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Background Airway mucus plugs in asthma are associated with exacerbation frequency, increased eosinophilia, and reduced lung function. The relationship between mucus plugs and spatially overlapping ventilation abnormalities observed at hyperpolarized gas MRI has not been assessed quantitatively. Purpose To assess regional associations between CT mucus plugs scored by individual bronchopulmonary segment and corresponding measurements of segmental ventilation defect percentage (VDP) at hyperpolarized helium 3 (3He) MRI. Materials and Methods In this secondary analysis of a Health Insurance Portability and Accountability Act-compliant prospective observational cohort, participants in the Severe Asthma Research Program (SARP) III (NCT01760915) between December 2012 and August 2015 underwent hyperpolarized 3He MRI to determine segmental VDP. Segmental mucus plugs at CT were scored by two readers, with segments scored as plugged only if both readers agreed independently. A linear mixed-effects model controlling for interpatient variability was then used to assess differences in VDP in plugged versus plug-free segments. Results Forty-four participants with asthma were assessed (mean age ± standard deviation, 47 years ± 15; 29 women): 19 with mild-to-moderate asthma and 25 with severe asthma. Mucus plugs were observed in 49 total bronchopulmonary segments across eight of 44 patients. Segments containing mucus plugs had a median segmental VDP of 25.9% (25th-75th percentile, 7.3%-38.3%) versus 1.4% (25th-75th percentile, 0.1%-5.2%; P < .001) in plug-free segments. Similarly, the model estimated a segmental VDP of 18.9% (95% CI: 15.7, 22.2) for mucus-plugged segments versus 5.1% (95% CI: 3.3, 7.0) for plug-free segments (P < .001). Participants with one or more mucus plugs had a median whole-lung VDP of 11.1% (25th-75th percentile, 7.1%-18.9%) versus 3.1% (25th-75th percentile, 1.1%-4.4%) in those without plugs (P < .001). Conclusion Airway mucus plugging at CT was associated with reduced ventilation in the same bronchopulmonary segment at hyperpolarized helium 3 MRI, suggesting that mucus plugging may be an important cause of ventilation defects in asthma. © RSNA, 2021 Online supplemental material is available for this article.

Additional file 1.

Additional file 3. Communication [11, 12, 30, 48].

Additional file 2. Incentives [6, 8, 17].

Additional file 1.

Additional file 1. SPIRIT 2013 Checklist: Recommended items to address in a clinical trial protocol and related documents*.

Additional file 3. Summary of data collection.

Additional file 4. MyComrade Logic Model.

Additional file 1. Additional methods, figures, tables.

This music score was submitted for the Kaleidoscope 2020 Call for Scores, an open access collaboration with the UCLA Music Library.

Additional file 4. Maternal age at recruitment.

Additional file 2. BMI at recruitment.

Additional file 1. SPIRIT 2013 Checklist: Recommended items to address in a clinical trial protocol and related documents

This music score was submitted for the Kaleidoscope 2020 Call for Scores, an open access collaboration with the UCLA Music Library.

This music score was submitted for the Kaleidoscope 2020 Call for Scores, an open access collaboration with the UCLA Music Library.

