A supervised, externally validated machine learning model for artifact and drainage detection in high-resolution intracranial pressure monitoring data.

OBJECTIVE: In neurocritical care, data from multiple biosensors are continuously measured, but only sporadically acknowledged by the attending physicians. In contrast, machine learning (ML) tools can analyze large amounts of data continuously, taking advantage of underlying information. However, the performance of such ML-based solutions is limited by different factors, for example, by patient motion, manipulation, or, as in the case of external ventricular drains (EVDs), the drainage of CSF to control intracranial pressure (ICP). The authors aimed to develop an ML-based algorithm that automatically classifies normal signals, artifacts, and drainages in high-resolution ICP monitoring data from EVDs, making the data suitable for real-time artifact removal and for future ML applications. METHODS: In their 2-center retrospective cohort study, the authors used labeled ICP data from 40 patients in the first neurocritical care unit (University Hospital Zurich) for model development. The authors created 94 descriptive features that were used to train the model. They compared histogram-based gradient boosting with extremely randomized trees after building pipelines with principal component analysis, hyperparameter optimization via grid search, and sequential feature selection. Performance was measured with nested 5-fold cross-validation and multiclass area under the receiver operating characteristic curve (AUROC). Data from 20 patients in a second, independent neurocritical care unit (Charité - Universitätsmedizin Berlin) were used for external validation with bootstrapping technique and AUROC. RESULTS: In cross-validation, the best-performing model achieved a mean AUROC of 0.945 (95% CI 0.92-0.969) on the development dataset. On the external validation dataset, the model performed with a mean AUROC of 0.928 (95% CI 0.908-0.946) in 100 bootstrapping validation cycles to classify normal signals, artifacts, and drainages. CONCLUSIONS: Here, the authors developed a well-performing supervised model with external validation that can detect normal signals, artifacts, and drainages in ICP signals from patients in neurocritical care units. For future analyses, this is a powerful tool to discard artifacts or to detect drainage events in ICP monitoring signals.

Cortical Thickness Patterns of Cognitive Impairment Phenotypes in Drug-Resistant Temporal Lobe Epilepsy.

OBJECTIVE: In temporal lobe epilepsy (TLE), a taxonomy classifying patients into 3 cognitive phenotypes has been adopted: minimally, focally, or multidomain cognitively impaired (CI). We examined gray matter (GM) thickness patterns of cognitive phenotypes in drug-resistant TLE and assessed potential use for predicting postsurgical cognitive outcomes. METHODS: TLE patients undergoing presurgical evaluation were categorized into cognitive phenotypes. Network edge weights and distances were calculated using type III analysis of variance F-statistics from comparisons of GM regions within each TLE cognitive phenotype and age- and sex-matched healthy participants. In resected patients, logistic regression models (LRMs) based on network analysis results were used for prediction of postsurgical cognitive outcome. RESULTS: A total of 124 patients (63 females, mean age ± standard deviation [SD] = 36.0 ± 12.0 years) and 117 healthy controls (63 females, mean age ± SD = 36.1 ± 12.0 years) were analyzed. In the multidomain CI group (n = 66, 53.2%), 28 GM regions were significantly thinner compared to healthy controls. Focally impaired patients (n = 37, 29.8%) showed 13 regions, whereas minimally impaired patients (n = 21, 16.9%) had 2 significantly thinner GM regions. Regions affected in both multidomain and focally impaired patients included the anterior cingulate cortex, medial prefrontal cortex, medial temporal, and lateral temporal regions. In 69 (35 females, mean age ± SD = 33.6 ± 18.0 years) patients who underwent surgery, LRMs based on network-identified GM regions predicted postsurgical verbal memory worsening with a receiver operating curve area under the curve of 0.70 ± 0.15. INTERPRETATION: A differential pattern of GM thickness can be found across different cognitive phenotypes in TLE. Including magnetic resonance imaging with clinical measures associated with cognitive profiles has potential in predicting postsurgical cognitive outcomes in drug-resistant TLE. ANN NEUROL 2024;95:984-997.

Machine learning using multimodal and autonomic nervous system parameters predicts clinically apparent stroke-associated pneumonia in a development and testing study.

