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AbstractBackgroundIn familial frontotemporal lobar degeneration (f‐FTLD), plasma levels of neurofilament light (NfL) are elevated at least two years prior to symptom onset and predict future clinical status. The Neurofilament Surveillance Project (NSP), an actively recruiting observational study, seeks to obtain higher resolution data of temporal changes in plasma NfL to better understand its clinical utility in f‐FTLD interventional trials. The NSP is a pre‐competitive collaboration sponsored by the Bluefield Project to Cure FTD and funded by biotechnology and pharmaceutical companies and nonprofit organizations.MethodParticipants must be members of families with disease‐causing mutations in C9orf72, MAPT or GRN and enrolled in the ALLFTD natural history study. Up to 335 participants, with roughly equal representation across mutation type, provide plasma quarterly for 3 years (4355 samples total) to enable the observation of peripheral NfL levels longitudinally during disease onset and progression. Plasma is collected by certified mobile research nurses during remote visits, and NfL is measured in batch at least every 6 months. NfL measurements from one plasma aliquot per draw will be correlated with annual assessments obtained in the ALLFTD study, including measures of disease severity (CDRÒ+NACC‐FTLD, CGI, etc.), cognitive function from the UDS Neuropsychology Battery, and volumetric MRI data. Additional plasma aliquots are reserved for future analyses of other biomarkers during the course of the study; another subset of aliquots is reserved to share with interested investigators after the study ends.ResultAs of 1/19/22, the NSP had enrolled 161 participants, including 62 from C9orf72 kindreds, 60 from MAPT kindreds, and 28 from GRN kindreds. One‐hundred forty‐five participants had completed their initial visit, and 109 had completed more than 3, and up to 5, quarterly blood draws. Enrollment and study visits are ongoing despite COVID‐related limitations.ConclusionThe quarterly blood sampling from this study, combined with annual comprehensive clinical assessments through the ALLFTD natural history study, will enable a more detailed understanding of when and how NfL levels change during phenoconversion and/or disease progression in f‐FTLD mutation carriers. These data will be especially informative in the context of the targeted investigational therapeutics for f‐FTLD now entering clinical trials.

AbstractBackgroundDyadic (self and study partner) subjective reports of an individual’s recent cognitive and functional decline are an efficient approach to facilitate identification of older adults with, and at risk for, Mild Cognitive Impairment (MCI). Such methods can be easily adapted for remote administration, but few studies have investigated the ability of remotely‐collected, unsupervised dyadic subjective reports to accurately detect MCI and cognitive decline.MethodsIn 289 older adults enrolled in the Brain health Registry (BHR), with clinically‐confirmed diagnoses of Cognitively‐unimpaired (CU) or MCI, we identified the optimal combination of remote measures to accurately distinguish CU from MCI. We then externally validated our predictive model in 217 participants enrolled in a separate cohort, Validation of Online Methods to Predict Cognitive Decline (eVAL). Predictors included Cogstate Brief Battery One Card Learning (OCL) accuracy; self‐ and study partner‐reported memory concern (SMCs); performance on online adaptations of Everyday Cognition Scale (ECog, self‐ and study partner‐report versions), Geriatric Depression Scale Short Form (GDS); family history of AD, age, gender, and education. We calculated 10‐fold cross validated area under the ROC curves (cvAUC), 95% confidence intervals (CI), and the percentage of participants correctly classified (accuracy) for all models. Likelihood ratio (LR) tests were used to compare model fits.ResultsIn the BHR cohort, models including dyad‐report ECog, family history of AD, GDS, and Cogstate OCL scores distinguished CU from MCI with the highest accuracy (cvAUC = 0.90; 95% CI = 0.83, 0.92; accuracy = 85.5%). Models excluding study partner‐report ECog distinguished diagnostic groups with moderate accuracy (cvAUC = 0.83; 95% CI = 0.78, 0.85; accuracy = 75.9%) When applied to the validation cohort, the optimal training model including dyadic report performed with moderate accuracy ( Table 2 ). In the validation cohort, inclusion of study partner (but not self) report SMC, in addition to dyadic‐report ECog, improved model fit (LR test p<0.01).ConclusionsRemotely‐collected dyadic subjective measures, along with other remote measures, accurately distinguish CU from MCI. Subjective cognitive and functional decline and concerns about memory problems independently contribute to diagnostic discrimination. Results support future utilization of remote dyadic subjective measures to efficiently identify older adults with MCI for prodromal AD clinical trials, and in clinical care settings.

