Whole-Exome Sequencing Analyses Support a Role of Vitamin D Metabolism in Ischemic Stroke.

BACKGROUND: Ischemic stroke (IS) is a highly heritable trait, and genome-wide association studies have identified several commonly occurring susceptibility risk loci for this condition. However, there are limited data on the contribution of rare genetic variation to IS. METHODS: We conducted an exome-wide study using whole-exome sequencing data from 152 058 UK Biobank participants, including 1777 IS cases. We performed single-variant analyses for rare variants and gene-based analyses for loss-of-function and deleterious missense rare variants. We validated these results through (1) gene-based testing using summary statistics from MEGASTROKE-a genome-wide association study of IS that included 67 162 IS cases and 454 450 controls, (2) gene-based testing using individual-level data from 1706 IS survivors, including 142 recurrent IS cases, enrolled in the VISP trial (Vitamin Intervention for Stroke Prevention); and (3) gene-based testing against neuroimaging phenotypes related to cerebrovascular disease using summary-level data from 42 310 UK Biobank participants with available magnetic resonance imaging data. RESULTS: In single-variant association analyses, none of the evaluated variants were associated with IS at genome-wide significance levels (P<5×10-8). In the gene-based analysis focused on loss-of-function and deleterious missense variants, rare genetic variation at CYP2R1 was significantly associated with IS risk (P=2.6×10-6), exceeding the Bonferroni-corrected threshold for 16 074 tests (P<3.1×10-6). Validations analyses indicated that CYP2R1 was associated with IS risk in MEGASTROKE (gene-based test, P=0.003), with IS recurrence in the VISP trial (gene-based test, P=0.001) and with neuroimaging traits (white matter hyperintensity, mean diffusivity, and fractional anisotropy) in the UK Biobank neuroimaging study (all gene-based tests, P<0.05). CONCLUSIONS: Because CYP2R1 plays an important role in vitamin D metabolism and existing observational evidence suggests an association between vitamin D levels and cerebrovascular disease, our results support a role of this pathway in the occurrence of IS.

Higher Hospital Frailty Risk Score Is Associated With Increased Risk of Stroke: Observational and Genetic Analyses.

BACKGROUND: Frailty is a prevalent state associated with several aging-related traits and conditions. The relationship between frailty and stroke remains understudied. Here we aim to investigate whether the hospital frailty risk score (HFRS) is associated with the risk of stroke and determine whether a significant association between genetically determined frailty and stroke exists. DESIGN: Observational study using data from All of Us research program and Mendelian Randomization analyses. METHODS: Participants from All of Us with available electronic health records were selected for analysis. All of Us began national enrollment in 2018 and is expected to continue for at least 10 years. All of Us is recruiting members of groups that have traditionally been underrepresented in research. All participants provided informed consent at the time of enrollment, and the date of consent was recorded for each participant. Incident stroke was defined as stroke event happening on or after the date of consent to the All of Us study HFRS was measured with a 3-year look-back period before the date of consent for stroke risk. The HFRS was stratified into 4 categories: no-frailty (HFRS=0), low (HFRS ≥1 and <5), intermediate (≥5 and <15), and high (HFRS ≥15). Last, we implemented Mendelian Randomization analyses to evaluate whether genetically determined frailty is associated with stroke risk. RESULTS: Two hundred fifty-three thousand two hundred twenty-six participants were at risk of stroke. In multivariable analyses, frailty status was significantly associated with risk of any (ischemic or hemorrhagic) stroke following a dose-response way: not-frail versus low HFRS (HR, 4.9 [CI, 3.5-6.8]; P<0.001), not-frail versus intermediate HFRS (HR, 11.4 [CI, 8.3-15.7]; P<0.001) and not-frail versus high HFRS (HR, 42.8 [CI, 31.2-58.6]; P<0.001). We found similar associations when evaluating ischemic and hemorrhagic stroke separately (P value for all comparisons <0.05). Mendelian Randomization confirmed this association by indicating that genetically determined frailty was independently associated with risk of any stroke (OR, 1.45 [95% CI, 1.15-1.84]; P=0.002). CONCLUSIONS: Frailty, based on the HFRS was associated with higher risk of any stroke. Mendelian Randomization analyses confirmed this association providing evidence to support a causal relationship.

Genetically Determined Low-Density Lipoprotein Cholesterol and Risk of Subarachnoid Hemorrhage.