BACKGROUND: Stroke-associated pneumonia (SAP) is a preventable determinant for poor outcome after stroke. Machine learning (ML) using large-scale clinical data warehouses may be able to predict SAP and identify patients for targeted interventions. The aim of this study was to develop a prediction model for identifying clinically apparent SAP using automated ML. METHODS: The ML model used clinical and laboratory parameters along with heart rate (HR), heart rate variability (HRV), and blood pressure (BP) values obtained during the first 48 h after stroke unit admission. A logistic regression classifier was developed and internally validated with a nested-cross-validation (nCV) approach. For every shuffle, the model was first trained and validated with a fixed threshold for 0.9 sensitivity, then finally tested on the out-of-sample data and benchmarked against a widely validated clinical score (A2DS2). RESULTS: We identified 2390 eligible patients admitted to two-stroke units at Charité between October 2020 and June 2023, of whom 1755 had all parameters available. SAP was diagnosed in 96/1755 (5.5%). Circadian profiles in HR, HRV, and BP metrics during the first 48 h after admission exhibited distinct differences between patients with SAP diagnosis vs. those without. CRP, mRS at admission, leukocyte count, high-frequency power in HRV, stroke severity at admission, sex, and diastolic BP were identified as the most informative ML features. We obtained an AUC of 0.91 (CI 0.88-0.95) for the ML model on the out-of-sample data in comparison to an AUC of 0.84 (CI 0.76-0.91) for the previously established A2DS2 score (p < 0.001). The ML model provided a sensitivity of 0.87 (CI 0.75-0.97) with a corresponding specificity of 0.82 (CI 0.78-0.85) which outperformed the A2DS2 score for multiple cutoffs. CONCLUSIONS: Automated, data warehouse-based prediction of clinically apparent SAP in the stroke unit setting is feasible, benefits from the inclusion of vital signs, and could be useful for identifying high-risk patients or prophylactic pneumonia management in clinical routine.

Blood biomarker changes following therapeutic hypothermia in ischemic stroke.

INTRODUCTION: Therapeutic hypothermia is a promising candidate for stroke treatment although its efficacy has not yet been demonstrated in patients. Changes in blood molecules could act as surrogate markers to evaluate the efficacy and safety of therapeutic cooling. METHODS: Blood samples from 54 patients included in the EuroHYP-1 study (27 treated with hypothermia, and 27 controls) were obtained at baseline, 24 ± 2 h, and 72 ± 4 h. The levels of a panel of 27 biomarkers, including matrix metalloproteinases and cardiac and inflammatory markers, were measured. RESULTS: Metalloproteinase-3 (MMP-3), fatty-acid-binding protein (FABP), and interleukin-8 (IL-8) increased over time in relation to the hypothermia treatment. Statistically significant correlations between the minimum temperature achieved by each patient in the hypothermia group and the MMP-3 level measured at 72 h, FABP level measured at 24 h, and IL-8 levels measured at 24 and 72 h were found. No differential biomarker levels were observed in patients with poor or favorable outcomes according to modified Rankin Scale scores. CONCLUSION: Although the exact roles of MMP3, FABP, and IL-8 in hypothermia-treated stroke patients are not known, further exploration is needed to confirm their roles in brain ischemia.

Datawarehouse-enabled quality control of atrial fibrillation detection in the stroke unit setting.

Objective: (1) To assess the accuracy of a standard operating procedure (SOP) regarding the utilization of atrial fibrillation (AF) alarms in everyday clinical practice, and (2) to evaluate the performance of automated continuous surveillance for atrial fibrillation (AF) in hospitalized acute stroke patients. Design: Retrospective cohort study. Setting: Two stroke units from two tertiary care hospitals in Berlin, Germany. Participants: We identified 635 patients with ischemic stroke diagnosis for the time period between 01. January and 30. September 2021 of which 176 patients had recorded AF alarms during monitoring. Of those, 115 patients were randomly selected for evaluation. After excluding 6 patients with hemorrhagic stroke in their records, 109 patients (mean age: 79.1 years, median NIHSS at admission: 6, 57% female) remained for analysis. Intervention: Using a clinical data warehouse for comprehensive data storage we retrospectively downloaded and visualized ECG data segments of 65 s duration around the automated AF alarms. We restricted the maximum number of ECG segments to ten per patient. Each ECG segment plot was uploaded into a REDCap database and categorized as either AF, non-AF or artifact by manual review. Atrial flutter was subsumed as AF. These classifications were then matched with 1) medical history and known diseases before stroke, 2) discharge diagnosis, and 3) recommended treatment plan in the medical history using electronic health records. Main outcome measures: The primary outcome was the proportion of previously unknown AF diagnoses correctly identified by the monitoring system but missed by the clinical team during hospitalization. Secondary outcomes included the proportion of patients in whom a diagnosis of AF would likely have led to anticoagulant therapy. We also evaluated the accuracy of the automated detection system in terms of its positive predictive value (PPV). Results: We evaluated a total of 717 ECG alarm segments from 109 patients. In 4 patients (3.7, 95% confidence interval [CI] 1.18-9.68%) physicians had missed AF despite at least one true positive alarm. All four patients did not receive long-term secondary prevention in form of anticoagulant therapy. 427 out of 717 alarms were rated true positives, resulting in a positive predictive value of 0.6 (CI 0.56-0.63) in this cohort. Conclusion: By connecting a data warehouse, electronic health records and a REDCap survey tool, we introduce a path to assess the monitoring quality of AF in acute stroke patients. We find that implemented standards of procedure to detect AF during stroke unit care are effective but leave room for improvement. Such data warehouse-based concepts may help to adjust internal processes or identify targets of further investigations.

