PURPOSE: International clinical practice guidelines call for initial volume resuscitation of at least 30 mL/kg body weight for patients with sepsis-induced hypotension or shock. Although not considered in the guidelines, preexisting cardiac dysfunction may be an important factor clinicians weigh in deciding the quantity of volume resuscitation for patients with septic shock. METHODS: We conducted a multicenter survey of clinicians who routinely treat patients with sepsis to evaluate their beliefs, behaviors, knowledge, and perceived structural barriers regarding initial volume resuscitation for patients with sepsis and concomitant heart failure with reduced ejection fraction (HFrEF) <40%. Initial volume resuscitation preferences were captured as ordinal values, and additional testing for volume resuscitation preferences was performed using McNemar and Wilcoxon signed rank tests as indicated. Univariable logistic regression models were used to identify significant predictors of ≥30 mL/kg fluid administration. RESULTS: A total of 317 clinicians at 9 US hospitals completed the survey (response rate 47.3%). Most respondents were specialists in either internal medicine or emergency medicine. Substantial heterogeneity was found regarding sepsis resuscitation preferences for patients with concomitant HFrEF. The belief that patients with septic shock and HFrEF should be exempt from current sepsis bundle initiatives was shared by 39.4% of respondents. A minimum fluid challenge of ∼30 mL/kg or more was deemed appropriate in septic shock by only 56.4% of respondents for patients with concomitant HFrEF, compared to 89.1% of respondents for patients without HFrEF (P < .01). Emergency medicine physicians were most likely to feel that <30 mL/kg was most appropriate in patients with septic shock and HFrEF. CONCLUSIONS: Clinical equipoise exists regarding initial volume resuscitation for patients with sepsis-induced hypotension or shock and concomitant HFrEF. Future studies and clinical practice guidelines should explicitly address resuscitation in this subpopulation.

RATIONALE: Chronic obstructive pulmonary disease (COPD) comprises distinct phenotypes, all characterised by airflow limitation. OBJECTIVES: We hypothesised that somatotype changes - as a surrogate of adiposity - from early adulthood follow different trajectories to reach distinct phenotypes. METHODS: Using the validated Stunkard's Pictogram, 356 COPD patients chose the somatotype that best reflects their current body build and those at ages 18, 30, 40 and 50 years. An unbiased group-based trajectory modelling was used to determine somatotype trajectories. We then compared the current COPD-related clinical and phenotypic characteristics of subjects belonging to each trajectory. MEASUREMENTS AND MAIN RESULTS: At 18 years of age, 88% of the participants described having a lean or medium somatotype (estimated body mass index (BMI) between 19 and 23 kg·m-2) while the other 12% a heavier somatotype (estimated BMI between 25 and 27 kg·m-2). From age 18 onwards, five distinct trajectories were observed. Four of them demonstrating a continuous increase in adiposity throughout adulthood with the exception of one, where the initial increase was followed by loss of adiposity after age 40. Patients with this trajectory were primarily females with low BMI and D LCO (diffusing capacity of the lung for carbon monoxide). A persistently lean trajectory was seen in 14% of the cohort. This group had significantly lower forced expiratory volume in 1 s (FEV1), D LCO, more emphysema and a worse BODE (BMI, airflow obstruction, dyspnoea and exercise capacity) score thus resembling the multiple organ loss of tissue (MOLT) phenotype. CONCLUSIONS: COPD patients have distinct somatotype trajectories throughout adulthood. Those with the MOLT phenotype maintain a lean trajectory throughout life. Smoking subjects with this lean phenotype in early adulthood deserve particular attention as they seem to develop more severe COPD.

The location of the mitral and aortic valves in dynamic cardiac imaging is useful for extracting functional derived parameters such as ejection fraction, valve excursions, and global longitudinal strain, and when performing anatomical structures tracking using slice following or valve intervention's planning. Completely automatic segmentation methods are still challenging tasks because of their fast movements and the different positions that prevent good visibility of the leaflets along the full cardiac cycle. In this article, we propose a processing pipeline to track the displacement of the aortic and mitral valve annuli from high-resolution cardiac four-dimensional computed tomographic angiography (4D-CTA). The proposed method is based on the dynamic separation of left ventricle, left atrium and aorta using statistical shape modeling and an energy minimization algorithm based on graph-cuts and has been evaluated on a set of 15 electrocardiography-gated 4D-CTAs. We report a mean agreement distance between manual annotations and our proposed method of 2.52±1.06 mm for the mitral annulus and 2.00±0.69 mm for the aortic valve annulus based on valve locations detected from manual anatomical landmarks. In addition, we show the effect of detecting the valvular planes on derived functional parameters (ejection fraction, global longitudinal strain, and excursions of the mitral and aortic valves).

