Prediction Of Neurological Outcome Research Articles

WITHDRAWN: Malignant transformation of ischemic stroke in the era of mechanical thrombectomy: When is decompressive craniectomy required?

ObjectiveThe treatment of choice for ischemic stroke due to intracranial arterial occlusion of the anterior circulation has been mechanical thrombectomy (MT). The success rate of MT is described as 71%, but malignant infarction can nonetheless ensue. In these cases, decompressive craniectomy (DHC) is often necessary. To date, only few factors are known to correlate significantly with the need for DHC after MT. MethodsWe conducted a retrospective analysis of patients admitted with anterior circulation stroke at our department who underwent MT with and without subsequent DHC. Patients were matched according to ASPECT score on pre-MT CT scan. Established predictors for neurological outcome, TICI grade of recanalization, as well as imaging were then compared between the groups to elucidate which of them were predictive of DHC. ResultsA total of 86 patients were included in the analysis. DHC was performed in 43 patients (50%). In a univariate analysis, male sex (p = 0.037), lower TICI grade of recanalization (p < 0.001) and hemorrhagic transformation (p = 0.014) were predictive of DHC. Patients who received later intravenous thrombolysis were also at higher risk for needing DHC (p = 0.050). Patients suffering from hypercholesterolemia (p < .001) and who were on antiplatlet agents (p = .039) were at lower risk for needing DHC. ConclusionsPatients with higher rates of recanalization were protected against DHC. Later administration of IV tPA was correlated with DHC, thus suggesting a synergistic effect of IV tPA and MT in protecting against malignant transformation. Prior use of antiplatelet agents appears to be a protective factor against DHC in stroke, which warrants further investigation.

The relationship between the initial pH and neurological outcome in patients with out-of-hospital cardiac arrest is affected by the status of recovery of spontaneous circulation on hospital arrival.

The early prediction of neurological outcomes is useful for out-of-hospital cardiac arrest (OHCA). The initial pH was associated with neurological outcomes, but the values varied among the studies. Patients admitted to our division with OHCA of cardiac origin between January 2015 and December 2022 were retrospectively examined (N = 199). A good neurological outcome was defined as a Glasgow-Pittsburgh cerebral performance category (CPC) of 1-2 at discharge. Patients were divided according to the achievement of recovery of spontaneous circulation (ROSC) on hospital arrival, and the efficacy of pH in predicting good neurological outcomes was compared. In patients with ROSC on hospital arrival (N = 100), the initial pH values for good and poor neurological outcomes were 7.26 ± 0.14 and 7.09 ± 0.18, respectively (p < 0.001). In patients without ROSC on hospital arrival (N = 99), the initial pH values for good and poor neurological outcomes were 7.06 ± 0.23 and 6.92 ± 0.15, respectively (p = 0.007). The pH associated with good neurological outcome was much lower in patients without ROSC than in those with ROSC on hospital arrival (P = 0.003). A higher initial pH is associated with good neurological outcomes in patients with OHCA. However, the pH for a good or poor neurological outcome depends on the ROSC status on hospital arrival.

Ferritin as an Effective Predictor of Neurological Outcomes in Children With Acute Necrotizing Encephalopathy

BackgroundAcute necrotizing encephalopathy (ANE) is a fulminant disease with poor prognosis. Cytokine storm is the important phenomenon of ANE that affects the brain and multiple organs. The study aimed to identify whether hyperferritinemia was associated with poor prognosis in patients with ANE. MethodsAll patients with ANE had multiple symmetric lesions located in the bilateral thalami and other regions such as brainstem tegmentum, cerebral white matter, and cerebellum. Neurological outcome at discharge was evaluated by pediatric neurologists using the Pediatric Cerebral Performance Category Scale. All risk factors associated with poor prognosis were further analyzed using receiver operating characteristic curve analysis. ResultsTwenty-nine patients with ANE were enrolled in the current study. Nine (31%) patients achieved a favorable neurological outcome, and 20 (69%) patients had poor neurological outcomes. results The group of poor neurological outcome had significantly higher proportion of shock on admission and brainstem involvement. Based on multivariate logistic regression analysis, ferritin, aspartate aminotransferase (AST), and ANE severity score (ANE-SS) were the predictors associated with outcomes. The appropriate cutoff value for predicting neurological outcomes in patients with ANE was 1823 ng/mL for ferritin, 78 U/L for AST, and 4.5 for ANE-SS. Besides, comparison analyses showed that higher level of ferritin and ANE-SS were significantly correlated with brainstem involvement (P < 0.05). ConclusionsFerritin may potentially be a prognostic factor in patients with ANE. Hyperferritinemia is associated with poor neurological outcomes in patients with ANE and ferritin levels more than 1823 ng/mL have about eightfold increased risk of poor neurological outcome.

