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Delirium is a detrimental mental condition often seen in older, hospitalized patients and is currently hard to predict. In this study, we leverage electronic health records (EHR) to identify 7,492 UCSF patients and 19,417 UC health system patients with an inpatient delirium diagnosis and the same number of control patients without delirium. We found significant associations between comorbidities or laboratory values and an inpatient delirium diagnosis, including metabolic abnormalities and psychiatric diagnoses. Some associations were sex-specific, including dementia subtypes and infections. We further explored the associations with anemia and bipolar disorder by conducting longitudinal analyses from the time of first diagnosis to development of delirium, demonstrating a significant relationship across time. Finally, we show that an inpatient delirium diagnosis leads to increased risk of mortality. These results demonstrate the powerful application of the EHR to shed insights into prior diagnoses and laboratory values that could help predict development of inpatient delirium and the importance of sex when making these assessments.
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BACKGROUND: Frontotemporal lobar degeneration (FTLD) is a leading cause of dementia in individuals aged <65 years. Several challenges to conducting in-person evaluations in FTLD illustrate an urgent need to develop remote, accessible, and low-burden assessment techniques. Studies of unobtrusive monitoring of at-home computer use in older adults with mild cognitive impairment show that declining function is reflected in reduced computer use; however, associations with smartphone use are unknown. OBJECTIVE: This study aims to characterize daily trajectories in smartphone battery use, a proxy for smartphone use, and examine relationships with clinical indicators of severity in FTLD. METHODS: Participants were 231 adults (mean age 52.5, SD 14.9 years; n=94, 40.7% men; n=223, 96.5% non-Hispanic White) enrolled in the Advancing Research and Treatment of Frontotemporal Lobar Degeneration (ARTFL study) and Longitudinal Evaluation of Familial Frontotemporal Dementia Subjects (LEFFTDS study) Longitudinal Frontotemporal Lobar Degeneration (ALLFTD) Mobile App study, including 49 (21.2%) with mild neurobehavioral changes and no functional impairment (ie, prodromal FTLD), 43 (18.6%) with neurobehavioral changes and functional impairment (ie, symptomatic FTLD), and 139 (60.2%) clinically normal adults, of whom 55 (39.6%) harbored heterozygous pathogenic or likely pathogenic variants in an autosomal dominant FTLD gene. Participants completed the Clinical Dementia Rating plus National Alzheimer's Coordinating Center Frontotemporal Lobar Degeneration Behavior and Language Domains (CDR+NACC FTLD) scale, a neuropsychological battery; the Neuropsychiatric Inventory; and brain magnetic resonance imaging. The ALLFTD Mobile App was installed on participants' smartphones for remote, passive, and continuous monitoring of smartphone use. Battery percentage was collected every 15 minutes over an average of 28 (SD 4.2; range 14-30) days. To determine whether temporal patterns of battery percentage varied as a function of disease severity, linear mixed effects models examined linear, quadratic, and cubic effects of the time of day and their interactions with each measure of disease severity on battery percentage. Models covaried for age, sex, smartphone type, and estimated smartphone age. RESULTS: The CDR+NACC FTLD global score interacted with time on battery percentage such that participants with prodromal or symptomatic FTLD demonstrated less change in battery percentage throughout the day (a proxy for less smartphone use) than clinically normal participants (P<.001 in both cases). Additional models showed that worse performance in all cognitive domains assessed (ie, executive functioning, memory, language, and visuospatial skills), more neuropsychiatric symptoms, and smaller brain volumes also associated with less battery use throughout the day (P<.001 in all cases). CONCLUSIONS: These findings support a proof of concept that passively collected data about smartphone use behaviors associate with clinical impairment in FTLD. This work underscores the need for future studies to develop and validate passive digital markers sensitive to longitudinal clinical decline across neurodegenerative diseases, with potential to enhance real-world monitoring of neurobehavioral change.