BackgroundChronic obstructive pulmonary disease (COPD) remains a major cause of morbidity and mortality. Present-day diagnostic criteria are largely based solely on spirometric criteria. Accumulating evidence has identified a substantial number of individuals without spirometric evidence of COPD who suffer from respiratory symptoms and/or increased morbidity and mortality. There is a clear need for an expanded definition of COPD that is linked to physiologic, structural (computed tomography [CT]) and clinical evidence of disease. Using data from the COPD Genetic Epidemiology study (COPDGene®), we hypothesized that an integrated approach that includes environmental exposure, clinical symptoms, chest CT imaging and spirometry better defines disease and captures the likelihood of progression of respiratory obstruction and mortality.MethodsFour key disease characteristics - environmental exposure (cigarette smoking), clinical symptoms (dyspnea and/or chronic bronchitis), chest CT imaging abnormalities (emphysema, gas trapping and/or airway wall thickening), and abnormal spirometry - were evaluated in a group of 8784 current and former smokers who were participants in COPDGene® Phase 1. Using these 4 disease characteristics, 8 categories of participants were identified and evaluated for odds of spirometric disease progression (FEV1 > 350 ml loss over 5 years), and the hazard ratio for all-cause mortality was examined.ResultsUsing smokers without symptoms, CT imaging abnormalities or airflow obstruction as the reference population, individuals were classified as Possible COPD, Probable COPD and Definite COPD. Current Global initiative for obstructive Lung Disease (GOLD) criteria would diagnose 4062 (46%) of the 8784 study participants with COPD. The proposed COPDGene® 2019 diagnostic criteria would add an additional 3144 participants. Under the new criteria, 82% of the 8784 study participants would be diagnosed with Possible, Probable or Definite COPD. These COPD groups showed increased risk of disease progression and mortality. Mortality increased in patients as the number of their COPD characteristics increased, with a maximum hazard ratio for all cause-mortality of 5.18 (95% confidence interval [CI]: 4.15-6.48) in those with all 4 disease characteristics.ConclusionsA substantial portion of smokers with respiratory symptoms and imaging abnormalities do not manifest spirometric obstruction as defined by population normals. These individuals are at significant risk of death and spirometric disease progression. We propose to redefine the diagnosis of COPD through an integrated approach using environmental exposure, clinical symptoms, CT imaging and spirometric criteria. These expanded criteria offer the potential to stimulate both current and future interventions that could slow or halt disease progression in patients before disability or irreversible lung structural changes develop.

Testing Protocol. (PDF 1055 kb)

Figure S2. (a) Internal properties in different clustering methods to find the best clustering approaches as well as the optimal number of clusters; (b) Bootstrapping stability analysis between K-means and hierarchical clustering with 4 or 5 numbers of clusters. (DOCX 58 kb)

CONSORT checklist. (PDF 66 kb)

Figure S3. Predicting imaged-based cluster using only 5 important variables. Variables are βtissueRV (Total), Jacobian (Total), βtissueTLC (Total), Dh* (RMB) and ADI (Total) with 81% accuracy. (DOCX 59 kb)

Table S1. The confusion matrices to assess the possible overlap between current and former smoker clusters. Values are presented as the number of subjects (%). (DOCX 15 kb)

Move for Life questionnaire. (PDF 452 kb)

Figure S1. A scree plot: eigenvalues (magnitude of variances) according to the number of principal components for determining the optimal number of components. (DOCX 65 kb)

BackgroundThe link between mucus plugs and airflow obstruction has not been established in chronic severe asthma, and the role of eosinophils and their products in mucus plug formation is unknown.MethodsIn clinical studies, we developed and applied a bronchopulmonary segment-based scoring system to quantify mucus plugs on multidetector computed tomography (MDCT) lung scans from 146 subjects with asthma and 22 controls, and analyzed relationships among mucus plug scores, forced expiratory volume in 1 second (FEV1), and airway eosinophils. Additionally, we used airway mucus gel models to explore whether oxidants generated by eosinophil peroxidase (EPO) oxidize cysteine thiol groups to promote mucus plug formation.ResultsMucus plugs occurred in at least 1 of 20 lung segments in 58% of subjects with asthma and in only 4.5% of controls, and the plugs in subjects with asthma persisted in the same segment for years. A high mucus score (plugs in ≥ 4 segments) occurred in 67% of subjects with asthma with FEV1 of less than 60% of predicted volume, 19% with FEV1 of 60%-80%, and 6% with FEV1 greater than 80% (P < 0.001) and was associated with marked increases in sputum eosinophils and EPO. EPO catalyzed oxidation of thiocyanate and bromide by H2O2 to generate oxidants that crosslink cysteine thiol groups and stiffen thiolated hydrogels.ConclusionMucus plugs are a plausible mechanism of chronic airflow obstruction in severe asthma, and EPO-generated oxidants may mediate mucus plug formation. We propose an approach for quantifying airway mucus plugging using MDCT lung scans and suggest that treating mucus plugs may improve airflow in chronic severe asthma.Trial registrationClinicaltrials.gov NCT01718197, NCT01606826, NCT01750411, NCT01761058, NCT01761630, NCT01759186, NCT01716494, and NCT01760915.FundingNIH grants P01 HL107201, R01 HL080414, U10 HL109146, U10 HL109164, U10 HL109172, U10 HL109086, U10 HL109250, U10 HL109168, U10 HL109257, U10 HL109152, and P01 HL107202 and National Center for Advancing Translational Sciences grants UL1TR0000427, UL1TR000448, and KL2TR000428.