Chest computed tomography (CT) imaging is a useful tool that provides in vivo information regarding lung structure. Imaging has contributed to a better understanding of COPD, allowing for the detection of early structural changes and the quantification of extra-pulmonary structures. Novel CT imaging techniques have provided insight into the progression of the main COPD subtypes, such as emphysema and small airway disease. This article serves as a review of new information relevant to COPD imaging. CT abnormalities, such as emphysema and loss of airways, are present even in smokers who do not meet the criteria for COPD and in those with mild-to-moderate disease. Subjects with mild-to-moderate COPD, with the highest loss of airways, also experience the highest decline in lung function. Extra-pulmonary manifestations of COPD, such as right ventricle enlargement and low muscle mass measured on CT, are associated with increased risk for all-cause mortality. CT longitudinal data has also given insight into the progression of COPD. Mechanically affected areas of lung parenchyma adjacent to emphysematous areas are associated with a greater decline in FEV1. Subjects with the greatest percentage of small airway disease, as measured on matched inspiratory-expiratory CT scan, also present with the greatest decline in lung function.

BACKGROUND: Risk factor identification is a proven strategy in advancing treatments and preventive therapy for many chronic conditions. Quantifying the impact of those risk factors on health outcomes can consolidate and focus efforts on individuals with specific high-risk profiles. Using multiple risk factors and longitudinal outcomes in 2 independent cohorts, we developed and validated a risk score model to predict mortality in current and former cigarette smokers. METHODS: We obtained extensive data on current and former smokers from the COPD Genetic Epidemiology (COPDGene®) study at enrollment. Based on physician input and model goodness-of-fit measures, a subset of variables was selected to fit final Weibull survival models separately for men and women. Coefficients and predictors were translated into a point system, allowing for easy computation of mortality risk scores and probabilities. We then used the SubPopulations and InteRmediate Outcome Measures In COPD Study (SPIROMICS) cohort for external validation of our model. RESULTS: Of 9867 COPDGene participants with standard baseline data, 17.6% died over 10 years of follow-up, and 9074 of these participants had the full set of baseline predictors (standard plus 6-minute walk distance and computed tomography variables) available for full model fits. The average age of participants in the cohort was 60 for both men and women, and the average predicted 10-year mortality risk was 18% for women and 25% for men. Model time-integrated area under the receiver operating characteristic curve statistics demonstrated good predictive model accuracy (0.797 average), validated in the external cohort (0.756 average). Risk of mortality was impacted most by 6-minute walk distance, forced expiratory volume in 1 second and age, for both men and women. CONCLUSIONS: Current and former smokers exhibited a wide range of mortality risk over a 10- year period. Our models can identify higher risk individuals who can be targeted for interventions to reduce risk of mortality, for participants with or without chronic obstructive pulmonary disease (COPD) using current Global initiative for obstructive Lung Disease (GOLD) criteria.