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Demência Frontotemporal , Smartphone , Humanos , Feminino , Masculino , Pessoa de Meia-Idade , Demência Frontotemporal/diagnóstico , Demência Frontotemporal/fisiopatologia , Idoso , Índice de Gravidade de Doença , Estudo de Prova de Conceito , Adulto , Estudos Longitudinais , Testes Neuropsicológicos , Aplicativos MóveisRESUMO
Importance: Frontotemporal lobar degeneration (FTLD) is relatively rare, behavioral and motor symptoms increase travel burden, and standard neuropsychological tests are not sensitive to early-stage disease. Remote smartphone-based cognitive assessments could mitigate these barriers to trial recruitment and success, but no such tools are validated for FTLD. Objective: To evaluate the reliability and validity of smartphone-based cognitive measures for remote FTLD evaluations. Design, Setting, and Participants: In this cohort study conducted from January 10, 2019, to July 31, 2023, controls and participants with FTLD performed smartphone application (app)-based executive functioning tasks and an associative memory task 3 times over 2 weeks. Observational research participants were enrolled through 18 centers of a North American FTLD research consortium (ALLFTD) and were asked to complete the tests remotely using their own smartphones. Of 1163 eligible individuals (enrolled in parent studies), 360 were enrolled in the present study; 364 refused and 439 were excluded. Participants were divided into discovery (n = 258) and validation (n = 102) cohorts. Among 329 participants with data available on disease stage, 195 were asymptomatic or had preclinical FTLD (59.3%), 66 had prodromal FTLD (20.1%), and 68 had symptomatic FTLD (20.7%) with a range of clinical syndromes. Exposure: Participants completed standard in-clinic measures and remotely administered ALLFTD mobile app (app) smartphone tests. Main Outcomes and Measures: Internal consistency, test-retest reliability, association of smartphone tests with criterion standard clinical measures, and diagnostic accuracy. Results: In the 360 participants (mean [SD] age, 54.0 [15.4] years; 209 [58.1%] women), smartphone tests showed moderate-to-excellent reliability (intraclass correlation coefficients, 0.77-0.95). Validity was supported by association of smartphones tests with disease severity (r range, 0.38-0.59), criterion-standard neuropsychological tests (r range, 0.40-0.66), and brain volume (standardized ß range, 0.34-0.50). Smartphone tests accurately differentiated individuals with dementia from controls (area under the curve [AUC], 0.93 [95% CI, 0.90-0.96]) and were more sensitive to early symptoms (AUC, 0.82 [95% CI, 0.76-0.88]) than the Montreal Cognitive Assessment (AUC, 0.68 [95% CI, 0.59-0.78]) (z of comparison, -2.49 [95% CI, -0.19 to -0.02]; P = .01). Reliability and validity findings were highly similar in the discovery and validation cohorts. Preclinical participants who carried pathogenic variants performed significantly worse than noncarrier family controls on 3 app tasks (eg, 2-back ß = -0.49 [95% CI, -0.72 to -0.25]; P < .001) but not a composite of traditional neuropsychological measures (ß = -0.14 [95% CI, -0.42 to 0.14]; P = .32). Conclusions and Relevance: The findings of this cohort study suggest that smartphones could offer a feasible, reliable, valid, and scalable solution for remote evaluations of FTLD and may improve early detection. Smartphone assessments should be considered as a complementary approach to traditional in-person trial designs. Future research should validate these results in diverse populations and evaluate the utility of these tests for longitudinal monitoring.
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Demência Frontotemporal , Degeneração Lobar Frontotemporal , Adulto , Idoso , Feminino , Humanos , Masculino , Pessoa de Meia-Idade , Estudos de Coortes , Demência Frontotemporal/diagnóstico , Degeneração Lobar Frontotemporal/diagnóstico , Degeneração Lobar Frontotemporal/patologia , Degeneração Lobar Frontotemporal/psicologia , Testes Neuropsicológicos , Reprodutibilidade dos Testes , Smartphone , Ensaios Clínicos como AssuntoRESUMO
Background and Objectives: TMEM106B has been proposed as a modifier of disease risk in FTLD-TDP, particularly in GRN mutation carriers. Furthermore, TMEM106B has been investigated as a disease modifier in the context of healthy aging and across multiple neurodegenerative diseases. The objective of this study is to evaluate and compare the effect of TMEM106B on gray matter volume and cognition in each of the common genetic FTD groups and in sporadic FTD patients. Methods: Participants were enrolled through the ARTFL/LEFFTDS Longitudinal Frontotemporal Lobar Degeneration (ALLFTD) study, which includes symptomatic and presymptomatic individuals with a pathogenic mutation in C9orf72, GRN, MAPT, VCP, TBK1, TARDBP, symptomatic non-mutation carriers, and non-carrier family controls. All participants were genotyped for the TMEM106B rs1990622 SNP. Cross-sectionally, linear mixed-effects models were fitted to assess an association between TMEM106B and genetic group interaction with each outcome measure (gray matter volume and UDS3-EF for cognition), adjusting for education, age, sex and CDR®+NACC-FTLD sum of boxes. Subsequently, associations between TMEM106B and each outcome measure were investigated within the genetic group. For longitudinal modeling, linear mixed-effects models with time by TMEM106B predictor interactions were fitted. Results: The minor allele of TMEM106B rs1990622, linked to a decreased risk of FTD, associated with greater gray matter volume in GRN mutation carriers under the recessive dosage model. This was most pronounced in the thalamus in the left hemisphere, with a retained association when considering presymptomatic GRN mutation carriers only. The minor allele of TMEM106B rs1990622 also associated with greater cognitive scores among all C9orf72 mutation carriers and in presymptomatic C9orf72 mutation carriers, under the recessive dosage model. Discussion: We identified associations of TMEM106B with gray matter volume and cognition in the presence of GRN and C9orf72 mutations. This further supports TMEM106B as modifier of TDP-43 pathology. The association of TMEM106B with outcomes of interest in presymptomatic GRN and C9orf72 mutation carriers could additionally reflect TMEM106B's impact on divergent pathophysiological changes before the appearance of clinical symptoms.
