Neurocritical Care最新文献

Background: A multimodal approach may prove effective for predicting clinical outcomes following cardiac arrest (CA). We aimed to develop a practical predictive model that incorporates clinical factors related to CA and multiple prognostic tests using machine learning methods.

Methods: The neurological outcomes after CA (NOCA) method for predicting poor outcomes were developed using data from 390 patients with CA between May 2018 and June 2023. The outcome was poor neurological outcome, defined as a Cerebral Performance Category score of 3-5 at discharge. We analyzed 31 variables describing the circumstances at CA, demographics, comorbidities, and prognostic studies. The prognostic method was developed based on an extreme gradient-boosting algorithm with threefold cross-validation and hyperparameter optimization. The performance of the predictive model was evaluated using the receiver operating characteristic curve analysis and calculating the area under the curve (AUC).

Results: Of the 390 total patients (mean age 64.2 years; 71.3% male), 235 (60.3%) experienced poor outcomes at discharge. We selected variables to predict poor neurological outcomes using least absolute shrinkage and selection operator regression. The Glasgow Coma Scale-M (best motor response), electroencephalographic features, the neurological pupil index, time from CA to return of spontaneous circulation, and brain imaging were found to be important key parameters in the NOCA score. The AUC of the NOCA method was 0.965 (95% confidence interval 0.941-0.976).

Conclusions: The NOCA score represents a simple method for predicting neurological outcomes, with good performance in patients with CA, using a machine learning analysis that incorporates widely available variables.

Background: Neurologic complications contribute significantly to morbidity and mortality in acute liver failure (ALF). However, clinical assessment of neurologic function in this population is often challenging. Continuous electroencephalography (cEEG) is a low-risk, noninvasive diagnostic tool that can monitor real-time cerebral function. We aimed to investigate cEEG findings and prognostic significance of specific EEG features in a cohort of strictly defined patients with ALF.

Methods: This was a retrospective, single-center study of adult patients with ALF who underwent cEEG monitoring for at least 6 h between 2013 and 2022. Clinical, laboratory, imaging, and treatment characteristics were evaluated. cEEG variables included background continuity, background frequency, the presence of sporadic epileptiform discharges, rhythmic or periodic patterns, and electrographic or electroclinical seizures. The primary outcome was mortality or transition to end-of-life care during the index admission.

Results: A total of 32 patients with ALF were included. 56.3% of patients had rhythmic or periodic patterns, of which the majority were generalized periodic discharges (37.5%). 12.5% of patients had sporadic epileptiform discharges, and 6.3% of patients demonstrated electrographic or clinical seizures. Eighteen (56.3%) patients died or were transitioned to end-of-life care during the index admission. Worsening background continuity or frequency over the course of the cEEG recording was significantly associated with poor outcome (p = 0.001, p = 0.007, respectively), with a 100% mortality rate in patients demonstrating these EEG trends. A worst recorded continuity of suppression, attenuation, and burst-suppression was also associated with poor outcome (p = 0.012). The presence of rhythmic or periodic patterns, sporadic epileptiform discharges, or seizures was not predictive of outcome.

Conclusions: Worsening cEEG background continuity or frequency is associated with poor outcome in adults with ALF. cEEG may contribute useful prognostic information in these patients, in conjunction with other laboratory and clinical markers of disease severity.

Background: Intracranial pressure (ICP) monitoring is a cornerstone of neurointensive care. However, some limitations of invasive techniques for ICP monitoring to acknowledge are the risk for complications and the lack of robust evidence supporting individualized ICP safety thresholds. Cerebrospinal compliance (CSC) may serve as a more reliable indicator of brain health than ICP alone. Previously, intracranial compliance (Ci), was described as a mathematical model from invasive ICP to assess CSC, using ICP waveform amplitudes and cerebral arterial blood volume (CaBV) waveform amplitudes via transcranial Doppler (TCD). This study aimed to compare Ci with a surrogate parameter based on CaBV waveform amplitudes and pulsatile micrometric skull waveforms (Skw) amplitudes. This noninvasive parameter was named Bcomp.