We evaluated whether genetically elevated low-density lipoprotein cholesterol (LDL-C) levels are associated with lower risk of intracranial aneurysms and subarachnoid hemorrhage (IA/SAH). We conducted a 2-sample Mendelian randomization (MR) study. Our primary analysis used the inverse-variance weighted method. In secondary analyses, we implemented the MR-PRESSO method, restricted our analysis to LDL-C-specific instruments, and performed multivariate MR. A 1-mmol/l increase in genetically instrumented LDL-C levels was associated with a 17% lower risk of IA/SAH (odds ratio = 0.83, 95% confidence interval = 0.73-0.94, p = 0.004). Results remained consistent in secondary and multivariate analyses (all p < 0.05). Our results provide evidence for an inverse causal relationship between LDL-C levels and risk of IA/SAH. ANN NEUROL 2022;91:145-149.

The Need for Medical Artificial Intelligence That Incorporates Prior Images.

The use of artificial intelligence (AI) has grown dramatically in the past few years in the United States and worldwide, with more than 300 AI-enabled devices approved by the U.S. Food and Drug Administration (FDA). Most of these AI-enabled applications focus on helping radiologists with detection, triage, and prioritization of tasks by using data from a single point, but clinical practice often encompasses a dynamic scenario wherein physicians make decisions on the basis of longitudinal information. Unfortunately, benchmark data sets incorporating clinical and radiologic data from several points are scarce, and, therefore, the machine learning community has not focused on developing methods and architectures suitable for these tasks. Current AI algorithms are not suited to tackle key image interpretation tasks that require comparisons to previous examinations. Focusing on the curation of data sets and algorithm development that allow for comparisons at different points will be required to advance the range of relevant tasks covered by future AI-enabled FDA-cleared devices.

Stroke Disparities Among Nonracial Minorities in the All of Us Research Program.

[Figure: see text].

Genetically Determined Smoking Behavior and Risk of Nontraumatic Subarachnoid Hemorrhage.

BACKGROUND AND PURPOSE: Animal and observational studies indicate that smoking is a risk factor for aneurysm formation and rupture, leading to nontraumatic subarachnoid hemorrhage (SAH). However, a definitive causal relationship between smoking and the risk of SAH has not been established. Using Mendelian randomization (MR) analyses, we tested the hypothesis that smoking is causally linked to the risk of SAH. METHODS: We conducted a 1-sample MR study using data from the UK Biobank, a large cohort study that enrolled over 500 000 Britons aged 40 to 69 from 2006 to 2010. Participants of European descent were included. SAH cases were ascertained using a combination of self-reported, electronic medical record, and death registry data. As the instrument, we built a polygenic risk score using independent genetic variants known to associate (P<5×10-8) with smoking behavior. This polygenic risk score represents the genetic susceptibility to smoking initiation. The primary MR analysis utilized the ratio method. Secondary MR analyses included the inverse variance weighted and weighted median methods. RESULTS: A total of 408 609 study participants were evaluated (mean age, 57 [SD 8], female sex, 220 937 [54%]). Among these, 132 566 (32%) ever smoked regularly, and 904 (0.22%) had a SAH. Each additional SD of the smoking polygenic risk score was associated with 21% increased risk of smoking (odds ratio [OR], 1.21 [95% CI, 1.20-1.21]; P<0.001) and a 10% increased risk of SAH (OR, 1.10 [95% CI, 1.03-1.17]; P=0.006). In the primary MR analysis, genetic susceptibility to smoking was associated with a 63% increase in the risk of SAH (OR, 1.63 [95% CI, 1.15-2.31]; P=0.006). Secondary analyses using the inverse variance weighted method (OR, 1.57 [95% CI, 1.13-2.17]; P=0.007) and the weighted median method (OR, 1.74 [95% CI, 1.06-2.86]; P=0.03) yielded similar results. There was no significant pleiotropy (MR-Egger intercept P=0.39; MR Pleiotropy Residual Sum and Outlier global test P=0.69). CONCLUSIONS: These findings provide evidence for a causal link between smoking and the risk of SAH.

Admission Hemoglobin Levels Are Associated With Functional Outcome in Spontaneous Intracerebral Hemorrhage.

