Access for ALL in ALS: A large-scale, inclusive, collaborative consortium to unlock the molecular and genetic mechanisms of amyotrophic lateral sclerosis.

Recent progress in therapeutics for amyotrophic lateral sclerosis (ALS) has spurred development and imbued the field of ALS with hope for more breakthroughs, yet substantial scientific gaps persist. This unmet need remains a stark reminder that innovative paradigms are needed to invigorate ALS research. To move toward more informative, targeted, and personalized drug development, the National Institutes of Health (NIH) established a national ALS clinical research consortium called Access for ALL in ALS (ALL ALS). This new consortium is a multi-institutional effort that aims to organize the ALS clinical research landscape in the United States. ALL ALS is operating in partnership with several stakeholders to operationalize the recommendations of the Accelerating Access to Critical Therapies for ALS Act (ACT for ALS) Public Private Partnership. ALL ALS will provide a large-scale, centralized, and readily accessible infrastructure for the collection and storage of a wide range of data from people living with ALS (symptomatic cohort) or who may be at risk of developing ALS (asymptomatic ALS gene carriers). Importantly, ALL ALS is designed to encourage community engagement, equity, and inclusion. The consortium is prioritizing the enrollment of geographically, ethnoculturally, and socioeconomically diverse participants. Collected data include longitudinal clinical data and biofluids, genomic, and digital biomarkers that will be harmonized and linked to the central Accelerating Medicines Partnership for ALS (AMP ALS) portal for sharing with the research community. The aim of ALL ALS is to deliver a comprehensive, inclusive, open-science dataset to help researchers answer important scientific questions of clinical relevance in ALS.

Generalizable clinical note section identification with large language models.

Objectives: Clinical note section identification helps locate relevant information and could be beneficial for downstream tasks such as named entity recognition. However, the traditional supervised methods suffer from transferability issues. This study proposes a new framework for using large language models (LLMs) for section identification to overcome the limitations. Materials and Methods: We framed section identification as question-answering and provided the section definitions in free-text. We evaluated multiple LLMs off-the-shelf without any training. We also fine-tune our LLMs to investigate how the size and the specificity of the fine-tuning dataset impacts model performance. Results: GPT4 achieved the highest F1 score of 0.77. The best open-source model (Tulu2-70b) achieved 0.64 and is on par with GPT3.5 (ChatGPT). GPT4 is also found to obtain F1 scores greater than 0.9 for 9 out of the 27 (33%) section types and greater than 0.8 for 15 out of 27 (56%) section types. For our fine-tuned models, we found they plateaued with an increasing size of the general domain dataset. We also found that adding a reasonable amount of section identification examples is beneficial. Discussion: These results indicate that GPT4 is nearly production-ready for section identification, and seemingly contains both knowledge of note structure and the ability to follow complex instructions, and the best current open-source LLM is catching up. Conclusion: Our study shows that LLMs are promising for generalizable clinical note section identification. They have the potential to be further improved by adding section identification examples to the fine-tuning dataset.

Body mass index is lower in asymptomatic C9orf72 expansion carriers but not in SOD1 pathogenic variant carriers compared to gene negatives.

