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BackgroundCystic fibrosis (CF) is the most common inherited disease in Caucasians, affecting around 10,000 individuals in the UK today. Prognosis has improved considerably over recent decades with ongoing improvements in treatment and care. Providing up-to-date survival predictions is important for patients, clinicians and health services planning.MethodsFlexible parametric survival modelling of UK CF Registry data from 2011 to 2015, capturing 602 deaths in 10,428 individuals. Survival curves were estimated from birth; conditional on reaching older ages; and projected under different assumptions concerning future mortality trends, using baseline characteristics of sex, CFTR genotype (zero, one, two copies of F508del) and age at diagnosis.FindingsMale sex was associated with better survival, as was older age at diagnosis, but only in F508del non-homozygotes. Survival did not differ by genotype among individuals diagnosed at birth. Median survival ages at birth in F508del homozygotes were 46 years (males) and 41 years (females), and similar in non-homozygotes diagnosed at birth. F508del heterozygotes diagnosed aged 5 had median survival ages of 57 (males) and 51 (females). Conditional on survival to 30, median survival age rises to 52 (males) and 49 (females) in homozygotes. Mortality rates decreased annually by 2% during 2006–2015. Future improvements at this rate suggest median survival ages for F508del homozygous babies of 65 (males) and 56 (females).InterpretationOver half of babies born today, and of individuals aged 30 and above today, can expect to survive into at least their fifth decade.Research in contextEvidence before this studyWe searched PubMed with terms “(cystic fibrosis survival) and (projection OR model OR registry OR United Kingdom OR UK)” to identify relevant studies on survival estimates for individuals with cystic fibrosis (CF). We also considered the most recent annual report from the UK Cystic Fibrosis Registry (Cystic Fibrosis Trust, 2016), a review by Buzzetti and colleagues (2009), the chapter on Epidemiology of Cystic Fibrosis by MacNeill (2016), the study of MacKenzie and colleagues (2014), and references therein. There have been many studies of factors associated with survival in CF; most have focused on identifying risk factors, and only a few have presented estimated survival curves, which are the focus of this work. The most recent study of survival in the UK is by Dodge and colleagues (2007), who used data obtained from CF clinics and the national death register, and gave an estimate of survival for babies born in 2003. We found no previous studies that have obtained detailed information on survival using UK Cystic Fibrosis Registry data. Jackson and colleagues obtained survival estimates for the US and Ireland using registry data (Jackson et al., 2011). MacKenzie and colleagues used US Cystic Fibrosis Foundation Patient Registry data from 2000 to 2010 to project survival for children born and diagnosed with CF in 2010, accounting for sex, genotype and age at diagnosis (MacKenzie e...

Metabolomics is the comprehensive study of small molecule metabolites in biological systems. By assaying and analyzing thousands of metabolites in biological samples, it provides a whole picture of metabolic status and biochemical events happening within an organism and has become an increasingly powerful tool in the disease research. In metabolomics, it is common to deal with large amounts of data generated by nuclear magnetic resonance (NMR) and/or mass spectrometry (MS). Moreover, based on different goals and designs of studies, it may be necessary to use a variety of data analysis methods or a combination of them in order to obtain an accurate and comprehensive result. In this review, we intend to provide an overview of computational and statistical methods that are commonly applied to analyze metabolomics data. The review is divided into five sections. The first two sections will introduce the background and the databases and resources available for metabolomics research. The third section will briefly describe the principles of the two main experimental methods that produce metabolomics data: MS and NMR, followed by the fourth section that describes the preprocessing of the data from these two approaches. In the fifth and the most important section, we will review four main types of analysis that can be performed on metabolomics data with examples in metabolomics. These are unsupervised learning methods, supervised learning methods, pathway analysis methods and analysis of time course metabolomics data. We conclude by providing a table summarizing the principles and tools that we discussed in this review.

).q RSNA, 2014 Purpose:To quantify regional lung ventilation in healthy volunteers and patients with severe asthma (both before and after thermoplasty) by using a combination of helium 3 ( 3 He) magnetic resonance (MR) imaging and computed tomography (CT), with the intention of developing more effective image-guided treatments for obstructive lung diseases. Materials andMethods:With approval of the local institutional review board, informed consent, and an Investigational New Drug Exemption, six healthy volunteers and 10 patients with severe asthma were imaged in compliance with HIPAA regulations by using both multidetector CT and 3He MR imaging. Individual bronchopulmonary segments were labeled voxel by voxel from the CT images and then registered to the Results:Ventilation measures for healthy volunteers yielded smaller segment-to-segment variation (mean SVP, 100% 6 18 [standard deviation]) than did the measures for patients with severe asthma (mean SVP, 97% 6 23). Patients with asthma also demonstrated larger segmental defect percentages (median, 13.5%; interquartile range, 8.9%-17.8%) than healthy volunteers (median, 6%; interquartile range, 5.6%-6.3%). These quantitative results confirm what is visually observed on the 3 He images. A Spearman correlation of r = 20.82 was found between the change in whole-lung defect percentage and the number of days between final treatment and second 3 He imaging. Conclusion:Regional quantification of lung ventilation is indeed feasible and may be a useful technique for image-guided treatment of obstructive lung diseases, such as bronchial thermoplasty for severe asthma. In these patients, ventilation defects decreased as a function of time after treatment.q RSNA, 2014

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Background Forced expiratory volume in 1 second (FEV1) is an established marker of cystic fibrosis (CF) disease progression that is used to capture clinical course and evaluate therapeutic efficacy. The research community has established FEV1 surveillance data through a variety of observational data sources such as patient registries, and there is a growing pipeline of new CF therapies demonstrated to be efficacious in clinical trials by establishing improvements in FEV1. Results In this review, we summarize from a statistical perspective the clinical relevance of FEV1 based on its association with morbidity and mortality in CF, its role in epidemiologic studies of disease progression and comparative effectiveness, and its utility in clinical trials. In addition, we identify opportunities to advance epidemiologic research and the clinical development pipeline through further statistical considerations. Conclusions Our understanding of CF disease course, therapeutics, and clinical care has evolved immensely in the past decades, in large part due to the thoughtful application of rigorous research methods and meaningful clinical endpoints such as FEV1. A continued commitment to conduct research that minimizes the potential for bias, maximizes the limited patient population, and harmonizes approaches to FEV1 analysis while maintaining clinical relevance, will facilitate further opportunities to advance CF care.

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