BACKGROUND: Genetic factors influence chronic obstructive pulmonary disease (COPD) risk, but the individual variants that have been identified have small effects. We hypothesised that a polygenic risk score using additional variants would predict COPD and associated phenotypes. METHODS: We constructed a polygenic risk score using a genome-wide association study of lung function (FEV1 and FEV1/forced vital capacity [FVC]) from the UK Biobank and SpiroMeta. We tested this polygenic risk score in nine cohorts of multiple ethnicities for an association with moderate-to-severe COPD (defined as FEV1/FVC <0·7 and FEV1 <80% of predicted). Associations were tested using logistic regression models, adjusting for age, sex, height, smoking pack-years, and principal components of genetic ancestry. We assessed predictive performance of models by area under the curve. In a subset of studies, we also studied quantitative and qualitative CT imaging phenotypes that reflect parenchymal and airway pathology, and patterns of reduced lung growth. FINDINGS: The polygenic risk score was associated with COPD in European (odds ratio [OR] per SD 1·81 [95% CI 1·74-1·88] and non-European (1·42 [1·34-1·51]) populations. Compared with the first decile, the tenth decile of the polygenic risk score was associated with COPD, with an OR of 7·99 (6·56-9·72) in European ancestry and 4·83 (3·45-6·77) in non-European ancestry cohorts. The polygenic risk score was superior to previously described genetic risk scores and, when combined with clinical risk factors (ie, age, sex, and smoking pack-years), showed improved prediction for COPD compared with a model comprising clinical risk factors alone (AUC 0·80 [0·79-0·81] vs 0·76 [0·75-0·76]). The polygenic risk score was associated with CT imaging phenotypes, including wall area percent, quantitative and qualitative measures of emphysema, local histogram emphysema patterns, and destructive emphysema subtypes. The polygenic risk score was associated with a reduced lung growth pattern. INTERPRETATION: A risk score comprised of genetic variants can identify a small subset of individuals at markedly increased risk for moderate-to-severe COPD, emphysema subtypes associated with cigarette smoking, and patterns of reduced lung growth. FUNDING: US National Institutes of Health, Wellcome Trust.

Computerized tomography in chronic obstructive pulmonary disease (COPD) has been the subject of intense interest in the research and clinical community. Methods have been developed to objectively detect and quantify processes affecting the lung parenchyma, airways and vasculature, as well as extrapulmonary manifestations of the noxious effects of chronic inhalational exposures, such as tobacco smoke. This article provides a brief overview of image-based advances in COPD research and then discusses how these advances have translated to clinical care, finishing with a brief description of a path forward for the convergence of research and care at the bedside.

Approximately 30-50% of hemodynamically stable patients presenting with acute pulmonary embolism (PE) have evidence of right ventricular (RV) dysfunction. These patients are classified as submassive PE and the role of reperfusion therapy remains unclear. We sought to identify the circumstances under which catheter-directed thrombolysis (CDT) would represent high-value care for submassive PE. We used a computer-based, individual-level, state-transition model with one million simulated patients to perform a cost-effectiveness analysis comparing the treatment of submassive PE with CDT followed by anticoagulation to treatment with anticoagulation alone. Because RV function impacts prognosis and is commonly used in PE outcomes research, our model used RV dysfunction to differentiate health states. One-way, two-way, and probabilistic sensitivity analyses were used to quantify model uncertainty. Our base case analysis generated an incremental cost-effectiveness ratio (ICER) of $119,326 per quality adjusted life year. Sensitivity analyses resulted in ICERs consistent with high-value care when CDT conferred a reduction in the absolute probability of RV dysfunction of 3.5% or more. CDT yielded low-value ICERs if the absolute reduction was less than 1.56%. Our model suggests that catheter-directed thrombolytics represents high-value care compared to anticoagulation alone when CDT offers an absolute improvement in RV dysfunction of 3.5% or more, but there is substantial uncertainly around these results. We estimated the monetary value of clarifying the costs and consequences surrounding RV dysfunction after submassive PE to be approximately $268 million annually, suggesting further research in this area could be highly valuable.

The Framingham Heart Study (FHS) is one of the largest and established longitudinal populational cohorts. CT cohorts of the FHS since 2002 provided a unique opportunity to assess non-cardiac thoracic imaging findings. This review deals with image-based phenotyping studies from recent major publications regarding interstitial lung abnormalities (ILAs), pulmonary cysts, emphysema, pulmonary nodules, pleural plaques, normal spectrum of the thymus, and anterior mediastinal masses, concluding with the discussion of future directions of FHS CT cohorts studies in the era of radiomics and artificial intelligence.