Objective: To examine the relationship between body mass index (BMI) and genotype among pre-symptomatic carriers of different pathogenic variants associated with amyotrophic lateral sclerosis. Methods: C9orf72+ carriers, SOD1+ carriers, and pathogenic variant negative controls (Gene-Negatives) were included from 3 largely independent cohorts: ALS Families Project (ALS-Families); Dominantly inherited ALS (DIALS); and Pre-symptomatic Familial ALS (Pre-fALS). First reported (ALS-Families) or measured (DIALS and Pre-fALS) weight and height were used to calculate BMI. Age at weight measurement, self-reported sex (male vs. female), and highest education (high school or below vs. college education vs. graduate school or above) were extracted. The associations between BMI and genotype in each cohort were examined with multivariable linear regression models, adjusted for age, sex, and education. Results: A total of 223 C9orf72+ carriers, 135 SOD1+ carriers, and 191 Gene-Negatives were included, deriving from ALS-Families (n = 114, median age 46, 37% male), DIALS (n = 221, median age 46, 30% male), and Pre-fALS (n = 214, median age 44, 39% male). Adjusting for age, sex, and education, the mean BMI of C9orf72+ carriers was lower than Gene-Negatives by 2.4 units (95% confidence interval [CI] = 0.3-4.6, p = 0.02) in ALS-Families; 2.7 units (95% CI = 0.9-4.4, p = 0.003) in DIALS; and 1.9 units (95% CI = 0.5-4.2, p = 0.12) in Pre-fALS. There were no significant differences in BMI between SOD1+ carriers and Gene-Negatives in any of the 3 cohorts. Conclusions: Compared to Gene-Negatives, average BMI is lower in asymptomatic C9orf72+ carriers across 3 cohorts while no significant difference was found between Gene-Negatives and SOD1+ carriers.

The TRIPOD-LLM Statement: A Targeted Guideline For Reporting Large Language Models Use.

Large Language Models (LLMs) are rapidly being adopted in healthcare, necessitating standardized reporting guidelines. We present TRIPOD-LLM, an extension of the TRIPOD+AI statement, addressing the unique challenges of LLMs in biomedical applications. TRIPOD-LLM provides a comprehensive checklist of 19 main items and 50 subitems, covering key aspects from title to discussion. The guidelines introduce a modular format accommodating various LLM research designs and tasks, with 14 main items and 32 subitems applicable across all categories. Developed through an expedited Delphi process and expert consensus, TRIPOD-LLM emphasizes transparency, human oversight, and task-specific performance reporting. We also introduce an interactive website ( https://tripod-llm.vercel.app/ ) facilitating easy guideline completion and PDF generation for submission. As a living document, TRIPOD-LLM will evolve with the field, aiming to enhance the quality, reproducibility, and clinical applicability of LLM research in healthcare through comprehensive reporting. COI: DSB: Editorial, unrelated to this work: Associate Editor of Radiation Oncology, HemOnc.org (no financial compensation); Research funding, unrelated to this work: American Association for Cancer Research; Advisory and consulting, unrelated to this work: MercurialAI. DDF: Editorial, unrelated to this work: Associate Editor of JAMIA, Editorial Board of Scientific Data, Nature; Funding, unrelated to this work: the intramural research program at the U.S. National Library of Medicine, National Institutes of Health. JWG: Editorial, unrelated to this work: Editorial Board of Radiology: Artificial Intelligence, British Journal of Radiology AI journal and NEJM AI. All other authors declare no conflicts of interest.

Lipid-siRNA conjugate accesses a perivascular transport mechanism and achieves widespread and durable knockdown in the central nervous system.

Short-interfering RNA (siRNA) has gained significant interest for treatment of neurological diseases by providing the capacity to achieve sustained inhibition of nearly any gene target. Yet, achieving efficacious drug delivery throughout deep brain structures of the CNS remains a considerable hurdle. We herein describe a lipid-siRNA conjugate that, following delivery into the cerebrospinal fluid (CSF), is transported effectively through perivascular spaces, enabling broad dispersion within CSF compartments and through the CNS parenchyma. We provide a detailed examination of the temporal kinetics of gene silencing, highlighting potent knockdown for up to five months from a single injection without detectable toxicity. Single-cell RNA sequencing further demonstrates gene silencing activity across diverse cell populations in the parenchyma and at brain borders, which may provide new avenues for neurological disease-modifying therapies.

SOFIA Aspiration System as first-line Technique (SOFAST): a prospective, multicenter study to assess the efficacy and safety of the 6 French SOFIA Flow Plus aspiration catheter for endovascular stroke thrombectomy.