Optimal PHASES Scoring for Risk Stratification of Surgically Treated Unruptured Aneurysms

The PHASES (Population, Hypertension, Age, Size, Earlier subarachnoid hemorrhage, Site) score was developed to facilitate risk stratification for management of unruptured intracranial aneurysms (UIAs). This study aimed to identify the optimal PHASES score cutoff for predicting neurologic outcomes in patients with surgically treated aneurysms. All patients who underwent microneurosurgical treatment for UIA at a large quaternary center from January 1, 2014, to December 31, 2020, were retrospectively reviewed. Inclusion criteria included a modified Rankin Scale (mRS) score of ≤2 at admission. The primary outcome was 1-year mRS score, with a "poor" neurologic outcome defined as an mRS score >2. In total, 375 patients were included in the analysis. The mean (SD) PHASES score for the entire study population was 4.47 (2.67). Of 375 patients, 116 (31%) had a PHASES score ≥6, which was found to maximize prediction of poor neurologic outcome. Patients with PHASES scores ≥6 had significantly higher rates of poor neurologic outcome than patients with PHASES scores <6 at discharge (58 [50%] vs. 90 [35%], P= 0.005) and follow-up (20 [17%] vs. 18 [6.9%], P= 0.002). After adjusting for age, Charlson Comorbidity Index score, nonsaccular aneurysm, andaneurysm size, PHASES score ≥6 remained a significant predictor of poor neurologic outcome at follow-up (odds ratio, 2.75; 95% confidence interval, 1.42-5.36, P= 0.003). In this retrospective analysis, a PHASES score ≥6 was associated with significantly greater proportions of poor outcome, suggesting that awareness of this threshold in PHASES scoring could be useful in risk stratification and UIA management.

Additional predictive value of optic nerve sheath diameter for neurological prognosis after cardiac arrest: a prospective cohort study

BackgroundThe goal is to estimate the additional value of ultrasonographic optic nerve sheath diameter (ONSD) measurement on days 1–3, on top of electroencephalography (EEG), pupillary light reflexes (PLR), and somatosensory evoked potentials (SSEP), for neurological outcome prediction of comatose cardiac arrest patients. We performed a prospective longitudinal cohort study in adult comatose patients after cardiac arrest. ONSD was measured on days 1–3 using ultrasound. Continuous EEG, PLR, and SSEP were acquired as standard care. Poor outcome was defined as cerebral performance categories 3–5 at 3–6 months. Logistic regression models were created for outcome prediction based on the established predictors with and without ONSD. Additional predictive value was assessed by increase in sensitivity for poor (at 100% specificity) and good outcome (at 90% specificity).ResultsWe included 100 patients, 54 with poor outcome. Mean ONSD did not differ significantly between patients with good and poor outcome. Sensitivity for predicting poor outcome increased by adding ONSD to EEG and SSEP from 25% to 41% in all patients and from 27% to 50% after exclusion of patients with non-neurological death.ConclusionsONSD on days 1–3 after cardiac arrest holds potential to add to neurological outcome prediction.Trialregistration: clinicaltrials.gov, NCT04084054. Registered 10 September 2019, https://www.clinicaltrials.gov/study/NCT04084054.

The role of intracranial pressure variability as a predictor of intracranial hypertension and mortality in critically ill patients.