Supplemental material, Supplemental Material3 for Analysis of revascularisation in ischaemic stroke with EmboTrap (ARISE I study) and meta-analysis of thrombectomy by Heinrich P Mattle, Carl Scarrott, Mairsil Claffey, John Thornton, Juan Macho, Christian Riedel, Michael Söderman, Alain Bonafé, Michel Piotin, John Newell, Tommy Andersson and for the ARISE I Study Group in Interventional Neuroradiology

Application of ADePT process to patient attrition. (DOCX 19Â kb)

Supplemental material, Supplemental Material2 for Analysis of revascularisation in ischaemic stroke with EmboTrap (ARISE I study) and meta-analysis of thrombectomy by Heinrich P Mattle, Carl Scarrott, Mairsil Claffey, John Thornton, Juan Macho, Christian Riedel, Michael Söderman, Alain Bonafé, Michel Piotin, John Newell, Tommy Andersson and for the ARISE I Study Group in Interventional Neuroradiology

Table S1. Standardized loadings of seven principal components based upon correlation matrix. Table S2. Major structural and functional imaging-based variables in four imaging-based clusters for 45 current smokers from longitudinal study. Figure S1. Clustering analysis, a: Internal property in different clustering methods; b: Clustering stability analysis between K-means and Hierarchical clustering with different number of clusters. Figure S2. Cluster analysis in training set (a) and validation set (b) with four clusters. Figure S3. A scree plot for determining the optimal number of principal components for longitudinal study. (DOCX 276 kb)

SPIRIT 2013 Checklist: Recommended items to address in a clinical trial protocol and related documents*. (DOC 121 kb)

Supplemental material, Supplemental Material3 for Analysis of revascularisation in ischaemic stroke with EmboTrap (ARISE I study) and meta-analysis of thrombectomy by Heinrich P Mattle, Carl Scarrott, Mairsil Claffey, John Thornton, Juan Macho, Christian Riedel, Michael Söderman, Alain Bonafé, Michel Piotin, John Newell, Tommy Andersson and for the ARISE I Study Group in Interventional Neuroradiology

CHARMS CONSORT Extension Checklist. (DOC 226Â kb)

Application of ADePT process to staff perceptions. (DOCX 14Â kb)

Supplemental material, Supplemental Material2 for Analysis of revascularisation in ischaemic stroke with EmboTrap (ARISE I study) and meta-analysis of thrombectomy by Heinrich P Mattle, Carl Scarrott, Mairsil Claffey, John Thornton, Juan Macho, Christian Riedel, Michael Söderman, Alain Bonafé, Michel Piotin, John Newell, Tommy Andersson and for the ARISE I Study Group in Interventional Neuroradiology

SPIRIT 2013 Checklist: Recommended items to address in a clinical trial protocol and related documents. (DOC 122 kb)

Multidetector row computed tomography (MDCT) is increasingly taking a central role in identifying subphenotypes within chronic obstructive pulmonary disease (COPD), asthma, and other lung-related disease populations, allowing for the quantification of the amount and distribution of altered parenchyma along with the characterization of airway and vascular anatomy. The embedding of quantitative CT (QCT) into a multicenter trial with a variety of scanner makes and models along with the variety of pressures within a clinical radiology setting has proven challenging, especially in the context of a longitudinal study. SPIROMICS (Subpopulations and Intermediate Outcome Measures in COPD Study), sponsored by the National Institutes of Health, has established a QCT lung assessment system (QCT-LAS), which includes scanner-specific imaging protocols for lung assessment at total lung capacity and residual volume. Also included are monthly scanning of a standardized test object and web-based tools for subject registration, protocol assignment, and data transmission coupled with automated image interrogation to assure protocol adherence. The SPIROMICS QCT-LAS has been adopted and contributed to by a growing number of other multicenter studies in which imaging is embedded. The key components of the SPIROMICS QCT-LAS along with evidence of implementation success are described herein. While imaging technologies continue to evolve, the required components of a QCT-LAS provide the framework for future studies, and the QCT results emanating from SPIROMICS and the growing number of other studies using the SPIROMICS QCT-LAS will provide a shared resource of image-derived pulmonary metrics.

