Deep Learning Classification of Usual Interstitial Pneumonia Predicts Outcomes

Rationale: Computed tomography (CT) enables noninvasive diagnosis of usual interstitial pneumonia (UIP), but enhanced image analyses are needed to overcome the limitations of visual assessment. Objectives: Apply multiple instance learning (MIL) to develop an explainable deep learning algorithm for prediction of UIP from CT and validate its performance in independent cohorts. Methods: We trained an MIL algorithm using a pooled dataset (n = 2,143) and tested it in three independent populations: data from a prior publication (n = 127), a single-institution clinical cohort (n = 239), and a national registry of patients with pulmonary fibrosis (n = 979). We tested UIP classification performance using receiver operating characteristic analysis, with histologic UIP as ground truth. Cox proportional hazards and linear mixed-effects models were used to examine associations between MIL predictions and survival or longitudinal FVC. Measurements and Main Results: In two cohorts with biopsy data, MIL improved accuracy for histologic UIP (area under the curve, 0.77 [n = 127] and 0.79 [n = 239]) compared with visual assessment (area under the curve, 0.65 and 0.71). In cohorts with survival data, MIL-UIP classifications were significant for mortality (n = 239, mortality to April 2021: unadjusted hazard ratio, 3.1; 95% confidence interval [CI], 1.96-4.91; P < 0.001; and n = 979, mortality to July 2022: unadjusted hazard ratio, 3.64; 95% CI, 2.66-4.97; P < 0.001). Individuals classified as UIP positive by the algorithm had a significantly greater annual decline in FVC than those classified as UIP negative (-88 ml/yr vs. -45 ml/yr; n = 979; P < 0.01), adjusting for extent of lung fibrosis. Conclusions: Computerized assessment using MIL identifies clinically significant features of UIP on CT. Such a method could improve confidence in radiologic assessment of patients with interstitial lung disease, potentially enabling earlier and more precise diagnosis.

Deep Learning-based Fibrosis Extent on Computed Tomography Predicts Outcome of Fibrosing Interstitial Lung Disease Independent of Visually Assessed Computed Tomography Pattern

Rationale: Radiologic pattern has been shown to predict survival in patients with fibrosing interstitial lung disease. The additional prognostic value of fibrosis extent by quantitative computed tomography (CT) is unknown. Objectives: We hypothesized that fibrosis extent provides information beyond visually assessed CT pattern that is useful for outcome prediction. Methods: We performed a retrospective analysis of chest CT, demographics, longitudinal pulmonary function, and transplantation-free survival among participants in the Pulmonary Fibrosis Foundation Patient Registry. CT pattern was classified visually according to the 2018 usual interstitial pneumonia criteria. Extent of fibrosis was objectively quantified using data-driven textural analysis. We used Kaplan-Meier plots and Cox proportional hazards and linear mixed-effects models to evaluate the relationships between CT-derived metrics and outcomes. Results: Visual assessment and quantitative analysis were performed on 979 enrollment CT scans. Linear mixed-effect modeling showed that greater baseline fibrosis extent was significantly associated with the annual rate of decline in forced vital capacity. In multivariable models that included CT pattern and fibrosis extent, quantitative fibrosis extent was strongly associated with transplantation-free survival independent of CT pattern (hazard ratio, 1.04; 95% confidence interval, 1.04-1.05; P < 0.001; C statistic = 0.73). Conclusions: The extent of lung fibrosis by quantitative CT is a strong predictor of physiologic progression and survival, independent of visually assessed CT pattern.

Quantification of Emphysema Progression at CT Using Simultaneous Volume, Noise, and Bias Lung Density Correction