Fighting Post-COVID and ME/CFS - development of curative therapies.

The sequela of COVID-19 include a broad spectrum of symptoms that fall under the umbrella term post-COVID-19 condition or syndrome (PCS). Immune dysregulation, autoimmunity, endothelial dysfunction, viral persistence, and viral reactivation have been identified as potential mechanisms. However, there is heterogeneity in expression of biomarkers, and it is unknown yet whether these distinguish different clinical subgroups of PCS. There is an overlap of symptoms and pathomechanisms of PCS with postinfectious myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS). No curative therapies are available for ME/CFS or PCS. The mechanisms identified so far provide targets for therapeutic interventions. To accelerate the development of therapies, we propose evaluating drugs targeting different mechanisms in clinical trial networks using harmonized diagnostic and outcome criteria and subgrouping patients based on a thorough clinical profiling including a comprehensive diagnostic and biomarker phenotyping.

Screening Newborns for Low T Cell Receptor Excision Circles (TRECs) Fails to Detect Immunodeficiency, Centromeric Instability, and Facial Anomalies Syndrome.

BACKGROUND: Assessment of T-cell receptor excision circles (TRECs) in dried blood spots of newborns allows the detection of severe combined immunodeficiency (SCID) (T cells <300/µL at birth) with a presumed sensitivity of 100%. TREC screening also identifies patients with selected combined immunodeficiency (CID) (T cells >300/µL, yet <1500/µL at birth). Nevertheless, relevant CIDs that would benefit from early recognition and curative treatment pass undetected. OBJECTIVE: We hypothesized that TREC screening at birth cannot identify CIDs that develop with age. METHODS: We analyzed the number of TRECs in dried blood spots in archived Guthrie cards of 22 children who had been born in the Berlin-Brandenburg area between January 2006 and November 2018 and who had undergone hematopoietic stem-cell transplantation (HSCT) for inborn errors of immunity. RESULTS: All patients with SCID would have been identified by TREC screening, but only 4 of 6 with CID. One of these patients had immunodeficiency, centromeric instability, and facial anomalies syndrome type 2 (ICF2). Two of 3 patients with ICF whom we have been following up at our institution had TREC numbers above the cutoff value suggestive of SCID at birth. Yet all patients with ICF had a severe clinical course that would have justified earlier HSCT. CONCLUSIONS: In ICF, naïve T cells may be present at birth, yet they decline with age. Therefore, TREC screening cannot identify these patients. Early recognition is nevertheless crucial, as patients with ICF benefit from HSCT early in life.

Explainable AI for wearable seizure logging: Impact of data quality, patient age, and antiseizure medication on performance.