AbstractBackgroundThe U.S. Study to Protect Brain Health Through Lifestyle Intervention to Reduce Risk (U.S. POINTER) is a 2‐year randomized controlled trial to evaluate the effect of lifestyle interventions in older adults (60‐79 years). The POINTER Imaging ancillary study is collecting Arterial Spin Labeling (ASL) MRI data to map cerebral blood flow (CBF) and evaluate the effect of interventions in the perfusion imaging biomarker. The optimization of timing parameters is essential for robust and efficient acquisition of ASL MRI.MethodA subset (n=83; F/M=51/32; age=69.0±5.5) of POINTER Imaging participants at two imaging sites underwent two ASL MRI scans during the same baseline session. Two 3D Pulsed ASL were obtained with TI1 of 790ms and TI2 values of 2000ms or 2750ms, which provide the delay times (between labeling and imaging) of 1210ms or 1960ms. ASL images were quantified into a physiological unit of CBF, ml/100g/min, using a single compartment kinetic model. CBF maps from two scans were normalized into a standard space and a voxel‐wise paired t‐test was performed after site variations were harmonized and covariates (age and sex) were adjusted. The age and sex effects in the difference of CBF values between two scans were also examined using linear regression. To control false positives, clusters smaller than 41ml were removed based on a simulation with α=0.05.ResultThe CBF values in ASL MRI rely on the timing of image acquisition: only a fraction of label bolus arrives in the tissue with a short delay while images suffer from low signal due to the magnetization decay with a long delay. Figure 1 shows the regions that require a long delay time (red) or a short delay (blue). The difference in CBF is proportional to arterial transit time (ATT) and the analysis shows females have shorter ATT mainly in the peri‐Sylvian region (Figure 2). No cluster survived correction in the analysis of the aging effect.ConclusionThe acquisition of two CBF maps not only offers optimization of acquisition timing but provides an additional perfusion parameter, arterial transit time, which may be a potential biomarker of cerebrovascular pathophysiology.

AbstractBackgroundGiven the triplication of chromosome 21 and the location of the amyloid precursor protein gene on chromosome 21, almost all adults with Down syndrome (DS) develop Alzheimer disease (AD)‐like pathology and dementia during their lifetime. Comparing amyloid accumulation in DS to autosomal dominant AD (ADAD), another genetic form of AD, may improve our understanding of early AD pathology development.MethodWe assessed amyloid positron emission tomography (PET) imaging in 192 participants with DS and 33 sibling controls from the Alzheimer’s Biomarker Consortium‐Down Syndrome (ABC‐DS) and 265 mutation‐carriers (MC) and 169 familial controls from the Dominantly Inherited Alzheimer Network (DIAN) (Table 1). We calculated regional standard uptake value ratios (SUVR) using a cerebellar cortex reference region and converted global amyloid burden SUVR to centiloids. We compared amyloid PET by cognitive status and estimated‐years‐to‐symptom‐onset (EYO). EYO was calculated for DIAN participants by subtracting their age from parental age of symptom onset and for ABC‐DS participants by subtracting their age from 50.2 years, a published average age of symptom onset in a large sample of individuals with DS (Fortea et al., 2020). In a subset of participants, we assessed the relationship between amyloid PET and CSF Aβ42/40.ResultThe relationship between CSF Aβ42/40 and amyloid PET was similar in DS and MC participants (Figure 1). We did not observe significant differences between MC and DS grouped by cognitive status (Figure 2). However, when assessed over EYO, global amyloid burden was significantly elevated in MC at EYO ≥ ‐23 but was not elevated in DS until EYO ≥ ‐15 (Figure 3). We observed early cortical and subcortical amyloid PET increases in both groups, but we also measured some regional differences in amyloid PET changes between MC and DS, specifically in the medial occipital region (Figure 4 and 5).ConclusionThese results demonstrate similarities in the relationship between amyloid biomarkers and the levels of amyloid accumulation in ADAD and DS. However, we also observed a 5‐10 year delay and some regional differences in amyloid accumulation in DS. This is important for future clinical trials to consider when recruiting participants and determining treatment efficacy.