Background CT attenuation is affected by lung volume, dosage, and scanner bias, leading to inaccurate emphysema progression measurements in multicenter studies. Purpose To develop and validate a method that simultaneously corrects volume, noise, and interscanner bias for lung density change estimation in emphysema progression at CT in a longitudinal multicenter study. Materials and Methods In this secondary analysis of the prospective Genetic Epidemiology of Chronic Obstructive Pulmonary Disease (COPDGene) study, lung function data were obtained from participants who completed baseline and 5-year follow-up visits from January 2008 to August 2017. CT emphysema progression was measured with volume-adjusted lung density (VALD) and compared with the joint volume-noise-bias-adjusted lung density (VNB-ALD). Reproducibility was studied under change of dosage protocol and scanner model with repeated acquisitions. Emphysema progression was visually scored in 102 randomly selected participants. A stratified analysis of clinical characteristics was performed that considered groups based on their combined lung density change measured by VALD and VNB-ALD. Results A total of 4954 COPDGene participants (mean age, 60 years ± 9 [SD]; 2511 male, 2443 female) were analyzed (1329 with repeated reduced-dose acquisition in the follow-up visit). Mean repeatability coefficients were 30 g/L ± 0.46 for VALD and 14 g/L ± 0.34 for VNB-ALD. VALD measurements showed no evidence of differences between nonprogressors and progressors (mean, -5.5 g/L ± 9.5 vs -8.6 g/L ± 9.6; P = .11), while VNB-ALD agreed with visual readings and showed a difference (mean, -0.67 g/L ± 4.8 vs -4.2 g/L ± 5.5; P < .001). Analysis of progression showed that VNB-ALD progressors had a greater decline in forced expiratory volume in 1 second (-42 mL per year vs -32 mL per year; Tukey-adjusted P = .002). Conclusion Simultaneously correcting volume, noise, and interscanner bias for lung density change estimation in emphysema progression at CT improved repeatability analyses and agreed with visual readings. It distinguished between progressors and nonprogressors and was associated with a greater decline in lung function metrics. Clinical trial registration no. NCT00608764 © RSNA, 2024 Supplemental material is available for this article. See also the editorial by Goo in this issue.

Exacerbation of Renal, Cardiovascular, and Respiratory Outcomes Associated with Changes in Climate

Exposure to environmental variables including declining air quality and increasing temperatures can exert detrimental effects on human health including acute exacerbations of chronic diseases. We aim to investigate the association between these exposures and acute health outcomes in a rural community in Colorado. Meteorological and adult emergency department visit data were retrospectively collected (2013-2017); for asthma outcomes, additional data were available (2003-2017). Daily environmental exposure data included PM₁₀, maximum daily temperature (MDT), and mean humidity and precipitation. Total daily counts of emergency department (ED) diagnoses for myocardial infarction, congestive heart failure, urolithiasis, and exacerbation of chronic obstructive pulmonary disease (COPD) and asthma, were calculated during the study period. Time series models using generalized estimating equations were fit for each disease and included all four environmental factors. Between 2013 and 2017, asthma and COPD exacerbation accounted for 30.8% and 25.4% of all ED visits (n=5,113), respectively. We found that for every 5˚C increase in MDT, the rate of urolithiasis visits increased by 13% (95% CI: 2%, 26%) and for every 10μg/m³ increase in 3-day moving average PM₁₀, the rate of urolithiasis visits increased by 7% (95% CI: 1%, 13%). The magnitude of association between 3-day moving average PM₁₀ and rate of urolithiasis visits increased with increasing MDT. The rate of asthma exacerbation significantly increased as 3-day, 7-day, and 21-day moving average PM₁₀ increased. This retrospective study on ED visits is one of the first to investigate the impact of several environmental exposures on adverse health outcomes in a rural community. Research into mitigating the negative impacts of these environmental exposures on health outcomes is needed.

Quantitative CT Evaluation of Emphysema Progression over 10 Years in the COPDGene Study