OBJECTIVE: Objective seizure count estimates are crucial for ambulatory epilepsy management. Wearables have shown promise for the detection of tonic-clonic seizures but may suffer from false alarms and undetected seizures. Seizure signatures recorded by wearables often occur over prolonged periods, including increased levels of electrodermal activity and heart rate long after seizure EEG onset, however, previous detection methods only partially exploited these signatures. Understanding the utility of these prolonged signatures for seizure count estimation and what factors generally determine seizure logging performance, including the role of data quality vs. algorithm performance, is thus crucial for improving wearables-based epilepsy monitoring and determining which patients benefit most from this technology. METHODS: In this retrospective study we examined 76 pediatric epilepsy patients during multiday video-EEG monitoring equipped with a wearable (Empatica E4; records of electrodermal activity, EDA, accelerometry, ACC, heart rate, HR; 1983 h total recording time; 45 tonic-clonic seizures). To log seizures on prolonged data trends, we applied deep learning on continuous overlapping 1-hour segments of multimodal data in a leave-one-subject-out approach. We systematically examined factors influencing logging performance, including patient age, antiseizure medication (ASM) load, seizure type and duration, and data artifacts. To gain insights into algorithm function and feature importance we applied Uniform Manifold Approximation and Projection (UMAP, to represent the separability of learned features) and SHapley Additive exPlanations (SHAP, to represent the most informative data signatures). RESULTS: Performance for tonic-clonic seizure logging increased systematically with patient age (AUC 0.61 for patients 〈 11 years, AUC 0.77 for patients between 11-15 years, AUC 0.85 for patients 〉 15 years). Across all ages, AUC was 0.75 corresponding to a sensitivity of 0.52 and a false alarm rate of 0.28/24 h. Seizures under high ASM load or with shorter duration were detected worse (P=.025, P=.033, respectively). UMAP visualized discriminatory power at the individual patient level, SHAP analyses identified clonic motor activity and peri/postictal increases in HR and EDA as most informative. In contrast, in missed seizures, these features were absent indicating that recording quality but not the algorithm caused the low sensitivity in these patients. SIGNIFICANCE: Our results demonstrate the utility of prolonged, postictal data segments for seizure logging, contribute to algorithm explainability and point to influencing factors, including high ASM dose and short seizure duration. Collectively, these results may help to identify patients who particularly benefit from such technology.

The spinal cord injury-induced immune deficiency syndrome: results of the SCIentinel study.

Infections are prevalent after spinal cord injury (SCI), constitute the main cause of death and are a rehabilitation confounder associated with impaired recovery. We hypothesize that SCI causes an acquired lesion-dependent (neurogenic) immune suppression as an underlying mechanism to facilitate infections. The international prospective multicentre cohort study (SCIentinel; protocol registration DRKS00000122; n = 111 patients) was designed to distinguish neurogenic from general trauma-related effects on the immune system. Therefore, SCI patient groups differing by neurological level, i.e. high SCI [thoracic (Th)4 or higher]; low SCI (Th5 or lower) and severity (complete SCI; incomplete SCI), were compared with a reference group of vertebral fracture (VF) patients without SCI. The primary outcome was quantitative monocytic Human Leukocyte Antigen-DR expression (mHLA-DR, synonym MHC II), a validated marker for immune suppression in critically ill patients associated with infection susceptibility. mHLA-DR was assessed from Day 1 to 10 weeks after injury by applying standardized flow cytometry procedures. Secondary outcomes were leucocyte subpopulation counts, serum immunoglobulin levels and clinically defined infections. Linear mixed models with multiple imputation were applied to evaluate group differences of logarithmic-transformed parameters. Mean quantitative mHLA-DR [ln (antibodies/cell)] levels at the primary end point 84 h after injury indicated an immune suppressive state below the normative values of 9.62 in all groups, which further differed in its dimension by neurological level: high SCI [8.95 (98.3% confidence interval, CI: 8.63; 9.26), n = 41], low SCI [9.05 (98.3% CI: 8.73; 9.36), n = 29], and VF without SCI [9.25 (98.3% CI: 8.97; 9.53), n = 41, P = 0.003]. Post hoc analysis accounting for SCI severity revealed the strongest mHLA-DR decrease [8.79 (95% CI: 8.50; 9.08)] in the complete, high SCI group, further demonstrating delayed mHLA-DR recovery [9.08 (95% CI: 8.82; 9.38)] and showing a difference from the VF controls of -0.43 (95% CI: -0.66; -0.20) at 14 days. Complete, high SCI patients also revealed constantly lower serum immunoglobulin G [-0.27 (95% CI: -0.45; -0.10)] and immunoglobulin A [-0.25 (95% CI: -0.49; -0.01)] levels [ln (g/l × 1000)] up to 10 weeks after injury. Low mHLA-DR levels in the range of borderline immunoparalysis (below 9.21) were positively associated with the occurrence and earlier onset of infections, which is consistent with results from studies on stroke or major surgery. Spinal cord injured patients can acquire a secondary, neurogenic immune deficiency syndrome characterized by reduced mHLA-DR expression and relative hypogammaglobulinaemia (combined cellular and humoral immune deficiency). mHLA-DR expression provides a basis to stratify infection-risk in patients with SCI.