BACKGROUND: Mechanical thrombectomy (MT) is the standard of care for acute ischemic stroke (AIS) patients with large vessel occlusion (LVO). The SOFAST study collected clinical evidence on the safety and efficacy of the 6 French SOFIA Flow Plus aspiration catheter (SOFIA 6F) when used as first-line treatment. METHODS: This was a prospective, multicenter investigation to assess the safety and efficacy of SOFIA 6F used for first-line aspiration. Anterior circulation LVO stroke patients were enrolled. The primary endpoint was the final modified Thrombolysis in Cerebral Infarction (mTICI)≥2b rate. Secondary endpoints included first-pass and first-line mTICI≥2b rates, times from arteriotomy to clot contact and mTICI≥2b, and 90-day modified Rankin Scale (mRS)≤2. First-line and final mTICI scores were adjudicated by an independent imaging core lab. Safety events were assessed by an independent clinical events adjudicator. RESULTS: A total of 108 patients were enrolled across 12 centers from July 2020 to June 2022. Median age was 67 years, median National Institutes of Health Stroke Scale (NIHSS) was 15.5, and 56.5% of patients received intravenous thrombolytics. At the end of the procedure, 97.2%, 85.2%, and 55.6% of patients achieved mTICI≥2b, ≥2c, and 3, respectively. With SOFIA 6F first-line aspiration, 87.0%, 79.6%, and 52.8% achieved mTICI≥2b, ≥2c, and 3, respectively. After the first pass, 75.0%, 70.4%, and 50.9% achieved mTICI≥2b, ≥2c, and 3, respectively. Median times from arteriotomy to clot contact and successful revascularization were 12 and 17 min, respectively. At 90 days, 66.7% of patients achieved mRS≤2. CONCLUSIONS: First-line aspiration with SOFIA 6F is safe and effective with high revascularization rates and short procedure times.

Cumulus: a federated electronic health record-based learning system powered by Fast Healthcare Interoperability Resources and artificial intelligence.

OBJECTIVE: To address challenges in large-scale electronic health record (EHR) data exchange, we sought to develop, deploy, and test an open source, cloud-hosted app "listener" that accesses standardized data across the SMART/HL7 Bulk FHIR Access application programming interface (API). METHODS: We advance a model for scalable, federated, data sharing and learning. Cumulus software is designed to address key technology and policy desiderata including local utility, control, and administrative simplicity as well as privacy preservation during robust data sharing, and artificial intelligence (AI) for processing unstructured text. RESULTS: Cumulus relies on containerized, cloud-hosted software, installed within a healthcare organization's security envelope. Cumulus accesses EHR data via the Bulk FHIR interface and streamlines automated processing and sharing. The modular design enables use of the latest AI and natural language processing tools and supports provider autonomy and administrative simplicity. In an initial test, Cumulus was deployed across 5 healthcare systems each partnered with public health. Cumulus output is patient counts which were aggregated into a table stratifying variables of interest to enable population health studies. All code is available open source. A policy stipulating that only aggregate data leave the institution greatly facilitated data sharing agreements. DISCUSSION AND CONCLUSION: Cumulus addresses barriers to data sharing based on (1) federally required support for standard APIs, (2) increasing use of cloud computing, and (3) advances in AI. There is potential for scalability to support learning across myriad network configurations and use cases.

The MexTAg collaborative cross: host genetics affects asbestos related disease latency, but has little influence once tumours develop.

