spontaneous circulation. We calculated lactate clearance and time-weighted cumulative lactate (TWCL), which represent the area under the time-lactate curve. The area under the receiver operating characteristic curve (AUC) and the adjusted odds ratios (AORs) of lactate-related variables for predicting 6-month poor outcome (Cerebral Performance Category 3–5) were evaluated. Interactions between lactate variables and characteristics of OHCA were evaluated by a multivariable logistic model with interaction terms and subgroup analysis. ResultsA total of 347 OHCA patients were included. After adjustment, higher lactate levels at the three time points were associated with a poor outcome (AOR 1.10 [95% CI, 1.03–1.18], AOR 1.15 [95% CI, 1.02–1.29], and AOR 1.36 [95% CI, 1.15–1.60], respectively), while TWCL was the only lactate kinetics variable associated with a poor outcome (AOR 1.29 [95% CI, 1.12–1.49]). We identified several interactions between lactate-related variables and OHCA characteristics. In particular, the AUC of TWCL was excellent in cases of noncardiac etiology (AUC 0.92 [95% CI, 0.86–0.96] but only moderate in cardiac etiology (AUC 0.69 [95% CI, 0.62–0.75]). ConclusionsEarly lactate levels, especially at 24 h, and TWCL were independent predictors of neurologic outcome in these patients, whereas lactate clearance was not. The prognostic ability of lactate-related variables varied depending on the OHCA characteristics.

Grey-to-White Matter Ratio Values in Early Head Computed Tomography (CT) as a Predictor of Neurologic Outcomes in Survivors of Out-of-Hospital Cardiac Arrest Based on Severity of Hypoxic-Ischemic Brain Injury

BackgroundHypoxic-ischemic brain injury (HIBI) is a common complication of out-of-hospital cardiac arrest (OHCA). ObjectivesWe investigated whether grey-to-white matter ratio (GWR) values, measured using early head computed tomography (HCT), were associated with neurologic outcomes based on the severity of HIBI in survivors of OHCA. MethodsThis retrospective multicenter study included adult comatose OHCA survivors who underwent an HCT scan within 2 h after the return of spontaneous circulation. HIBI severity was assessed using the revised post-Cardiac Arrest Syndrome for Therapeutic hypothermia (rCAST) scale (low, moderate, and severe). Poor neurologic outcomes were defined as Cerebral Performance Categories 3 to 5 at 6 months after OHCA. ResultsAmong 354 patients, 27% were women and 224 (63.3%) had poor neurologic outcomes. The distribution of severity was 19.5% low, 47.5% moderate, and 33.1% severe. The area under the receiver operating curves of the GWR values for predicting rCAST severity (low, moderate, and severe) were 0.52, 0.62, and 0.79, respectively. The severe group had significantly higher predictive performance than the moderate group (p = 0.02). Multivariate logistic regression analysis revealed a significant association between GWR values and poor neurologic outcomes in the moderate group (adjusted odds ratio = 0.012, 95% CI 0.0–0.54, p = 0.02). ConclusionsIn this cohort study, GWR values measured using early HCT demonstrated variations in predicting neurologic outcomes based on HIBI severity. Furthermore, GWR in the moderate group was associated with poor neurologic outcomes.

Brain Swelling versus Infarct Size: A Problematizing Review.

In human stroke, brain swelling is an important predictor of neurological outcome and mortality, yet treatments to reduce or prevent brain swelling are extremely limited, due in part to an inadequate understanding of mechanisms. In preclinical studies on cerebroprotection in animal models of stroke, historically, the focus has been on reducing infarct size, and in most studies, a reduction in infarct size has been associated with a corresponding reduction in brain swelling. Unfortunately, such findings on brain swelling have little translational value for treating brain swelling in patients with stroke. This is because, in humans, brain swelling usually becomes evident, either symptomatically or radiologically, days after the infarct size has stabilized, requiring that the prevention or treatment of brain swelling target mechanism(s) that are independent of a reduction in infarct size. In this problematizing review, we highlight the often-neglected concept that brain edema and brain swelling are not simply secondary, correlative phenomena of stroke but distinct pathological entities with unique molecular and cellular mechanisms that are worthy of direct targeting. We outline the advances in approaches for the study of brain swelling that are independent of a reduction in infarct size. Although straightforward, the approaches reviewed in this study have important translational relevance for identifying novel treatment targets for post-ischemic brain swelling.

Advanced Magnetic Resonance Imaging Biomarkers of the Injured Spinal Cord: A Comparative Study of Imaging and Histology in Human Traumatic Spinal Cord Injury.

A significant problem in the diagnosis and management of traumatic spinal cord injury (tSCI) is the heterogeneity of secondary injury and the prediction of neurological outcome. Imaging biomarkers specific to myelin loss and inflammation after tSCI would enable detailed assessment of the pathophysiological processes underpinning secondary damage to the cord. Such biomarkers could be used to biologically stratify injury severity and better inform prognosis for neurological recovery. While much work has been done to establish magnetic resonance imaging (MRI) biomarkers for SCI in animal models, the relationship between imaging findings and the underlying pathology has been difficult to discern in human tSCI because of the paucity of human spinal cord tissue. We utilized post-mortem spinal cords from individuals who had a tSCI to examine this relationship by performing ex vivo MRI scans before histological analysis. We investigated the correlation between the histological distribution of myelin loss and inflammatory cells in the injured spinal cord and a number of myelin and inflammation-sensitive MRI measures: myelin water fraction (MWF), inhomogeneous magnetization transfer ratio (ihMTR), and diffusion tensor and diffusion kurtosis imaging-derived fractional anisotropy (FA) and axial, radial, and mean diffusivity (AD, RD, MD). The histological features were analyzed by staining with Luxol Fast Blue (LFB) for myelin lipids and Class II major histocompatibility complex (Class II MHC) and CD68 for microglia and macrophages. Both MWF and ihMTR were strongly correlated with LFB staining for myelin, supporting the use of both as biomarkers for myelin loss after SCI. A decrease in ihMTR was also correlated with the presence of Class II MHC positive immune cells. FA and RD correlated with both Class II MHC and CD68 and may therefore be useful biomarkers for inflammation after tSCI. Our work demonstrates the utility of advanced MRI techniques sensitive to biological tissue damage after tSCI, which is an important step toward using these MRI techniques in the clinic to aid in decision-making.