Methods: Neurocritical patients undergoing ICP monitoring were evaluated using TCD and the skull micrometric deformation sensor (B4C). ICP waveform (from invasive ICP probes) and Skw (from noninvasive B4C) were analyzed to extract pulse amplitudes, whereas TCD provided cerebral blood velocities from the middle cerebral arteries for CaBV calculation. CSC was measured using the volume/pressure relationship, with CaBV amplitude serving as the volume surrogate, and ICP and B4C pulse amplitudes as surrogates for ICP values. Agreement and correlation analysis was calculated between Ci and Bcomp.

Results: Data from 71 patients were analyzed, with 68% of the sample having suffered traumatic brain injury. Maximum CaBV was significantly delayed in patients with poor CSC (p < 0.001). Ci and Bcomp showed strong agreement and linear correlation (mean difference of - 0.28 and Spearman correlation of 0.88, p < 0.001).

Conclusions: Using CaBV, which reflects changes in arterial blood volume during the cardiac cycle and Skw pulse amplitudes, Bcomp demonstrated high agreement and correlation with Ci, defined as the product of CaBV and ICP pulse amplitude. The observed shift in CaBV among patients with poor CSC suggests that this vascular marker is influenced by intracranial resistance. These findings are promising for the real-time, noninvasive assessment of CSC in clinical settings and warrant further research.

Background: Early postoperative cerebral infarction (ePCI) significantly worsens outcomes in patients with spontaneous intracerebral hemorrhage (ICH) undergoing surgery. This study aimed to develop and externally validate a nomogram to assess ePCI risk.

Methods: Adult patients with spontaneous supratentorial ICH who underwent surgery between May 2015 and September 2022 at a large tertiary referral center (development cohort) and another tertiary referral center (external validation cohort) were retrospectively included. ePCI was defined as a newly identified permanent low-density lesion observed within 72 h of surgery on computed tomography. We developed a nomogram using predictors identified through least absolute shrinkage and selection operator analysis. The model's discrimination, calibration, and clinical utility were evaluated.

Results: The development cohort (n = 453) had 51 ePCI cases, and the external validation cohort (n = 184) had 20. The model incorporated the Glasgow Coma Scale (GCS), the Original Intracerebral Hemorrhage Scale (oICH), uncal herniation stage, and hematoma volume, demonstrating strong discrimination with an area under the receiver operating characteristic curve (AUC) of 0.915 (95% confidence interval [CI] 0.882-0.948) in the development cohort and an AUC of 0.942 (95% CI 0.897-0.988) in the external independent cohort. The model also showed excellent calibration and clinical applicability.

Conclusions: This nomogram, including the GCS, the oICH, uncal herniation stage, and hematoma volume, effectively predicts ePCI risk in patients with spontaneous supratentorial ICH.

Background: From visual, ocular, and pupillomotor abnormalities to qualitative and more importantly rapid quantitative disturbances of consciousness, top of the basilar artery syndrome (TOBS) represents a diagnostic challenge in neurocritical care. In this monocentric retrospective cross-sectional study, we will describe this particular patient group in detail and highlight its variability and the associated implications.

Methods: Consecutive patients with radiologically confirmed TOBS presenting to our comprehensive stroke center were analyzed from 2010 to 2022. Baseline parameters at admission, including clinical symptoms, National Institutes of Health Stroke Scale (NIHSS) score, Glasgow Coma Scale (GCS) score, and imaging parameters (mode and success of recanalization measured by the Thrombolysis in Cerebral Infarction [TICI] score, extent of infarct, and infarct localization), were assessed. Functional dependence at discharge was analyzed with the modified Rankin scale (mRS) and Barthel Index.

Results: We assessed 96 eligible patients with a mean age of 70 (SD ± 14) years, 41.67% of whom were female. The median NIHSS score at admission was 19 (interquartile range [IQR] 8-35), and the median GCS score was 7 (IQR 3-15). Dysphagia was identified in 51.72% of patients, with a significant number discharged with nasogastric tubes. Most patients received both intravenous thrombolysis (IVT) and mechanical thrombectomy (MT) (47%), whereas 32% received MT only, and 10% received no acute recanalizing therapy. Patients receiving both IVT and MT had higher frequencies of successful vessel revascularization (higher TICI scores) and better clinical outcomes compared to those receiving only MT (median mRS score 4 [IQR 2-5] vs. 5 [IQR 2-6], p = 0.046). Multivariable regression analysis confirmed that successful recanalization (TICI) and GCS score at admission were key predictors of functional outcomes.