Data lake-driven analytics identify nocturnal non-dipping of heart rate as predictor of unfavorable stroke outcome at discharge.

BACKGROUND: Post-stroke heart rate (HR) and heart rate variability (HRV) changes have been proposed as outcome predictors after stroke. We used data lake-enabled continuous electrocardiograms to assess post-stroke HR and HRV, and to determine the utility of HR and HRV to improve machine learning-based predictions of stroke outcome. METHODS: In this observational cohort study, we included stroke patients admitted to two stroke units in Berlin, Germany, between October 2020 and December 2021 with final diagnosis of acute ischemic stroke or acute intracranial hemorrhage and collected continuous ECG data through data warehousing. We created circadian profiles of several continuously recorded ECG parameters including HR and HRV parameters. The pre-defined primary outcome was short-term unfavorable functional outcome after stroke indicated through modified Rankin Scale (mRS) score of > 2. RESULTS: We included 625 stroke patients, 287 stroke patients remained after matching for age and National Institute of Health Stroke Scale (NIHSS; mean age 74.5 years, 45.6% female, 88.9% ischemic, median NIHSS 5). Both higher HR and nocturnal non-dipping of HR were associated with unfavorable functional outcome (p < 0.01). The examined HRV parameters were not associated with the outcome of interest. Nocturnal non-dipping of HR ranked highly in feature importance of various machine learning models. CONCLUSIONS: Our data suggest that a lack of circadian HR modulation, specifically nocturnal non-dipping, is associated with short-term unfavorable functional outcome after stroke, and that including HR into machine learning-based prediction models may lead to improved stroke outcome prediction.

Correction: Spatial and temporal correlations in human cortex are inherently linked and predicted by functional hierarchy, vigilance state as well as antiepileptic drug load.

[This corrects the article DOI: 10.1371/journal.pcbi.1010919.].

Determination of the Cancer Genome Atlas (TCGA) Endometrial Cancer Molecular Subtypes Using the Variant Interpretation and Clinical Decision Support Software MH Guide.

BACKGROUND: The Cancer Genome Atlas (TCGA) network (United States National Cancer Institute) identified four molecular endometrial cancer (EC) subtypes using an extensive multi-method approach. The aim of this study was to determine the four TCGA EC molecular subtypes using a single-method whole-exome sequencing (WES)-based approach provided by MH Guide (Molecular Health, Heidelberg, Germany). METHODS: WES and clinical data of n = 232 EC patients were obtained from TCGA. The four TCGA EC molecular subtypes designated as (i) Mutated Polymerase ε (POLE), (ii) Microsatellite Instability (MSI), (iii) Copy Number (CN) low and, (iv) CN-high were determined using the MH Guide software. The prognostic value of the subtypes determined by MH Guide were compared with the TCGA classification. RESULTS: Analysis of WES data using the MH Guide software led to the precise identification of the four EC molecular subtypes analogous to the TCGA classification. Both approaches displayed high concordance in terms of prognostic significance. CONCLUSIONS: The multi-method-based TCGA EC molecular subtypes can reliably be reproduced by the single-method-based MH Guide approach. The easy-to-implement single-method MH Guide approach represents a promising diagnostic tool.

Lesional Antibody Synthesis and Complement Deposition Associate With De Novo Antineuronal Antibody Synthesis After Spinal Cord Injury.