Activation of toll-like receptors (TLR1, TLR5, TLR6) and downstream markers (CCR1, MAPK14, ICAM1) leads to increased systemic inflammation. Our objective was to study the association between the gene expression levels of these six genes and lung function (Forced Expiratory Volume in one second (FEV1), Forced Vital Capacity (FVC) and FEV1/FVC). We studied gene expression levels and lung function in the Coronary Artery Risk Development in Young Adults study. Spirometry testing was used to measure lung function and gene expression levels were measured using the Nanostring platform. Multivariate linear regression models were used to study the association between lung function measured at year 30, 10-year decline from year 20 to year 30, and gene expression levels (highest quartile divided into two levels - 75th to 95th and>95th to 100th percentile) adjusting for center, smoking and BMI, measured at year 25. Year 30 FEV1 and FVC were lower in the highest level of TLR5 compared to the lowest quartile with difference of 4.00% (p for trend: 0.04) and 3.90% (p for trend: 0.05), respectively. The 10-year decline of FEV1 was faster in the highest level of CCR1 as compared to the lowest quartile with a difference of 1.69% (p for trend: 0.01). There was no association between gene expression and FEV1/FVC. Higher gene expression levels in TLR5 and CCR1 are associated with lower lung function and faster decline in FEV1 over 10 years, in a threshold manner, providing new insights into the role of inflammation in lung function.

Background: This document provides clinical recommendations for the pharmacologic treatment of chronic obstructive pulmonary disease (COPD). It represents a collaborative effort on the part of a panel of expert COPD clinicians and researchers along with a team of methodologists under the guidance of the American Thoracic Society.Methods: Comprehensive evidence syntheses were performed on all relevant studies that addressed the clinical questions and critical patient-centered outcomes agreed upon by the panel of experts. The evidence was appraised, rated, and graded, and recommendations were formulated using the Grading of Recommendations, Assessment, Development, and Evaluation approach.Results: After weighing the quality of evidence and balancing the desirable and undesirable effects, the guideline panel made the following recommendations: 1) a strong recommendation for the use of long-acting β2-agonist (LABA)/long-acting muscarinic antagonist (LAMA) combination therapy over LABA or LAMA monotherapy in patients with COPD and dyspnea or exercise intolerance; 2) a conditional recommendation for the use of triple therapy with inhaled corticosteroids (ICS)/LABA/LAMA over dual therapy with LABA/LAMA in patients with COPD and dyspnea or exercise intolerance who have experienced one or more exacerbations in the past year; 3) a conditional recommendation for ICS withdrawal for patients with COPD receiving triple therapy (ICS/LABA/LAMA) if the patient has had no exacerbations in the past year; 4) no recommendation for or against ICS as an additive therapy to long-acting bronchodilators in patients with COPD and blood eosinophilia, except for those patients with a history of one or more exacerbations in the past year requiring antibiotics or oral steroids or hospitalization, for whom ICS is conditionally recommended as an additive therapy; 5) a conditional recommendation against the use of maintenance oral corticosteroids in patients with COPD and a history of severe and frequent exacerbations; and 6) a conditional recommendation for opioid-based therapy in patients with COPD who experience advanced refractory dyspnea despite otherwise optimal therapy.Conclusions: The task force made recommendations regarding the pharmacologic treatment of COPD based on currently available evidence. Additional research in populations that are underrepresented in clinical trials is needed, including studies in patients with COPD 80 years of age and older, those with multiple chronic health conditions, and those with a codiagnosis of COPD and asthma.

We provide a single-cell atlas of idiopathic pulmonary fibrosis (IPF), a fatal interstitial lung disease, by profiling 312,928 cells from 32 IPF, 28 smoker and nonsmoker controls, and 18 chronic obstructive pulmonary disease (COPD) lungs. Among epithelial cells enriched in IPF, we identify a previously unidentified population of aberrant basaloid cells that coexpress basal epithelial, mesenchymal, senescence, and developmental markers and are located at the edge of myofibroblast foci in the IPF lung. Among vascular endothelial cells, we identify an ectopically expanded cell population transcriptomically identical to bronchial restricted vascular endothelial cells in IPF. We confirm the presence of both populations by immunohistochemistry and independent datasets. Among stromal cells, we identify IPF myofibroblasts and invasive fibroblasts with partially overlapping cells in control and COPD lungs. Last, we confirm previous findings of profibrotic macrophage populations in the IPF lung. Our comprehensive catalog reveals the complexity and diversity of aberrant cellular populations in IPF.