Background Long-term studies of chronic obstructive pulmonary disease (COPD) can evaluate emphysema progression. Adjustment for differences in equipment and scanning protocols of individual CT examinations have not been studied extensively. Purpose To evaluate emphysema progression in current and former smokers in the COPDGene cohort over three imaging points obtained at 5-year intervals accounting for individual CT parameters. Materials and Methods Current and former cigarette smokers enrolled between 2008 and 2011 from the COPDGene study were prospectively followed for 10 years between 2008 and 2020. Extent of emphysema as adjusted lung density (ALD) from quantitative CT was measured at baseline and at 5- and 10-year follow-up. Linear mixed models adjusted for CT technical characteristics were constructed to evaluate emphysema progression. Mean annual changes in ALD over consecutive 5-year study periods were estimated by smoking status and baseline emphysema. Results Of 8431 participants at baseline (mean age, 60 years ± 9 [SD]; 3905 female participants), 4913 were at 5-year follow-up and 1544 participants were at 10-year follow-up. There were 4134 (49%) participants who were current smokers, and 4449 (53%) participants had more than trace emphysema at baseline. Current smokers with more than trace emphysema showed the largest decline in ALD, with mean annual decreases of 1.4 g/L (95% CI: 1.2, 1.5) in the first 5 years and 0.9 g/L (95% CI: 0.7, 1.2) in the second 5 years. Accounting for CT noise, field of view, and scanner model improved model fit for estimation of emphysema progression (P < .001 by likelihood ratio test). Conclusion Evaluation at CT of emphysema progression in the COPDGene study showed that, during the span of 10 years, participants with pre-existing emphysema who continued smoking had the largest decline in ALD. Adjusting for CT equipment and protocol factors improved these longitudinal estimates. Clinical trial registration no. NCT00608764 © RSNA, 2023 Supplemental material is available for this article. See the editorial by Parraga and Kirby in this issue.

Quantitative computed tomography predicts outcomes in idiopathic pulmonary fibrosis

BACKGROUND AND OBJECTIVE

Prediction of disease course in patients with progressive pulmonary fibrosis remains challenging. The purpose of this study was to assess the prognostic value of lung fibrosis extent quantified at computed tomography (CT) using data-driven texture analysis (DTA) in a large cohort of well-characterized patients with idiopathic pulmonary fibrosis (IPF) enrolled in a national registry.

METHODS

This retrospective analysis included participants in the Australian IPF Registry with available CT between 2007 and 2016. CT scans were analysed using the DTA method to quantify the extent of lung fibrosis. Demographics, longitudinal pulmonary function and quantitative CT metrics were compared using descriptive statistics. Linear mixed models, and Cox analyses adjusted for age, gender, BMI, smoking history and treatment with anti-fibrotics were performed to assess the relationships between baseline DTA, pulmonary function metrics and outcomes.

RESULTS

CT scans of 393 participants were analysed, 221 of which had available pulmonary function testing obtained within 90 days of CT. Linear mixed-effect modelling showed that baseline DTA score was significantly associated with annual rate of decline in forced vital capacity and diffusing capacity of carbon monoxide. In multivariable Cox proportional hazard models, greater extent of lung fibrosis was associated with poorer transplant-free survival (hazard ratio [HR] 1.20, p < 0.0001) and progression-free survival (HR 1.14, p < 0.0001).

CONCLUSION

In a multi-centre observational registry of patients with IPF, the extent of fibrotic abnormality on baseline CT quantified using DTA is associated with outcomes independent of pulmonary function.

Signatures of Mitochondrial Dysfunction and Impaired Fatty Acid Metabolism in Plasma of Patients with Post-Acute Sequelae of COVID-19 (PASC)

Exercise intolerance is a major manifestation of post-acute sequelae of severe acute respiratory syndrome coronavirus infection (PASC, or "long-COVID"). Exercise intolerance in PASC is associated with higher arterial blood lactate accumulation and lower fatty acid oxidation rates during graded exercise tests to volitional exertion, suggesting altered metabolism and mitochondrial dysfunction. It remains unclear whether the profound disturbances in metabolism that have been identified in plasma from patients suffering from acute coronavirus disease 2019 (COVID-19) are also present in PASC. To bridge this gap, individuals with a history of previous acute COVID-19 infection that did not require hospitalization were enrolled at National Jewish Health (Denver, CO, USA) and were grouped into those that developed PASC (n = 29) and those that fully recovered (n = 16). Plasma samples from the two groups were analyzed via mass spectrometry-based untargeted metabolomics and compared against plasma metabolic profiles of healthy control individuals (n = 30). Observational demographic and clinical data were retrospectively abstracted from the medical record. Compared to plasma of healthy controls or individuals who recovered from COVID-19, PASC plasma exhibited significantly higher free- and carnitine-conjugated mono-, poly-, and highly unsaturated fatty acids, accompanied by markedly lower levels of mono-, di- and tricarboxylates (pyruvate, lactate, citrate, succinate, and malate), polyamines (spermine) and taurine. Plasma from individuals who fully recovered from COVID-19 exhibited an intermediary metabolic phenotype, with milder disturbances in fatty acid metabolism and higher levels of spermine and taurine. Of note, depletion of tryptophan-a hallmark of disease severity in COVID-19-is not normalized in PASC patients, despite normalization of kynurenine levels-a tryptophan metabolite that predicts mortality in hospitalized COVID-19 patients. In conclusion, PASC plasma metabolites are indicative of altered fatty acid metabolism and dysfunctional mitochondria-dependent lipid catabolism. These metabolic profiles obtained at rest are consistent with previously reported mitochondrial dysfunction during exercise, and may pave the way for therapeutic intervention focused on restoring mitochondrial fat-burning capacity.