Intracranial pressure (ICP) monitoring is a widely utilized and essential tool for tracking neurosurgical patients, but there are limitations to the use of a solely ICP-based paradigm for guiding management. It has been suggested that ICP variability (ICPV), in addition to mean ICP, may be a useful predictor of neurological outcomes, as it represents an indirect measure of intact cerebral pressure autoregulation. However, the current literature regarding the applicability of ICPV shows conflicting associations between ICPV and mortality. Thus, the authors aimed to investigate the effect of ICPV on intracranial hypertensive episodes and mortality using the eICU Collaborative Research Database version 2.0. The authors extracted from the eICU database 1,815,676 ICP readings from 868 patients with neurosurgical conditions. ICPV was computed using two methods: the rolling standard deviation (RSD) and the absolute deviation from the rolling mean (DRM). An episode of intracranial hypertension was defined as at least 25 minutes of ICP > 22 mm Hg in any 30-minute window. The effects of mean ICPV on intracranial hypertension and mortality were computed using multivariate logistic regression. A recurrent neural network with long short-term memory was used for time-series predictions of ICP and ICPV to prognosticate future episodes of intracranial hypertension. A higher mean ICPV was significantly associated with intracranial hypertension using both ICPV definitions (RSD: aOR 2.82, 95% CI 2.07-3.90, p < 0.001; DRM: aOR 3.93, 95% CI 2.77-5.69, p < 0.001). ICPV was significantly associated with mortality in patients with intracranial hypertension (RSD: aOR 1.28, 95% CI 1.04-1.61, p = 0.026, DRM: aOR 1.39, 95% CI 1.10-1.79, p = 0.007). In the machine learning models, both definitions of ICPV achieved similarly good results, with the best F1 score of 0.685 ± 0.026 and an area under the curve of 0.980 ± 0.003 achieved with the DRM definition over 20 minutes. ICPV may be useful as an adjunct for the prognostication of intracranial hypertensive episodes and mortality in neurosurgical critical care as part of neuromonitoring. Further research on predicting future intracranial hypertensive episodes with ICPV may help clinicians react expediently to ICP changes in patients.

An innovative MedIcaL dEcision Support tool for neurological ouTcome predictiOn from post-resuscitatioN Electrocardiograms (MILESTONE)

Abstract Background The prediction of poor neurological outcome after Out-of-Hospital Cardiac Arrest (OHCA) represents a major challenge in post-resuscitation care. This is currently based on a complex multiparametric prognostication approach and the existing decision support tools are characterized by unsatisfactory performance. Purpose The aim of the present study is to develop a machine learning (ML) tool for clinical decision support in the prediction of neurological outcome in patients who have experienced an OHCA. We present an innovative MedIcaL dEcision Support tool for neurological ouTcome predictiOn from post-resuscitatioN Electrocardiograms (MILESTONE), which can be used in a post-resuscitation setting. Methods MILESTONE uses an ECG image as an input from which it extracts features by computer vision techniques. Those features, used to feed the ML model for the prediction of the neurological outcome, were retrieved from a dataset of 1341 ECGs collected from a Utstein-style OHCA registry: 431 post-ROSC ECGs of patients with poor neurological outcome (defined according to the Cerebral Performance Category &amp;gt; 2) at discharge (class 1); 568 post-ROSC ECGs of patients with good neurological outcome (CPC≤2) and 342 ECGs of healthy subjects (class 0). Image features were extracted by several computer vision algorithms derived from the scale-invariant feature transform (SIFT) method. The extracted features, patient’s age and gender were used to train an XGBoost classifier. Nested cross-validation was applied to optimize the model’s parameters and to test its generalization capability. Class weightage technique was introduced to make our classifier more prone to detect positive cases. Model’s performance was evaluated by the balanced accuracy, the Area Under the Curve of the Receiver Operating Characteristic curve (AUC-ROC), and the F1-score in both the validation and the test sets. Results Our model achieved an AUC-ROC and F1-score of 88.5±4.1% and 78.2±7.3% respectively in the hold-out set (Figure). MILESTONE reached a balanced accuracy of over 80% as a proof of the robustness of the results and the high discrimination capability for both classes, although it has an intended bias towards the identification of patients with worse outcomes. In terms of explainability, MILESTONE has the potential to correlate machine-readable ECG features with more well-known macro features used in clinical practice such as QT intervals, peak shapes, etc. Conclusions We present the first use of computer vision-based ECG features able to predict poor neurological outcome in patients resuscitated from OHCA. Poor neurological outcome represents a major challenge in post-resuscitation care and this innovative and flexible tool showed an extremely promising performance. Moreover, this tool can be re-trained either for different pathologies or on any time length of ECG recording thus continuously improving its performance.

Electroencephalography Prediction of Neurological Outcomes After Hypoxic-Ischemic Brain Injury: A Systematic Review and Meta-Analysis.

