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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.

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.

Abstract Rationale Idiopathic pulmonary fibrosis (IPF) is a complex and heterogeneous disease. Given this, we reasoned that differences in genetic profiles may be associated with unique clinical and radiologic features. Computational image analysis, sometimes referred to as radiomics, provides objective, quantitative assessments of radiologic features in subjects with pulmonary fibrosis. Objectives To determine if the genetic risk profile of patients with IPF identifies unique computational imaging phenotypes. Methods Participants with IPF were included in this study if they had genotype data and computed tomography (CT) scans of the chest available for computational image analysis. The extent of lung fibrosis and the likelihood of a usual interstitial pneumonia (UIP) pattern were scored automatically using two separate, previously validated deep learning techniques for CT analysis. UIP pattern was also classified visually by radiologists according to established criteria. Results Among 329 participants with IPF, MUC5B and ZKSCAN1 were independently associated with the deep learning–based UIP score. None of the common variants were associated with fibrosis extent by computational imaging. We did not find an association between MUC5B or ZKSCAN1 and visually assessed UIP pattern. Conclusions Select genetic variants are associated with computer-based classification of UIP on CT in this IPF cohort. Analysis of radiologic features using deep learning may enhance our ability to identify important genotype–phenotype associations in fibrotic lung diseases.

AbstractBackgroundIdiopathic rapid eye movement sleep behavior disorder (RBD) is a strong known predictor of a final clinicopathological diagnosis of a Lewy type α‐synucleinopathy (LTS). Olfactory dysfunction is an early symptom of synucleinopathies and has been repeatedly associated with the presence of post‐mortem LTS.ObjectiveTo assess the combined value of a clinician diagnosis of probable RBD (PRBD) and hyposmia in predicting the post‐mortem presence of LTS in a broader, less‐selected, volunteer elderly population.MethodsWe studied 652 autopsied subjects from the Arizona Study of Aging and Neurodegenerative Disorders, which were evaluated for PRBD, had completed annual movement and cognitive assessments, and had at least one the University of Pennsylvania Smell Identification Test (UPSIT) olfactory test.ResultsHistological evidence of LTS was significantly more frequent in those who had PRBD (112/152: 73.7%) than those without (177/494: 35.8%) (P < 0.001). LTS was more frequent in cases with PRBD and a low UPSIT score (90.8%) compared to cases with PRBD only (73.7%) (P < 0.001) or cases with a low UPSIT score only (69.4%) (P < 0.001). Sensitivity of PRBD diagnosis for predicting LTS was 38.8% and specificity 88.8%, whereas sensitivity of a low UPSIT score was 74.4% and specificity 73.4% (Youden's index = 0.276 for PRBD, 0.478 for UPSIT). When combining both measures, sensitivity was 34.3% and specificity increased to 97.2%.ConclusionPRBD, diagnosed without sleep study confirmation, combined with a reduced olfactory performance is highly specific for predicting post‐mortem presence of LTS. The combination of both measures may provide a cost‐effective means of predicting LTS in a broader community.

Objective We provide evidence‐based recommendations regarding the treatment of interstitial lung disease (ILD) in adults with systemic autoimmune rheumatic diseases (SARDs). Methods We developed clinically relevant population, intervention, comparator, and outcomes questions. A systematic literature review was then performed, and the available evidence was rated using the Grading of Recommendations, Assessment, Development, and Evaluation methodology. A panel of clinicians and patients reached consensus on the direction and strength of the recommendations. Results Thirty‐five recommendations were generated (including two strong recommendations) for first‐line SARD‐ILD treatment, treatment of SARD‐ILD progression despite first‐line ILD therapy, and treatment of rapidly progressive ILD. The strong recommendations were against using glucocorticoids in systemic sclerosis–ILD as a first‐line ILD therapy and after ILD progression. Otherwise, glucocorticoids are conditionally recommended for first‐line ILD treatment in all other SARDs. Conclusion This clinical practice guideline presents the first recommendations endorsed by the American College of Rheumatology and American College of Chest Physicians for the treatment of ILD in people with SARDs.

Objective We provide evidence‐based recommendations regarding the treatment of interstitial lung disease (ILD) in adults with systemic autoimmune rheumatic diseases (SARDs). Methods We developed clinically relevant population, intervention, comparator, and outcomes questions. A systematic literature review was then performed, and the available evidence was rated using the Grading of Recommendations, Assessment, Development, and Evaluation methodology. A panel of clinicians and patients reached consensus on the direction and strength of the recommendations. Results Thirty‐five recommendations were generated (including two strong recommendations) for first‐line SARD‐ILD treatment, treatment of SARD‐ILD progression despite first‐line ILD therapy, and treatment of rapidly progressive ILD. The strong recommendations were against using glucocorticoids in systemic sclerosis–ILD as a first‐line ILD therapy and after ILD progression. Otherwise, glucocorticoids are conditionally recommended for first‐line ILD treatment in all other SARDs. Conclusion This clinical practice guideline presents the first recommendations endorsed by the American College of Rheumatology and American College of Chest Physicians for the treatment of ILD in people with SARDs.

