Citations

Objective To develop evidence-based clinical practice guidelines for non-invasive neuromodulation devices in acute and preventive migraine treatment. Methods A systematic review was conducted across six databases from 1946 to April 2025. Randomized controlled trials evaluating Food and Drug Administration-cleared or Conformité Européenne (CE)-marked non-invasive neuromodulation devices were included. The quality of evidence was assessed using the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) methodology, and recommendations were developed through consensus following GRADE Evidence-to-Decision frameworks. The working group comprised 15 senior members and six junior members. Results From 1536 initial records, 15 studies met the inclusion criteria and were finally used to develop evidence-based recommendations. Evidence quality ranged from very low to moderate. Weak recommendations were issued for SAVI Dual, Cefaly, Relivion, and Nerivio in the treatment of acute migraine attacks, and for gammaCore Sapphire, Cefaly, and Nerivio in the preventive migraine treatment. Other cleared devices received no recommendations or have no eligible studies for the GRADE assessment. The primary limitations across studies included imprecision due to small sample sizes and various methodological concerns. Additionally, expert consensus recommendations were developed for devices and clinical scenarios not adequately covered by randomized controlled trials, including potential applications in pediatric populations, vestibular migraine, chronic migraine, menstrual migraine, and medication overuse headache. Conclusion Non-invasive neuromodulation devices offer promising alternatives to drug treatment for migraine management. These devices are safe and generally well tolerated and devoid of drug interactions. While current evidence quality varies, ongoing research and technological advancements show encouraging potential. Future studies should adhere to International Headache Society guidelines for neuromodulation device trials, address proper sham controls and blinding assessment, and account for patient adherence challenges in device use. Expanded insurance coverage would enhance cost-effectiveness and device accessibility. These guidelines provide a framework for clinical decision-making while highlighting areas requiring further research.

Immune checkpoint inhibitors (ICIs) are an important class of cancer treatment. Endocrine immune-related adverse events (E-irAEs) account for a significant proportion of irAEs in ICI-treated patients. Diagnosing E-irAEs accurately and timely can be challenging in the absence of clear categorization and standardization across oncology, endocrinology and other specialties. While existing guidelines provide some broad directions to diagnosis and management, they lack clarity on irAE-specific symptom evaluation and work-up, as well as assessment of severity. These limitations can then impact triage, time-sensitive management and care escalation to specialists such as oncoendocrinologists. The objective of this study was to develop consensus-based statements on disease definitions for E-irAEs. A core working group of endocrinologists with expertise in the oncoendocrinology field drafted a survey with statements outlining the general approach to E-irAEs, disease definition and management approach of six specific diagnoses: ir-thyroiditis thyrotoxic phase, ir-thyroiditis hypothyroidism, ir-Graves’ disease, ir-hypophysitis, ir-adrenalitis and ir-type 1 diabetes mellitus. Severity grading tables were drafted for three disease categories—“thyroid”, “pituitary and adrenal”, and “diabetes mellitus”. A two-round modified Delphi process, using the RAND/University of California Los Angeles (RAND/UCLA) Appropriateness Method, was employed. In this process, a 25-member voting panel consisting of endocrinologists, oncologists and other specialists and healthcare providers with experience in management of ICI-treated patients and irAEs was recruited. The panel rated anonymously on usability, accuracy, appropriateness or agreement of 41 items on a 9-point scale in Survey 1, after which a meeting was convened. Statements were modified based on voting results from Survey 1 and Meeting 1, and the process was repeated in Survey 2 and Meeting 2. At the end of this process, consensus was achieved for all statements. Our study findings address the gap in standardized nomenclature, clinical, laboratory and radiological evaluations, and management principles of E-irAEs. With consensus achieved from a panel of experts from a variety of disciplines, we anticipate that the statements can form the basis for standardization of the diagnostic process and improvement of patient care.