Additional file 1. List of Research Ethics Committees and letters of approval.

Purpose Overweight and underweight conditions are considered relative contraindications to lung transplantation due to their association with excess mortality. Yet, recent work suggests that body mass index (BMI) does not accurately reflect adipose tissue mass in adults with advanced lung diseases. Alternative and more accurate measures of adiposity are needed. Chest fat estimation by routine computed tomography (CT) imaging may therefore be important for identifying high-risk lung transplant candidates. In this paper, an approach to chest fat quantification and quality assessment based on a recently formulated concept of standardized anatomic space (SAS) is presented. The goal of the paper is to seek answers to several key questions related to chest fat quantity and quality assessment based on a single slice CT (whether in the chest, abdomen, or thigh) versus a volumetric CT, which have not been addressed in the literature. Methods Unenhanced chest CT image data sets from 40 adult lung transplant candidates (age 58 ± 12 yrs and BMI 26.4 ± 4.3 kg/m2), 16 with chronic obstructive pulmonary disease (COPD), 16 with idiopathic pulmonary fibrosis (IPF), and the remainder with other conditions were analyzed together with a single slice acquired for each patient at the L5 vertebral level and mid-thigh level. The thoracic body region and the interface between subcutaneous adipose tissue (SAT) and visceral adipose tissue (VAT) in the chest were consistently defined in all patients and delineated using Live Wire tools. The SAT and VAT components of chest were then segmented guided by this interface. The SAS approach was used to identify the corresponding anatomic slices in each chest CT study, and SAT and VAT areas in each slice as well as their whole volumes were quantified. Similarly, the SAT and VAT components were segmented in the abdomen and thigh slices. Key parameters of the attenuation (Hounsfield unit (HU) distributions) were determined from each chest slice and from the whole chest volume separately for SAT and VAT components. The same parameters were also computed from the single abdominal and thigh slices. The ability of the slice at each anatomic location in the chest (and abdomen and thigh) to act as a marker of the measures derived from the whole chest volume was assessed via Pearson correlation coefficient (PCC) analysis. Results The SAS approach correctly identified slice locations in different subjects in terms of vertebral levels. PCC between chest fat volume and chest slice fat area was maximal at the T8 level for SAT (0.97) and at the T7 level for VAT (0.86), and was modest between chest fat volume and abdominal slice fat area for SAT and VAT (0.73 and 0.75, respectively). However, correlation was weak for chest fat volume and thigh slice fat area for SAT and VAT (0.52 and 0.37, respectively), and for chest fat volume for SAT and VAT and BMI (0.65 and 0.28, respectively). These same single slice locations with maximal PCC were found for SAT and VAT within both COPD and IPF groups. Most of the attenuation properties derived from the whole chest volume and single best chest slice for VAT (but not for SAT) were significantly different between COPD and IPF groups. Conclusions This study demonstrates a new way of optimally selecting slices whose measurements may be used as markers of similar measurements made on the whole chest volume. The results suggest that one or two slices imaged at T7 and T8 vertebral levels may be enough to estimate reliably the total SAT and VAT components of chest fat and the quality of chest fat as determined by attenuation distributions in the entire chest volume.

Additional file 1. List of Research Ethics Committees and letters of approval.

