Identification of disease phenotypes in acetylcholine receptor-antibody myasthenia gravis using proteomics-based consensus clustering.

BACKGROUND: The clinical heterogeneity of myasthenia gravis (MG), an autoimmune disease defined by antibodies (Ab) directed against the postsynaptic membrane, constitutes a challenge for patient stratification and treatment decision making. Novel strategies are needed to classify patients based on their biological phenotypes aiming to improve patient selection and treatment outcomes. METHODS: For this purpose, we assessed the serum proteome of a cohort of 140 patients with anti-acetylcholine receptor-Ab-positive MG and utilised consensus clustering as an unsupervised tool to assign patients to biological profiles. For in-depth analysis, we used immunogenomic sequencing to study the B cell repertoire of a subgroup of patients and an in vitro assay using primary human muscle cells to interrogate serum-induced complement formation. FINDINGS: This strategy identified four distinct patient phenotypes based on their proteomic patterns in their serum. Notably, one patient phenotype, here named PS3, was characterised by high disease severity and complement activation as defining features. Assessing a subgroup of patients, hyperexpanded antibody clones were present in the B cell repertoire of the PS3 group and effectively activated complement as compared to other patients. In line with their disease phenotype, PS3 patients were more likely to benefit from complement-inhibiting therapies. These findings were validated in a prospective cohort of 18 patients using a cell-based assay. INTERPRETATION: Collectively, this study suggests proteomics-based clustering as a gateway to assign patients to a biological signature likely to benefit from complement inhibition and provides a stratification strategy for clinical practice. FUNDING: CN and CBS were supported by the Forschungskommission of the Medical Faculty of the Heinrich Heine University Düsseldorf. CN was supported by the Else Kröner-Fresenius-Stiftung (EKEA.38). CBS was supported by the Deutsche Forschungsgemeinschaft (DFG-German Research Foundation) with a Walter Benjamin fellowship (project 539363086). The project was supported by the Ministry of Culture and Science of North Rhine-Westphalia (MODS, "Profilbildung 2020" [grant no. PROFILNRW-2020-107-A]).

Preventing long-term disability in CIDP: the role of timely diagnosis and treatment monitoring in a multicenter CIDP cohort.

BACKGROUND: Chronic inflammatory demyelinating polyneuropathy (CIDP) is an inflammatory disease affecting the peripheral nerves and the most frequent autoimmune polyneuropathy. Given the lack of established biomarkers or risk factors for the development of CIDP and patients' treatment response, this research effort seeks to identify potential clinical factors that may influence disease progression and overall treatment efficacy. METHODS: In this multicenter, retrospective analysis, we have screened 197 CIDP patients who presented to the University Hospitals in Düsseldorf, Berlin, Cologne, Essen, Magdeburg and Munich between 2018 and 2022. We utilized the respective hospital information system and examined baseline data with clinical examination, medical letters, laboratory results, antibody status, nerve conduction studies, imaging and biopsy findings. Aside from clinical baseline data, we analyzed treatment outcomes using the Standard of Care (SOC) definition, as well as a comparison of an early (within the first 12 months after manifestation) versus late (more than 12 months after manifestation) onset of therapy. RESULTS: In terms of treatment, most patients received intravenous immunoglobulin (56%) or prednisolone (39%) as their first therapy. Patients who started their initial treatment later experienced a worsening disease course, as reflected by a significant deterioration in their Inflammatory Neuropathy Cause and Treatment (INCAT) leg disability score. SOC-refractory patients had worse clinical outcomes than SOC-responders. Associated factors for SOC-refractory status included the presence of fatigue as a symptom and alcohol dependence. CONCLUSION: Timely diagnosis, prompt initiation of treatment and careful monitoring of treatment response are essential for the prevention of long-term disability in CIDP and suggest a "hit hard and early" treatment paradigm.

Triage Performance Across Large Language Models, ChatGPT, and Untrained Doctors in Emergency Medicine: Comparative Study.