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The pathophysiological mechanisms driving disease progression of frontotemporal lobar degeneration (FTLD) and corresponding biomarkers are not fully understood. We leveraged aptamer-based proteomics (> 4,000 proteins) to identify dysregulated communities of co-expressed cerebrospinal fluid proteins in 116 adults carrying autosomal dominant FTLD mutations (C9orf72, GRN, MAPT) compared to 39 noncarrier controls. Network analysis identified 31 protein co-expression modules. Proteomic signatures of genetic FTLD clinical severity included increased abundance of RNA splicing (particularly in C9orf72 and GRN) and extracellular matrix (particularly in MAPT) modules, as well as decreased abundance of synaptic/neuronal and autophagy modules. The generalizability of genetic FTLD proteomic signatures was tested and confirmed in independent cohorts of 1) sporadic progressive supranuclear palsy-Richardson syndrome and 2) frontotemporal dementia spectrum syndromes. Network-based proteomics hold promise for identifying replicable molecular pathways in adults living with FTLD. 'Hub' proteins driving co-expression of affected modules warrant further attention as candidate biomarkers and therapeutic targets.
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Purpose To compare quantitative measures of tumor metabolism and perfusion using fluorine 18 (18F) fluorodeoxyglucose (FDG) dedicated breast PET (dbPET) and breast dynamic contrast-enhanced (DCE) MRI during early treatment with neoadjuvant chemotherapy (NAC). Materials and Methods Prospectively collected DCE MRI and 18F-FDG dbPET examinations were analyzed at baseline (T0) and after 3 weeks (T1) of NAC in 20 participants with 22 invasive breast cancers. FDG dbPET-derived standardized uptake value (SUV), metabolic tumor volume, and total lesion glycolysis (TLG) and MRI-derived percent enhancement (PE), signal enhancement ratio (SER), and functional tumor volume (FTV) were calculated at both time points. Differences between FDG dbPET and MRI parameters were evaluated after stratifying by receptor status, Ki-67 index, and residual cancer burden. Parameters were compared using Wilcoxon signed rank and Mann-Whitney U tests. Results High Ki-67 tumors had higher baseline SUVmean (difference, 5.1; P = .01) and SUVpeak (difference, 5.5; P = .04). At T1, decreases were observed in FDG dbPET measures (pseudo-median difference T0 minus T1 value [95% CI]) of SUVmax (-6.2 [-10.2, -2.6]; P < .001), SUVmean (-2.6 [-4.9, -1.3]; P < .001), SUVpeak (-4.2 [-6.9, -2.3]; P < .001), and TLG (-29.1 mL3 [-71.4, -6.8]; P = .005) and MRI measures of SERpeak (-1.0 [-1.3, -0.2]; P = .02) and FTV (-11.6 mL3 [-22.2, -1.7]; P = .009). Relative to nonresponsive tumors, responsive tumors showed a difference (95% CI) in percent change in SUVmax of -34.3% (-55.9%, 1.5%; P = .06) and in PEpeak of -42.4% (95% CI: -110.5%, 8.5%; P = .08). Conclusion 18F-FDG dbPET was sensitive to early changes during NAC and provided complementary information to DCE MRI that may be useful for treatment response evaluation. Keywords: Breast, PET, Dynamic Contrast-enhanced MRI Clinical trial registration no. NCT01042379 Supplemental material is available for this article. © RSNA, 2024.
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Neoplasias da Mama , Fluordesoxiglucose F18 , Humanos , Feminino , Fluordesoxiglucose F18/uso terapêutico , Terapia Neoadjuvante , Antígeno Ki-67 , Tomografia por Emissão de Pósitrons/métodos , Neoplasias da Mama/diagnóstico por imagem , Neoplasias da Mama/tratamento farmacológico , Neoplasias da Mama/metabolismo , Imageamento por Ressonância MagnéticaRESUMO
COVID-19 increased the prevalence of clinically significant anxiety in the United States. To investigate contributing factors we analyzed anxiety, reported online via monthly Generalized Anxiety Disorders-7 (GAD-7) surveys between April 2020 and May 2022, in association with self-reported worry about the health effects of COVID-19, economic difficulty, personal COVID-19 experience, and subjective social status. 333,292 anxiety surveys from 50,172 participants (82% non-Hispanic white; 73% female; median age 55, IQR 42-66) showed high levels of anxiety, especially early in the pandemic. Anxiety scores showed strong independent associations with worry about the health effects of COVID-19 for oneself or family members (GAD-7 score +3.28 for highest vs. lowest category; 95% confidence interval: 3.24, 3.33; p<0.0001 for trend) and with difficulty paying for basic living expenses (+2.06; 1.97, 2.15, p<0.0001) in multivariable regression models after adjusting for demographic characteristics, COVID-19 case rates and death rates, and personal COVID-19 experience. High levels of COVID-19 health worry and economic stress were each more common among participants reporting lower subjective social status, and median anxiety scores for those experiencing both were in the range considered indicative of moderate to severe clinical anxiety disorders. In summary, health worry and economic difficulty both contributed to high rates of anxiety during the first two years of the COVID-19 pandemic in the US, especially in disadvantaged socioeconomic groups. Programs to address both health concerns and economic insecurity in vulnerable populations could help mitigate pandemic impacts on anxiety and mental health.