Emphysema Progression at CT by Deep Learning Predicts Functional Impairment and Mortality: Results from the COPDGene Study

Background Visual assessment remains the standard for evaluating emphysema at CT; however, it is time consuming, is subjective, requires training, and is affected by variability that may limit sensitivity to longitudinal change. Purpose To evaluate the clinical and imaging significance of increasing emphysema severity as graded by a deep learning algorithm on sequential CT scans in cigarette smokers. Materials and Methods A secondary analysis of the prospective Genetic Epidemiology of Chronic Obstructive Pulmonary Disease (COPDGene) study participants was performed and included baseline and 5-year follow-up CT scans from 2007 to 2017. Emphysema was classified automatically according to the Fleischner emphysema grading system at baseline and 5-year follow-up using a deep learning model. Baseline and change in clinical and imaging parameters at 5-year follow-up were compared in participants whose emphysema progressed versus those who did not. Kaplan-Meier analysis and multivariable Cox regression were used to assess the relationship between emphysema score progression and mortality. Results A total of 5056 participants (mean age, 60 years ± 9 [SD]; 2566 men) were evaluated. At 5-year follow-up, 1293 of the 5056 participants (26%) had emphysema progression according to the Fleischner grading system. This group demonstrated progressive airflow obstruction (forced expiratory volume in 1 second [percent predicted]: -3.4 vs -1.8), a greater decline in 6-minute walk distance (-177 m vs -124 m), and greater progression in quantitative emphysema extent (adjusted lung density: -1.4 g/L vs 0.5 g/L; percentage of lung voxels with CT attenuation less than -950 HU: 0.6 vs 0.2) than those with nonprogressive emphysema (P < .001 for each). Multivariable Cox regression analysis showed a higher mortality rate in the group with emphysema progression, with an estimated hazard ratio of 1.5 (95% CI: 1.2, 1.8; P < .001). Conclusion An increase in Fleischner emphysema grade on sequential CT scans using an automated deep learning algorithm was associated with increased functional impairment and increased risk of mortality. ClinicalTrials.gov registration no. NCT00608764 © RSNA, 2022 Online supplemental material is available for this article. See also the editorial by Grenier in this issue.

Predicting COPD Progression in Current and Former Smokers Using a Joint Model for Forced Expiratory Volume in 1 Second and Forced Expiratory Volume in 1 Second to Forced Vital Capacity Ratio

Understanding baseline characteristics that can predict the progression of lung disease such as chronic obstructive pulmonary disease (COPD) for current or former smokers may allow for therapeutic intervention, particularly for individuals at high risk of rapid disease progression or transition from non-COPD to COPD. Classic diagnostic criteria for COPD and disease severity such as the Global Initiative for Chronic Obstructive Lung Disease document are based on forced expiratory volume in 1 second (FEV1) and FEV1 to forced vital capacity (FVC) ratio. Modeling changes in these outcomes jointly is beneficial given that they are correlated, and they are both required for specific disease classifications. Here, linear mixed models were used to model changes in FEV1 and FEV1/FVC jointly for 5- and 10-year intervals, using important baseline predictors to better understand the factors that affect disease progression. Participants with predicted loss of FEV1 and/or FEV1/FVC of at least 5% tended to have more emphysema, higher functional residual capacity, higher airway wall thickness as measured by Pi10, lower FVC to total lung capacity ratio and a lower body mass index at baseline, all relative to overall cohort averages. The model developed can be used to predict progression for any potential COPD individual, based on demographic, symptom, computed tomography, and comorbidity variables.