OBJECTIVES: To test the hypothesis that admission hemoglobin levels are associated with outcome in primary, nontraumatic intracerebral hemorrhage. DESIGN: Individual patient data meta-analysis of three studies of intracerebral hemorrhage. SETTING: Two randomized clinical trials and one multiethnic observational study. PATIENTS: Patients with spontaneous, nontraumatic intracerebral hemorrhage. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Our exposure of interest was admission hemoglobin levels and the primary outcome was 3-month postintracerebral hemorrhage-dichotomized modified Rankin Scale (0-3 vs 4-6). Intermediate outcomes were admission hematoma volume and hematoma expansion defined as 6 mL or 33% increase in hemorrhage size on repeat CT. A total of 4,172 intracerebral hemorrhage patients were included in the study (mean age 63 [sd = 14]; female sex 1,668 [40%]). Each additional g/dL of admission hemoglobin was associated with 14% (odds ratio, 0.86; 95% CI, 0.82-0.91) and 7% (odds ratio, 0.93; 95% CI, 0.88-0.98) reductions in the risk of poor outcome in unadjusted and adjusted analyses, respectively. Dose-response analyses indicated a linear relationship between admission hemoglobin levels and poor outcome across the entire evaluated range (test-for-trend p < 0.001). No consistent associations were found between the admission hemoglobin levels and hematoma volume or hematoma expansion. CONCLUSIONS: Higher hemoglobin levels are associated with better outcome in intracerebral hemorrhage. Further research is needed to evaluate admission hemoglobin levels as both a therapeutic target and predictor of outcome.

Genetically Elevated LDL Associates with Lower Risk of Intracerebral Hemorrhage.

OBJECTIVE: Observational studies point to an inverse correlation between low-density lipoprotein (LDL) cholesterol levels and risk of intracerebral hemorrhage (ICH), but it remains unclear whether this association is causal. We tested the hypothesis that genetically elevated LDL is associated with reduced risk of ICH. METHODS: We constructed one polygenic risk score (PRS) per lipid trait (total cholesterol, LDL, high-density lipoprotein [HDL], and triglycerides) using independent genomewide significant single nucleotide polymorphisms (SNPs) for each trait. We used data from 316,428 individuals enrolled in the UK Biobank to estimate the effect of each PRS on its corresponding trait, and data from 1,286 ICH cases and 1,261 matched controls to estimate the effect of each PRS on ICH risk. We used these estimates to conduct Mendelian Randomization (MR) analyses. RESULTS: We identified 410, 339, 393, and 317 lipid-related SNPs for total cholesterol, LDL, HDL, and triglycerides, respectively. All four PRSs were strongly associated with their corresponding trait (all p < 1.00 × 10-100 ). While one SD increase in the PRSs for total cholesterol (odds ratio [OR] = 0.92; 95% confidence interval [CI] = 0.85-0.99; p = 0.03) and LDL cholesterol (OR = 0.88; 95% CI = 0.81-0.95; p = 0.002) were inversely associated with ICH risk, no significant associations were found for HDL and triglycerides (both p > 0.05). MR analyses indicated that 1mmol/L (38.67mg/dL) increase of genetically instrumented total and LDL cholesterol were associated with 23% (OR = 0.77; 95% CI = 0.65-0.98; p = 0.03) and 41% lower risks of ICH (OR = 0.59; 95% CI = 0.42-0.82; p = 0.002), respectively. INTERPRETATION: Genetically elevated LDL levels were associated with lower risk of ICH, providing support for a potential causal role of LDL cholesterol in ICH. ANN NEUROL 2020 ANN NEUROL 2020;88:56-66.

Admission computed tomography radiomic signatures outperform hematoma volume in predicting baseline clinical severity and functional outcome in the ATACH-2 trial intracerebral hemorrhage population.

BACKGROUND AND PURPOSE: Radiomics provides a framework for automated extraction of high-dimensional feature sets from medical images. We aimed to determine radiomics signature correlates of admission clinical severity and medium-term outcome from intracerebral hemorrhage (ICH) lesions on baseline head computed tomography (CT). METHODS: We used the ATACH-2 (Antihypertensive Treatment of Acute Cerebral Hemorrhage II) trial dataset. Patients included in this analysis (n = 895) were randomly allocated to discovery (n = 448) and independent validation (n = 447) cohorts. We extracted 1130 radiomics features from hematoma lesions on baseline noncontrast head CT scans and generated radiomics signatures associated with admission Glasgow Coma Scale (GCS), admission National Institutes of Health Stroke Scale (NIHSS), and 3-month modified Rankin Scale (mRS) scores. Spearman's correlation between radiomics signatures and corresponding target variables was compared with hematoma volume. RESULTS: In the discovery cohort, radiomics signatures, compared to ICH volume, had a significantly stronger association with admission GCS (0.47 vs. 0.44, p = 0.008), admission NIHSS (0.69 vs. 0.57, p < 0.001), and 3-month mRS scores (0.44 vs. 0.32, p < 0.001). Similarly, in independent validation, radiomics signatures, compared to ICH volume, had a significantly stronger association with admission GCS (0.43 vs. 0.41, p = 0.02), NIHSS (0.64 vs. 0.56, p < 0.001), and 3-month mRS scores (0.43 vs. 0.33, p < 0.001). In multiple regression analysis adjusted for known predictors of ICH outcome, the radiomics signature was an independent predictor of 3-month mRS in both cohorts. CONCLUSIONS: Limited by the enrollment criteria of the ATACH-2 trial, we showed that radiomics features quantifying hematoma texture, density, and shape on baseline CT can provide imaging correlates for clinical presentation and 3-month outcome. These findings couldtrigger a paradigm shift where imaging biomarkers may improve current modelsfor prognostication, risk-stratification, and treatment triage of ICH patients.