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COVID-19 , Ciência do Cidadão , Humanos , Feminino , Estados Unidos , Pessoa de Meia-Idade , Masculino , COVID-19/epidemiologia , Pandemias , SARS-CoV-2 , Depressão/epidemiologia , Ansiedade/psicologia , Transtornos de Ansiedade/epidemiologiaRESUMO
Purpose To investigate the impact of longitudinal variation in functional tumor volume (FTV) underestimation and overestimation in predicting pathologic complete response (pCR) after neoadjuvant chemotherapy (NAC). Materials and Methods Women with breast cancer who were enrolled in the prospective I-SPY 2 TRIAL (Investigation of Serial Studies to Predict Your Therapeutic Response with Imaging and Molecular Analysis 2) from May 2010 to November 2016 were eligible for this retrospective analysis. Participants underwent four MRI examinations during NAC treatment. FTV was calculated based on automated segmentation. Baseline FTV before treatment (FTV0) and the percentage of FTV change at early treatment and inter-regimen time points relative to baseline (∆FTV1 and ∆FTV2, respectively) were classified into high-standard or standard groups based on visual assessment of FTV under- and overestimation. Logistic regression models predicting pCR using single predictors (FTV0, ∆FTV1, and ∆FTV2) and multiple predictors (all three) were developed using bootstrap resampling with out-of-sample data evaluation with the area under the receiver operating characteristic curve (AUC) independently in each group. Results This study included 432 women (mean age, 49.0 years ± 10.6 [SD]). In the FTV0 model, the high-standard and standard groups showed similar AUCs (0.61 vs 0.62). The high-standard group had a higher estimated AUC compared with the standard group in the ∆FTV1 (0.74 vs 0.63), ∆FTV2 (0.79 vs 0.62), and multiple predictor models (0.85 vs 0.64), with a statistically significant difference for the latter two models (P = .03 and P = .01, respectively). Conclusion The findings in this study suggest that longitudinal variation in FTV estimation needs to be considered when using early FTV change as an MRI-based criterion for breast cancer treatment personalization. Keywords: Breast, Cancer, Dynamic Contrast-enhanced, MRI, Tumor Response ClinicalTrials.gov registration no. NCT01042379 Supplemental material is available for this article. © RSNA, 2023 See also the commentary by Ram in this issue.
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Neoplasias da Mama , Feminino , Humanos , Pessoa de Meia-Idade , Neoplasias da Mama/diagnóstico por imagem , Neoplasias da Mama/tratamento farmacológico , Terapia Neoadjuvante/métodos , Carga Tumoral , Estudos Retrospectivos , Estudos Prospectivos , Resultado do Tratamento , Imageamento por Ressonância Magnética/métodosRESUMO
Background Although several clinical breast cancer risk models are used to guide screening and prevention, they have only moderate discrimination. Purpose To compare selected existing mammography artificial intelligence (AI) algorithms and the Breast Cancer Surveillance Consortium (BCSC) risk model for prediction of 5-year risk. Materials and Methods This retrospective case-cohort study included data in women with a negative screening mammographic examination (no visible evidence of cancer) in 2016, who were followed until 2021 at Kaiser Permanente Northern California. Women with prior breast cancer or a highly penetrant gene mutation were excluded. Of the 324 009 eligible women, a random subcohort was selected, regardless of cancer status, to which all additional patients with breast cancer were added. The index screening mammographic examination was used as input for five AI algorithms to generate continuous scores that were compared with the BCSC clinical risk score. Risk estimates for incident breast cancer 0 to 5 years after the initial mammographic examination were calculated using a time-dependent area under the receiver operating characteristic curve (AUC). Results The subcohort included 13 628 patients, of whom 193 had incident cancer. Incident cancers in eligible patients (additional 4391 of 324 009) were also included. For incident cancers at 0 to 5 years, the time-dependent AUC for BCSC was 0.61 (95% CI: 0.60, 0.62). AI algorithms had higher time-dependent AUCs than did BCSC, ranging from 0.63 to 0.67 (Bonferroni-adjusted P < .0016). Time-dependent AUCs for combined BCSC and AI models were slightly higher than AI alone (AI with BCSC time-dependent AUC range, 0.66-0.68; Bonferroni-adjusted P < .0016). Conclusion When using a negative screening examination, AI algorithms performed better than the BCSC risk model for predicting breast cancer risk at 0 to 5 years. Combined AI and BCSC models further improved prediction. © RSNA, 2023 Supplemental material is available for this article.
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Neoplasias da Mama , Feminino , Humanos , Neoplasias da Mama/diagnóstico por imagem , Neoplasias da Mama/epidemiologia , Inteligência Artificial , Estudos Retrospectivos , Estudos de Coortes , Mamografia/métodos , Algoritmos , Detecção Precoce de Câncer/métodosRESUMO
BACKGROUND: Complex systems models of breast cancer have previously focused on prediction of prognosis and clinical events for individual women. There is a need for understanding breast cancer at the population level for public health decision-making, for identifying gaps in epidemiologic knowledge and for the education of the public as to the complexity of this most common of cancers. METHODS AND FINDINGS: We developed an agent-based model of breast cancer for the women of the state of California using data from the U.S. Census, the California Health Interview Survey, the California Cancer Registry, the National Health and Nutrition Examination Survey and the literature. The model was implemented in the Julia programming language and R computing environment. The Paradigm II model development followed a transdisciplinary process with expertise from multiple relevant disciplinary experts from genetics to epidemiology and sociology with the goal of exploring both upstream determinants at the population level and pathophysiologic etiologic factors at the biologic level. The resulting model reproduces in a reasonable manner the overall age-specific incidence curve for the years 2008-2012 and incidence and relative risks due to specific risk factors such as BRCA1, polygenic risk, alcohol consumption, hormone therapy, breastfeeding, oral contraceptive use and scenarios for environmental toxin exposures. CONCLUSIONS: The Paradigm II model illustrates the role of multiple etiologic factors in breast cancer from domains of biology, behavior and the environment. The value of the model is in providing a virtual laboratory to evaluate a wide range of potential interventions into the social, environmental and behavioral determinants of breast cancer at the population level.