BACKGROUND AND OBJECTIVES: Spinal cord injury (SCI) disrupts the fine-balanced interaction between the CNS and immune system and can cause maladaptive aberrant immune responses. The study examines emerging autoantibody synthesis after SCI with binding to conformational spinal cord epitopes and surface peptides located on the intact neuronal membrane. METHODS: This is a prospective longitudinal cohort study conducted in acute care and inpatient rehabilitation centers in conjunction with a neuropathologic case-control study in archival tissue samples ranging from acute injury (baseline) to several months thereafter (follow-up). In the cohort study, serum autoantibody binding was examined in a blinded manner using tissue-based assays (TBAs) and dorsal root ganglia (DRG) neuronal cultures. Groups with traumatic motor complete SCI vs motor incomplete SCI vs isolated vertebral fracture without SCI (controls) were compared. In the neuropathologic study, B cell infiltration and antibody synthesis at the spinal lesion site were examined by comparing SCI with neuropathologically unaltered cord tissue. In addition, the CSF in an individual patient was explored. RESULTS: Emerging autoantibody binding in both TBA and DRG assessments was restricted to an SCI patient subpopulation only (16%, 9/55 sera) while being absent in vertebral fracture controls (0%, 0/19 sera). Autoantibody binding to the spinal cord characteristically detected the substantia gelatinosa, a less-myelinated region of high synaptic density involved in sensory-motor integration and pain processing. Autoantibody binding was most frequent after motor complete SCI (grade American Spinal Injury Association impairment scale A/B, 22%, 8/37 sera) and was associated with neuropathic pain medication. In conjunction, the neuropathologic study demonstrated lesional spinal infiltration of B cells (CD20, CD79a) in 27% (6/22) of patients with SCI, the presence of plasma cells (CD138) in 9% (2/22). IgG and IgM antibody syntheses colocalized to areas of activated complement (C9neo) deposition. Longitudinal CSF analysis of an additional single patient demonstrated de novo (IgM) intrathecal antibody synthesis emerging with late reopening of the blood-spinal cord barrier. DISCUSSION: This study provides immunologic, neurobiological, and neuropathologic proof-of-principle for an antibody-mediated autoimmunity response emerging approximately 3 weeks after SCI in a patient subpopulation with a high demand of neuropathic pain medication. Emerging autoimmunity directed against specific spinal cord and neuronal epitopes suggests the existence of paratraumatic CNS autoimmune syndromes.

Spatial and temporal correlations in human cortex are inherently linked and predicted by functional hierarchy, vigilance state as well as antiepileptic drug load.

The ability of neural circuits to integrate information over time and across different cortical areas is believed an essential ingredient for information processing in the brain. Temporal and spatial correlations in cortex dynamics have independently been shown to capture these integration properties in task-dependent ways. A fundamental question remains if temporal and spatial integration properties are linked and what internal and external factors shape these correlations. Previous research on spatio-temporal correlations has been limited in duration and coverage, thus providing only an incomplete picture of their interdependence and variability. Here, we use long-term invasive EEG data to comprehensively map temporal and spatial correlations according to cortical topography, vigilance state and drug dependence over extended periods of time. We show that temporal and spatial correlations in cortical networks are intimately linked, decline under antiepileptic drug action, and break down during slow-wave sleep. Further, we report temporal correlations in human electrophysiology signals to increase with the functional hierarchy in cortex. Systematic investigation of a neural network model suggests that these dynamical features may arise when dynamics are poised near a critical point. Our results provide mechanistic and functional links between specific measurable changes in the network dynamics relevant for characterizing the brain's changing information processing capabilities.

Prediction of epilepsy surgery outcome using foramen ovale EEG - A machine learning approach.

INTRODUCTION: Patients with drug-resistant focal epilepsy may benefit from ablative or resective surgery. In presurgical work-up, intracranial EEG markers have been shown to be useful in identification of the seizure onset zone and prediction of post-surgical seizure freedom. However, in most cases, implantation of depth or subdural electrodes is performed, exposing patients to increased risks of complications. METHODS: We analysed EEG data recorded from a minimally invasive approach utilizing foramen ovale (FO) and epidural peg electrodes using a supervised machine learning approach to predict post-surgical seizure freedom. Power-spectral EEG features were incorporated in a logistic regression model predicting one-year post-surgical seizure freedom. The prediction model was validated using repeated 5-fold cross-validation and compared to outcome prediction based on clinical and scalp EEG variables. RESULTS: Forty-seven patients (26 patients with post-surgical 1-year seizure freedom) were included in the study, with 31 having FO and 27 patients having peg onset seizures. The area under the receiver-operating curve for post-surgical seizure freedom (Engel 1A) prediction in patients with FO onset seizures was 0.74 ± 0.23 using electrophysiology features, compared to 0.66 ± 0.22 for predictions based on clinical and scalp EEG variables (p < 0.001). The most important features for prediction were spectral power in the gamma and high gamma ranges. EEG data from peg electrodes was not informative in predicting post-surgical outcomes. CONCLUSION: In this hypothesis-generating study, a data-driven approach based on EEG features derived from FO electrodes recordings outperformed the predictive ability based solely on clinical and scalp EEG variables. Pending validation in future studies, this method may provide valuable post-surgical prognostic information while minimizing risks of more invasive diagnostic approaches.

Neurofilament light-chain response during therapy with antisense oligonucleotide tofersen in SOD1-related ALS: Treatment experience in clinical practice.