Mental health disorders among ovarian cancer survivors in a population-based cohort

BACKGROUND

Ovarian cancer is the fifth most common female cancer in the United States. There have been very few studies investigating mental health diagnoses among ovarian cancer survivors with long-term follow up. The aim of this study is to examine the incidence of mental illness among ovarian cancer survivors compared to a general population cohort. A secondary aim is to investigate risk factors for mental illnesses among ovarian cancer survivors.

PATIENTS AND METHODS

Cohorts of 1689 ovarian cancer patients diagnosed between 1996 and 2012 and 7038 women without cancer matched by age and birth state from the general population were identified. Mental health diagnoses were identified from electronic medical records and statewide healthcare facilities data. Cox proportional hazard models were used to estimate hazard ratios (HRs).

RESULTS

Ovarian cancer survivors experienced increased risks of mental illnesses within the first 2 years after cancer diagnosis (HR = 3.55, 95% CI = 3.04-4.14). The risks of depression among ovarian cancer survivors were nearly 3-fold within the first 2 years of cancer diagnosis (HR = 2.59, 95% CI = 1.94-3.47), and 1.69-fold at 2-5 years after cancer diagnosis (HR = 1.69, 95% CI = 1.18-2.42). Ovarian cancer survivors experienced an 80% increased risk of death with a mental illness diagnosis (HR = 1.80, 95% CI = 1.48-2.18) and a 94% increased risk of death with a depression diagnosis (HR = 1.94, 95% CI = 1.56-2.40).

CONCLUSIONS

Higher risks of mental illnesses were observed among ovarian cancer survivors throughout the follow-up periods of 0-2 years and 2-5 years after cancer diagnosis. Multidisciplinary care is needed to monitor and treat mental illnesses among ovarian cancer survivors.

Improved executive function in patients with systemic lupus erythematosus following interactive digital training

OBJECTIVE

This study aimed to evaluate the sensitivity of a digital platform to assess attentional and executive function in systemic lupus erythematosus (SLE) patients, and to evaluate the impact of an at-home interactive digital treatment to improve cognitive dysfunction in this clinical population.

BACKGROUND

Deficits in attention and executive function are common in patients with SLE. Despite these cognitive difficulties, there are limited brief assessment techniques and few treatment options to improve cognitive abilities in patients with SLE. Interactive digital treatment approaches (use of video game-based software) have been successful in identifying and improving cognition in other clinical populations.

METHODS

Sixty SLE patients completed baseline neuropsychological tests (of attention, psychomotor speed, and executive function), a tablet-based digital platform (EVO^TM Monitor), and biobehavioral measures. The patients were randomized into treatment SLE (n = 30) or no contact control SLE (n = 30) groups, and returned 4 weeks later for follow-up cognitive, EVO Monitor, and biobehavioral testing. The SLE treatment group was trained on a tablet-based digital treatment (AKL-T01) and was instructed to complete 5 sessions at least 5 days per week for 4-weeks for a total of approximately 25 min of gameplay per day.

RESULTS

Systemic lupus erythematosus patients demonstrated impairment in visuomotor and processing speed, visual attention, and cognitive flexibility/sequencing skills at baseline. The video game-like treatment group (AKL-T01) had significant improvement in visuomotor speed (Trail Making A) and cognitive flexibility/sequencing (Trail Making B) compared to the control group at 4-week follow-up. The treatment group also demonstrated significant improvement in EVO Monitor multitasking at follow-up (with no change in controls). At baseline, a multitasking metric from EVO Monitor was associated with performance on tasks of cognitive flexibility (Trail Making B) and psychomotor speed (WAIS-IV Coding).

CONCLUSIONS

These findings provide evidence that SLE patients who participated in a 4-week interactive digital video game-like activity had significant improvement in motor speed and executive functions, and would benefit from participation in digital interventions designed to target frontoparietal networks of the brain. Preliminary findings also suggest specific metrics from EVO Monitor may also be useful to detect cognitive impairment and cognitive changes in patients with SLE.

Whole genome sequencing identifies novel genetic mutations in patients with eczema herpeticum

BACKGROUND

Eczema herpeticum (EH) is a rare complication of atopic dermatitis (AD) caused by disseminated herpes simplex virus (HSV) infection. The role of rare and/or deleterious genetic variants in disease etiology is largely unknown. This study aimed to identify genes that harbor damaging genetic variants associated with HSV infection in AD with a history of recurrent eczema herpeticum (ADEH+).

