Unsupervised learning technique identifies bronchiectasis phenotypes with distinct clinical characteristics

Biomarkers in bronchiectasis

Bronchiectasis is a heterogeneous disease with multiple aetiologies and diverse clinical features. There is a general consensus that optimal treatment requires precision medicine approaches focused on specific treatable disease characteristics, known as treatable traits. Identifying subtypes of conditions with distinct underlying biology (endotypes) depends on the identification of biomarkers that are associated with disease features, prognosis or treatment response and which can be applied in clinical practice. Bronchiectasis is a disease characterised by inflammation, infection, structural lung damage and impaired mucociliary clearance. Increasingly there are available methods to measure each of these components of the disease, revealing heterogeneous inflammatory profiles, microbiota, radiology and mucus and epithelial biology in patients with bronchiectasis. Using emerging biomarkers and omics technologies to guide treatment in bronchiectasis is a promising field of research. Here we review the most recent data on biomarkers in bronchiectasis.

Clinical Characteristics and Outcomes of the Phenotypes of COPD-Bronchiectasis Association

INTRODUCTION

Although COPD may frequently co-exist with bronchiectasis [COPD-bronchiectasis associated (CBA)], little is known regarding the clinical heterogeneity. We aimed to identify the phenotypes and compare the clinical characteristics and prognosis of CBA.

METHODS

We conducted a retrospective cohort study involving 2928 bronchiectasis patients, 5158 COPD patients, and 1219 patients with CBA hospitalized between July 2017 and December 2020. We phenotyped CBA with a two-step clustering approach and validated in an independent retrospective cohort with decision-tree algorithms.

RESULTS

Compared with patients with COPD or bronchiectasis alone, patients with CBA had significantly longer disease duration, greater lung function impairment, and increased use of intravenous antibiotics during hospitalization. We identified five clusters of CBA. Cluster 1 (N=120, CBA-MS) had predominantly moderate-severe bronchiectasis, Cluster 2 (N=108, CBA-FH) was characterized by frequent hospitalization within the previous year, Cluster 3 (N=163, CBA-BI) had bacterial infection, Cluster 4 (N=143, CBA-NB) had infrequent hospitalization but no bacterial infection, and Cluster 5 (N=113, CBA-NHB) had no hospitalization or bacterial infection in the past year. The decision-tree model predicted the cluster assignment in the validation cohort with 91.8% accuracy. CBA-MS, CBA-BI, and CBA-FH exhibited higher risks of hospital re-admission and intensive care unit admission compared with CBA-NHB during follow-up (all P<0.05). Of the five clusters, CBA-FH conferred the worst clinical prognosis.

CONCLUSION

Bronchiectasis severity, recent hospitalizations and sputum culture findings are three defining variables accounting for most heterogeneity of CBA, the characterization of which will help refine personalized clinical management.

Bronchiectasis

Bronchiectasis is a final common pathway of a wide variety of underlying conditions including infectious, autoimmune, allergic, genetic and inflammatory conditions. Patients experience a chronic disease with variable clinical symptoms and course, but most experience cough, sputum production and recurrent exacerbations. Symptoms of bronchiectasis lead to poor quality of life and exacerbations are the major driver of morbidity and mortality. Patients are often chronically infected with bacteria with the most common being Pseudomonas aeruginosa and Haemophilus influenzae. Treatment of bronchiectasis includes standardised testing to identify the underlying cause with targeted treatment if immune deficiency, allergic bronchopulmonary aspergillosis or non-tuberculous mycobacterial infection, for example, are identified. Airway clearance is the mainstay of therapy for patients with symptoms of cough and sputum production. Frequently exacerbating patients may benefit from long term antibiotic or mucoactive therapies. Bronchiectasis is a heterogeneous disease and increasingly precision medicine approaches are advocated to target treatments most appropriately and to limit the emergence of antimicrobial resistance.

Predicting ICU Mortality in Rheumatic Heart Disease: Comparison of XGBoost and Logistic Regression

Background

Rheumatic heart disease (RHD) accounts for a large proportion of Intensive Care Unit (ICU) deaths. Early prediction of RHD can help with timely and appropriate treatment to improve survival outcomes, and the XGBoost machine learning technology can be used to identify predictive factors; however, its use has been limited in the past. We compared the performance of logistic regression and XGBoost in predicting hospital mortality among patients with RHD from the Medical Information Mart for Intensive Care IV (MIMIC-IV) database.

Methods

The patients with RHD in the MIMIC-IV database were divided into two groups retrospectively according to the availability of data and its clinical significance based on whether they survived or died. Backward stepwise regression was used to analyze the independent factors influencing patients with RHD, and to compare the differences between the two groups. The XGBoost algorithm and logistic regression were used to establish two prediction models, and the areas under the receiver operating characteristic curves (AUCs) and decision-curve analysis (DCA) were used to test and compare the models. Finally, DCA and the clinical impact curve (CIC) were used to validate the model.

Results

Data on 1,634 patients with RHD were analyzed, comprising 207 who died during hospitalization and 1,427 survived. According to estimated results for the two models using AUCs [0.838 (95% confidence interval = 0.786–0.891) and 0.815 (95% confidence interval = 0.765–0.865)] and DCA, the logistic regression model performed better. DCA and CIC verified that the logistic regression model had convincing predictive value.