INTRODUCTION/AIMS: In amyotrophic lateral sclerosis (ALS) caused by superoxide dismutase 1 (SOD1) gene mutations (SOD1-ALS), the antisense oligonucleotide tofersen had been investigated in a phase III study (VALOR) and subsequently introduced in an expanded access program. In this study we assess neurofilament light chain (NfL) before and during tofersen treatment. METHODS: In six SOD1-ALS patients treated with tofersen at three specialized ALS centers in Germany, NfL in cerebrospinal fluid (CSF-NfL) and/or serum (sNfL) were investigated using the ALS Functional Rating Scale Revised (ALSFRS-R) and ALS progression rate (ALS-PR), defined by monthly decline of ALSFRS-R. RESULTS: Three of the six SOD1-ALS patients reported a negative family history. Three patients harbored a homozygous c.272A > C, p.(Asp91Ala) mutation. These and two other patients showed slower progressing ALS (defined by ALS-PR <0.9), whereas one patient demonstrated rapidly progressing ALS (ALS-PR = 2.66). Mean treatment duration was 6.5 (range 5 to 8) months. In all patients, NfL decreased (mean CSF-NfL: -66%, range -52% to -86%; mean sNfL: -62%, range -36% to -84%). sNfL after 5 months of tofersen treatment was significantly reduced compared with the nearest pretreatment measurement (P = .017). ALS-PR decreased in two patients, whereas no changes in ALSFRS-R were observed in four participants who had very low ALS-PR or ALSFRS-R values before treatment. DISCUSSION: In this case series, the significant NfL decline after tofersen treatment confirmed its value as response biomarker in an expanded clinical spectrum of SOD1-ALS. Given the previously reported strong correlation between sNfL and ALS progression, the NfL treatment response supports the notion of tofersen having disease-modifying activity.

The brain reacting to COVID-19: analysis of the cerebrospinal fluid proteome, RNA and inflammation.

Patients with COVID-19 can have a variety of neurological symptoms, but the active involvement of central nervous system (CNS) in COVID-19 remains unclear. While routine cerebrospinal fluid (CSF) analyses in patients with neurological manifestations of COVID-19 generally show no or only mild inflammation, more detailed data on inflammatory mediators in the CSF of patients with COVID-19 are scarce. We studied the inflammatory response in paired CSF and serum samples of patients with COVID-19 (n = 38). Patients with herpes simplex virus encephalitis (HSVE, n = 10) and patients with non-inflammatory, non-neurodegenerative neurological diseases (n = 28) served as controls. We used proteomics, enzyme-linked immunoassays, and semiquantitative cytokine arrays to characterize inflammatory proteins. Autoantibody screening was performed with cell-based assays and native tissue staining. RNA sequencing of long-non-coding RNA and circular RNA was done to study the transcriptome. Proteomics on single protein level and subsequent pathway analysis showed similar yet strongly attenuated inflammatory changes in the CSF of COVID-19 patients compared to HSVE patients with, e.g., downregulation of the apolipoproteins and extracellular matrix proteins. Protein upregulation of the complement system, the serpin proteins pathways, and other proteins including glycoproteins alpha-2 and alpha-1 acid. Importantly, calculation of interleukin-6, interleukin-16, and CXCL10 CSF/serum indices suggest that these inflammatory mediators reach the CSF from the systemic circulation, rather than being produced within the CNS. Antibody screening revealed no pathological levels of known neuronal autoantibodies. When stratifying COVID-19 patients into those with and without bacterial superinfection as indicated by elevated procalcitonin levels, inflammatory markers were significantly (p < 0.01) higher in those with bacterial superinfection. RNA sequencing in the CSF revealed 101 linear RNAs comprising messenger RNAs, and two circRNAs being significantly differentially expressed in COVID-19 than in non-neuroinflammatory controls and neurodegenerative patients. Our findings may explain the absence of signs of intrathecal inflammation upon routine CSF testing despite the presence of SARS-CoV2 infection-associated neurological symptoms. The relevance of blood-derived mediators of inflammation in the CSF for neurological COVID-19 and post-COVID-19 symptoms deserves further investigation.

[Outpatient long-term video EEG as new diagnostic approach in Germany: results of a feasibility study]. / Ambulantes Langzeit-Video-EEG als neuer diagnostischer Ansatz in Deutschland: Ergebnisse einer Machbarkeitsstudie.