Background. Predicting neurological outcomes after hypoxic-ischemic brain injury (HIBI) is difficult. Objective. Electroencephalography (EEG) can identify acute and subacute brain abnormalities after hypoxic brain injury and predict HIBI recovery. We examined EEG's ability to predict neurologic outcomes following HIBI. Method. A PRISMA-compliant search was conducted in the Medline, Embase, Cochrane, and Central databases until January 2023. EEG-predicted neurological outcomes in HIBI patients were selected from relevant perspective and retrospective cohort studies. RevMan did meta-analysis, while QDAS2 assessed research quality. Results. Eleven studies with 3761 HIBI patients met the inclusion and exclusion criteria. We aggregated study-level estimates of sensitivity and specificity for EEG patterns determined a priori using random effect bivariate and univariate meta-analysis when appropriate. Positive indicators and anatomical area heterogeneity impacted prognosis accuracy. Funnel plots analyzed publication bias. Significant heterogeneity of greater than 80% was among the included studies with P < 0.001. The area under the curve was 0.94, the threshold effect was P < 0.001, and the sensitivity and specificity, with 95% confidence intervals, were 0.91 (0.84-0.99) and 0.86 (0.75-0.97). EEG detects status epilepticus and burst suppression with good sensitivity, specificity, and little probability of false-negative impairment result attribution. Study quality varied by domain, but patient flow and timing were well conducted in all. Conclusion. EEG can predict the outcome of HIBI with good prognostic accuracy, but more standardized cross-study protocols and descriptions of EEG patterns are needed to better evaluate its prognostic use for patients with HIBI.

Relationship between Kidney Dysfunction, Stroke Severity, and Outcomes in a Nigerian Tertiary Hospital: A Prospective Study.

Stroke is a common neurologic disease associated with fatal outcomes. Kidney dysfunction may be an important predictor of stroke severity and outcome. To determine the relationship between kidney dysfunction at admission and stroke severity and 30-day outcome. This was a prospective study that involved 150 stroke patients. Stroke severity at admission was assessed using the National Institutes of Health Stroke Scale (NIHSS). Renal dysfunction was assessed by the presence of albuminuria and or reduced glomerular filtration rate (GFR) at admission. Neurological outcome was assessed using mortality, modified Rankin Scale (mRS), and Glasgow Outcome Scale (GCS). The mean age of the study participants was 61.0 ± 13.2 years. Renal dysfunction was present in 66% of the participants while the case fatality rate of stroke was 26%. Poor neurological outcome at 30 days was found in 44.1% of survivors. Those with albuminuria had lower GCS (P = 0.041), lower GFR (P = 0.004), higher mRS score on day 14 (P = 0.041) and day 30 (P = 0.032), and higher NIHSS score (P = 0.034). Independent predictors of 30-day mortality were albuminuria (Adjusted Odd Ratio (AOR) 3.60, 95%CI: 1.07-12.17) and increasing NIHSS score (AOR = 1.15, 95%CI: 1.04-1.28). Lower GCS (P < 0.001), elevated white blood cells (P = 0.003), serum creatinine (P = 0.048), and NIHSS score (P < 0.001) were associated with poor neurological outcome. NIHSS score was the only significant predictor of neurologic outcome (AOR: 1.25; CI: 1.11-1.41; P ≤ 0.001). Kidney dysfunction was associated with stroke severity and mortality. However, it was not an independent predictor of neurological outcome.

189 Role of Total Spectral Power in Prediction of Neurological Outcome After Cardiac Arrest

189 Role of Total Spectral Power in Prediction of Neurological Outcome After Cardiac Arrest

Quantitative analysis of early apparent diffusion coefficient values from MRIs for predicting neurological prognosis in survivors of out-of-hospital cardiac arrest: an observational study