Objectives: This study combines two innovative mouse models in a major gene discovery project to assess the influence of host genetics on asbestos related disease (ARD). Conventional genetics studies provided evidence that some susceptibility to mesothelioma is genetic. However, the identification of host modifier genes, the roles they may play, and whether they contribute to disease susceptibility remain unknown. Here we report a study designed to rapidly identify genes associated with mesothelioma susceptibility by combining the Collaborative Cross (CC) resource with the well-characterised MexTAg mesothelioma mouse model. Methods: The CC is a powerful mouse resource that harnesses over 90% of common genetic variation in the mouse species, allowing rapid identification of genes mediating complex traits. MexTAg mice rapidly, uniformly, and predictably develop mesothelioma, but only after asbestos exposure. To assess the influence of host genetics on ARD, we crossed 72 genetically distinct CC mouse strains with MexTAg mice and exposed the resulting CC-MexTAg (CCMT) progeny to asbestos and monitored them for traits including overall survival, the time to ARD onset (latency), the time between ARD onset and euthanasia (disease progression) and ascites volume. We identified phenotype-specific modifier genes associated with these traits and we validated the role of human orthologues in asbestos-induced carcinogenesis using human mesothelioma datasets. Results: We generated 72 genetically distinct CCMT strains and exposed their progeny (2,562 in total) to asbestos. Reflecting the genetic diversity of the CC, there was considerable variation in overall survival and disease latency. Surprisingly, however, there was no variation in disease progression, demonstrating that host genetic factors do have a significant influence during disease latency but have a limited role once disease is established. Quantitative trait loci (QTL) affecting ARD survival/latency were identified on chromosomes 6, 12 and X. Of the 97-protein coding candidate modifier genes that spanned these QTL, eight genes (CPED1, ORS1, NDUFA1, HS1BP3, IL13RA1, LSM8, TES and TSPAN12) were found to significantly affect outcome in both CCMT and human mesothelioma datasets. Conclusion: Host genetic factors affect susceptibility to development of asbestos associated disease. However, following mesothelioma establishment, genetic variation in molecular or immunological mechanisms did not affect disease progression. Identification of multiple candidate modifier genes and their human homologues with known associations in other advanced stage or metastatic cancers highlights the complexity of ARD and may provide a pathway to identify novel therapeutic targets.

Prevalence of anti-myelin oligodendrocyte glycoprotein antibodies across neuroinflammatory and neurodegenerative diseases.

Inflammatory central nervous system (CNS) diseases, such as multiple sclerosis (MS) and myelin oligodendrocyte glycoprotein (MOG) antibody-associated disease (MOGAD), are characterized by humoral immune abnormalities. Anti-MOG antibodies are not specific to MOGAD, with their presence described in MS. Autoantibodies may also be present and play a role in various neurodegenerative diseases. Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease driven by motor neuron dysfunction. While immune involvement in ALS has been recognized, the presence of antibodies targeting CNS myelin antigens has not been established. We aimed to establish a live cell-based assay for quantification of serum anti-MOG IgG1 in patients with CNS diseases, including MS and ALS. In total, 771 serum samples from the John L. Trotter MS Center and the Northeast ALS Consortium were examined using a live cell-based assay for detection of anti-MOG IgG1. Samples from three cohorts were tested in blinded fashion: healthy control (HC) subjects, patients with clinically diagnosed MOGAD, and an experimental group of ALS and MS patients. All samples from established MOGAD cases were positive for anti-MOG antibodies, while all HC samples were negative. Anti-MOG IgG1 was detected in 65 of 658 samples (9.9%) from MS subjects and 4 of 108 (3.7%) samples from ALS subjects. The presence of serum anti-MOG IgG1 in MS and ALS patients raises questions about the contribution of these antibodies to disease pathophysiology as well as accuracy of diagnostic approaches for CNS inflammatory diseases.

Bringing platform trials closer to reality by enabling with digital research environment (DRE) connectivity.

Platform trials are generally regarded as an innovative approach to address clinical valuation of early stage candidates, regardless of modality as the evidence evolves. As a type of randomized clinical trial (RCT) design construct in which multiple interventions are evaluated concurrently against a common control group allowing new interventions to be added and the control group to be updated throughout the trial, they provide a dynamic and efficient mechanism to compare and potentially discriminate new treatment candidates. Their recent use in the evaluation of new therapies for COVID-19 has spurred new interest in the approach. The paucity of platform trials is less influenced by the novelty and operational requirements as opposed to concerns regarding the sharing of intellectual property (IP) and the lack of infrastructure to operationalize the conduct in the context of IP and data sharing. We provide a mechanism how this can be accomplished through the use of a digital research environment (DRE) providing a safe and secure platform for clinical researchers, quantitative and physician scientists to analyze and develop tools (e.g., models) on sensitive data with the confidence that the data and models developed are protected. A DRE, in this context, expands on the concept of a trusted research environment (TRE) by providing remote access to data alongside tools for analysis in a securely controlled workspace, while allowing data and tools to be findable, accessible, interoperable, and reusable (FAIR), version-controlled, and dynamically grow in size or quality as a result of each treatment evaluated in the trial.