Importance Accumulation of amyloid plaque drives pathogenesis of Alzheimer disease (AD). Reduction of amyloid via amyloid-targeting therapies may result in clinical benefit. Objective To assess the correlation of posttreatment amyloid levels with clinical outcomes and biomarkers in AD. Design, Setting, and Participants This was a post hoc exploratory analysis from the randomized, placebo-controlled phase 3 trial, TRAILBLAZER-ALZ 2, conducted June 2020 through April 2023 at 277 medical research centers/hospitals in 8 countries. A total of 8240 participants aged 60 to 85 years with early symptomatic AD with amyloid and tau pathology based on positron emission tomography (PET) imaging were assessed for eligibility. Of these, 6504 participants were excluded predominantly due to inadequate amyloid or tau pathology. The current analysis included 1582 participants (766 in the donanemab group and 816 in the placebo group) with baseline and at least 1 posttreatment assessment. Data analysis took place from July 2024 to March 2025. Interventions Participants were randomized 1:1 to receive donanemab (700 mg for the first 3 doses and 1400 mg thereafter) or placebo intravenously every 4 weeks for up to 72 weeks, with outcomes assessed through 76 weeks. Main Outcomes and Measures Participants were categorized into 1 of 10 groups (deciles) based on their lowest amyloid value observed posttreatment. Clinical progression was assessed via changes in integrated Alzheimer’s Disease Rating Scale (iADRS) and Clinical Dementia Rating–Sum of Boxes (CDR-SB) scores. Plasma biomarkers measured included phosphorylated tau 217 (p-tau217), p-tau181, glial fibrillary acidic protein (GFAP), and neurofilament light chain (NfL). Correlations between the median amyloid level in each decile were assessed with 76-week least-squares mean changes in each outcome and biomarker. Results Analyses included 1582 participants, including 766 treated with donanemab and 816 with placebo. The mean (SD) age was 72.9 (6.2) years, and 900 participants (56.9%) were female. Participants who received donanemab had lower posttreatment amyloid values than those in the placebo group. Across the trial population, lower posttreatment amyloid levels were correlated with slower clinical progression as measured by iADRS score ( R 2 , 0.73 [95% CI, 0.37-0.97]) and CDR-SB score ( R 2 , 0.87 [95% CI, 0.70-0.97]) and with decreases in p-tau217 ( R 2 , 0.86 [95% CI, 0.73-0.97]), p-tau181 ( R 2 , 0.88 [95% CI, 0.77-0.97]), and GFAP ( R 2 , 0.87 [95% CI, 0.76-0.97]). There was no correlation between posttreatment amyloid value and NfL ( R 2 , 0.03 [95% CI, 0.00-0.54]). Conclusions and Relevance The findings in this secondary analysis of a randomized clinical trial demonstrating a correlation between posttreatment amyloid plaque level and clinical benefit support amyloid plaque removal as the mechanism of action for donanemab treatment and the level of amyloid plaque as a potential surrogate biomarker in amyloid-targeting therapies. Trial Registration ClinicalTrials.gov Identifier: NCT04437511

Abstract Nephrotic syndrome is a rare, heterogeneous kidney disorder characterized by proteinuria, hypoalbuminemia, and edema. To elucidate its genetic architecture, we conducted a large-scale, electronic health record (EHR)-linked, multi-ancestry genome-wide association study comprising 5,214 cases and 1,601,060 controls. We identified 37 distinct loci associated with disease risk, including novel associations at JAML and STPG2 , and confirmed prior signals at PLA2R1 , HMCN1 , and APOL1 . Fine-mapping of the major histocompatibility complex (MHC) revealed the strongest association at DRB103:01:01G in individuals of European ancestry (OR = 2.01, P = 1.4×10⁻²⁰), alongside nominal ancestry-specific associations in African ( DOA01:01:05 ) and Latino ( DPB1*14:01:01G ) populations. Transcriptome-wide association analysis (TWAS) identified 484 significant gene-tissue associations, including C4A in kidney cortex. Expression quantitative trait locus (eQTL) mapping revealed numerous cis-eQTLs in glomerular and tubular renal tissues, largely within the MHC region. We observed significant genetic correlation between adult and pediatric nephrotic syndrome (Rg = 0.63, P = 3.5x10 -11 ), suggesting shared genetic etiology. Pathway analyses implicated estrogen receptor signaling and histone modification. Mendelian randomization implicated APOM expression and apomorphine exposure with increased disease risk (OR = 4.93, P = 1.8x10 -19 ). These results expand the understanding of nephrotic syndrome pathogenesis and highlight ancestry-informed targets for therapeutic development.

566 Background: Video telemedicine visits (VTVs) have the potential to overcome disparities in access to cancer care for many socially, geographically, and economically disadvantaged populations. Internet connectivity, digital literacy, and socioeconomic factors are traditionally cited as influencing VTV utilization. However, digital diffusion has occurred rapidly across several sectors, including healthcare, since the onset of the COVID-19 pandemic, and less is known about how these factors have evolved to influence VTV utilization behavior over this time. Through a qualitative approach, we aimed to: (1) explore the lived experiences of cancer patients who transitioned to VTV use as compared to those who persistently did not, and (2) identify similarities and differences between these two groups that could inform targeted interventions to support access and patient acceptance of VTVs in the setting of cancer care. Methods: During June 2024, we conducted semi-structured interviews with 24 patients (Mean age 71.3 years, range 42-88; female 50%) receiving ongoing, longitudinal cancer care at our academic medical center since July 2021 who had not used VTVs through at least October 2022. Using purposeful sampling, patients were equally divided according to VTV utilization behaviors defined as “transitioned users” (TU; i.e. those who transitioned to VTV utilization after October 2022) and “persistent non-users” (PNU). Transcripts were analyzed using a combined deductive–inductive thematic approach and categorized into themes of barriers and facilitators across three domains consistent with the COM-B model: capabilities (C), opportunities (O), and motivations (M). Results: TU patients frequently cited a reduction in travel and logistical considerations as a primary reason for uptake of VTVs. PNU patients frequently reported digital literacy barriers, particularly among older patients, as primary reasons for lack of VTV utilization. TU patients reported more experience and comfort in technical skills as compared to PNU participants. PNU patients also reported less frequent offering of VTV as an option by their care teams over time, implying they may have transitioned to VTV utilization if it had been offered with more regularity. Both study groups desired improved internet access and technical support to enhance (or, in the case of PNU patients, enable) VTV utilization. Conclusions: These findings suggest that multi-modal care delivery may best serve current patient populations, not requiring, yet offering VTV services as an adjunct to in-person care. Longer term, individual barriers to VTV use will also need to be addressed to further optimize access for patients who desire to incorporate this care modality.

