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Abstract Objective This study explores and documents the patient experience during the prodrome phase of migraine. Background Migraine attacks can be divided into four stages: the prodrome (or premonitory phase), the aura, the headache phase, and postdrome. Qualitative data on the range of symptoms during the prodrome and their timing relative to headache onset are sparse. Prodromal symptoms may predict the onset of migraine headache pain and provide a clinically useful benchmark for initiation of treatment early in an attack before pain begins. Design/Methods Eligible participants with a clinician‐confirmed diagnosis of migraine and at least one prodromal symptom were consented, screened, and then participated in 60‐min interviews. The interview guide included open‐ended questions to elicit spontaneous reports and specific probes about prodromal symptoms based on a clinician‐established list. A theory approach was used to analyze the qualitative data collected in interviews to identify key themes and gather insights; data were analyzed using ATLAS.ti. Results Data collection for this study occurred from March 8, 2022, to May 16, 2022. Twenty interviews were conducted, and analyses demonstrated that concept saturation was achieved. Participants reported 36 unique prodromal symptoms, and each participant experienced a mean of 13 symptoms (standard deviation [SD] = 6.6) and a median of 11 symptoms (interquartile range [IQR] = 7.8–17.0) during the prodrome phase. The most commonly reported prodromal symptoms were nausea ( n = 17/20, 85%), fatigue/tiredness ( n = 16/20, 80%), sensitivity to light ( n = 13/20, 65%), neck pain/stiffness ( n = 12/20, 60%), and dizziness/vertigo/light‐headedness ( n = 10/20, 50%). Of the symptoms reported by at least four participants ( n ≥ 4/20, 20%), neck pain/stiffness was rated most bothersome (8.9 out of 10) and sensitivity to light was rated most severe (8.5 out of 10). Almost 40% of all symptoms reported occurred less than 2 h before the start of migraine headache. Of the commonly reported symptoms, nausea (0.8h), sensitivity to light (1.0 h), and dizziness/vertigo/lightheadedness (2.0 h) began closest to headache onset; fatigue/tiredness (4.0 h) and neck pain/stiffness (4.8 h) were the most remote from headache onset. Conclusion This study identified frequently reported prodromal symptoms, with participants commonly reporting that a migraine headache would follow their experience of prodrome within a 1‐ to 6‐h window. Characterizing the prodrome experience may improve measurement strategies for the burden of migraine and create opportunities to treat during the prodromal phase to prevent the onset of moderate or severe headaches.

Objective Individuals with serum elevations of anti–cyclic citrullinated peptide (anti‐CCP) antibodies are at increased risk for future rheumatoid arthritis (RA). No pharmacologic interventions have been approved for the prevention of RA in such at‐risk individuals. However, hydroxychloroquine (HCQ) is used without supporting clinical trial evidence. Methods In this phase 2 randomized trial, individuals at risk for RA with anti‐CCP3 ≥2 times the upper limit of normal (ULN) were assigned to receive HCQ or placebo for 12 months, with up to 24 months of postdrug follow‐up. The primary outcome was the development of clinical RA, as defined in the protocol, at 36 months. Secondary outcomes included safety, development of inflammatory arthritis (IA), and participant‐reported joint symptoms. Results Of 144 randomized participants, 71 were assigned to HCQ and 73 to placebo. In the modified intent‐to‐treat population, clinical RA occurred in 21 of 69 (30.4%) participants in the HCQ group and 24 of 73 (32.9%) in the placebo group. The risk of clinical RA at 36 months was 0.336 with HCQ and 0.394 with placebo (difference −0.058; 95% confidence interval −0.336 to 0.220; P = 0.52). Results for IA were similar. The occurrence and severity of joint symptoms were not observed to differ between groups. Adverse event incidence was similar between groups. Conclusion In this trial involving individuals with anti‐CCP3 levels ≥2 times the ULN, 12 months of HCQ did not prevent the development of clinical RA at 36 months. image