Conclusions: A large proportion of patients presenting with TOBS were severely affected by a significant reduction in vigilance, a condition that persists in the absence of recanalization and is then associated with a relevant dependency.

Background: Early prediction of hematoma expansion (HE) following nontraumatic intracerebral hemorrhage (ICH) may inform preemptive therapeutic interventions. We sought to identify how accurately machine learning (ML) radiomics models predict HE compared with expert clinicians using head computed tomography (HCT).

Methods: We used data from 900 study participants with ICH enrolled in the Antihypertensive Treatment of Acute Cerebral Hemorrhage 2 Study. ML models were developed using baseline HCT images, as well as admission clinical data in a training cohort (n = 621), and their performance was evaluated in an independent test cohort (n = 279) to predict HE (defined as HE by 33% or > 6 mL at 24 h). We simultaneously surveyed expert clinicians and asked them to predict HE using the same initial HCT images and clinical data. Area under the receiver operating characteristic curve (AUC) were compared between clinician predictions, ML models using radiomic data only (a random forest classifier and a deep learning imaging model) and ML models using both radiomic and clinical data (three random forest classifier models using different feature combinations). Kappa values comparing interrater reliability among expert clinicians were calculated. The best performing model was compared with clinical predication.

Results: The AUC for expert clinician prediction of HE was 0.591, with a kappa of 0.156 for interrater variability, compared with ML models using radiomic data only (a deep learning model using image input, AUC 0.680) and using both radiomic and clinical data (a random forest model, AUC 0.677). The intraclass correlation coefficient for clinical judgment and the best performing ML model was 0.47 (95% confidence interval 0.23-0.75).

Conclusions: We introduced supervised ML algorithms demonstrating that HE prediction may outperform practicing clinicians. Despite overall moderate AUCs, our results set a new relative benchmark for performance in these tasks that even expert clinicians find challenging. These results emphasize the need for continued improvements and further enhanced clinical decision support to optimally manage patients with ICH.

Background: This study aimed to assess the organization, infrastructure, workforce, and adherence to protocols in neurocritical care across low- and middle-income countries (LMICs), with the goal of identifying key gaps and opportunities for improvement.

Methods: We conducted a cross-sectional survey of 408 health care providers from 42 LMICs. The survey collected data on the presence of dedicated neurointensive care units, workforce composition, access to critical care technologies, and adherence to evidence-based protocols. Data were analyzed using descriptive statistics, and comparisons were made across different geographical regions (East Asia and the Pacific, Europe and Central Asia, Latin America and the Caribbean, the Middle East and North Africa, and South Asia and sub-Saharan Africa) and economic strata [low-income countries (LICs), lower middle-income countries (LoMICs), and upper middle-income countries (UMICs)].

Results: Only 36.8% of respondents reported access to dedicated neurointensive care units: highest in the Middle East (100%), lowest in sub-Saharan Africa (11.5%), highest in LoMICs (42%), and lowest in LICs (13%). Access to critical care technologies, such as portable computed tomography scanners (9.3%; UMICs 11%, LICs 0%) and tele-intensive care unit services (14.9%; UMICs 19%, LICs 10%), was limited. Workforce shortages were evident, with many institutions relying on anesthesia residents for 24-h care. Adherence to protocols, including those for acute ischemic stroke (61.7%) and traumatic brain injury (55.6%), was highest in Latin America and the Caribbean (72% and 73%, respectively) and higher in UMICs (66% and 60%, respectively) but remained low in LICs (22% and 32%, respectively).

Conclusions: The study highlights critical gaps in infrastructure, workforce, and technology across LMICs, yet it also underscores the potential for improvement. Strategic investments in neurointensive care unit capacity, workforce development, and affordable technologies are an unmet need in resource-limited settings. These findings offer a road map for policymakers and global health stakeholders to prioritize neurocritical care and reduce the disparities in patient outcomes globally.