BackgroundThis study aimed to quantitatively analyse ultra-early brain diffusion-weighted magnetic resonance imaging (DW-MRI) findings to determine the apparent diffusion coefficient (ADC) threshold associated with neurological outcomes in comatose survivors of out-of-hospital cardiac arrest (OHCA).MethodsThis retrospective study included adult survivors of comatose OHCA who underwent DW-MRI imaging scans using a 3-T MRI scanner within 6 h of the return of spontaneous circulation (ROSC). We investigated the association between neurological outcomes and ADC values obtained through voxel-based analysis on DW-MRI. Additionally, we constructed multivariable logistic regression models with pupillary light reflex (PLR), serum neuron-specific enolase (NSE), and ADC values as independent variables to predict poor neurological outcomes. The primary outcome was poor neurological outcome 6 months after ROSC, determined by the Cerebral Performance Category 3–5.ResultsOverall, 131 patients (26% female) were analysed, of whom 74 (57%) showed poor neurological outcomes. The group with a poor neurological outcome had lower mean whole brain ADC values (739.1 vs. 787.1 × 10–6 mm/s) and higher percentages of voxels with ADC below threshold in all ranges (250–1150) (all P < 0.001). The mean whole brain ADC values (area under the receiver operating characteristic curve [AUC] 0.83) and the percentage of voxels with ADC below 600 (AUC 0.81) had the highest sensitivity of 51% (95% confidence interval [CI] 39.4–63.1; cut-off value ≤ 739.2 × 10−6 mm2/s and > 17.2%, respectively) when the false positive rate (FPR) was 0%. In the multivariable model, which also included PLR, NSE, and mean whole brain ADC values, poor neurological outcome was predicted with the highest accuracy (AUC 0.91; 51% sensitivity). This model showed more accurate prediction and sensitivity at an FPR of 0% than did the combination of PLR and NSE (AUC 0.86; 30% sensitivity; P = 0.03).ConclusionsIn this cohort study, early voxel-based quantitative ADC analysis after ROSC was associated with poor neurological outcomes 6 months after cardiac arrest. The mean whole brain ADC value demonstrated the highest sensitivity when the FPR was 0%, and including it in the multivariable model improved the prediction of poor neurological outcomes.

Predictors of neurologic outcomes and mortality in physically abused and unintentionally injured children: a retrospective observation study

ObjectivesThis study aimed to identify the predictors of neurologic outcomes and mortality in physically abused and unintentionally injured children admitted to intensive care units (ICUs).MethodsAll maltreated children were admitted to pediatric, neurosurgical, and trauma ICUs between 2001 and 2019. Clinical factors, including age, sex, season of admission, identifying settings, injury severity score, etiologies, length of stay in the ICU, neurologic outcomes, and mortality, were analyzed and compared between the physically abused and unintentionally injured groups. Neurologic assessments were conducted using the Pediatric Cerebral Performance Category scale. The study was approved by the Institutional Review Board of Chang Gung Memorial Hospital and the Ethics Committee waived the requirement for informed consent because of the anonymized nature of the data.ResultsA total of 2481 children were investigated; of them, there were 480 (19.3%) victims admitted to the ICUs, including 156 physically abused and 324 unintentionally injured. Age, history of prematurity, clinical outcomes, head injury, neurosurgical interventions, clinical manifestations, brain computed tomography findings, and laboratory findings significantly differed between them (all p < 0.05). Traumatic brain injury was the major etiology for admission to the ICU. The incidence of abusive head trauma was 87.1% among the physically abused group. Only 46 (29.4%) and 268 (82.7%) cases achieved favorable neurologic outcomes in the physically abused and unintentionally injured groups, respectively. Shock within 24 h, spontaneous hypothermia (body temperature, < 35 °C), and post-traumatic seizure were strongly associated with poor neurologic outcomes and mortality in both groups.ConclusionsInitial presentation with shock, spontaneous hypothermia at ICU admission, and post-traumatic seizure were associated with poor neurologic outcomes and mortality in physically abused and unintentionally injured children.

Whether serum albumin/leukocyte ratio combined with Go-FAR score more accurately predicts the neurological outcome of patients who suffered cardiac arrest

