Citations

AbstractBackgroundIn this pilot study, the diagnostic agreement for sleep biomarker‐based neurodegenerative disorder (NDD) risk probabilities was evaluated in patients with Alzheimer’s disease dementia (AD), Lewy body disease (LBD), mild cognitive impairment (MCI), and controls (CG) with a Mini‐Mental State Exam scores ≥28.MethodsSleep biomarkers recorded with the Sleep Profiler (Advanced Brain Monitoring, Inc.) were used as inputs to a 4‐class machine learning algorithm trained to assign NDD risk probabilities to AD=27, LBD=19, isolated REM sleep behavior disorder=15, and CG=58. Input variables included age, time‐REM, non‐REM hypertonia, autonomic‐activation index, spindle‐duration, atypical‐N3, time‐supine, sleep‐efficiency, relative‐theta, and theta/alpha. Distributions of NDD risk were then assigned to AD=37, LBD=19, MCI=71, and CG=106 records.Records with CG output probabilities ≥70% were labeled Probably‐Normal, and from 45‐70% Likely‐Normal with indications of a NDD added when the LBD, AD or prodromal synucleinopathy (pSYN) risks exceeded approximately 25%. Similar thresholds were used to assign Likely‐ or Probably‐ LBD, AD, pSYN, or Mixed based on the group probabilities. Likely‐Normal with indications consistent with the diagnosis trended toward agreement.ResultsThe proportions of each group with classifications consistent with the diagnosis or trended toward agreement were CG=86%, MCI=75%, LBD=79%, and AD=87%.In the CG group, NDD risk assignments were Probably‐Normal=68% and Likely‐Normal with NDD indications=18%, Likely‐NDD=10%, and Probable‐NDD=4%.The MCI group was assigned Probable‐NDD=38% (AD=26%, Mixed=10%, LBD/pSYN=2%), Likely‐NDD=17% (AD=7%, Mixed=6%, LBD=3%, pSYN=1%), Likely‐Normal with indications=20% (AD=9%, pSYN=7%, Mixed=4%) and Probably‐Normal=25%The LBD group’s assigned risks consistent with the diagnoses included Probable‐NDD=74% and Likely‐Normal with pSYN indications=6% but included inconsistencies from Probable‐AD=5%, Likely‐Normal with AD indications=5%, and Probably‐Normal=11%.AD group’s consistent risk classifications included Probable‐NDD=60%, Likely‐NDD=11%, Likely‐Normal with AD/Mixed indications=13% and countered by inconsistencies from Probable‐LBD/pSYN=8%, Likely‐Normal with pSYN indications=3%, and Probably‐Normal=5%.ConclusionsConsistencies between the diagnoses and assigned NDD risks agreements exceeded 75% in all four groups. Studies are underway to confirm the test‐retest reliability and/or evaluate NDD risk probabilities in other patient populations.Support: NIH grants R44AG050326, R44AG054256, RO1AG060477, R33AG064271, ULRR024150, R34AG056639.

AbstractBackgroundPopulation dementia prevalence is traditionally estimated using cohort studies, surveys, routinely‐collected administrative data, and registries. Hospital Electronic Health Records (EHRs) are comprised of rich structured and unstructured (text) clinical data that are underutilised for this purpose.We aimed to develop a suite of algorithms using routinely‐collected EHR data to reliably identify cases of dementia, as a key step towards incorporating such data in prevalence estimation. Towards this, we developed a novel predictive framework integrating data‐science and biostatistical methods.MethodTraining data were sourced via the National Centre for Healthy Ageing (NCHA) Data Platform, a linked, curated, EHR‐derived data warehouse. Individuals within the platform catchment aged >60 years with confirmed dementia were identified through hospital specialist dementia clinics. A comparison group of individuals aged >60years with EHR records without dementia was recruited from the community.A panel of clinical experts (Neurology, Geriatric Medicine) informed variable and concept selection and guided data cleaning efforts within both streams. Algorithms were developed via two work‐streams; a traditional biostatistical approach to fit logistic regression models using structured data elements, and a data science stream used Natural Language Processing (NLP) to fit models to the unstructured (text) parts of the EHR, for the same individuals.ResultOf 568 individuals (362 with dementia), 434 had clinical notes available. In the data science stream using unstructured data, among a range of NLP derived models, the Random Forest classifier performed best in assigning dementia status, with Area Under the Curve (AUC) 0.95, specificity 90.2% and sensitivity 88.4%. In the biostatistics stream, 15 structured variables were included in the final model, covering demographics, health service attendance, medications, and ICD‐10 Codes, with AUC 0.94, specificity 85.9% and sensitivity 85.6%.ConclusionArtificial intelligence techniques applied to unstructured electronic health data and guided by human clinical expertise may be powerful tools in capturing the presence of dementia, at least comparable to traditional techniques using structured data, and conferring practical and scientific advantages for dementia prevalence estimation. Future validation is required in less crisply delineated real‐world settings.