Abstract INTRODUCTION Plasma biomarkers’ utility for predicting incident mild cognitive impairment (MCI) remains unclear. We evaluated associations of plasma Alzheimer's disease (AD) biomarkers and amyloid positron emission tomography (PET) with transitions from cognitively unimpaired (CU) to MCI in the Mayo Clinic Study of Aging (MCSA) and BioFINDER‐2 studies. METHODS Associations of continuous baseline plasma biomarker levels and amyloid PET Centiloid with progression to MCI, adjusting for age, sex, and education, were evaluated with Cox proportional hazards models. RESULTS The study included 381 MCSA and 584 BioFINDER‐2 participants. Amyloid PET and percent phosphorylated to non‐phosphorylated tau217 (%p‐tau217) were strong predictors of progression to MCI in both cohorts: hazard ratios of 1.49 and 1.23 in the MCSA and 1.72 and 1.65 in BioFINDER, respectively. Amyloid beta 42/40 was a significant predictor in BioFINDER‐2 only (hazard ratio 2.20). DISCUSSION Plasma %p‐tau217 was associated with progression from CU to MCI in both cohorts, although differences in biomarker associations may be related to differences in the two cohorts. Highlights Mass‐spectrometry‐based plasma phosphorylated tau217 was associated with cognitively unimpaired to mild cognitive impairment (MCI) progression. Plasma amyloid beta 42/40 was a significant predictor in BioFINDER but not the Mayo Clinic Study of Aging (MCSA). Amyloid positron emission tomography (PET) was the strongest predictor of progression to MCI in the MCSA. Plasma had added value to amyloid PET in BioFINDER but not the MCSA. Biomarker performance may vary with cohort and biomarker measurement differences.

BACKGROUND AND OBJECTIVES: Embolization is part of multidisciplinary brain arteriovenous malformations (AVMs) treatment, yet safety data for novel liquid embolization agents (LEAs) such as Squid and PHIL remain limited. This study reports complications and outcomes from curative embolization using Onyx, Squid, PHIL, and Glue. METHODS: Adult patients with brain AVMs treated with LEAs from July 2015 to December 2022 were reviewed for intraprocedural technical issues, postoperative hemorrhagic, and ischemic complications. Minor (modified Rankin Scale ≤2) and major complication rate (morbidity/modified Rankin Scale >2), mortality, and 6-month angiographic cure rate were evaluated. RESULTS: In total, 135 patients (62.2% male, mean age 41.7 ± 15.1 years) with 206 embolizations (34% Onyx, 18.9% Squid, 17.5% PHIL, 28.2% Glue) were included. Overall, 87.4% procedures were in ruptured AVMs and 84% treated by the transarterial technique (84%). The 6-month angiographic cure rate was 53.3%, higher when nonadhesive LEAs were used than Glue (65.3% vs 17.6%, P < .001). In adjusted analysis, only eloquent location (odds ratio [OR] = 4.4; P = .007) and Glue (OR = 9.1; 95% CI 2.9-28.9, P < .001) were predictors of lower angiographic occlusion rate, whereas size had an inverse correlation (OR = 0.67; P = .019). Technical procedural complications, hemorrhagic, and ischemic rates were 10.2% (2.2% morbidity), 13.1% (3.7% morbidity and 2.2% mortality), and 9.2% (2.2% morbidity, 0% mortality), respectively, regardless of the preferred LEA. Minor, major complications, and mortality rates were 3.7%, 4.4%, and 2.2%, respectively. Hemorrhage was the major contributor to morbidity and mortality, whereas deep venous drainage was an independent predictor of hemorrhage and mortality. CONCLUSION: PHIL and Squid demonstrated complication and angiographic cure rates comparable with Onyx. However, curative embolization of brain AVMs resulted in only moderate angiographic occlusion. Larger multicenter studies with standardized operator protocols, cost-effectiveness assessments, and extended follow-up are warranted to validate these findings.

Introduction: Endovascular thrombectomy (EVT) is an effective treatment for basilar artery occlusion (BAO) stroke in select patients. While there is a growing body of literature suggesting that advanced imaging modalities such as computed tomography perfusion (CTP) and magnetic resonance (MR) may not be necessary for selecting anterior circulation large vessel occlusion stroke patients for EVT, whether advanced imaging may be superior to conventional imaging (non-contrast CT and CT angiography) in identifying good treatment candidates among BAO patients is less clear. Patients and methods: This was a multicenter retrospective cohort study of BAO EVT patients treated from 2013 to 2022 in the Stroke Thrombectomy and Aneurysm Registry. Patients selected for EVT by advanced imaging (CTP or MR) were matched with those selected by conventional imaging using propensity score matching (PSM) accounting for possible confounders. Primary outcome was functional independence at 90 days. Other outcomes include bedridden state or death at 90-days and symptomatic intracranial hemorrhage (sICH). Results: 268 patients were included. 150 patients were selected for BAO EVT by conventional imaging, 86 by CTP, and 32 by MR. Patients selected by advanced imaging were significantly older than those selected by conventional imaging (median age 71 vs 64 years, p = 0.001); patient characteristics were otherwise similar between cohorts. After PSM, 90-day outcomes were similar between the two cohorts ( p = 0.56), with similar rates of functional independence (39.4% vs 35.1%, p = 0.65), bedridden state or death (40.4% vs 44.7%, p = 0.66), and sICH (3.3% vs 5.7%, p = 0.49) for conventional and advanced imaging groups, respectively. Results were similar across treatment time windows (all p > 0.05). Conclusions: Selecting patients for basilar EVT using conventional versus advanced imaging did not result in different clinical outcomes, regardless of treatment time windows. Conventional imaging appears sufficient as a first-line tool for selecting basilar EVT patients in routine clinical practice.