Objective: To investigate whether serum albumin/leukocyte ratio combined with good outcome score after resuscitation (GO-FAR score) can improve the prediction of neurological outcomes in patients with cardiac arrest, so that clinical decisions can be made earlier and futile resuscitation can be avoided. Methods:The data of 72 patients with cardiac arrest rescued 2020.01.01-2022.04.30 in the Seventh Affiliated Hospital of Sun Yat-sen University were collected, and the GO-FAR score and albumin/leukocyte ratio of all patients were calculated. The main research outcome was the neurological outcome within one month after cardiac arrest. The brain function performance score (CPC score) was used for assessing neurological outcomes, It was divided into two groups:a good prognosis group (CPC score 1-2) and a poor prognosis group (CPC score 3-5). Multivariate Logistic analysis and ROC curve analysis were performed on the data of the two groups, and conclusions were drawn. Result:Univariate and multivariate analysis found that the albumin/leukocyte ratio and GO-FAR score in the poor prognosis group were lower than those in the good prognosis group, and the difference between the two groups was statistically significant (P &lt; 0.05).The results of ROC curve analysis found that the area under the curve of ALB/WBC, GO-FAR score, and ALB/WBC + GO-FAR score were: 0.782 (95% CI: 0.662 to 0.903), 0.677 (95% CI: 0.493 to 0.862), 0.841 (95% CI: 746-0.936), P &lt; 0.05, and the difference was statistically significant. Malignant tumor, shock rhythm, albumin, and leukocytes were all influencing factors for functional outcomes in patients with cardiac arrest. Conclusion:The combination of GO-FAR score and albumin/leukocyte ratio can better predict neurological outcomes in patients with cardiac arrest, but still cannot make instructions to stop resuscitation. More research is needed to confirm.

Confounders for prognostic accuracy of neuron-specific enolase after cardiac arrest: A retrospective cohort study

AimTo evaluate neuron-specific enolase (NSE) thresholds for prediction of neurological outcome after cardiac arrest and to analyze the influence of hemolysis and confounders. MethodsRetrospective analysis from a cardiac arrest registry. Determination of NSE serum concentration and hemolysis-index (h-index) 48–96 hours after cardiac arrest. Evaluation of neurological outcome using the Cerebral Performance Category score (CPC) at hospital discharge. Separate analyses considering CPC 1–3 and CPC 1–2 as good neurological outcome. Analysis of specificity and sensitivity for poor and good neurological outcome prediction with and without exclusion of hemolytic samples (h-index larger than 50). ResultsAmong 356 survivors three days after cardiac arrest, hemolysis was detected in 28 samples (7.9%). At a threshold of 60 µg/L, NSE predicted poor neurological outcome (CPC 4–5) in all samples with a specificity of 92% (86–95%) and sensitivity of 73% (66–79%). In non-hemolytic samples, specificity was 94% (89–97%) and sensitivity 70% (62–76%). At a threshold of 100 µg/L, specificity was 98% (95–100%, all samples) and 99% (95–100%, non-hemolytic samples), and sensitivity 58% (51–65%) and 55% (47–63%), respectively. Possible confounders for elevated NSE in patients with good neurological outcome were ECMO, malignancies, blood transfusions and acute brain diseases. Nine patients with NSE below 17 µg/L had CPC 5, all had plausible death causes other than hypoxic-ischemic encephalopathy. ConclusionsNSE concentrations higher than 100 µg/L predicted poor neurological outcome with high specificity. An NSE less than 17 µg/L indicated absence of severe hypoxic-ischemic encephalopathy. Hemolysis and other confounders need to be considered. Institutional protocol numberThe local ethics committee (board name: Ethikkommission der Charité) approved this study by the number: EA2/066/23, approval date: 28th June 2023, study title “‘ROSC’ – Resuscitation Outcome Study.”

Initial Temporal Muscle Thickness and Area: Poor Predictors of Neurological Outcome in Aneurysmal Subarachnoid Hemorrhage in a Central European Patient Cohort.

The temporalis muscle area (TMA) has been proclaimed as a surrogate parameter for estimating skeletal muscle mass. Pilot studies in Asian populations suggested temporal muscle thickness (TMT) and TMA as prognostic factors for neurological outcomes in aneurysmal subarachnoid hemorrhage (aSAH) patients. This study aimed to validate these findings in a larger European patient cohort. We retrospectively analyzed age, sex, aneurysm location, treatment, World Federation of Neurosurgical Societies (WFNS) grade, Fisher score, and modified Rankin Score (mRS) at six months in patients with aSAH. TMT and TMA measurements were obtained from initial native CT scans. Logistic regression with the dichotomized six-month mRS as the outcome incorporating TMT, weighted average of TMT, or TMA as predictors was performed. Of the included 478 patients, 66% were female, the mean age was 56, and 48% of patients had an mRS of three to six after six months. The mean TMT at the level of the Sylvian fissure was 5.9 (±1.7) mm in males and 4.8 (±1.8) mm in females. The mean TMA was 234.5 (±107.9) mm2 in females and 380 (±134.1) mm2 in males. WFNS grade (p < 0.001), Fisher score (p < 0.001), and age (p < 0.05) correlated significantly with the mRS after six months. No correlation was found between mRS after six months and the TMT at the Sylvian fissure (p = 0.3), the weighted average of TMT (p = 0.1), or the TMA (p = 0.1). In this central European patient cohort of 478 individuals, no significant associations were found between TMT/TMA and neurological outcomes following aSAH. Further prospective studies in diverse patient populations are necessary to determine the prognostic value of TMA and TMT in aSAH patients.