Abstract Imaging-guided percutaneous core needle biopsy is currently the most common technique for the investigation of potentially malignant bone lesions. It allows precise needle placement and better visual guidance, leading to improved diagnostic accuracy. Needle tract seeding (NTS) is a rare complication of biopsies in general, and its true incidence remains unknown. This study aimed to assess the risk of NTS in patients with thyroid cancer who underwent bone biopsy. For our cohort, we extracted data from the electronic medical record at the Mayo Clinic in Rochester (Minnesota, USA). Inclusion criteria included patients with a history of thyroid cancer who underwent biopsy for bone metastasis between 1/1/2014 and 10/1/2023. We identified a cohort of 20 patients that fit our inclusion criteria. Of these 20 patients, 2 patients developed NTS after CT-guided bone biopsy. Cases of seeding had a larger tumor size, a more aggressive histopathological presentation, a significantly shorter duration between cancer diagnosis and bone metastasis, and underwent more tumor manipulation procedures, such as biopsy and radiofrequency ablation, in contrast to those without seeding. In conclusion, our study identified NTS to have an incidence of 10% after biopsies of bone metastasis related to thyroid carcinoma. These are likely the result of an interplay of risk factors, including tumor biology, penetrated tissues and procedural technical details. Further studies with larger sample sizes are needed to confirm our findings and identify strategies to mitigate NTS.

Ion radiotherapy requires accurate relative biological effectiveness (RBE) calculations to account for the markedly different biological effects of ions compared to photons. Microdosimetric RBE models rely on descriptions of the energy deposition at the microscopic scale, either through radial dose distributions (RDDs) or microdosimetric probability density distributions. While RDD approaches focus on the theoretical description of the energy deposition around the ion track, microdosimetric distributions offer the advantage of being experimentally measurable, which is crucial for quality assurance programs. As the results of microdosimetric RBE models depend on whether RDD or microdosimetric distributions are used, the model parameters are not interchangeable between these approaches. This study presents and validates a method to reproduce the published reference biological and clinical dose calculations at NIRS-QST for only carbon ion radiotherapy by using the modified microdosimetric kinetic model (MKM) alongside microdosimetric distributions instead of the reference RDD approach. To achieve this, Monte Carlo simulations were performed to estimate the variation of the radiation quality within and outside the field of pristine and spread-out Bragg peaks. By appropriately optimizing the modified MKM parameters for microdosimetric distributions assessed within water spheres, we successfully reproduced the results of calculations using the reference NIRS-QST RDD, generally within 2%.