Background: Despite advancements in the neuroscience of consciousness, no new medications for disorders of consciousness (DOC) have been discovered in more than a decade. Repurposing existing US Food and Drug Administration (FDA)-approved drugs for DOC is crucial for improving clinical management and patient outcomes.

Methods: To identify potential new treatments among existing FDA-approved drugs, we used a deep learning-based drug screening model to predict the efficacy of drugs as awakening agents based on their three-dimensional molecular structure. A retrospective cohort study from March 2012 to October 2024 tested the model's predictions, focusing on changes in Glasgow Coma Scale (GCS) scores in 4047 patients in a coma from traumatic, vascular, or anoxic brain injury.

Results: Our deep learning drug screens identified saxagliptin, a dipeptidyl peptidase-4 inhibitor, as a promising awakening drug for both acute and prolonged DOC. The retrospective clinical analysis showed that saxagliptin was associated with the highest recovery rate from acute coma among diabetes medications. After matching patients by age, sex, initial GCS score, coma etiology, and glycemic status, brain-injured patients with diabetes on incretin-based therapies, including dipeptidyl peptidase-4 inhibitors and glucagon-like peptide-1 analogues, recovered from coma at significantly higher rates compared to both brain-injured patients with diabetes on non-incretin-based diabetes medications (95% confidence interval of 1.8-14.1% higher recovery rate, P = 0.0331) and brain-injured patients without diabetes (95% confidence interval of 2-21% higher recovery rate, P = 0.0272). Post matching, brain-injured patients with diabetes on incretin-based therapies also recovered at a significantly higher rate than patients treated with amantadine (95% confidence interval for the difference 2.4-25.1.0%, P = 0.0364). A review of preclinical studies identified several pathways through which saxagliptin and other incretin-based medications may aid awakening from both acute and chronic DOC: restoring monoaminergic and GABAergic neurotransmission, reducing brain inflammation and oxidative damage, clearing hyperphosphorylated tau and amyloid-β, normalizing thalamocortical glucose metabolism, increasing neural plasticity, and mitigating excitotoxic brain damage.

Conclusions: Our findings suggest incretin-based medications in general, and saxagliptin in particular, as potential novel therapeutic agents for DOC. Further prospective clinical trials are needed to confirm their efficacy and safety in DOC.

Background: Understanding the risk factors for hematoma expansion (HE) in different regions of intracerebral hemorrhage (ICH) can help in the development of more accurate HE prediction tools and in implementing more effective clinical treatment interventions. This study aims to investigate the risk factors for HE in patients with lobar and deep ICH.

Methods: A retrospective analysis was conducted on 558 cases of primary supratentorial ICH from Tongji Hospital Affiliated to Tongji University. Patients were categorized into lobar ICH and deep ICH groups. Differential analysis of ICH characteristics at different locations was performed, followed by subgroup analysis based on HE occurrence. Binary logistic regression was used to identify independent risk factors for HE in each group.

Results: Among the 404 patients with ICH who underwent follow-up noncontrast computed tomography (NCCT) scans, the proportion with HE was similar in the deep ICH group (23.2%) and the lobar ICH group (22.7%). Binary logistic regression analysis revealed that fluid level (odds ratio [OR] 4.77, 95% confidence interval [CI] 1.74-13.06), admission Glasgow Coma Scale score (OR 0.87, 95% CI 0.80-0.96), and time from onset to NCCT examination (OR 0.84, 95% CI 0.75-0.94) were independently associated with HE in the deep ICH group. In the lobar ICH group, irregular shape (OR 4.96, 95% CI 1.37-18.01) and fibrinogen level (OR 0.42, 95% CI 0.21-0.86) were significant risk factors.

Conclusions: Fluid level, low admission Glasgow Coma Scale score, and shorter time from onset to NCCT are independent predictors of HE in deep ICH, whereas irregular shape and low fibrinogen levels are independent predictors of HE in lobar ICH. These findings are of great significance for elucidating the mechanisms underlying HE in different locations of ICH and for developing precise predictive models of HE.