METHODS

Whole genome sequencing (WGS) was performed on 49 recurrent ADEH+ (≥3 EH episodes), 491 AD without a history of eczema herpeticum (ADEH-) and 237 non-atopic control (NA) subjects. Variants were annotated, and a gene-based approach (SKAT-O) was used to identify genes harboring damaging genetic variants associated with ADEH+. Genes identified through WGS were studied for effects on HSV responses and keratinocyte differentiation.

RESULTS

Eight genes were identified in the comparison of recurrent ADEH+to ADEH-and NA subjects: SIDT2, CLEC7A, GSTZ1, TPSG1, SP110, RBBP8NL, TRIM15, and FRMD3. Silencing SIDT2 and RBBP8NL in normal human primary keratinocytes (NHPKs) led to significantly increased HSV-1 replication. SIDT2-silenced NHPKs had decreased gene expression of IFNk and IL1b in response to HSV-1 infection. RBBP8NL-silenced NHPKs had decreased gene expression of IFNk, but increased IL1b. Additionally, silencing SIDT2 and RBBP8NL also inhibited gene expression of keratinocyte differentiation markers keratin 10 (KRT10) and loricrin (LOR).

CONCLUSION

SIDT2 and RBBP8NL participate in keratinocyte's response to HSV-1 infection. SIDT2 and RBBP8NL also regulate expression of keratinocyte differentiation genes of KRT10 and LOR.

Abstract 900: Mental health disorders among ovarian cancer survivors in a population-based cohort

Ovarian cancer is the fifth most common female cancer in the United States. Although there have been many studies on self-reported QOL among ovarian cancer patients, there have been very few studies investigating mental disease diagnoses among ovarian cancer survivors with long term follow up. The aim of this study is to examine the incidence of mental illness among ovarian cancer survivors compared to a general population cohort. A secondary aim is to investigate risk factors for mental illnesses among ovarian cancer survivors. Cohorts of 1,689 ovarian cancer patients diagnosed between 1996 and 2012 and 7,038 women without cancer matched by age, birth state and follow up time from the general population were identified. Mental health diagnoses were identified from electronic medical records and statewide healthcare facilities data. Cox proportional hazard models were used to estimate hazard ratios (HRs). Ovarian cancer survivors experienced increased risks of mental illnesses within the first two years after cancer diagnosis (HR=3.48, 95%CI=2.98-4.05) compared to the general population. The risks of depression among ovarian cancer survivors were 3-fold within the first two years of cancer diagnosis (HR=3.11, 95%CI=2.53-3.83), and 1.67-fold at 2-5 years after cancer diagnosis (HR=1.67, 95%CI=1.17-2.38). The risk of anxiety disorder among ovarian cancer survivors was 3.54-fold at 0-2 years (HR= 3.54, 95% CI= 2.87-4.38), and 1.86-fold at 2-5 years (HR= 1.86, 95% CI= 1.14-3.01). Elevated risk for adjustment disorders was observed among ovarian cancer survivors compared with the general population cohort between 0-2 years (HR= 3.96, 95% CI= 1.00-15.84) and between 2-5 years (HR= 3.96, 95% CI= 1.00-15.84). Cancer treatment and later diagnosis year were associated with increased risk of any mental illness at 0-2 years after cancer diagnosis among ovarian cancer survivors. Distant-stage cancer was an important risk factor compared to early-stage for both mental illness and depression among ovarian cancer survivors in all time periods. Ovarian cancer patients who had a mucinous histology subtype had 47% decreased risk of any mental illness and 67% decreased risk of depression at 0-2 years, compare to those with high-grade serous histology subtype. In addition, a baseline CCI score of 1+ and older age at diagnosis (&gt;60 years old) were important for the increased risk of depression at 0-2 years or &gt;5 year after cancer diagnosis, respectively. Ovarian cancer survivors experienced an 80% increased risk of death with a mental illness diagnosis (HR=1.80, 95% CI=1.48-2.18) and a 94% increased risk of death with a depression diagnosis (HR=1.94, 95% CI=1.56-2.40).Higher risks of mental illnesses were observed among ovarian cancer survivors throughout the follow-up periods of 0-2 years and 2-5 years after cancer diagnosis. Multidisciplinary care is needed to monitor and treat mental illnesses among ovarian cancer survivors.