BACKGROUND: Large language models (LLMs) have demonstrated impressive performances in various medical domains, prompting an exploration of their potential utility within the high-demand setting of emergency department (ED) triage. This study evaluated the triage proficiency of different LLMs and ChatGPT, an LLM-based chatbot, compared to professionally trained ED staff and untrained personnel. We further explored whether LLM responses could guide untrained staff in effective triage. OBJECTIVE: This study aimed to assess the efficacy of LLMs and the associated product ChatGPT in ED triage compared to personnel of varying training status and to investigate if the models' responses can enhance the triage proficiency of untrained personnel. METHODS: A total of 124 anonymized case vignettes were triaged by untrained doctors; different versions of currently available LLMs; ChatGPT; and professionally trained raters, who subsequently agreed on a consensus set according to the Manchester Triage System (MTS). The prototypical vignettes were adapted from cases at a tertiary ED in Germany. The main outcome was the level of agreement between raters' MTS level assignments, measured via quadratic-weighted Cohen κ. The extent of over- and undertriage was also determined. Notably, instances of ChatGPT were prompted using zero-shot approaches without extensive background information on the MTS. The tested LLMs included raw GPT-4, Llama 3 70B, Gemini 1.5, and Mixtral 8x7b. RESULTS: GPT-4-based ChatGPT and untrained doctors showed substantial agreement with the consensus triage of professional raters (κ=mean 0.67, SD 0.037 and κ=mean 0.68, SD 0.056, respectively), significantly exceeding the performance of GPT-3.5-based ChatGPT (κ=mean 0.54, SD 0.024; P<.001). When untrained doctors used this LLM for second-opinion triage, there was a slight but statistically insignificant performance increase (κ=mean 0.70, SD 0.047; P=.97). Other tested LLMs performed similar to or worse than GPT-4-based ChatGPT or showed odd triaging behavior with the used parameters. LLMs and ChatGPT models tended toward overtriage, whereas untrained doctors undertriaged. CONCLUSIONS: While LLMs and the LLM-based product ChatGPT do not yet match professionally trained raters, their best models' triage proficiency equals that of untrained ED doctors. In its current form, LLMs or ChatGPT thus did not demonstrate gold-standard performance in ED triage and, in the setting of this study, failed to significantly improve untrained doctors' triage when used as decision support. Notable performance enhancements in newer LLM versions over older ones hint at future improvements with further technological development and specific training.

The PNS Nurse Program: A Health Care Support Concept for Patients With Immune-Mediated Peripheral Nervous System Diseases.

Background: Immune-mediated peripheral nervous system (PNS) disorders pose diagnostic and therapeutic challenges, necessitating collaborative, patient-centered care. Limited access to specialized centers leads to delayed diagnosis and care, as seen during the COVID-19 pandemic. To address these challenges, accessible specialized care is crucial. On-site support plays a vital role in advising and assisting patients and caregivers, enabling multidisciplinary care for PNS diseases. Recent Findings: The PNS Nurse Education Program tackles these complexities, using specialized nurses experienced in multiple sclerosis and Parkinson disease. Focusing on peripheral neuroimmunologic disorders, PNS nurses monitor disease severity, optimize communication, and provide therapeutic support in the recently started era of available immunotherapies. Collaboration with other healthcare sectors and support groups further enhances patient care. Implications for Practice: Ultimately, the PNS Nurse Education Program aims to bridge the gap between complex treatments and limited specialized care, improving patient outcomes and relieving burdens on patients, caregivers, and healthcare systems.

The three-year evolution of Germany's Digital Therapeutics reimbursement program and its path forward.

The 2019 German Digital Healthcare Act introduced the Digital Health Application program, known in German as 'Digitale Gesundheitsanwendungen' (DiGA). The program has established a pioneering model for integrating Digital Therapeutics (DTx) into a healthcare system with scalable and effective reimbursement strategies. To date, the continuous upward trend enabled by this framework has resulted in more than 374,000 DiGA prescriptions, increasingly cementing its role in the German healthcare system. This perspective provides a synthesis of the DiGA program's evolution since its inception three years ago, highlighting trends regarding prescriptions and pricing as well as criticisms and identified shortcomings. It further discusses forthcoming legislative amendments, including the anticipated integration of higher-risk medical devices, which have the potential to significantly transform the program. Despite encountering challenges related to effectiveness, evidence requirements, and integration within the healthcare system, the DiGA program continues to evolve and serves as a seminal example for the integration of DTx, offering valuable insights for healthcare systems globally.