BACKGROUND: Peak lung function and rate of decline predict future airflow obstruction and nonrespiratory comorbid conditions. Associations between lung function trajectories and emphysema have not been explored. METHODS: Using data from the population-based CARDIA Study, we sought to describe the prevalence of visually ascertained emphysema at multiple time points and contextualize its development based upon participant's adult life course measures of lung function. There were 3171 men and women enrolled at a mean age of 25 years, who underwent serial spirometric examinations through a mean age of 55 years. Trajectories for the change in percent-predicted forced expiratory volume in one second (FEV1) were determined by fitting a mixture model via maximum likelihood. Emphysema was visually identified on computed tomographic scans and its prevalence reported at mean ages of 40, 45, and 50 years. RESULTS: We identified 5 trajectories describing peak and change in FEV1: "Preserved Ideal," "Preserved Good," "Preserved Impaired," "Worsening," and "Persistently Poor." Ever smokers comprised part of all 5 trajectories. The prevalence of emphysema was 1.7% (n = 46; mean age of 40 years), 2.5% (n = 67; mean age of 45 years), and 7.1% (n = 189; mean age of 50 years). Of those with emphysema at a mean age of 50 years, 18.0% were never smokers. Worsening and poor lung health trajectories were associated with increased odds of future emphysema independent of chronic tobacco smoke exposure (odds ratio 5.06; confidence interval, 1.84-13.96; odds ratio 4.85; confidence interval, 1.43-16.44). CONCLUSIONS: Lower peak and accelerated decline in FEV1 are risk factors for future emphysema independent of smoking status.

Chronic obstructive pulmonary disease (COPD) is a complex and heterogeneous disease that can benefit from novel approaches to understanding its evolution and divergent trajectories. Artificial intelligence (AI) has revolutionized how we can use clinical, imaging, and molecular data to understand and model complex systems. AI has shown impressive results in areas related to automated clinical decision making, radiological interpretation and prognostication. The unique nature of COPD and the accessibility to well-phenotyped populations result in an ideal scenario for AI development. This review provides an introduction to AI and deep learning and presents some recent successes in applying AI in COPD. Finally, we will discuss some of the opportunities, challenges, and limitations for AI applications in the context of COPD.

Biomarker estimation methods from medical images have traditionally followed a segment-and-measure strategy. Deep-learning regression networks have changed such a paradigm, enabling the direct estimation of biomarkers in databases where segmentation masks are not present. While such methods achieve high performance, they operate as a black-box. In this work, we present a novel deep learning network structure that, when trained with only the value of the biomarker, can perform biomarker regression and the generation of an accurate localization mask simultaneously, thus enabling a qualitative assessment of the image locus that relates to the quantitative result. We showcase the proposed method with three different network structures and compare their performance against direct regression networks in four different problems: pectoralis muscle area (PMA), subcutaneous fat area (SFA), liver mass area in single slice computed tomography (CT), and Agatston score estimated from non-contrast thoracic CT images (CAC). Our results show that the proposed method improves the performance with respect to direct biomarker regression methods (correlation coefficient of 0.978, 0.998, and 0.950 for the proposed method in comparison to 0.971, 0.982, and 0.936 for the reference regression methods on PMA, SFA and CAC respectively) while achieving good localization (DICE coefficients of 0.875, 0.914 for PMA and SFA respectively, p < 0.05 for all pairs). We observe the same improvement in regression results comparing the proposed method with those obtained by quantify the outputs using an U-Net segmentation network (0.989 and 0.951 respectively). We, therefore, conclude that it is possible to obtain simultaneously good biomarker regression and localization when training biomarker regression networks using only the biomarker value.

Subtle interstitial changes in the lung parenchyma of smokers, known as Interstitial Lung Abnormalities (ILA), have been associated with clinical outcomes, including mortality, even in the absence of Interstitial Lung Disease (ILD). Although several methods have been proposed for the automatic identification of more advanced Interstitial Lung Disease (ILD) patterns, few have tackled ILA, which likely precedes the development ILD in some cases. In this context, we propose a novel methodology for automated identification and classification of ILA patterns in computed tomography (CT) images. The proposed method is an ensemble of deep convolutional neural networks (CNNs) that detect more discriminative features by incorporating two, two-and-a-half and three- dimensional architectures, thereby enabling more accurate classification. This technique is implemented by first training each individual CNN, and then combining its output responses to form the overall ensemble output. To train and test the system we used 37424 radiographic tissue samples corresponding to eight different parenchymal feature classes from 208 CT scans. The resulting ensemble performance including an average sensitivity of 91,41% and average specificity of 98,18% suggests it is potentially a viable method to identify radiographic patterns that precede the development of ILD.