Andexanet Alfa Versus 4-Factor Prothrombin Complex Concentrate for Reversal of Factor Xa Inhibitors in Intracranial Hemorrhage.

BACKGROUND/OBJECTIVE: There are limited data on the risks and benefits of using andexanet alfa (AA) in comparison with four-factor prothrombin complex concentrate (4F-PCC) to reverse factor Xa inhibitors (FXi) associated intracranial hemorrhage (ICH). We sought to describe our experience with AA or 4F-PCC in patients with oral FXi-related traumatic and spontaneous ICH. METHODS: We conducted a retrospective review of consecutive adult patients with FXi-related ICH who received AA or 4F-PCC. FXi-related ICH cases included traumatic and spontaneous intracranial hemorrhages. Our primary analysis evaluated ICH stability on head computed tomography scan (CT), defined as a similar amount of blood from the initial scan at the onset of ICH to subsequent scans, at 6-h and 24-h post-administration of AA or 4F-PCC. For the subset of spontaneous intraparenchymal hemorrhages, volume was measured at 6-h and 24-h post-reversal. In secondary analyses, we evaluated good functional outcome at discharge, defined as a Modified Rankin Score of less than 3, and the incidence of thrombotic events after AA or 4F-PCC adminstration, during hospitalization. RESULTS: A total of 44 patients (16 traumatic and 28 spontaneous ICH) with median age of 79 years [72-86], 36% females, with a FXi-related ICH, were included in this study. The majority of spontaneous ICHs were intraparenchymal 19 (68%). Twenty-eight patients (64%) received AA and 16 patients (36%) received 4F-PCC. There was no difference between AA and 4F-PCC in terms of CT stability at 6 h (21 [78%] vs 10 [71%], p = 0.71) and 24 h (15 [88%] vs 6 [60%], p = 0.15). In a subgroup of patients with spontaneous intraparenchymal hemorrhage, there was no difference in the degree of achieved hemostasis based on hematoma volume between AA and 4F-PCC at 6 h (9.3 mL [6.9-26.4] vs 10 mL [9.4-22.1], adjusted p = 0. 997) and 24-h (9.2 mL [6.1-18.8] vs 9.9 [9.4-21.1], adjusted p = 1). The number of patients with good outcome based on mRS on discharge were 10 (36%) and 6 (38%) in the AA and 4F-PCC groups, respectively (adjusted p = 0.81). The incidence of thromboembolic events was similar in the AA and 4F-PCC groups (2 [7%] vs 0, p = 0.53). CONCLUSION: In this limited sample of patients, we found no difference in neuroimaging stability, functional outcome and thrombotic events when comparing AA and 4F-PCC in patients with FXi-related ICH. Since our analysis is likely underpowered, a multi-center collaborative network devoted to this question is warranted.

Deep Neural Regression Prediction of Motor Imagery Skills Using EEG Functional Connectivity Indicators.

Motor imaging (MI) induces recovery and neuroplasticity in neurophysical regulation. However, a non-negligible portion of users presents insufficient coordination skills of sensorimotor cortex control. Assessments of the relationship between wakefulness and tasks states are conducted to foster neurophysiological and mechanistic interpretation in MI-related applications. Thus, to understand the organization of information processing, measures of functional connectivity are used. Also, models of neural network regression prediction are becoming popular, These intend to reduce the need for extracting features manually. However, predicting MI practicing's neurophysiological inefficiency raises several problems, like enhancing network regression performance because of the overfitting risk. Here, to increase the prediction performance, we develop a deep network regression model that includes three procedures: leave-one-out cross-validation combined with Monte Carlo dropout layers, subject clustering of MI inefficiency, and transfer learning between neighboring runs. Validation is performed using functional connectivity predictors extracted from two electroencephalographic databases acquired in conditions close to real MI applications (150 users), resulting in a high prediction of pretraining desynchronization and initial training synchronization with adequate physiological interpretability.