Citation Format: Siqi Hu, David Baraghoshi, Esther Chang, Kerry Rowe, John Snyder, Vikrant Deshmukh, Michael Newman, Alison Fraser, David Gaffney, Ken Smith, Kimberly Herget, Anita Peoples, Mia Hashibe. Mental health disorders among ovarian cancer survivors in a population-based cohort [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 900.

Factors Associated with Persistence of Severe Asthma from Late Adolescence to Early Adulthood

RATIONALE

Asthma severity in children generally starts mild but may progress and stay severe for unknown reasons.

OBJECTIVES

Identify factors in childhood that predict persistence of severe asthma in late adolescence and early adulthood.

METHODS

The Childhood Asthma Management Program is the largest and longest asthma trial in 1041 children aged 5-12 years with mild to moderate asthma. We evaluated 682 participants from the program with analyzable data in late adolescence (age 17-19) and early adulthood (age 21-23).

MEASUREMENTS

Severe asthma was defined using criteria from the American Thoracic Society and the National Asthma Education and Prevention Program to best capture severe asthma. Logistic regression with stepwise elimination was used to analyze clinical features, biomarkers, and lung function predictive of persistence of severe asthma.

MAIN RESULTS

In late adolescence and early adulthood 12% and 19% of the patents had severe asthma, respectively; only 6% were severe at both time periods. For every 5% decrease in post bronchodilator FEV1/FVC in childhood, the odds of persistence of severe asthma increased 2.36-fold (95% CI: 1.70-3.28; p <0.0001), for participants with maternal smoking during pregnancy odds of persistence of severe asthma increased 3.17-fold (95% CI: 1.18-8.53, p=0.02). Reduced growth lung function trajectory was significantly associated with persistence of severe asthma compared to normal growth.

CONCLUSIONS

Lung function and maternal smoking during pregnancy were significant predictors of severe asthma from late adolescence to early adulthood. Interventions to preserve lung function early may prevent disease progression.

Respiratory exacerbations are associated with muscle loss in current and former smokers

OBJECTIVES

Muscle wasting is a recognised extra-pulmonary complication in chronic obstructive pulmonary disease and has been associated with increased risk of death. Acute respiratory exacerbations are associated with reduction of muscle function, but there is a paucity of data on their long-term effect. This study explores the relationship between acute respiratory exacerbations and long-term muscle loss using serial measurements of CT derived pectoralis muscle area (PMA).

DESIGN AND SETTING

Participants were included from two prospective, longitudinal, observational, multicentre cohorts of ever-smokers with at least 10 pack-year history.

PARTICIPANTS

The primary analysis included 1332 (of 2501) participants from Evaluation of COPD Longitudinally to Identify Predictive Surrogate Endpoints (ECLIPSE) and 4384 (of 10 198) participants from Genetic Epidemiology of COPD (COPDGene) who had complete data from their baseline and follow-up visits.

INTERVENTIONS

PMA was measured on chest CT scans at two timepoints. Self-reported exacerbation data were collected from participants in both studies through the use of periodic longitudinal surveys.

MAIN OUTCOME MEASURES

Age-related and excess muscle loss over time.

RESULTS

Age, sex, race and body mass index were associated with baseline PMA. Participants experienced age-related decline at the upper end of reported normal ranges. In ECLIPSE, the exacerbation rate over time was associated with an excess muscle area loss of 1.3% (95% CI 0.6 to 1.9, p<0.001) over 3 years and in COPDGene with an excess muscle area loss of 2.1% (95% CI 1.2 to 2.8, p<0.001) over 5 years. Excess muscle area decline was absent in 273 individuals who participated in pulmonary rehabilitation.

CONCLUSIONS

Exacerbations are associated with accelerated skeletal muscle loss. Each annual exacerbation was associated with the equivalent of 6 months of age-expected decline in muscle mass. Ameliorating exacerbation-associated muscle loss represents an important therapeutic target.