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Neoplasias da Mama , Feminino , Humanos , Neoplasias da Mama/epidemiologia , Neoplasias da Mama/etiologia , Inquéritos Nutricionais , Fatores de Risco , Consumo de Bebidas Alcoólicas , IncidênciaRESUMO
Introduction: Remote smartphone assessments of cognition, speech/language, and motor functioning in frontotemporal dementia (FTD) could enable decentralized clinical trials and improve access to research. We studied the feasibility and acceptability of remote smartphone data collection in FTD research using the ALLFTD Mobile App (ALLFTD-mApp). Methods: A diagnostically mixed sample of 214 participants with FTD or from familial FTD kindreds (asymptomatic: CDR®+NACC-FTLD = 0 [N = 101]; prodromal: 0.5 [N = 49]; symptomatic ≥1 [N = 51]; not measured [N = 13]) were asked to complete ALLFTD-mApp tests on their smartphone three times within 12 days. They completed smartphone familiarity and participation experience surveys. Results: It was feasible for participants to complete the ALLFTD-mApp on their own smartphones. Participants reported high smartphone familiarity, completed â¼ 70% of tasks, and considered the time commitment acceptable (98% of respondents). Greater disease severity was associated with poorer performance across several tests. Discussion: These findings suggest that the ALLFTD-mApp study protocol is feasible and acceptable for remote FTD research. HIGHLIGHTS: The ALLFTD Mobile App is a smartphone-based platform for remote, self-administered data collection.The ALLFTD Mobile App consists of a comprehensive battery of surveys and tests of executive functioning, memory, speech and language, and motor abilities.Remote digital data collection using the ALLFTD Mobile App was feasible in a multicenter research consortium that studies FTD. Data was collected in healthy controls and participants with a range of diagnoses, particularly FTD spectrum disorders.Remote digital data collection was well accepted by participants with a variety of diagnoses.
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BACKGROUND: Measuring systemic inflammatory markers may improve clinical prognosis and help identify targetable pathways for treatment in patients with autosomal dominant forms of frontotemporal lobar degeneration (FTLD). METHODS: We measured plasma concentrations of IL-6, TNFα and YKL-40 in pathogenic variant carriers (MAPT, C9orf72, GRN) and non-carrier family members enrolled in the ARTFL-LEFFTDS Longitudinal Frontotemporal Lobar Degeneration consortium. We evaluated associations between baseline plasma inflammation and rate of clinical and neuroimaging changes (linear mixed effects models with standardised (z) outcomes). We compared inflammation between asymptomatic carriers who remained clinically normal ('asymptomatic non-converters') and those who became symptomatic ('asymptomatic converters') using area under the curve analyses. Discrimination accuracy was compared with that of plasma neurofilament light chain (NfL). RESULTS: We studied 394 participants (non-carriers=143, C9orf72=117, GRN=62, MAPT=72). In MAPT, higher TNFα was associated with faster functional decline (B=0.12 (0.02, 0.22), p=0.02) and temporal lobe atrophy. In C9orf72, higher TNFα was associated with faster functional decline (B=0.09 (0.03, 0.16), p=0.006) and cognitive decline (B=-0.16 (-0.22, -0.10), p<0.001), while higher IL-6 was associated with faster functional decline (B=0.12 (0.03, 0.21), p=0.01). TNFα was higher in asymptomatic converters than non-converters (ß=0.29 (0.09, 0.48), p=0.004) and improved discriminability compared with plasma NfL alone (ΔR2=0.16, p=0.007; NfL: OR=1.4 (1.03, 1.9), p=0.03; TNFα: OR=7.7 (1.7, 31.7), p=0.007). CONCLUSIONS: Systemic proinflammatory protein measurement, particularly TNFα, may improve clinical prognosis in autosomal dominant FTLD pathogenic variant carriers who are not yet exhibiting severe impairment. Integrating TNFα with markers of neuronal dysfunction like NfL could optimise detection of impending symptom conversion in asymptomatic pathogenic variant carriers and may help personalise therapeutic approaches.