LCD Benchmark: Long Clinical Document Benchmark on Mortality Prediction for Language Models.

Objective: The application of Natural Language Processing (NLP) in the clinical domain is important due to the rich unstructured information in clinical documents, which often remains inaccessible in structured data. When applying NLP methods to a certain domain, the role of benchmark datasets is crucial as benchmark datasets not only guide the selection of best-performing models but also enable the assessment of the reliability of the generated outputs. Despite the recent availability of language models (LMs) capable of longer context, benchmark datasets targeting long clinical document classification tasks are absent. Materials and Methods: To address this issue, we propose LCD benchmark, a benchmark for the task of predicting 30-day out-of-hospital mortality using discharge notes of MIMIC-IV and statewide death data. We evaluated this benchmark dataset using baseline models, from bag-of-words and CNN to instruction-tuned large language models. Additionally, we provide a comprehensive analysis of the model outputs, including manual review and visualization of model weights, to offer insights into their predictive capabilities and limitations. Results and Discussion: Baseline models showed 28.9% for best-performing supervised models and 32.2% for GPT-4 in F1-metrics. Notes in our dataset have a median word count of 1687. Our analysis of the model outputs showed that our dataset is challenging for both models and human experts, but the models can find meaningful signals from the text. Conclusion: We expect our LCD benchmark to be a resource for the development of advanced supervised models, or prompting methods, tailored for clinical text. The benchmark dataset is available at https://github.com/Machine-Learning-for-Medical-Language/long-clinical-doc.

Reduce intraoperative albumin utilisation in cardiac surgical patients: a quality improvement initiative.

BACKGROUND: Albumin continues to be used routinely by cardiac anaesthesiologists perioperatively despite lack of evidence for improved outcomes. The Multicenter Perioperative Outcomes Group (MPOG) data ranked our institution as one of the highest intraoperative albumin users during cardiac surgery. Therefore, we designed a quality improvement project (QIP) to introduce a bundle of interventions to reduce intraoperative albumin use in cardiac surgical patients. METHODS: Our institutional MPOG data were used to analyse the FLUID-01-C measure that provides the number of adult cardiac surgery cases where albumin was administered intraoperatively by anaesthesiologists from 1 July 2019 to 30 June 2022. The QIP involved introduction of the following interventions: (1) education about appropriate albumin use and indications (January 2021), (2) email communications reinforced with OR teaching (March 2021), (3) removal of albumin from the standard pharmacy intraoperative medication trays (April 2021), (4) grand rounds presentation discussing the QIP and highlighting the interventions (May 2021) and (5) quarterly provider feedback (starting July 2021). Multivariable segmented regression models were used to assess the changes from preintervention to postintervention time period in albumin utilisation, and its total monthly cost. RESULTS: Among the 5767 cardiac surgery cases that met inclusion criteria over the 3-year study period, 16% of patients received albumin intraoperatively. The total number of cases that passed the metric (albumin administration was avoided), gradually increased as our interventions went into effect. Intraoperative albumin utilisation (beta=-101.1, 95% CI -145 to -56.7) and total monthly cost of albumin (beta=-7678, 95% CI -10712 to -4640) demonstrated significant decrease after starting the interventions. CONCLUSIONS: At a single academic cardiac surgery programme, implementation of a bundle of simple and low-cost interventions as part of a coordinated QIP were effective in significantly decreasing intraoperative use of albumin, which translated into considerable costs savings.

Moving Biosurveillance Beyond Coded Data Using AI for Symptom Detection From Physician Notes: Retrospective Cohort Study.