Deep Learning for Automated Measurement of Hemorrhage and Perihematomal Edema in Supratentorial Intracerebral Hemorrhage.

Background and Purpose- Volumes of hemorrhage and perihematomal edema (PHE) are well-established biomarkers of primary and secondary injury, respectively, in spontaneous intracerebral hemorrhage. An automated imaging pipeline capable of accurately and rapidly quantifying these biomarkers would facilitate large cohort studies evaluating underlying mechanisms of injury. Methods- Regions of hemorrhage and PHE were manually delineated on computed tomography scans of patients enrolled in 2 intracerebral hemorrhage studies. Manual ground-truth masks from the first cohort were used to train a fully convolutional neural network to segment images into hemorrhage and PHE. The primary outcome was automated-versus-human concordance in hemorrhage and PHE volumes. The secondary outcome was voxel-by-voxel overlap of segmentations, quantified by the Dice similarity coefficient (DSC). Algorithm performance was validated on 84 scans from the second study. Results- Two hundred twenty-four scans from 124 patients with supratentorial intracerebral hemorrhage were used for algorithm derivation. Median volumes were 18 mL (interquartile range, 8-43) for hemorrhage and 12 mL (interquartile range, 5-30) for PHE. Concordance was excellent (0.96) for automated quantification of hemorrhage and good (0.81) for PHE, with DSC of 0.90 (interquartile range, 0.85-0.93) and 0.54 (0.39-0.65), respectively. External validation confirmed algorithm accuracy for hemorrhage (concordance 0.98, DSC 0.90) and PHE (concordance 0.90, DSC 0.55). This was comparable with the consistency observed between 2 human raters (DSC 0.90 for hemorrhage, 0.57 for PHE). Conclusions- We have developed a deep learning-based imaging algorithm capable of accurately measuring hemorrhage and PHE volumes. Rapid and consistent automated biomarker quantification may accelerate powerful and precise studies of disease biology in large cohorts of intracerebral hemorrhage patients.

Diagnosis of Rapidly Progressive Dementia in a Referral Center in Argentina.

INTRODUCTION: Rapidly progressive dementia (RPD) is a broadly defined clinical syndrome. Our aim was to describe clinical and ancillary study findings in patients with RPD and evaluate their diagnostic performance for the identification of nonchronic neurodegenerative rapidly progressive dementia (ncnRPD). METHODS: We reviewed clinical records and ancillary methods of patients evaluated for RPD at our institution in Buenos Aires, Argentina from 2011 to 2017. We compared findings between chronic neurodegenerative RPD and ncnRPD and evaluated the diagnostic metrics using receiver operating characteristic curves. RESULTS: We included 104 patients with RPD, 29 of whom were chronic neurodegenerative RPD and 75 of whom were ncnRPD. The 6-month time to dementia cutpoint had a sensitivity of 89% and specificity of 100% for ncnRPD, with an area under the receiver operating characteristic curve of 0.965 (95% confidence interval=0.935-0.99; P<0.001). A decision tree that included time to dementia, brain magnetic resonance imaging, and cerebrospinal fluid analysis identified ncnRPD patients with a sensitivity of 100%, specificity of 79%, positive predictive value of 93%, and negative predictive value of 100% overall. DISCUSSION: RPD is a clinical syndrome that comprises different diagnoses, many of them for treatable diseases. Using the time to dementia, brain magnetic resonance imaging, and cerebrospinal fluid analysis when triaging these patients could help identify those diseases that need to be studied more aggressively.

Trigeminal Neuralgia Crisis - Intravenous Phenytoin as Acute Rescue Treatment.

OBJECTIVE: The aim of this retrospective cohort study was to analyze responses to intravenous (IV) phenytoin (PHT) for trigeminal neuralgia (TN) crisis in a group of patients treated at our institution. BACKGROUND: TN is one of the most common causes of facial pain. Its treatment relies on preventive therapy with either carbamazepine or oxcarbazepine. During severe pain episodes, patients may be unable to eat, drink, or even swallow oral medication, requiring in-hospital treatment. There is scarce evidence to support IV medication use for TN, making management of this condition difficult. METHODS: We reviewed clinical records of patients with TN crisis consulting the emergency department at a tertiary neurological referral center in Buenos Aires, Argentina, treated with IV PHT as analgesic strategy, and with at least 1-month posttreatment follow-up. Demographic features, magnetic resonance imaging findings, and therapeutic management were analyzed. RESULTS: Thirty-nine patients with TN were included, 18 (46.2%) receiving IV PHT more than once (total number of infusions administered, 65). Immediate pain relief was observed in 89.2% (58/65) and 15.4% (10/65) presented side effects. CONCLUSIONS: We recommend IV PHT as acute rescue treatment in TN crisis.