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Demência Frontotemporal , Degeneração Lobar Frontotemporal , Humanos , Proteína C9orf72/genética , Progressão da Doença , Demência Frontotemporal/diagnóstico , Degeneração Lobar Frontotemporal/diagnóstico , Degeneração Lobar Frontotemporal/genética , Degeneração Lobar Frontotemporal/patologia , Inflamação , Interleucina-6 , Mutação , Proteínas tau/genética , Fator de Necrose Tumoral alfaRESUMO
Background: Few prospective studies of Long COVID risk factors have been conducted. The purpose of this study was to determine whether sociodemographic factors, lifestyle, or medical history preceding COVID-19 or characteristics of acute severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection are associated with Long COVID. Methods: In March 26, 2020, the COVID-19 Citizen Science study, an online cohort study, began enrolling participants with longitudinal assessment of symptoms before, during, and after SARS-CoV-2 infection. Adult participants who reported a positive SARS-CoV-2 test result before April 4, 2022 were surveyed for Long COVID symptoms. The primary outcome was at least 1 prevalent Long COVID symptom greater than 1 month after acute infection. Exposures of interest included age, sex, race/ethnicity, education, employment, socioeconomic status/financial insecurity, self-reported medical history, vaccination status, variant wave, number of acute symptoms, pre-COVID depression, anxiety, alcohol and drug use, sleep, and exercise. Results: Of 13 305 participants who reported a SARS-CoV-2 positive test, 1480 (11.1%) responded. Respondents' mean age was 53 and 1017 (69%) were female. Four hundred seventy-six (32.2%) participants reported Long COVID symptoms at a median 360 days after infection. In multivariable models, number of acute symptoms (odds ratio [OR], 1.30 per symptom; 95% confidence interval [CI], 1.20-1.40), lower socioeconomic status/financial insecurity (OR, 1.62; 95% CI, 1.02-2.63), preinfection depression (OR, 1.08; 95% CI, 1.01-1.16), and earlier variants (OR = 0.37 for Omicron compared with ancestral strain; 95% CI, 0.15-0.90) were associated with Long COVID symptoms. Conclusions: Variant wave, severity of acute infection, lower socioeconomic status, and pre-existing depression are associated with Long COVID symptoms.
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INTRODUCTION: Empathy relies on fronto-cingular and temporal networks that are selectively vulnerable in behavioral variant frontotemporal dementia (bvFTD). This study modeled when in the disease process empathy changes begin, and how they progress. METHODS: Four hundred thirty-one individuals with asymptomatic genetic FTD (n = 114), genetic and sporadic bvFTD (n = 317), and 163 asymptomatic non-carrier controls were enrolled. In sub-samples, we investigated empathy measured by the informant-based Interpersonal Reactivity Index (IRI) at each disease stage and over time (n = 91), and its correspondence to underlying atrophy (n = 51). RESULTS: Empathic concern (estimate = 4.38, 95% confidence interval [CI] = 2.79, 5.97; p < 0.001) and perspective taking (estimate = 5.64, 95% CI = 3.81, 7.48; p < 0.001) scores declined between the asymptomatic and very mild symptomatic stages regardless of pathogenic variant status. More rapid loss of empathy corresponded with subcortical atrophy. DISCUSSION: Loss of empathy is an early and progressive symptom of bvFTD that is measurable by IRI informant ratings and can be used to monitor behavior in neuropsychiatry practice and treatment trials.
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Empatia , Demência Frontotemporal , Humanos , Demência Frontotemporal/diagnóstico , Testes Neuropsicológicos , Atrofia , Imageamento por Ressonância MagnéticaRESUMO
Importance: Prolonged symptoms following SARS-CoV-2 infection, or Long COVID, is common, but few prospective studies of Long COVID risk factors have been conducted. Objective: To determine whether sociodemographic factors, lifestyle, or medical history preceding COVID-19 or characteristics of acute SARS-CoV-2 infection are associated with Long COVID. Design: Cohort study with longitudinal assessment of symptoms before, during, and after SARS-CoV-2 infection, and cross-sectional assessment of Long COVID symptoms using data from the COVID-19 Citizen Science (CCS) study. Setting: CCS is an online cohort study that began enrolling March 26, 2020. We included data collected between March 26, 2020, and May 18, 2022. Participants: Adult CCS participants who reported a positive SARS-CoV-2 test result (PCR, Antigen, or Antibody) more than 30 days prior to May 4, 2022, were surveyed. Exposures: Age, sex, race/ethnicity, education, employment, socioeconomic status/financial insecurity, self-reported medical history, vaccination status, time of infection (variant wave), number of acute symptoms, pre-COVID depression, anxiety, alcohol and drug use, sleep, exercise. Main Outcome: Presence of at least 1 Long COVID symptom greater than 1 month after acute infection. Sensitivity analyses were performed considering only symptoms beyond 3 months and only severe symptoms. Results: 13,305 participants reported a SARS-CoV-2 positive test more than 30 days prior, 1480 (11.1% of eligible) responded to a survey about Long COVID symptoms, and 476 (32.2% of respondents) reported Long COVID symptoms (median 360 days after infection).Respondents' mean age was 53 and 1017 (69%) were female. Common Long COVID symptoms included fatigue, reported by 230/476 (48.3%), shortness of breath (109, 22.9%), confusion/brain fog (108, 22.7%), headache (103, 21.6%), and altered taste or smell (98, 20.6%). In multivariable models, number of acute COVID-19 symptoms (OR 1.30 per symptom, 95%CI 1.20-1.40), lower socioeconomic status/financial insecurity (OR 1.62, 95%CI 1.02-2.63), pre-infection depression (OR 1.08, 95%CI 1.01-1.16), and earlier variants (OR 0.37 for Omicron compared to ancestral strain, 95%CI 0.15-0.90) were associated with Long COVID symptoms. Conclusions and Relevance: Variant wave, severity of acute infection, lower socioeconomic status and pre-existing depression are associated with Long COVID symptoms. Key Points: Question: What are the patterns of symptoms and risk factors for Long COVID among SARS-CoV-2 infected individuals?Findings: Persistent symptoms were highly prevalent, especially fatigue, shortness of breath, headache, brain fog/confusion, and altered taste/smell, which persisted beyond 1 year among 56% of participants with symptoms; a minority of participants reported severe Long COVID symptoms. Number of acute symptoms during acute SARS-CoV-2 infection, financial insecurity, pre-existing depression, and infection with earlier variants are associated with prevalent Long COVID symptoms independent of vaccination, medical history, and other factors.Meaning: Severity of acute infection, SARS-CoV-2 variant, and financial insecurity and depression are associated with Long COVID symptoms.