BACKGROUND: Real-time surveillance of emerging infectious diseases necessitates a dynamically evolving, computable case definition, which frequently incorporates symptom-related criteria. For symptom detection, both population health monitoring platforms and research initiatives primarily depend on structured data extracted from electronic health records. OBJECTIVE: This study sought to validate and test an artificial intelligence (AI)-based natural language processing (NLP) pipeline for detecting COVID-19 symptoms from physician notes in pediatric patients. We specifically study patients presenting to the emergency department (ED) who can be sentinel cases in an outbreak. METHODS: Subjects in this retrospective cohort study are patients who are 21 years of age and younger, who presented to a pediatric ED at a large academic children's hospital between March 1, 2020, and May 31, 2022. The ED notes for all patients were processed with an NLP pipeline tuned to detect the mention of 11 COVID-19 symptoms based on Centers for Disease Control and Prevention (CDC) criteria. For a gold standard, 3 subject matter experts labeled 226 ED notes and had strong agreement (F1-score=0.986; positive predictive value [PPV]=0.972; and sensitivity=1.0). F1-score, PPV, and sensitivity were used to compare the performance of both NLP and the International Classification of Diseases, 10th Revision (ICD-10) coding to the gold standard chart review. As a formative use case, variations in symptom patterns were measured across SARS-CoV-2 variant eras. RESULTS: There were 85,678 ED encounters during the study period, including 4% (n=3420) with patients with COVID-19. NLP was more accurate at identifying encounters with patients that had any of the COVID-19 symptoms (F1-score=0.796) than ICD-10 codes (F1-score =0.451). NLP accuracy was higher for positive symptoms (sensitivity=0.930) than ICD-10 (sensitivity=0.300). However, ICD-10 accuracy was higher for negative symptoms (specificity=0.994) than NLP (specificity=0.917). Congestion or runny nose showed the highest accuracy difference (NLP: F1-score=0.828 and ICD-10: F1-score=0.042). For encounters with patients with COVID-19, prevalence estimates of each NLP symptom differed across variant eras. Patients with COVID-19 were more likely to have each NLP symptom detected than patients without this disease. Effect sizes (odds ratios) varied across pandemic eras. CONCLUSIONS: This study establishes the value of AI-based NLP as a highly effective tool for real-time COVID-19 symptom detection in pediatric patients, outperforming traditional ICD-10 methods. It also reveals the evolving nature of symptom prevalence across different virus variants, underscoring the need for dynamic, technology-driven approaches in infectious disease surveillance.

Automated stratification of trauma injury severity across multiple body regions using multi-modal, multi-class machine learning models.

OBJECTIVE: The timely stratification of trauma injury severity can enhance the quality of trauma care but it requires intense manual annotation from certified trauma coders. The objective of this study is to develop machine learning models for the stratification of trauma injury severity across various body regions using clinical text and structured electronic health records (EHRs) data. MATERIALS AND METHODS: Our study utilized clinical documents and structured EHR variables linked with the trauma registry data to create 2 machine learning models with different approaches to representing text. The first one fuses concept unique identifiers (CUIs) extracted from free text with structured EHR variables, while the second one integrates free text with structured EHR variables. Temporal validation was undertaken to ensure the models' temporal generalizability. Additionally, analyses to assess the variable importance were conducted. RESULTS: Both models demonstrated impressive performance in categorizing leg injuries, achieving high accuracy with macro-F1 scores of over 0.8. Additionally, they showed considerable accuracy, with macro-F1 scores exceeding or near 0.7, in assessing injuries in the areas of the chest and head. We showed in our variable importance analysis that the most important features in the model have strong face validity in determining clinically relevant trauma injuries. DISCUSSION: The CUI-based model achieves comparable performance, if not higher, compared to the free-text-based model, with reduced complexity. Furthermore, integrating structured EHR data improves performance, particularly when the text modalities are insufficiently indicative. CONCLUSIONS: Our multi-modal, multiclass models can provide accurate stratification of trauma injury severity and clinically relevant interpretations.