Tracing Digital Therapeutics Research Across Medical Specialties: Evidence from ClinicalTrials.gov.

Digital therapeutics (DTx), evidence-based software interventions for preventing, managing, or treating medical disorders, have rapidly evolved with healthcare's shift toward online, patient-centric solutions. This study scrutinizes DTx clinical trials from 2005 to 2022, analyzing their growth, funding, underlying medical specialties, and other R&D characteristics, using ClinicalTrials.gov data. Our analysis includes trials categorized via the ICD-11 system, covering active, recruiting, or completed studies and considering trials listing multiple conditions. In analyzing 5,889 registered DTx trials, we document a more than five-fold increase in such trials since 2011, and a compound annual growth rate of 22.82% since 2005. While most trials were single-center, the median number of study subjects increased in recent years, driven by larger interventional trials. The key disciplines driving this growth were psychiatry, neurology, oncology, and endocrinology. Mental health dominated DTx trials in recent years, led by neurocognitive disorders, substance abuse disorders, and mood disorders. Industry funding varied across disciplines and was particularly high in visual system diseases and dermatology. DTx trials have surged since 2005, accelerated by recent growth in mental health trials. These trends mirror developments toward remote healthcare delivery, amplified by digital health investments during the COVID-19 pandemic. Growing numbers of participants in DTx trials point to increased demand for more robust trials. However, because most trials are single-center and country-specific, more international cooperation and harmonized evaluation standards will be essential for DTx trials to become more efficient and provide validation across countries, health systems, and groups of individuals.

[Digital medicine in neurological research-Between hype and evidence]. / Digitale Medizin in der neurologischen Forschung ­ zwischen Hype und Evidenz.

BACKGROUND: The rapid advancement of digital medicine and health technologies in neurology offers both significant potential and challenges. This article outlines fundamental aspects of digital medicine related to neurological research and highlights application examples of digital technologies in neurological research. AIM: To provide a comprehensive overview of current digital developments in neurology and their impact on neurological research. MATERIAL AND METHODS: In this narrative review articles from various sources and references related to digital medicine and health technologies in neurology were compiled and analyzed. RESULTS AND DISCUSSION: The data presented indicate that digital health technologies and digital therapeutics have the potential to decisively shape neurological care and research; however, it is emphasized that a critical evaluation and evidence-based approach to these technologies are essential to determine their actual value in neurology.

Introducing electronic monitoring of disease activity in patients with chronic inflammatory demyelinating polyneuropathy (EMDA CIDP): trial protocol of a proof of concept study.

INTRODUCTION: Chronic inflammatory demyelinating polyneuropathy (CIDP) is one of the most common immune neuropathies leading to severe impairments in daily life. Current treatment options include intravenous immunoglobulins (IVIG), which are administered at intervals of 4-12 weeks. Determination of individual treatment intervals is challenging since existing clinical scores lack sensitivity to objectify small, partially fluctuating deficits in patients. End-of-dose phenomena described by patients, manifested by increased fatigue and worsening of (motor) symptoms, are currently difficult to detect. From a medical and socio-economic point of view, it is necessary to identify and validate new, more sensitive outcome measures for accurate mapping of disease progression and, thus, for interval finding. Digital health technologies such as wearables may be particularly useful for this purpose, as they record real-life data and consequently, in contrast to classic clinical 'snapshots', can continuously depict the disease course. METHODS: In this prospective, observational, non-interventional, single-center, investigator-initiated study, CIDP patients treated with IVIG will be continuously monitored over a period of 6 months. Clinical scores and blood analyses will be assessed and collected during three visits (V1, V2, V3). Additionally, activity, sleep, and cardiac parameters will be recorded over the entire period using a wearable device. Further, patients' subjective disease development and quality of life will be recorded at various visits (read-outs). The usability of the smartwatch will be assessed at the end of the study. PERSPECTIVE: The study aims to evaluate different digital measurements obtained with the smartwatch and blood-based analyses for monitoring disease activity and progress in CIDP patients. In conjunction, both means of monitoring might offer detailed insights into behavioral and biological patterns associated with treatment-related fluctuations such as end-of-dose phenomena. TRIAL REGISTRATION: The study protocol was registered at ClinicalTrials.gov. Identifier: NCT05723848. Initially, the protocol was submitted prospectively on January 10, 2023. The trial was publicly released after formal improvements on February 13, 2023, after first patients were included according to the original protocol.