Fleischner Society Visual Emphysema CT Patterns Help Predict Progression of Emphysema in Current and Former Smokers: Results from the COPDGene Study

Background The correlation between visual emphysema patterns and subsequent progression of disease may provide a way to enrich a study population for treatment trials of emphysema. Purpose To evaluate the potential relationship between emphysema visual subtypes and progression of emphysema and gas trapping. Materials and Methods Current and former smokers with and without chronic obstructive pulmonary disease (COPD) enrolled in the prospective Genetic Epidemiology of COPD (COPDGene) study (ClinicalTrials.gov identifier: NCT02445183) between 2008 and 2011 had their Fleischner Society visual CT scores assessed at baseline, quantitative inspiratory, and expiratory CT and at 5 years. They also underwent pulmonary function testing at baseline CT and at 5 years. The dependent variables were inspiratory lung density at 15th percentile (adjusted for lung volume) as a measure of emphysema and percentage of lung volume with attenuation less than -856 HU at expiratory CT as a measure of air trapping. Statistical analysis used a linear mixed model, adjusted for age, height, sex, race, smoking status, and scanner make. Results A total of 4166 participants (mean age, 60 years ± 9 [standard deviation]; 2091 [50%] men) were evaluated. In participants with COPD (1655 participants, 40%), those with visual presence of mild, moderate, and confluent emphysema at baseline CT showed a mean decline in lung density of 4.6 g/L ± 1.1 (P < .001), 6.7 g/L ± 1.1 (P < .001), and 6.4 g/L ± 1.2 (P < .001), respectively, compared with 2.4 g/L ± 1.3 (P < .001) for those with trace emphysema. For participants without COPD, those with visual presence of mild and moderate emphysema at baseline CT showed a mean decline in lung density of 3.6 g/L ± 1.0 (P < .001) and 3.1 g/L ± 1.6 (P < .001), respectively, compared with 1.8 g/L ± 1.0 (P < .001) for those with trace emphysema. Conclusion The pattern of parenchymal emphysema at baseline CT was an independent predictor of subsequent progression of emphysema in participants who are current or former cigarette smokers with and without chronic obstructive pulmonary disease. © RSNA, 2020 Online supplemental material is available for this article.

Long-term risk of cardiovascular disease among colorectal cancer survivors in a population-based cohort study

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Background: In the United States, colorectal cancer is the fourth most common cancer and one of the leading causes of cancer death. Few studies have examined the relationship between colorectal cancer survivorship and long-term cardiovascular disease (CVD) risk. Methods: Individuals diagnosed with colorectal cancer were identified using the Utah Population Database. For a comparison group, up to 5 cancer-free individuals were matched by birth year, birth state, follow-up time and sex to each cancer case. For individuals with > 10 years of follow-up, we estimated CVD risk > 10 years after cancer diagnosis. Cox regression models were used to estimate hazard ratios (HR) and 95% Confidence Intervals. Results: Among 1,749 colorectal cancer survivors who had survived for at least 10 years, 1,001 (57.2%) were diagnosed with CVD > 10 years after cancer diagnosis. Compared to the general population, colorectal cancer survivors had an increased risk of CVD > 10 years after cancer diagnosis: HR = 2.84 (95% CI = 2.59, 3.11) for hypertension; HR = 2.66 (95% CI 2.37, 2.98) for diseases of the heart; HR = 3.91 (95% CI = 3.33, 4.58) for diseases of the arteries, arterioles and capillaries; HR = 2.58 (95% CI = 2.46, 2.99) for diseases of the veins and lymphatics; HR = 2.98 (95% CI = 2.36, 3.76) for cerebrovascular disease. Colorectal cancer survivors with ≥1 comorbidity had an increased risk of CVD > 10 years after cancer diagnosis compared to survivors with no comorbidities (HR = 1.7, 95% CI = 1.49, 1.95). Colorectal cancer survivors who were ≥65 years had an increased risk of CVD > 10 years after cancer diagnosis. Colorectal cancer survivors who were obese at the time of diagnosis had an increased risk of CVD > 10 years after cancer diagnosis when compared to survivors with normal BMIs (HR = 1.25; 95% CI = 1.06, 1.49). Conclusions: Compared to the general population, colorectal cancer survivors had an increased risk of CVD during the > 10 year follow-up period. Within colorectal cancer survivors, there was an increased risk of CVD for those that were older, had ≥1 comorbidity and were obese. The increased risk of CVD among survivors may be attributable to the lifestyle risk factors shared by colorectal cancer and CVD.