A method comparison study compares two or more different methods measuring some quantity of interest and the aim is to determine the level of agreement between the methods. Most of the methodological developments in this research area have focused on studies where the response variable of interest is a univariate continuous response. More recently there has been interest on method comparison studies where the response is functional in nature (i.e. a discrete realisations of an underlying functional form). This is still a new and growing field of research in Statistics with many open questions. The specific aim of this thesis is to extend the Linear Mixed Modelling (LMM) framework to method agreement studies i) involving a continuous response when the observed bias between the methods of measurement is non-linear and ii) where the response variable is functional in nature. Initially the focus is on method comparison studies involving a univariate continuous response where the LMM is used to extend the “classical” Bland and Altman approach to account for non-linear bias between two methods of measurement. Following this, a further extension of the LMM framework is proposed for method comparison studies (of increasing design complexity) involving functional responses. A natural alternative analytical approach to consider is the use of Functional Data Analysis (FDA), given the nature of the response. A detailed description of the use of FDA in method comparison studies is given including a new functional equivalent to the Bland-Altman plot. An approach to adapt the LMM framework to accommodate functional responses is then given and the benefits of this approach for study designs with increasing complexity are discussed. The computational issues that arise when fitting a nonparametric LMM are highlighted and a new elegant solution to circumvent this problem is proposed using an eigenbasis for the random-effects regressor matrix. A simulation study is presented to investigate the performance of the FDA and LMM when used to generate functional limits of agreement in studies with no replicates. The performance of the eigenbasis approach to a full B-spline basis implementation is compared in terms of coverage and computational time. All the graphical and analytical approaches proposed are demonstrated using data from two case studies in elite sports: one relating to blood biomarkers with a univariate continuous responses and the other a comparison of two motion capture systems with functional responses. 2025-01-09

Data Science is the new and exciting interdisciplinary response that has emerged as a consequence of the staggering amounts of data generated in many new forms from digital images to audio to text. It is an interdisciplinary field involving Statistics, Computer Science and Mathematics. It involves the study of data, how they are collected, stored, accessed, visualised, modelled and ultimately used to inform decision making by turning data into intelligence. Despite this ’data revolution’ and the development of Data Science as a consequence, the aim of any data analysis is still the same, to make inference about unknown population parameters using sample statistics. One fundamental challenge in inference is the identification of outliers. Such oddities, or atypical observations, could be indicative of poor data management or biased sampling. In this situation the presence of such outliers are considered a negative aspect and efforts are needed to account for them (e.g. correct data entry errors) accordingly to avoid introducing bias in parameter estimation. On the other hand, finding an outlier may be the key focus of the exercise as an outlier may represent something new and novel. Many statistical methods have been developed to identify outlying data points and robust methods developed to account for outliers in statistical models. A central property of all such methods is that an observation is classified as an outlier or not (i.e. a binary decision); being able to quantify an observations ’outlyingness’ is clearly an attractive alternative. In this thesis, a novel method is presented for outlier detection in multivariate data based on the idea of a statistical depth function. The proposed approach enables outlier detection in multivariate data while taking into consideration the local geometry of the underlying probability distribution. 2021-09-14

The overall aims of this thesis are to use modern approaches in Biostatistics to help clinicians diagnose Prostate Cancer (PCa) early, and treat effectively, and to help patients choose between treatment options. Biostatistics is both a primary and an enabling discipline, a fundamental requirement of all quantitative research, upon which the validity and integrity of research findings are dependent. This thesis encompasses both aspects. The primary (methodological) component involved the development of new and novel methods for generating adaptive ranges in longitudinal monitoring of biomarkers. The enabling component was to deliver on the requirements outlined by my funders (Prostate Cancer Institute (PCI) in the National University of Ireland, Galway), namely to build a decision support system that i) displays useful summary information of PCI data from several sources and ii) presents the results of several statistical analyses relating to treatment comparisons and outcomes in a manner that was informative to clinicians and patients. The thesis is comprised of three Work Packages (WP). In the first WP, methods to generate personalised adaptive reference ranges (i.e. ranges that adapt and account for an individual's previous data) will be developed that allow clinicians to identify meaningful changes in an individual's blood test results more quickly compared to decision making using conventional normal ranges. Application of biomarker monitoring in elite sports will be presented also. Current techniques involve implementation of a Bayesian approach when the variability within individuals is assumed to be fixed. This thesis will further extend the current literature by accommodating different within individual variability structure that is more realistic, and will result in wider applicability. Additionally, the use of an approximate EM algorithm to produce computationally efficient adaptive ranges for large streaming datasets will also be proposed. A comprehensive simulation study will be undertaken to assess the performance of the methods proposed. The second WP relates to identifying and assessing the main health outcomes following PCa treatment. In particular, the PCa treatment outcomes under different treatment options and based on different risk factors will be compared using suitable statistical methods. Finally, in the third WP a modern statistical decision support system will be developed to enable patients make more informed and reliable decisions about their treatment choices. To conclude, areas of further work across the three WPs will be outlined. 2021-12-02