The search for non-invasive, fast, and low-cost diagnostic tools has gained significant traction among many researchers worldwide. Dielectric properties calculated from microwave signals offer unique insights into biological tissue. Material properties, such as relative permittivity (εr) and conductivity (σ), can vary significantly between healthy and unhealthy tissue types at a given frequency. Understanding this difference in properties is key for identifying the disease state. The frequency-dependent nature of the dielectric measurements results in large datasets, which can be postprocessed using artificial intelligence (AI) methods. In this work, the dielectric properties of liver tissues in three mouse models of liver disease are characterized using dielectric spectroscopy. The measurements are grouped into four categories based on the diets or disease state of the mice, i.e., healthy mice, mice with non-alcoholic steatohepatitis (NASH) induced by choline-deficient high-fat diet, mice with NASH induced by western diet, and mice with liver fibrosis. Multi-class classification machine learning (ML) models are then explored to differentiate the liver tissue groups based on dielectric measurements. The results show that the support vector machine (SVM) model was able to differentiate the tissue groups with an accuracy up to 90%. This technology pipeline, thus, shows great potential for developing the next generation non-invasive diagnostic tools.

ImportanceLate-onset meningitis (LOM) has been associated with adverse neurodevelopmental outcomes in children born extremely preterm.ObjectiveTo report the incidence of LOM during birth hospitalization and neurodevelopmental outcomes at 18 to 26 months’ corrected age.Design, Setting, and ParticipantsThis cohort study is a secondary analysis of a multicenter prospective cohort of children born at 22 to 26 weeks’ gestation between 2003 and 2017 with follow-up from 2004 to 2021. The study was conducted at 25 Eunice Kennedy Shriver National Institute of Child Health and Human Development Neonatal Research Network centers.ExposuresCulture-confirmed LOM.Main Outcomes and MeasuresIncidence and microbiology of LOM (2003-2017); lumbar puncture (LP) performance in late-onset sepsis (LOS) evaluations (2011-2017); composite outcome of death or neurodevelopmental impairment (NDI; 2004-2021).ResultsAmong 13 372 infants (median [IQR] gestational age, 25.4 [24.4-26.1] weeks; 6864 [51%] boys), LOM was diagnosed in 167 (1%); LOS without LOM in 4564 (34%); and neither LOS nor LOM in 8641 (65%). The observed incidence of LOM decreased from 2% (95% CI, 1%-3%) in 2003 to 0.4% (95% CI, 0.7%-1.0%) in 2017 (P &amp;lt; .001). LP performance in LOS evaluations decreased from 36% (95% CI, 33%-40%) in 2011 to 24% (95% CI, 21%-27%) in 2017 (P &amp;lt; .001). Among infants with culture-confirmed LOS, LP performance decreased from 58% (95% CI, 51%-65%) to 45% (95% CI, 38%-51%; P = .008). LP performance varied by center among all LOS evaluations (10%-59%, P &amp;lt; .001) and among those with culture-confirmed LOS (23%-79%, P &amp;lt; .001). LOM occurred in the absence of concurrent LOS in 27 of 167 cases (16%). The most common LOM isolates were coagulase-negative Staphylococcus (98 [59%]), Candida albicans (38 [23%]), and Escherichia coli (27 [16%]). Death or NDI occurred in 22 of 46 children (48%) with LOM due to coagulase-negative Staphylococcus, 43 of 67 (64%) due to all other bacterial pathogens, and 26 of 33 (79%) due to fungal pathogens. The adjusted relative risk of death or NDI was increased among children with LOM (aOR, 1.53; 95% CI, 1.04-2.25) and among those with LOS without LOM (aOR, 1.41; 95% CI, 1.29-1.54) compared with children with neither infection.Conclusions and RelevanceIn this cohort study, LP was performed with decreasing frequency, and the observed incidence of LOM also decreased. Both LOM and LOS were associated with increased risk of death or NDI; risk varied by LOM pathogen. The full association of LOM with outcomes of children born extremely preterm may be underestimated by current diagnostic practices.