Evidence from ClinicalTrials.gov on the growth of Digital Health Technologies in neurology trials.

Digital Health Technologies (DHTs) such as connected sensors offer particular promise for improving data collection and patient empowerment in neurology research and care. This study analyzed the recent evolution of the use of DHTs in trials registered on ClinicalTrials.gov for four chronic neurological disorders: epilepsy, multiple sclerosis, Alzheimer's, and Parkinson's disease. We document growth in the collection of both more established digital measures (e.g., motor function) and more novel digital measures (e.g., speech) over recent years, highlighting contexts of use and key trends.

Driving time-based identification of gaps in specialised care coverage: An example of neuroinflammatory diseases in Germany.

Objective: Due to the growing complexity in monitoring and treatment of many disorders, disease-specific care and research networks offer patients certified healthcare. However, the networks' ability to provide health services close to patients' homes usually remains vague. Digital Health Technologies (DHTs) help to provide better care, especially if implemented in a targeted manner in regions undersupplied by specialised networks. Therefore, we used a car travel time-based isochrone approach to identify care gaps using the example of the neuroinflammation-focused German healthcare and research networks for multiple sclerosis (MS), myasthenia gravis (MG), myositis and immune-mediated neuropathy. Methods: Excellence centres were mapped, and isochrones for 30, 60, 90 and 120 minutes were calculated. The resulting geometric figures were aggregated and used to mask the global human settlement population grid 2019 to estimate German inhabitants that can reach centres within the given periods. Results: While 96.48% of Germans can drive to an MS-focused centre within one hour, coverage is lower for the rare disease networks for MG (48.3%), myositis (43.1%) and immune-mediated neuropathy (56.7%). Within 120 minutes, more than 80% of Germans can reach a centre of any network. Besides the generally worse covered rural regions such as North-Eastern Germany, the rare disease networks also show network-specific regional underrepresentation. Conclusion: An isochrone-based approach helps identify regions where specialised care is hard to reach, which might be especially troublesome in the case of an often disabled patient collective. Patient care could be improved by focusing deployments of disease-specific DHTs on these areas.

Myositis in Germany: epidemiological insights over 15 years from 2005 to 2019.

BACKGROUND: The medical care of patients with myositis is a great challenge in clinical practice. This is due to the rarity of these disease, the complexity of diagnosis and management as well as the lack of systematic analyses. OBJECTIVES: Therefore, the aim of this project was to obtain an overview of the current care of myositis patients in Germany and to evaluate epidemiological trends in recent years. METHODS: In collaboration with BARMER Insurance, retrospective analysis of outpatient and inpatient data from an average of approximately 8.7 million insured patients between January 2005 and December 2019 was performed using ICD-10 codes for myositis for identification of relevant data. In addition, a comparative analysis was performed between myositis patients and an age-matched comparison group from other populations insured by BARMER. RESULTS: 45,800 BARMER-insured individuals received a diagnosis of myositis during the observation period, with a relatively stable prevalence throughout. With regard to comorbidities, a significantly higher rate of cardiovascular disease as well as neoplasm was observed compared to the control group within the BARMER-insured population. In addition, myositis patients suffer more frequently from psychiatric disorders, such as depression and somatoform disorders. However, the ICD-10 catalogue only includes the specific coding of "dermatomyositis" and "polymyositis" and thus does not allow for a sufficient analysis of all idiopathic inflammatory myopathies subtypes. CONCLUSION: The current data provide a comprehensive epidemiological analysis of myositis in Germany, highlighting the multimorbidity of myositis patients. This underlines the need for multidisciplinary management. However, the ICD-10 codes currently still in use do not allow for specific analysis of the subtypes of myositis. The upcoming ICD-11 coding may improve future analyses in this regard.

Detecting ongoing disease activity in mildly affected multiple sclerosis patients under first-line therapies.