Background: Substance use disorders (SUDs) are highly prevalent, affecting over 48.5 million Americans. Available treatments for SUD remain insufficient, and many patients do not respond to existing interventions despite adequate adherence to treatments. While novel therapies for SUD are urgently needed, the use of psychedelic drugs for the treatment of SUDs has shown promise. Objectives: This overview of systematic reviews summarizes existing evidence on hallucinogens—serotonergic psychedelics and ketamine—for the treatment of SUD. Methods: A comprehensive search of the literature was conducted to identify relevant evidence for using serotonergic and non-serotonergic psychedelics for the treatment of SUDs. After initial screening (n = 468 studies), 62 studies were retrieved and assessed for eligibility, and a total of 16 systematic reviews were included. Conclusions: Although preliminary, evidence suggests that the use of serotonergic and non-serotonergic psychedelics for the treatment of SUD may provide advantages over traditional therapeutics, and these compounds may eventually become part of the next generation of treatments for SUD under specific circumstances. Research with these drugs has faced significant challenges, though, and caution when interpreting results is warranted, given high risk of bias and several other methodological limitations from the studies to date. Furthermore, risks associated with these drugs are not negligible. For now, the use of psychedelic drugs for the treatment of SUDs remains experimental, and existing evidence is insufficient to support its use in clinical practice.

Abstract Background Lung adenocarcinoma in never-smokers is a major public health burden, especially among East Asian women. Polygenic risk scores (PRSs) are promising for risk stratification but are primarily developed in European-ancestry populations. We aimed to develop and validate single- and multi-ancestry PRSs for East Asian never-smokers to improve lung adenocarcinoma risk prediction. Methods PRSs were developed using genome-wide association study summary statistics from East Asian (8002 cases; 20 782 controls) and European (2058 cases; 5575 controls) populations. Single-ancestry models included a PRS with 25 single nucleotide variants (PRS-25), PRS clumping and thresholding (CT), and LDpred2; multi-ancestry models included LDpred2 plus European PRS-128, PRS-CSx, and clumping and thresholding, super learning, empirical Bayes (CT-SLEB). Performance was evaluated in independent East Asian data from the Female Lung Cancer Consortium and externally validated in the Nanjing Lung Cancer Cohort. We assessed predictive accuracy via area under the curve operating procedure (AUC), with 10-year and (ages 30-80 years) absolute risks estimates. Results The best multi-ancestry PRS, using East Asian and European data via CT-SLEB, outperformed the best East Asian-only (LDpred2; AUC = 0.629, 95% confidence interval [CI] = 0.618 to 0.641), achieving an AUC of 0.640 (95% CI = 0.629 to 0.653) and odds ratio of 1.71 (95% CI = 1.61 to 1.82) per standard deviation increase. Nanjing Lung Cancer Cohort validation confirmed robust performance (AUC = 0.649, 95% CI = 0.623 to 0.676). The top 20% PRS group had a 3.92-fold higher lung adenocarcinoma risk than the bottom 20%. Further, the top 5% PRS group reached a 6.69% lifetime absolute risk. Notably, this group reached the average population 10-year lung adenocarcinoma risk at age 50 years (0.42%) by age 41 years, 9 years earlier. Conclusions Multi-ancestry PRS approaches enhance lung adenocarcinoma risk stratification in East Asian never-smokers, with consistent external validation, suggesting future clinical utility.

Abstract Error analysis is a critical step in evaluating and improving clinical concept extraction models, the most common clinical natural language processing (NLP) task. Unlike corpus annotation, which follows standardized protocols for creating gold-standard datasets, error analysis requires nuanced judgment grounded in both clinical expertise and NLP knowledge. This is especially important given the heterogeneity of clinical text, where variations in documentation style, note structure, and terminology can substantially influence model behavior. Despite its importance, there is currently no standardized, user-level framework to support systematic error analysis in clinical concept extraction task. In this study, we developed and validated MedError, a machine-assisted, human-in-the-loop framework designed to standardize and enhance error analysis for clinical concept extraction tasks. We collected and manually curated a corpus of 1,187 unique errors from a total of 4,237 notes across three different distinct hospitals. The error categories were defined using our previously validated error taxonomy and included 480 false negatives and 707 false positives across 25 error types and 48 clinical concept categories. We evaluated the performance of three proprietary and three open-source large language models (LLMs) in automatically classifying these errors into 26 and 15 predefined categories. We further developed a machine-assisted framework, MedError, which integrates best practices in error analysis, LLM-assisted classification and reasoning, and a user-friendly interface to enable more efficient, reproducible, and context-aware error analysis. The framework supports both single-site and federated multisite error analysis, facilitating the effective translation of clinical NLP systems into real-world settings.