Enhanced analysis of somatosensory evoked potentials at 20–30 milliseconds can predict neurological outcome after cardiac arrest

ObjectiveThis study attempted to test the effectiveness of an enhanced analysis of the 20–30 ms complex of somatosensory evoked potentials, in predicting the short-term outcome of comatose survivors of out of hospital cardiac arrest and compare it with the current clinical practice. MethodsSingle-centre, prospective, observational study. Median nerve SSEP recording performed at 24–36 h post-return of spontaneous circulation. Recording was analysed using amplitude measurements of P25/30 and Peak-To-Trough of 20–30 ms complex and thresholds to decide P25/30 presence. Neurological outcome was dichotomised into favourable and unfavourable. Results89 participants were analysed. 43.8% had favourable and 56.2% unfavourable outcome. The sensitivity, specificity, positive and negative predictive values of the present SSEP and favourable outcome were calculated. P25/30 presence and size of PTT improved positive predictive value and specificity, while maintained similar negative predictive value and sensitivity, compared to the current practice. Inter-interpreter agreement was also improved. ConclusionsEnhanced analysis of the SSEP at 20–30 ms complex could improve the short-term prognostic accuracy for short-term neurological outcome in comatose survivors of cardiac arrest. SignificancePeak-To-Trough analysis of the 20–30 ms SSEP waveform appears to be the best predictor of neurological outcome following out of hospital cardiac arrest. It is also the easiest and most reliable to analyse.

The use of neurone specific enolase to prognosticate neurological recovery and long term neurological outcomes in OOHCA patients.

Hypoxic-ischaemic brain injury (HIBI), is a common sequalae following out-of-hospital cardiac arrest (OOHCA), it is reported as the cause of death in 68% of patients who survive to ICU admission, while other patients can be left with permanent neurological disability. Prediction of neurological outcome follows a multimodal approach, including use of the biomarker, neurone specific enolase (NSE). There is however no definitive cut-off value for poor neurological outcome, and little research has analysed NSE and long-term outcomes in survivors. We investigated an NSE threshold for poor short-term neurological outcome and the relationship between NSE and poor neurological outcome in survivors. A retrospective study was conducted of all adult OOHCA patients admitted to the Royal County Sussex Hospital ICU between April 2017 and November 2018. NSE levels, Targeted Temperature Management (TTM), cross-sectional imaging, mortality and GCS on ICU discharge were recorded. Assessment of neurological function after a median of 19 months (range 14-32 months) post ICU discharge was undertaken following ICU discharge and related to NSE. NSE levels were measured in 59 patients; of these 36 (61%) had a poor neurological outcome due to hypoxic ischaemic brain injury. Youden's index and ROC analysis established an NSE cut-off value of 64.5 μg/L, with AUC of 0.901, sensitivity of 77.8% and specificity of 100%. Follow-up of 26 survivors after 19 months did not show a significant relationship between NSE after OOHCA and long-term neurological outcome. Our results show that NSE >64.5 µg/L has a poor short-term neurological outcome with 100% specificity. Whilst limited by a low sample size, NSE in survivors showed no relationship with neurological outcome post OOHCA in the long term.

Skin sympathetic nerve activity as a biomarker for outcomes in spontaneous intracerebral hemorrhage.