Abstract Background All Hitachi proton pencil beam scanning facilities currently use discrete spot scanning (DSS). Mayo Clinic Florida (MCF) is installing a Hitachi particle therapy system with advanced technologies, including fast scan speeds, high beam intensity, rapid beam off control (RBOC), a skip spot function, and proton pencil beam scanning using dose driven continuous scanning (DDCS). A potential concern of RBOC is the generation of a shoulder at the end of the normal spot delivery due to a flap spot (FS) with a flap dose (FD), which has been investigated for carbon synchrotron but not for proton delivery. While investigated, for instance, for Hitachi's installation at MCF, this methodology could be applicable for all future high intensity proton deliveries. Purpose No Hitachi proton facility currently uses the proposed RBOC. This study aimed to understand the dosimetric impact of proton FD at MCF by simulating the FS with a Hitachi proton machine in research mode, reflecting the higher proton intensities expected with RBOC at MCF. Method Experiments were conducted to simulate MCF RBOC at Kyoto Prefecture University of Medicine (KPUM) in research mode, reducing delay time (Td) from 1.5 ms to 0.1 ms. 5,000 contiguous spots were delivered on the central axis for proton energies of 70.2, 142.5, and 220.0 MeV; at normal, high dose rate (HDR), and ultra‐high dose rate (uHDR) intensities; and at vertical and horizontal gantry angles for different Td. Measurements were taken using a fast oscilloscope and the nozzle's spot position monitor (SPM) and dose monitor (DM). A model was developed to predict FD dependence on beam intensity and assess the dosimetric impact for prostate and brain treatment plans. Two simulation types were planned: a flap DSS plan with FS at every spot and a flap DDCS plan with FS only at the end of each layer. Result FD was observed for RBOC with Td = 0.1 ms, showing no gantry angle dependence. FD increased with higher delayed dose rate (DDR), that is, beam intensity. The planning study showed dose volume histogram deterioration with increased FD compared to the clinical plan, but it was only significant for uHDR intensities. Deterioration was marginal in flap DSS plans for the HDR intensities planned at MCF, and flap DDCS plans were even less sensitive than flap DSS plans. Conclusion MCF is installing proton DDCS with higher beam intensities, a skip spot function, and fast beam‐off control. The resulting FD had an insignificant impact on dose distribution for two patient plans with both DSS and DDCS at the anticipated MCF intensities. However, significant dependence was observed in the case of uHDR. A method to measure the position and dose of the FS during commissioning is described in addition to recommendations for regular QA and log‐based proton patient‐specific quality assurance.

BACKGROUND Previous epidemiologic studies of autoimmune diseases in the US have included a limited number of diseases or used metaanalyses that rely on different data collection methods and analyses for each disease. METHODS To estimate the prevalence of autoimmune diseases in the US, we used electronic health record data from 6 large medical systems in the US. We developed a software program using common methodology to compute the estimated prevalence of autoimmune diseases alone and in aggregate that can be readily used by other investigators to replicate or modify the analysis over time. RESULTS Our findings indicate that over 15 million people, or 4.6% of the US population, have been diagnosed with at least 1 autoimmune disease from January 1, 2011, to June 1, 2022, and 34% of those are diagnosed with more than 1 autoimmune disease. As expected, females (63% of those with autoimmune disease) were almost twice as likely as males to be diagnosed with an autoimmune disease. We identified the top 20 autoimmune diseases based on prevalence and according to sex and age. CONCLUSION Here, we provide, for what we believe to be the first time, a large-scale prevalence estimate of autoimmune disease in the US by sex and age. FUNDING Autoimmune Registry Inc., the National Heart Lung and Blood Institute, the National Center for Advancing Translational Sciences, the Intramural Research Program of the National Institute of Environmental Health Sciences.

Abstract Purpose: NFE2L2/KEAP1/CUL3 mutations have been validated for radioresistance in cell-based assays and animal models. However, clinical validation of these biomarkers has been challenging because of multimodality treatment regimens. This study aims to investigate the association between NFE2L2/KEAP1/CUL3 mutations and patient outcomes, including local failure, locoregional failure, disease-free survival (DFS), and overall survival, using samples from a phase III trial in which patients were treated with radiation monotherapy at two controlled doses. Patients and Methods: We investigated NFE2L2/KEAP1/CUL3 mutations in 250 randomized patients with T2N0 glottic squamous cell carcinoma receiving definitive radiotherapy in the NRG/RTOG 9512 trial. A total of 119 patients had available biospecimens that were subjected to amplicon-based next-generation sequencing to assess for the presence of NFE2L2/KEAP1/CUL3 mutations without regard to outcomes. Mutations in NFE2L2/KEAP1/CUL3 were assessed blinded to clinical outcomes. Cox models (two-sided α = 0.05) were used to evaluate the association with clinical outcomes, performed by an independent statistical team. Results: Nineteen of 119 patients (16.0%) had NFE2L2/KEAP1/CUL3 mutations. Patient, treatment, and tumor characteristics were similar between those with and without mutations. Patients with mutation compared with those without had significantly more local failure [HR = 3.50; 95% confidence interval (CI), 1.56–7.89; P = 0.0025] and locoregional failure (HR = 3.80; 95% CI, 1.80–8.03; P = 0.0005). DFS was significantly worse for the mutated compared with the nonmutated group in the first 2 years (HR = 2.88; 95% CI, 1.46–5.66; P = 0.0022). The median DFS was shorter in the mutation group (10.3 months) versus those with intact NFE2L2/KEAP1/CUL3 (4.2 years). Conclusions: NFE2L2/KEAP1/CUL3 mutations may predict radiation treatment failure in T2N0 glottic cancer. See related commentary by Rao, p. 1563

