Citations

AbstractBackground and purposeParaneoplastic neurological autoimmunity is well described with small‐cell lung cancer, but information is limited for other neuroendocrine neoplasms (NENs).MethodsAdult patients with histopathologically confirmed non‐pulmonary NENs, neurological autoimmunity within 5 years of NEN diagnosis, and neural antibody testing performed at the Mayo Clinic Neuroimmunology Laboratory (January 2008 to March 2023) were retrospectively identified. Control sera were available from patients with NENs without neurological autoimmunity (116).ResultsThirty‐four patients were identified (median age 68 years, range 31–87). The most common primary tumor sites were pancreas (nine), skin (Merkel cell, eight), small bowel/duodenum (seven), and unknown (seven). Five patients received immune checkpoint inhibitor (ICI) therapy before symptom onset; symptoms preceded cancer diagnosis in 62.1% of non‐ICI‐treated patients. The most frequent neurological phenotypes (non‐ICI‐treated) were movement disorders (12; cerebellar ataxia in 10), dysautonomia (six), peripheral neuropathy (eight), encephalitis (four), and neuromuscular junction disorders (four). Neural antibodies were detected in 55.9% of patients studied (most common specificities: P/Q‐type voltage‐gated calcium channel [seven], muscle‐type acetylcholine receptor [three], anti‐neuronal nuclear antibody type 1 [three], and neuronal intermediate filaments [two]), but in only 6.9% of controls. Amongst patients receiving cancer or immunosuppressive therapy, 51.6% had partial or complete recovery. Outcomes were unfavorable in 48.3% (non‐ICI‐treated) and neural autoantibody positivity was associated with poor neurological outcome.DiscussionNeurological autoimmunity associated with non‐pulmonary NENs is often multifocal and can be treatment responsive, underscoring the importance of rapid recognition and early treatment.

AbstractIntroduction/AimsGlucocorticoids (GC) are first‐line therapy for many neuromuscular diseases. There is a lack of guidelines regarding the prevention and management of GC complications in the context of neuromuscular disease, introducing the potential for practice variation, that may compromise quality of care. Our aim was to evaluate the practice patterns among Canadian adult neuromuscular neurologists on the screening, management, and treatment of GC‐related complications and to identify variances in practice.MethodsA web‐based anonymous questionnaire was disseminated to 99 Canadian adult neuromuscular neurologists. Questions addressed patterns of screening, prevention, monitoring, and treatment of GC‐induced adverse events, including infection prophylaxis, vaccination, bone health, hyperglycemia, and other complications.ResultsSeventy‐one percent completed the survey. Of those, 52% perform screening blood work prior to initiating GC, 56% screen for infections, and 18% for osteoporosis. The majority monitor glycemic control and blood pressure (>85%). Thirty‐two (46%) reported that they do not primarily monitor GC complications, but rather provide recommendations to the primary care physician. Pneumocystis jiroveci pneumonia prophylaxis was never used by 29%, and 29% recommend vaccinations prior to GC initiation. Calcium supplementation was recommended by 80% to prevent osteoporosis. Only 36% were aware of any existing guidelines for preventing GC complications, and 91% endorsed a need for neurology‐specific guidelines.DiscussionThere is substantial variability in the management of GC adverse effects among neuromuscular neurologists, often not corresponding to limited published literature. Our results support the need for improved education and neurology‐specific guidelines to help standardize practice and improve and prevent complications.

AbstractINTRODUCTIONIncreasing evidence suggests that amyloid reduction could serve as a plausible surrogate endpoint for clinical and cognitive efficacy. The double‐blind phase 3 DIAN‐TU‐001 trial tested clinical and cognitive declines with increasing doses of solanezumab or gantenerumab.METHODSWe used latent class (LC) analysis on data from the Dominantly Inherited Alzheimer Network Trials Unit 001 trial to test amyloid positron emission tomography (PET) reduction as a potential surrogate biomarker.RESULTSLC analysis categorized participants into three classes: amyloid no change, amyloid reduction, and amyloid growth, based on longitudinal amyloid Pittsburgh compound B PET standardized uptake value ratio data. The amyloid‐no‐change class was at an earlier disease stage for amyloid amounts and dementia. Despite similar baseline characteristics, the amyloid‐reduction class exhibited reductions in the annual decline rates compared to the amyloid‐growth class across multiple biomarker, clinical, and cognitive outcomes.DISCUSSIONLC analysis indicates that amyloid reduction is associated with improved clinical outcomes and supports its use as a surrogate biomarker in clinical trials.Highlights We used latent class (LC) analysis to test amyloid reduction as a surrogate biomarker. Despite similar baseline characteristics, the amyloid‐reduction class exhibited remarkably better outcomes compared to the amyloid‐growth class across multiple measures. LC analysis proves valuable in testing amyloid reduction as a surrogate biomarker in clinical trials lacking significant treatment effects.

AbstractBackgroundMixed model repeated measures (MMRM) is the most common analysis approach in clinical trials for Alzheimer’s disease. The model treats time as a categorical variable, which can be problematic when assessments occur off‐schedule, as including off‐schedule visits can induce bias, and excluding them ignores valuable information and violates the intention to treat principle. This problem has been exacerbated by clinical trial visits which have been delayed due to the COVID19 pandemic. Since MMRM entails a mean parameter for each group and visit, the model can be inefficient for longer trials of Preclinical Alzheimer’s. As an alternative to MMRM, we propose a constrained longitudinal data analysis with natural cubic splines that treats time as continuous and uses test version effects to model the mean over time.MethodWe use model selection techniques to compare the predictive value of spline models to slope models, MMRM, and models that assume a proportional treatment effect using data from several completed trials. These trials include the ADCS trial of donepezil in MCI, trials of intranasal insulin and a fyn kinase inhibitor in mild Alzheimer’s dementia, and the A4 trial of solanezumab in Preclinical Alzheimer’s. Simulation studies assess the power and Type I error of these models for trials in Preclinical Alzheimer’s, MCI, and mild dementia.ResultThe spline model with ADAS‐Cog version effect attained the best predictive value for the trials in mild dementia. The proportional model was preferred for the trial in MCI, but the proportional effect assumption was violated, and the treatment effect estimate was in the wrong direction. Considering that the spline with two degrees of freedom and version effect provide fits that are similar to MMRM in Figure 1, we concluded that two degrees of freedom appear to be sufficient to capture enough of the general trend to assess the treatment effect. AIC results from the A4 Study will be considered and included after the study is completed in Spring 2023.ConclusionThe spline model is shown to have better predictive value and precision in real datasets and has better power and Type I error control in a variety of simulation scenarios.