AbstractObjectiveThe aim of this study was to analyze a publicly available sample of rule-based phenotype definitions to characterize and evaluate the variability of logical constructs used.Materials and MethodsA sample of 33 preexisting phenotype definitions used in research that are represented using Fast Healthcare Interoperability Resources and Clinical Quality Language (CQL) was analyzed using automated analysis of the computable representation of the CQL libraries.ResultsMost of the phenotype definitions include narrative descriptions and flowcharts, while few provide pseudocode or executable artifacts. Most use 4 or fewer medical terminologies. The number of codes used ranges from 5 to 6865, and value sets from 1 to 19. We found that the most common expressions used were literal, data, and logical expressions. Aggregate and arithmetic expressions are the least common. Expression depth ranges from 4 to 27.DiscussionDespite the range of conditions, we found that all of the phenotype definitions consisted of logical criteria, representing both clinical and operational logic, and tabular data, consisting of codes from standard terminologies and keywords for natural language processing. The total number and variety of expressions are low, which may be to simplify implementation, or authors may limit complexity due to data availability constraints.ConclusionsThe phenotype definitions analyzed show significant variation in specific logical, arithmetic, and other operators but are all composed of the same high-level components, namely tabular data and logical expressions. A standard representation for phenotype definitions should support these formats and be modular to support localization and shared logic.

Background Chronic pain in cancer survivors negatively impacts quality of life. This study sought to investigate the relationship between high-impact chronic pain (HICP) -- defined as chronic pain that limits life or work activities on most days or every day in the past 3 months -- and cannabis in cancer survivors. Methods An electronic survey was developed in conjunction with the National Cancer Institute Comprehensive Cancer Centers in the United States. This survey was distributed to cancer survivors within a multi-site, single institution setting. Results The survey response rate was 23.0% (2304/10,000); 72.7% of these patients (1676/2304) did in fact have a confirmed cancer diagnosis. Among these cancer survivors, 16.5% (unweighted 278/1676) had HICP, and 12.4% (208/1676) reported cannabis use since their cancer diagnosis. The prevalence of past 30-day cannabis use was 12.3% (206/1676). Compared to cancer survivors without pain, those with HICP were more likely to believe in the benefits of cannabis (unweighted 92.1% vs. 74.7%; age-adjusted odds ratio [OR] = 3.1; 95% CI: 1.9-5.1) and less likely to believe in its risks (unweighted 48.2% vs. 58.4%; age-adjusted OR = 0.6; 95% CI: 0.4-0.7). Conclusions Cancer survivors with HICP have a higher prevalence of cannabis use compared to those patients without pain. More research is needed to advance pain and symptom management among cancer survivors and to identify clinical scenarios in which benefit is greater than potential harm.

Abstract Introduction Engaging learners in continuing medical education (CME) is challenging. Recently, CME courses have transitioned to livestreamed CME, with learners viewing live, in‐person courses online. The authors aimed to (1) compare learner engagement and teaching effectiveness in livestreamed with in‐person CME and (2) determine how livestream engagement and teaching effectiveness is associated with (A) interactivity metrics, (B) presentation characteristics and (C) medical knowledge. Methods A 3‐year, non‐randomised study of in‐person and livestream CME was performed. The course was in‐person for 2018 but transitioned to livestream for 2020 and 2021. Learners completed the Learner Engagement Inventory and Teaching Effectiveness Instrument after each presentation. Both instruments were supported by content, internal structure and relations to other variables' validity evidence. Interactivity metrics included learner use of audience response, questions asked by learners and presentation views. Presentation characteristics included presentations using audience response, using pre/post‐test format, time of day and words per slide. Medical knowledge was assessed by audience response. A repeated measures analysis of variance ( anova ) was used for comparisons and a mixed model approach for correlations. Results A total of 159 learners (response rate 27%) completed questionnaires. Engagement did not significantly differ between in‐person or livestream CME. (4.56 versus 4.53, p = 0.64, maximum 5 = highly engaged). However, teacher effectiveness scores were higher for in‐person compared with livestream (4.77 versus 4.71 p = 0.01, maximum 5 = highly effective). For livestreamed courses, learner engagement was associated with presentation characteristics, including presentation using of audience response (yes = 4.57, no = 4.45, p < .0001), use of a pre/post‐test (yes = 4.62, no = 4.54, p < .0001) and time of presentation (morning = 4.58, afternoon = 4.53, p = .0002). Significant associations were not seen for interactivity metrics or medical knowledge. Discussion Livestreaming may be as engaging as in‐person CME. Although teaching effectiveness in livestreaming was lower, this difference was small. CME course planners should consider offering livestream CME while exploring strategies to enhance teaching effectiveness in livestreamed settings.