In Germany, long-term video EEG as the gold standard for the diagnostics of epilepsy and other seizure disorders, is currently only available for inpatient monitoring in a limited number of specialized centers. These limited monitoring capacities and the large amount of associated time and work resources lead to a significant waiting time for this important diagnostic procedure nationwide. New portable sensor technology and automated data analysis methods are creating opportunities for gold standard long-term video EEG assessments in outpatient settings, which may help to resolve this barrier. Here, we report the results of a single-center feasibility study by implementing outpatient long-term video EEG (ALVEEG) as a diagnostic pathway in Germany. In the new diagnostic pathway, the use of innovative, portable video EEG monitoring systems along with artificial intelligence-assisted data analysis are intended to provide those patients affected by seizure disorders with a more rapid, efficient, and cross-sectoral access to gold standard diagnostics in the home environment. The diagnostics were well accepted by patients and clinicians and may represent a complementary option to inpatient monitoring to eliminate current bottlenecks in diagnostics and care.

Persistent but atypical germinal center reaction among 3rd SARS-CoV-2 vaccination after rituximab exposure.

Background: Durable vaccine-mediated immunity relies on the generation of long-lived plasma cells and memory B cells (MBCs), differentiating upon germinal center (GC) reactions. SARS-CoV-2 mRNA vaccination induces a strong GC response in healthy volunteers (HC), but limited data is available about response longevity upon rituximab treatment. Methods: We evaluated humoral and cellular responses upon 3rd vaccination in seven patients with rheumatoid arthritis (RA) who initially mounted anti-spike SARS-CoV-2 IgG antibodies after primary 2x vaccination and got re-exposed to rituximab (RTX) 1-2 months after the second vaccination. Ten patients with RA on other therapies and ten HC represented the control groups. As control for known long-lived induced immunity, we analyzed humoral and cellular tetanus toxoid (TT) immune responses in steady-state. Results: After 3rd vaccination, 5/7 seroconverted RTX patients revealed lower anti-SARS-CoV-2 IgG levels but similar neutralizing capacity compared with HC. Antibody levels after 3rd vaccination correlated with values after 2nd vaccination. Despite significant reduction of circulating total and antigen-specific B cells in RTX re-exposed patients, we observed the induction of IgG+ MBCs upon 3rd vaccination. Notably, only RTX treated patients revealed a high amount of IgA+ MBCs before and IgA+ plasmablasts after 3rd vaccination. IgA+ B cells were not part of the steady state TT+ B cell pool. TNF-secretion and generation of effector memory CD4 spike-specific T cells were significantly boosted upon 3rd vaccination. Summary: On the basis of pre-existing affinity matured MBCs within primary immunisation, RTX re-exposed patients revealed a persistent but atypical GC immune response accompanied by boosted spike-specific memory CD4 T cells upon SARS-CoV-2 recall vaccination.

M ycobacterium tuberculosis-specific CD4 T-cell scoring discriminates tuberculosis infection from disease.

BACKGROUND: Rapid and reliable diagnostic work-up of tuberculosis (TB) remains a major healthcare goal. In particular, discrimination of TB infection from TB disease with currently available diagnostic tools is challenging and time consuming. This study aimed at establishing a standardised blood-based assay that rapidly and reliably discriminates TB infection from TB disease based on multiparameter analysis of TB antigen-reactive CD4+ T-cells acting as sensors for TB stage-specific immune status. METHODS: 157 HIV-negative subjects with suspected TB infection or TB disease were recruited from local tertiary care hospitals in Berlin (Germany). Peripheral blood mononuclear cells were analysed for CD4+ T-cells reactive to the Mycobacterium tuberculosis antigens purified protein derivative and early secretory antigenic target 6 kDa/culture filtrate protein 10. The activation state of TB antigen-reactive T-cells, identified by surface expression of CD154, was evaluated according to the expression profile of proliferation marker Ki-67 and activation markers CD38 and HLA-DR. Using data from 81 subjects with clinically confirmed TB infection (n=34) or culture-proven pulmonary or extrapulmonary TB disease (n=47), 12 parameters were derived from the expression profile and integrated into a scoring system. RESULTS: Using the scoring system, our assay (TB-Flow Assay) allowed reliable discrimination of TB infection from both pulmonary and extrapulmonary TB disease with high sensitivity (90.9%) and specificity (93.3%) as was confirmed by Monte-Carlo cross-validation. CONCLUSION: With low time requirement, ease of sample collection, and high sensitivity and specificity both for pulmonary and extrapulmonary TB disease, we believe this novel standardised TB-Flow Assay will improve the work-up of patients with suspected TB disease, supporting rapid TB diagnosis and facilitating treatment decisions.