ObjectiveWe provide evidence‐based recommendations regarding screening for interstitial lung disease (ILD) and the monitoring for ILD progression in people with systemic autoimmune rheumatic diseases (SARDs), specifically rheumatoid arthritis, systemic sclerosis, idiopathic inflammatory myopathies, mixed connective tissue disease, and Sjögren disease.MethodsWe developed clinically relevant population, intervention, comparator, and outcomes questions related to screening and monitoring for ILD in patients with SARDs. A systematic literature review was performed, and the available evidence was rated using the Grading of Recommendations, Assessment, Development, and Evaluation methodology. A Voting Panel of interdisciplinary clinician experts and patients achieved consensus on the direction and strength of each recommendation.ResultsFifteen recommendations were developed. For screening people with these SARDs at risk for ILD, we conditionally recommend pulmonary function tests (PFTs) and high‐resolution computed tomography of the chest (HRCT chest); conditionally recommend against screening with 6‐minute walk test distance (6MWD), chest radiography, ambulatory desaturation testing, or bronchoscopy; and strongly recommend against screening with surgical lung biopsy. We conditionally recommend monitoring ILD with PFTs, HRCT chest, and ambulatory desaturation testing and conditionally recommend against monitoring with 6MWD, chest radiography, or bronchoscopy. We provide guidance on ILD risk factors and suggestions on frequency of testing to evaluate for the development of ILD in people with SARDs.ConclusionThis clinical practice guideline presents the first recommendations endorsed by the American College of Rheumatology and American College of Chest Physicians for the screening and monitoring of ILD in people with SARDs.

Objective We provide evidence‐based recommendations regarding screening for interstitial lung disease (ILD) and the monitoring for ILD progression in people with systemic autoimmune rheumatic diseases (SARDs), specifically rheumatoid arthritis, systemic sclerosis, idiopathic inflammatory myopathies, mixed connective tissue disease, and Sjögren disease. Methods We developed clinically relevant population, intervention, comparator, and outcomes questions related to screening and monitoring for ILD in patients with SARDs. A systematic literature review was performed, and the available evidence was rated using the Grading of Recommendations, Assessment, Development, and Evaluation methodology. A Voting Panel of interdisciplinary clinician experts and patients achieved consensus on the direction and strength of each recommendation. Results Fifteen recommendations were developed. For screening people with these SARDs at risk for ILD, we conditionally recommend pulmonary function tests (PFTs) and high‐resolution computed tomography of the chest (HRCT chest); conditionally recommend against screening with 6‐minute walk test distance (6MWD), chest radiography, ambulatory desaturation testing, or bronchoscopy; and strongly recommend against screening with surgical lung biopsy. We conditionally recommend monitoring ILD with PFTs, HRCT chest, and ambulatory desaturation testing and conditionally recommend against monitoring with 6MWD, chest radiography, or bronchoscopy. We provide guidance on ILD risk factors and suggestions on frequency of testing to evaluate for the development of ILD in people with SARDs. Conclusion This clinical practice guideline presents the first recommendations endorsed by the American College of Rheumatology and American College of Chest Physicians for the screening and monitoring of ILD in people with SARDs.

ImportancePersistent symptoms and disability following SARS-CoV-2 infection, known as post–COVID-19 condition or “long COVID,” are frequently reported and pose a substantial personal and societal burden.ObjectiveTo determine time to recovery following SARS-CoV-2 infection and identify factors associated with recovery by 90 days.Design, Setting, and ParticipantsFor this prospective cohort study, standardized ascertainment of SARS-CoV-2 infection was conducted starting in April 1, 2020, across 14 ongoing National Institutes of Health–funded cohorts that have enrolled and followed participants since 1971. This report includes data collected through February 28, 2023, on adults aged 18 years or older with self-reported SARS-CoV-2 infection.ExposurePreinfection health conditions and lifestyle factors assessed before and during the pandemic via prepandemic examinations and pandemic-era questionnaires.Main Outcomes and MeasuresProbability of nonrecovery by 90 days and restricted mean recovery times were estimated using Kaplan-Meier curves, and Cox proportional hazards regression was performed to assess multivariable-adjusted associations with recovery by 90 days.ResultsOf 4708 participants with self-reported SARS-CoV-2 infection (mean [SD] age, 61.3 [13.8] years; 2952 women [62.7%]), an estimated 22.5% (95% CI, 21.2%-23.7%) did not recover by 90 days post infection. Median (IQR) time to recovery was 20 (8-75) days. By 90 days post infection, there were significant differences in restricted mean recovery time according to sociodemographic, clinical, and lifestyle characteristics, particularly by acute infection severity (outpatient vs critical hospitalization, 32.9 days [95% CI, 31.9-33.9 days] vs 57.6 days [95% CI, 51.9-63.3 days]; log-rank P &amp;lt; .001). Recovery by 90 days post infection was associated with vaccination prior to infection (hazard ratio [HR], 1.30; 95% CI, 1.11-1.51) and infection during the sixth (Omicron variant) vs first wave (HR, 1.25; 95% CI, 1.06-1.49). These associations were mediated by reduced severity of acute infection (33.4% and 17.6%, respectively). Recovery was unfavorably associated with female sex (HR, 0.85; 95% CI, 0.79-0.92) and prepandemic clinical cardiovascular disease (HR, 0.84; 95% CI, 0.71-0.99). No significant multivariable-adjusted associations were observed for age, educational attainment, smoking history, obesity, diabetes, chronic kidney disease, asthma, chronic obstructive pulmonary disease, or elevated depressive symptoms. Results were similar for reinfections.Conclusions and RelevanceIn this cohort study, more than 1 in 5 adults did not recover within 3 months of SARS-CoV-2 infection. Recovery within 3 months was less likely in women and those with preexisting cardiovascular disease and more likely in those with COVID-19 vaccination or infection during the Omicron variant wave.