Entropy-Based Estimation of Event-Related De/Synchronization in Motor Imagery Using Vector-Quantized Patterns.

Assessment of brain dynamics elicited by motor imagery (MI) tasks contributes to clinical and learning applications. In this regard, Event-Related Desynchronization/Synchronization (ERD/S) is computed from Electroencephalographic signals, which show considerable variations in complexity. We present an Entropy-based method, termed VQEnt, for estimation of ERD/S using quantized stochastic patterns as a symbolic space, aiming to improve their discriminability and physiological interpretability. The proposed method builds the probabilistic priors by assessing the Gaussian similarity between the input measured data and their reduced vector-quantized representation. The validating results of a bi-class imagine task database (left and right hand) prove that VQEnt holds symbols that encode several neighboring samples, providing similar or even better accuracy than the other baseline sample-based algorithms of Entropy estimation. Besides, the performed ERD/S time-series are close enough to the trajectories extracted by the variational percentage of EEG signal power and fulfill the physiological MI paradigm. In BCI literate individuals, the VQEnt estimator presents the most accurate outcomes at a lower amount of electrodes placed in the sensorimotor cortex so that reduced channel set directly involved with the MI paradigm is enough to discriminate between tasks, providing an accuracy similar to the performed by the whole electrode set.

Differences between acute-onset chronic inflammatory demyelinating polyneuropathy and acute inflammatory demyelinating polyneuropathy in adult patients.

Acute inflammatory demyelinating polyneuropathy (AIDP) and acute-onset chronic inflammatory demyelinating polyneuropathy (A-CIDP) are conditions presenting overlapping clinical features during early stages (first 4 weeks), although the latter may progress after 8 weeks. The aim of this study was to identify predictive factors contributing to their differential diagnosis. Clinical records of adult patients with AIDP or A-CIDP diagnosed at our institution between January 2006 and July 2017 were retrospectively reviewed. Demographic characteristics, clinical manifestations, cerebrospinal-fluid (CSF) findings, treatment and clinical evolution were analyzed. Nerve conduction studies were performed in all patients with at least 12 months follow-up. A total of 91 patients were included (AIDP, n = 77; A-CIDP, n = 14). The median age was 55.5 years in patients with A-CIDP vs 43 years in AIDP (P = .07). The history of diabetes mellitus was more frequent in A-CIDP (29% vs 8%, P = .04). No significant differences between groups were observed with respect to: human immunodeficiency virus (HIV) status, presence of auto-immune disorder or oncologic disease. Cranial, motor and autonomic nerve involvement rates were similar in both groups. Patients in the A-CIDP group showed higher frequency of proprioceptive disturbances (83% vs 28%; P < .001), sensory ataxia (46% vs 16%; P = .01), and the use of combined immunotherapy with corticoids (29% vs 3%; P = .005). There were no significant differences in CSF findings, intensive care unit (ICU) admission, or mortality rates. During the first 8 weeks both entities are practically indistinguishable. Alterations in proprioception could suggest A-CIDP. Searching for markers that allow early differentiation could favor the onset of corticotherapy without delay.

Randomised controlled trials evaluating artificial intelligence in clinical practice: a scoping review.

This scoping review of randomised controlled trials on artificial intelligence (AI) in clinical practice reveals an expanding interest in AI across clinical specialties and locations. The USA and China are leading in the number of trials, with a focus on deep learning systems for medical imaging, particularly in gastroenterology and radiology. A majority of trials (70 [81%] of 86) report positive primary endpoints, primarily related to diagnostic yield or performance; however, the predominance of single-centre trials, little demographic reporting, and varying reports of operational efficiency raise concerns about the generalisability and practicality of these results. Despite the promising outcomes, considering the likelihood of publication bias and the need for more comprehensive research including multicentre trials, diverse outcome measures, and improved reporting standards is crucial. Future AI trials should prioritise patient-relevant outcomes to fully understand AI's true effects and limitations in health care.