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This study tested the hypothesis that a change in the apparent diffusion coefficient (ADC) measured in diffusion-weighted MRI (DWI) is an independent imaging marker, and ADC performs better than functional tumor volume (FTV) for assessing treatment response in patients with locally advanced breast cancer receiving neoadjuvant immunotherapy. A total of 249 patients were randomized to standard neoadjuvant chemotherapy with pembrolizumab (pembro) or without pembrolizumab (control). DCE-MRI and DWI, performed prior to and 3 weeks after the start of treatment, were analyzed. Percent changes of tumor ADC metrics (mean, 5th to 95th percentiles of ADC histogram) and FTV were evaluated for the prediction of pathologic complete response (pCR) using a logistic regression model. The area under the ROC curve (AUC) estimated for the percent change in mean ADC was higher in the pembro cohort (0.73, 95% confidence interval [CI]: 0.52 to 0.93) than in the control cohort (0.63, 95% CI: 0.43 to 0.83). In the control cohort, the percent change of the 95th percentile ADC achieved the highest AUC, 0.69 (95% CI: 0.52 to 0.85). In the pembro cohort, the percent change of the 25th percentile ADC achieved the highest AUC, 0.75 (95% CI: 0.55 to 0.95). AUCs estimated for percent change of FTV were 0.61 (95% CI: 0.39 to 0.83) and 0.66 (95% CI: 0.47 to 0.85) for the pembro and control cohorts, respectively. Tumor ADC may perform better than FTV to predict pCR at an early treatment time-point during neoadjuvant immunotherapy.
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Early detection of neurodegeneration, and prediction of when neurodegenerative diseases will lead to symptoms, are critical for developing and initiating disease modifying treatments for these disorders. While each neurodegenerative disease has a typical pattern of early changes in the brain, these disorders are heterogeneous, and early manifestations can vary greatly across people. Methods for detecting emerging neurodegeneration in any part of the brain are therefore needed. Prior publications have described the use of Bayesian linear mixed-effects (BLME) modeling for characterizing the trajectory of change across the brain in healthy controls and patients with neurodegenerative disease. Here, we use an extension of such a model to detect emerging neurodegeneration in cognitively healthy individuals at risk for dementia. We use BLME to quantify individualized rates of volume loss across the cerebral cortex from the first two MRIs in each person and then extend the BLME model to predict future values for each voxel. We then compare observed values at subsequent time points with the values that were expected from the initial rates of change and identify voxels that are lower than the expected values, indicating accelerated volume loss and neurodegeneration. We apply the model to longitudinal imaging data from cognitively normal participants in the Alzheimer's Disease Neuroimaging Initiative (ADNI), some of whom subsequently developed dementia, and two cognitively normal cases who developed pathology-proven frontotemporal lobar degeneration (FTLD). These analyses identified regions of accelerated volume loss prior to or accompanying the earliest symptoms, and expanding across the brain over time, in all cases. The changes were detected in regions that are typical for the likely diseases affecting each patient, including medial temporal regions in patients at risk for Alzheimer's disease, and insular, frontal, and/or anterior/inferior temporal regions in patients with likely or proven FTLD. In the cases where detailed histories were available, the first regions identified were consistent with early symptoms. Furthermore, survival analysis in the ADNI cases demonstrated that the rate of spread of accelerated volume loss across the brain was a statistically significant predictor of time to conversion to dementia. This method for detection of neurodegeneration is a potentially promising approach for identifying early changes due to a variety of diseases, without prior assumptions about what regions are most likely to be affected first in an individual.
Assuntos
Doença de Alzheimer , Demência Frontotemporal , Degeneração Lobar Frontotemporal , Doenças Neurodegenerativas , Humanos , Doença de Alzheimer/diagnóstico por imagem , Doenças Neurodegenerativas/diagnóstico por imagem , Teorema de Bayes , Encéfalo/diagnóstico por imagem , Demência Frontotemporal/diagnóstico por imagem , Imageamento por Ressonância MagnéticaRESUMO
This study evaluated the inter-reader agreement of tumor apparent diffusion coefficient (ADC) measurements performed on breast diffusion-weighted imaging (DWI) for assessing treatment response in a multi-center clinical trial of neoadjuvant chemotherapy (NAC) for breast cancer. DWIs from 103 breast cancer patients (mean age: 46 ± 11 years) acquired at baseline and after 3 weeks of treatment were evaluated independently by two readers. Three types of tumor regions of interests (ROIs) were delineated: multiple-slice restricted, single-slice restricted and single-slice tumor ROIs. Compared to tumor ROIs, restricted ROIs were limited to low ADC areas of enhancing tumor only. We found excellent agreement (intraclass correlation coefficient [ICC] ranged from 0.94 to 0.98) for mean ADC. Higher ICCs were observed in multiple-slice restricted ROIs (range: 0.97 to 0.98) than in other two ROI types (both in the range of 0.94 to 0.98). Among the three ROI types, the highest area under the receiver operating characteristic curves (AUCs) were observed for mean ADC of multiple-slice restricted ROIs (0.65, 95% confidence interval [CI]: 0.52-0.79 and 0.67, 95% CI: 0.53-0.81 for Reader 1 and Reader 2, respectively). In conclusion, mean ADC values of multiple-slice restricted ROI showed excellent agreement and similar predictive performance for pathologic complete response between the two readers.