BACKGROUND: The current range of disease-modifying treatments (DMTs) for relapsing-remitting multiple sclerosis (RRMS) has placed more importance on the accurate monitoring of disease progression for timely and appropriate treatment decisions. With a rising number of measurements for disease progression, it is currently unclear how well these measurements or combinations of them can monitor more mildly affected RRMS patients. OBJECTIVES: To investigate several composite measures for monitoring disease activity and their potential relation to the biomarker neurofilament light chain (NfL) in a clearly defined early RRMS patient cohort with a milder disease course. METHODS: From a total of 301 RRMS patients, a subset of 46 patients being treated with a continuous first-line therapy was analyzed for loss of no evidence of disease activity (lo-NEDA-3) status, relapse-associated worsening (RAW) and progression independent of relapse activity (PIRA), up to seven years after treatment initialization. Kaplan-Meier estimates were used for time-to-event analysis. Additionally, a Cox regression model was used to analyze the effect of NfL levels on outcome measures in this cohort. RESULTS: In this mildly affected cohort, both lo-NEDA-3 and PIRA frequently occurred over a median observational period of 67.2 months and were observed in 39 (84.8%) and 23 (50.0%) patients, respectively. Additionally, 12 out of 26 PIRA manifestations (46.2%) were observed without a corresponding lo-NEDA-3 status. Jointly, either PIRA or lo-NEDA-3 showed disease activity in all patients followed-up for at least the median duration (67.2 months). NfL values demonstrated an association with the occurrence of relapses and RAW. CONCLUSION: The complementary use of different disease progression measures helps mirror ongoing disease activity in mildly affected early RRMS patients being treated with continuous first-line therapy.

Platelet Inhibition by Low-Dose Acetylsalicylic Acid Reduces Neuroinflammation in an Animal Model of Multiple Sclerosis.

Aside from the established immune-mediated etiology of multiple sclerosis (MS), compelling evidence implicates platelets as important players in disease pathogenesis. Specifically, numerous studies have highlighted that activated platelets promote the central nervous system (CNS)-directed adaptive immune response early in the disease course. Platelets, therefore, present a novel opportunity for modulating the neuroinflammatory process that characterizes MS. We hypothesized that the well-known antiplatelet agent acetylsalicylic acid (ASA) could inhibit neuroinflammation by affecting platelets if applied at low-dose and investigated its effect during experimental autoimmune encephalomyelitis (EAE) as a model to study MS. We found that oral administration of low-dose ASA alleviates symptoms of EAE accompanied by reduced inflammatory infiltrates and less extensive demyelination. Remarkably, the percentage of CNS-infiltrated CD4+ T cells, the major drivers of neuroinflammation, was decreased to 40.98 ± 3.28% in ASA-treated mice compared to 56.11 ± 1.46% in control animals at the disease maximum as revealed by flow cytometry. More interestingly, plasma levels of thromboxane A2 were decreased, while concentrations of platelet factor 4 and glycoprotein VI were not affected by low-dose ASA treatment. Overall, we demonstrate that low-dose ASA could ameliorate the platelet-dependent neuroinflammatory response in vivo, thus indicating a potential treatment approach for MS.

The STING-IFN-ß-Dependent Axis Is Markedly Low in Patients with Relapsing-Remitting Multiple Sclerosis.

Cyclic GMP-AMP-synthase is a sensor of endogenous nucleic acids, which subsequently elicits a stimulator of interferon genes (STING)-dependent type I interferon (IFN) response defending us against viruses and other intracellular pathogens. This pathway can drive pathological inflammation, as documented for type I interferonopathies. In contrast, specific STING activation and subsequent IFN-ß release have shown beneficial effects on experimental autoimmune encephalomyelitis (EAE) as a model for multiple sclerosis (MS). Although less severe cases of relapse-remitting MS (RRMS) are treated with IFN-ß, there is little information correlating aberrant type I IFN signaling and the pathologic conditions of MS. We hypothesized that there is a link between STING activation and the endogenous production of IFN-ß during neuroinflammation. Gene expression analysis in EAE mice showed that Sting level decreased in the peripheral lymphoid tissue, while its level increased within the central nervous system over the course of the disease. Similar patterns could be verified in peripheral immune cells during the acute phases of RRMS in comparison to remitting phases and appropriately matched healthy controls. Our study is the first to provide evidence that the STING/IFN-ß-axis is downregulated in RRMS patients, meriting further intensified research to understand its role in the pathophysiology of MS and potential translational applications.