Translational Medicine, within biomedical and public health research domains, is defined as the convergence of basic and clinical research with the aim to transfer knowledge on the benefits and risks of therapies. The concept of Translational Statistics is proposed to facilitate the integration of biostatistics within clinical research to enhance communication of statistical research findings in an accurate and accessible manner to diverse audiences (e.g. policy makers, patients and the media). The use of appropriate visualisation is central to all areas of statistical research. Providing meaningful graphical representations of data is necessary to identify features about the population from which the data were sampled and may throw up an unsuspected view of the data such as a pattern or unusual observations. Informative graphical representations of statistical models play an important translational role. Static nomograms have been used to visualise statistical models. In this study, we propose the use of dynamic nomograms as a visualisation and translational tool to further aid the communication of the results of a statistical analysis to a non-statistical audience. A visualisation tool for time-to-event data is presented which contains a collection of useful graphical summaries, in particular, the Mean Residual Life function. It includes the classical survival summaries as well as the dynamic prediction for survival function and the mean residual life function as the two attractive alternatives. In theory, most regression-type models presented in the literature could have an accompanying web address to direct the reader to the corresponding dynamic nomogram allowing them to 'interact' with the model to gain insight into the effect of each explanatory variable on the primary response. 2020-03-20

In time-to-event studies subjects are followed until the event of interest has happened. Subjects who do not experience the event are referred to as censored. Due to censoring, methods of plotting individual survival time, such as density plots, are invalid. The graphical displays of time-to-event data usually take the form of a Kaplan-Meier survival plot. However, using a Kaplan-Meier survival plot might not be the most informative way to present the data to answer the typical questions of interest. The median survival is often used as a summary of the survival experience of a patients' population and it is easily read of the Kaplan-Meier plot. It is unlikely however that the median is a relevant summary at the patient level and a density plot of the data is perhaps more informative for communication than a single summary statistic. A fundamental idea in this thesis is to consider censored data as a form of missing, incomplete, data and use approaches from the missing data literature to handle this issue. In particular, we will use the idea of imputing the censored observations, based on the other information in the dataset and some form of assumed model. By imputing values for the censored observations and combining the original complete and imputed incomplete data, it is possible to plot the density of the full data to complement the information given by Kaplan-Meier plots. In this thesis, we consider using parametric Bayesian and non-parametric Bayesian methods to impute right censored survival data to achieve this aim. The imputation of censored observations not only allows more interpretable graphics to be produced for a wider general audience (physicians and patients), but it opens up the possibility of the use of standard formal methods of analysis for continuous responses. 2022-03-20

The main aim of my PhD is to create a prognostic model for invasive breast cancer patients for disease recurrence and death. The data were collected retrospectively and are comprised of 647 invasive breast cancer patients with patient characteristics and genetic markers measured. An additional complexity exists due to the presence of missing data. A complete case analysis with both clinical and pathological biomarkers reduces the number of cases to 103 patients. A major challenge is how best to build a prognostic model for breast cancer in the presence of missing data. The Kaplan Meier estimate of the survival function is the most commonly used method for the representation of the distribution of survival times. Extensions to graphical comparisons of these survival estimates were developed. Classical approaches to modelling survival data using complete case analysis are examined and then an empirical simulation study is used to examine the effect of missing data on variable selection and to compare the performance of variable selection techniques in imputed data. The final model identified Bilateral, Lymph Node status, Mitotic Count, Metastasis and UICC staging as being good predictors of Disease Free Survival and a subset of these for Overall Survival (Mitotic Count, Metastasis and UICC staging). These models have good concordance and were calibrated both internally and externally. Classification and Regression Trees (CART) are a non-parametric approach to regression modelling. The main feature of CART is the data are recursively partitioned into groups and a simple prediction model fitted to each partition. A novel approach using surrogate splits to create alternative competing trees with comparable prediction power are introduced. This helps identify underlying structure in the data.