Assuntos
Neoplasias da Mama , Adulto , Mama , Neoplasias da Mama/diagnóstico por imagem , Neoplasias da Mama/tratamento farmacológico , Neoplasias da Mama/patologia , Imagem de Difusão por Ressonância Magnética/métodos , Feminino , Humanos , Pessoa de Meia-Idade , Variações Dependentes do Observador , Reprodutibilidade dos TestesRESUMO
BACKGROUND AND OBJECTIVES: Changes in social behavior are common symptoms of frontotemporal lobar degeneration (FTLD) and Alzheimer's disease syndromes. For early identification of individual patients and differential diagnosis, sensitive clinical measures are required that are able to assess patterns of behaviors and detect syndromic differences in both asymptomatic and symptomatic stages. We investigated whether the examiner-based Social Behavior Observer Checklist (SBOCL) is sensitive to early behavior changes and reflects disease severity within and between neurodegenerative syndromes. METHODS: Asymptomatic individuals and neurodegenerative disease patients were selected from the multisite ALLFTD cohort study. In a sample of participants with at least one timepoint of SBOCL data, we investigated whether the Disorganized, Reactive, and Insensitive subscales of the SBOCL change as a function of disease stage within and between these syndromes. In a longitudinal subsample with both SBOCL and neuroimaging data, we examined whether change over time on each subscale corresponds to progressive gray matter atrophy. RESULTS: 1082 FTLD mutation carriers and non-carriers were enrolled (282 asymptomatic, 341 behavioral variant frontotemporal dementia, 114 semantic and 95 non-fluent variant primary progressive aphasia, 137 progressive supranuclear palsy, 113 Alzheimer's clinical syndrome). The Disorganized score increased between asymptomatic to very mild (p=0.016, estimate=-1.10, 95%CI=[-1.99, -0.22]), very mild to mild (p=0.013, -1.17, [-2.08, -0.26]), and mild to moderate/severe (p<0.001, -2.00, [-2.55, -1.45]) disease stages in behavioral variant frontotemporal dementia regardless of mutation status. Asymptomatic GRN pathogenic gene variant carriers showed more Reactive behaviors (preoccupation with time: p=0.001, 1.11, [1.06, 1.16]; self-consciousness: p=0.003, 1.77, [1.52, 2.01]) than asymptomatic non-carriers (1.01, [0.98, 1.03]; 1.31, [1.20, 1.41]). Insensitive score increased to a clinically abnormal level in advanced stages of behavioral variant frontotemporal dementia (p=0.003, -0.73, [-1.18, -0.29]). Higher scores on each subscale corresponded with higher caregiver burden (p<0.001). Greater change over time corresponded to greater fronto-subcortical atrophy in the semantic-appraisal and fronto-parietal intrinsically connected networks. DISCUSSION: The SBOCL is sensitive to early symptoms and reflects disease severity, with some evidence for progression across asymptomatic and symptomatic stages of FTLD syndromes; thus it may hold promise for early measurement and monitoring of behavioral symptoms in clinical practice and treatment trials. CLASSIFICATION OF EVIDENCE: This study provides Class II evidence that the Social Behavior Observer Checklist is sensitive to early behavioral changes in FTLD pathogenic variants and early symptomatic individuals in a highly educated patient cohort.
RESUMO
Background parenchymal enhancement (BPE) of breast fibroglandular tissue (FGT) in dynamic contrast-enhanced breast magnetic resonance imaging (MRI) has shown an association with response to neoadjuvant chemotherapy (NAC) in patients with breast cancer. Fully automated segmentation of FGT for BPE calculation is a challenge when image artifacts are present. Low spatial frequency intensity nonuniformity due to coil sensitivity variations is known as bias or inhomogeneity and can affect FGT segmentation and subsequent BPE measurement. In this study, we utilized the N4ITK algorithm for bias correction over a restricted bilateral breast volume and compared the contralateral FGT segmentations based on uncorrected and bias-corrected images in three MRI examinations at pre-treatment, early treatment and inter-regimen timepoints during NAC. A retrospective analysis of 2 cohorts was performed: one with 735 patients enrolled in the multi-center I-SPY 2 TRIAL and the sub-cohort of 340 patients meeting a high-quality benchmark for segmentation. Bias correction substantially increased the FGT segmentation quality for 6.3-8.0% of examinations, while it substantially decreased the quality for no examination. Our results showed improvement in segmentation quality and a small but statistically significant increase in the resulting BPE measurement after bias correction at all timepoints in both cohorts. Continuing studies are examining the effects on pCR prediction.