A rapid and accurate forecast for the early prognosis of ICH patients is challenging. This study investigated whether heart rate variability (HRV) and skin sympathetic nerve activity (SKNA) could prognosticate poor neurological outcomes in ICH patients. Between November 2020 and November 2021, we studied 92 spontaneous ICH patients in the First Affiliated Hospital of Nanjing Medical University. Glasgow Outcome Scale (GOS) score at 2 weeks after the ICH was used to categorize patients into good and poor outcome groups. The modified Rankin Scale (mRS) assessed patients' ability to live independently for 1 year. We utilized a portable high-frequency electrocardiogram (ECG) recording system to record the HRV and SKNA information in ICH patients and control participants. 77 patients were eligible for the prediction of neurological outcome and were allocated into the good (n = 22) or poor (n = 55) outcome groups based on the GOS grade. In univariate logistic regression analysis, significant variables that could differentiate the outcomes were age, hypertension, tracheal intubation, Glasgow Coma Scale (GCS) score, existing intraventricular hemorrhage, white blood cells, neutrophil, lnVLF, lnTP, and aSKNA. Variables in the best fit multivariable logistic regression model were age, hypertension, GCS score, neutrophils, and aSKNA. The GCS score was the only independent risk factor for poor outcomes. At 30 days and 1 year of follow-up, patients with lower aSKNA had poor outcomes. ICH patients had reduced aSKNA, which could be a prognostic indicator. A lower aSKNA suggested a worse prognosis. The present data indicate that ECG signals could be helpful for prognosticating ICH patients.

Assessment of Brain Computed Tomography as a Practical Way to Predict Neurological Outcomes in Post-Cardiac Arrest Survivors

Objective: To evaluate whether a visual inspection of ischemic changes in brain computed tomography (CT) images, which is very practical in clinical settings, could predict neurological outcomes in post-cardiac arrest survivors. Materials and Methods: The authors retrospectively reviewed medical records and brain CT images of 62 patients who remained comatose after surviving cardiac arrest and had undergone a CT scan within 24 hours to seven days. Neurological outcomes at one month were assessed using the Cerebral Performance Category (CPC). The CPC scores of 1-2 and 3-5 referred to good and poor neurological outcomes, respectively. Findings from the inspection of the images were graded into grade 1 for absence of acute ischemic change, grade 2 for decreased attenuation of the grey matter in some brain region(s), and grade 3 for diffused loss of grey-white matter differentiation or apparent brain swelling. An experienced neurologist and a consensus group of four pre-clerkship medical students, blinded to the neurological outcomes, evaluated the grade of ischemic changes in CT images. Results: Positive correlations were observed between CPC and CT grading by both the neurologist (ρ=0.76, 95% CI 0.63 to 0.90, p&lt;0.001) and medical students (ρ=0.57, 95% CI 0.38 to 0.77, p&lt;0.001). The CT grading of 2 or more by the neurologist could predict poor neurological outcomes with specificity of 1.00, sensitivity of 0.89, and receiver operating characteristic (ROC) AUC of 0.94 (95% CI 0.89 to 1.00). The evaluation by medical students showed an ROC AUC of 0.80 (95% CI 0.64 to 0.96). Conclusion: The simple visual inspection of ischemic changes in brain CT images showed a high diagnostic accuracy and could be a practical method for predicting neurological outcomes in post-cardiac arrest survivors. Keywords: Cardiac arrest; Post-cardiac arrest ischemic brain injury; Neurological outcomes; Computerized tomography

Explainable artificial intelligence-based prediction of poor neurological outcome from head computed tomography in the immediate post-resuscitation phase

Predicting poor neurological outcomes after resuscitation is important for planning treatment strategies. We constructed an explainable artificial intelligence-based prognostic model using head computed tomography (CT) scans taken immediately within 3 h of resuscitation from cardiac arrest and compared its predictive accuracy with that of previous methods using gray-to-white matter ratio (GWR). We included 321 consecutive patients admitted to our institution after resuscitation for out-of-hospital cardiopulmonary arrest with circulation resumption over 6 years. A machine learning model using head CT images with transfer learning was used to predict the neurological outcomes at 1 month. These predictions were compared with the predictions of GWR for multiple regions of interest in head CT using receiver operating characteristic (ROC)-area under curve (AUC) and precision recall (PR)-AUC. The regions of focus were visualized using a heatmap. Both methods had similar ROC-AUCs, but the machine learning model had a higher PR-AUC (0.73 vs. 0.58). The machine learning-focused area of interest for classification was the boundary between gray and white matter, which overlapped with the area of focus when diagnosing hypoxic– ischemic brain injury. The machine learning model for predicting poor outcomes had superior accuracy to conventional methods and could help optimize treatment.