Abstract Biomarkers that aid in early detection of neurodegeneration are needed to enable early symptomatic treatment and enable identification of people who may benefit from neuroprotective interventions. Increasing evidence suggests that sleep biomarkers may be useful, given the bi-directional relationship between sleep and neurodegeneration and the prominence of sleep disturbances and altered sleep architectural characteristics in several neurodegenerative disorders. This study aimed to demonstrate that sleep can accurately characterize specific neurodegenerative disorders (NDD). A four-class machine-learning algorithm was trained using age and nine sleep biomarkers from patients with clinically-diagnosed manifest and prodromal NDDs, including Alzheimer’s disease dementia (AD = 27), Lewy body dementia (LBD = 18), and isolated REM sleep behavior disorder (iRBD = 15), as well as a control group (CG = 58). The algorithm was validated in a total of 381 recordings, which included the training data set plus an additional AD = 10, iRBD = 18, Parkinson disease without dementia (PD = 29), mild cognitive impairment (MCI = 78) and CG = 128. Test-retest consistency was then assessed in LBD = 10, AD = 9, and CG = 46. The agreement between the NDD profiles and their respective clinical diagnoses exceeded 75% for the AD, LBD, and CG, and improved when NDD participants classified Likely Normal with NDD indications consistent with their clinical diagnosis were considered. Profiles for iRBD, PD and MCI participants were consistent with the heterogeneity of disease severities, with the majority of overt disagreements explained by normal sleep characterization in 27% of iRBD, 21% of PD, and 26% of MCI participants. For test-retest assignments, the same or similar NDD profiles were obtained for 88% of LBD, 86% in AD, and 98% of CG participants. The potential utility for NDD subtyping based on sleep biomarkers demonstrates promise and requires further prospective development and validation in larger NDD cohorts.

OBJECTIVE To evaluate a regimen of inhaled Technosphere insulin (TI) plus insulin degludec in adults with type 1 diabetes, who prestudy were predominately using either an automated insulin delivery (AID) system or multiple daily insulin injections (MDI) with continuous glucose monitoring. RESEARCH DESIGN AND METHODS At 19 sites, adults with type 1 diabetes were randomly assigned to TI plus insulin degludec (N = 62) or usual care (UC) with continuation of prestudy insulin delivery method (N = 61) for 17 weeks. RESULTS Prestudy, AID was used by 48% and MDI by 45%. Mean ± SD HbA1c was 7.57% ± 0.97% at baseline and 7.62% ± 1.06% at 17 weeks in the TI group and 7.59% ± 0.80% and 7.54% ± 0.77%, respectively, in the UC group (adjusted difference 0.11%, 95% CI −0.10 to 0.33, P value for noninferiority = 0.01). HbA1c improved from baseline to 17 weeks by >0.5% (5.5 mmol/mol) in 12 (21%) in the TI group and in 3 (5%) in the UC group and worsened by >0.5% (5.5 mmol/mol) in 15 (26%) in the TI group and in 2 (3%) in the UC group. The most common TI side effect was a brief cough; eight participants discontinued TI due to side effects. CONCLUSIONS In adults with type 1 diabetes, HbA1c after 17 weeks with a regimen of TI and degludec was noninferior to UC, which consisted predominately of either AID or MDI. TI should be considered an option for people with type 1 diabetes, particularly those who are motivated to further reduce postprandial hyperglycemia.