Since the discovery of the first psychoactive medications, their psychiatric and medical uses have overlapped. Designating a medication psychiatric or psychotropic is thus arbitrary, based on its most common usage, but labeling it so obscures the full range of its pharmacologic activity and clinical utility. Psychotropic medications (PMs) are frequently used to treat medical conditions or symptoms in the areas of neurology (e.g., migraine, seizure disorder), dermatology (e.g., itching), gastroenterology, and chronic pain, among many others. It is important for both primary care and specialty physicians to be aware of the potential non-psychiatric indications for the use of common PMs such as antidepressants, antipsychotics, and anxiolytics. This review examines the potential benefits and risks of PMs when used for non-psychiatric indications, highlighting how their pharmacologic effects on neurotransmitters contribute to both therapeutic outcomes and adverse effects. It also provides guiding principles for prescribers, including the importance of adjusting doses based on the specific indication, monitoring for harmful side effects, considering age-related factors, and addressing the risks associated with polypharmacy.

Introduction and Objective: Kidney stone growth and new stone formation are common clinical trial endpoints and are associated with future symptomatic events. To date, a manual review of CT scans has been required to assess stone growth and new stone formation, which is laborious. We validated the performance of a software algorithm that automatically identified, registered, and measured stones over longitudinal CT studies. Methods: We validated the performance of a pretrained machine learning algorithm to classify stone outcomes on longitudinal CT scan images at baseline and at the end of the 2-year follow-up period for 62 participants aged >18 years in the Prevention of Urinary Stones with Hydration (PUSH) randomized controlled trial. Stones were defined as an area of voxels with a minimum linear dimension of 2 mm that was higher in density than the mean plus 4 standard deviations of all nonnegative HU values within the kidney. The four outcomes assessed were: (1) growth of at least one existing stone by ≥2 mm, (2) formation of at least one new ≥2 mm stone, (3) no stone growth or new stone formation, and (4) loss of at least one stone. The accuracy of the algorithm was determined by comparing its outcomes to the gold standard of independent review of the CT images by at least two expert clinicians. Results: The algorithm correctly classified outcomes for 61 paired scans (98.4%). One pair that the algorithm incorrectly classified as stone growth was a new renal artery calcification on end-of-study CT. Conclusions: An automated image analysis method validated for the prospective PUSH trial was highly accurate for determining clinical outcomes of new stone formation, stone growth, stable stone size, and stone loss on longitudinal CT images. This method has the potential to improve the accuracy and efficiency of clinical care and endpoint determination for future clinical trials.

Artificial intelligence (AI) is reshaping neurosurgery, offering unprecedented opportunities to enhance diagnostics, personalize treatment, and predict outcomes. At the heart of this transformation is the ability to effectively harness big data (BD) within the electronic medical record. Understanding these data structures is essential for making sense of the vast volumes of information generated in modern neurosurgical practice. Equally important are the machine learning (ML) models driving these advancements. From supervised learning and convolutional neural networks to generative AI, these tools are already making a mark in areas such as brain tumor segmentation and spine surgery outcome predictions. Their versatility highlights the potential of ML to complement clinical expertise and streamline decision-making in neurosurgery. However, adopting BD and ML also brings ethical challenges that cannot be ignored. Bias in algorithms threatens to reinforce health disparities, whereas concerns about data privacy demand vigilance in handling sensitive patient information. In addition, the question of liability looms large as ML increasingly influences clinical decisions. The aim of the study was to provide a roadmap for neurosurgeons navigating the evolving intersection of BD, ML, and ethical responsibility in the AI era.