Conclusions

We used logistic regression analysis to establish a more meaningful prediction model for the final outcome of patients with RHD. This model might be clinically useful for patients with RHD and help clinicians to provide detailed treatments and precise management.

Digital phenotyping of sleep patterns among heterogenous samples of Latinx adults using unsupervised learning

OBJECTIVE

This study aimed to identify sleep disturbance subtypes ("phenotypes") among Latinx adults based on objective sleep data using a flexible unsupervised machine learning technique.

METHODS

This study was an analysis of sleep data from three cross-sectional studies of the Precision in Symptom Self-Management Center at Columbia University. All studies focused on sleep health in Latinx adults at increased risk for sleep disturbance. Data on total sleep time (TST), time in bed (TIB), wake after sleep onset (WASO), sleep efficiency (SE), number of awakenings (NOA) and the mean length of nightly awakenings were collected using wrist-mounted accelerometers. Cluster analysis of the sleep data was conducted using an unsupervised machine learning approach that relies on mixtures of multivariate generalized linear mixed models.

RESULTS

The analytic sample included 494 days of data from 118 adults (Ages 19-77). A 3-cluster model provided the best fit based on deviance indices (ie, DΔ∼ -75 and -17 from 1- and 2- to 3-cluster models, respectively) and likelihood ratio (P_diff ∼ 0.93). Phenotype 1 (n = 64) was associated with greater likelihood of overall adequate SE and less variability in SE and WASO. Phenotype 2 (n = 11) was characterized by higher NOAs, and greater WASO and TIB than the other phenotypes. Phenotype 3 (n = 43) was characterized by greater variability in SE, bed times and awakening times.

CONCLUSION

Robust digital data-driven modeling approaches can be useful for detecting sleep phenotypes from heterogenous patient populations, and have implications for designing precision sleep health strategies for management and early detection of sleep problems.

Personalized approaches to bronchiectasis

Introduction: Interest in bronchiectasis is increasing due to its rising prevalence, associated with aging populations and the extended use of high-resolution chest tomography (HRCT), and the resulting high morbidity, mortality, and demand for resources.Areas covered: This article provides an extensive review of bronchiectasis as a complex and heterogeneous disease, as well as examining the difficulty of establishing useful clinical phenotypes. In keeping with the aims of 'precision medicine', we address the disease of bronchiectasis from three specific perspectives: severity, activity, and impact. We used PubMed to search the literature for articles including the following keywords: personalized medicine, bronchiectasis, biomarkers, phenotypes, precision medicine, treatable traits. We reviewed the most relevant articles published over the last 5 years.Expert opinion: This article reflects on the usefulness of these three dimensions in 'control panels' and clinical fingerprinting, as well as approaches to personalized medicine and the treatable features of bronchiectasis non-cystic fibrosis.

The clinical phenotype of bronchiectasis and its clinical guiding implications

Bronchiectasis is a chronic airway disease with abnormal and persistent bronchial dilatation caused by a variety of reasons. In recent years, numerous reports have shown that bronchiectasis is heterogeneous, the clinical characteristics of patients with different phenotypes are different, and the efficacy of a treatment regimen may vary greatly in patients with different bronchiectasis phenotypes. This paper summarizes the current clinical phenotypic classification of bronchiectasis from the perspective of etiology, microbiology, and the frequency of acute exacerbation, and cluster analysis was used to determine new clinical phenotypes and their statistical and clinical significance. Different tools for assessing disease severity yield different outcomes. This article summarizes the research progress in the above areas, hoping to provide a more comprehensive understanding of the disease.

Impact of Pseudomonas aeruginosa Infection on Patients with Chronic Inflammatory Airway Diseases

Pseudomonas aeruginosa (P. aeruginosa) is a ubiquitous and opportunistic microorganism and is considered one of the most significant pathogens that produce chronic colonization and infection of the lower respiratory tract, especially in people with chronic inflammatory airway diseases such as asthma, chronic obstructive pulmonary disease (COPD), cystic fibrosis (CF), and bronchiectasis. From a microbiological viewpoint, the presence and persistence of P. aeruginosa over time are characterized by adaptation within the host that precludes any rapid, devastating injury to the host. Moreover, this microorganism usually develops antibiotic resistance, which is accelerated in chronic infections especially in those situations where the frequent use of antimicrobials facilitates the selection of “hypermutator P. aeruginosa strain”. This phenomenon has been observed in people with bronchiectasis, CF, and the “exacerbator” COPD phenotype. From a clinical point of view, a chronic bronchial infection of P. aeruginosa has been related to more severity and poor prognosis in people with CF, bronchiectasis, and probably in COPD, but little is known on the effect of this microorganism infection in people with asthma. The relationship between the impact and treatment of P. aeruginosa infection in people with airway diseases emerges as an important future challenge and it is the most important objective of this review.

A Cluster Analysis of Bronchiectasis Patients Based on the Airway Immune Profile

BACKGROUND

Clinical heterogeneity in bronchiectasis remains a challenge for improving the appropriate targeting of therapies and patient management. Antimicrobial peptides (AMPs) have been linked to disease severity and phenotype.

RESEARCH QUESTION

Can we identify clusters of patients based on the levels of AMPs, airway inflammation, tissue remodeling, and tissue damage to establish their relationship with disease severity and clinical outcomes?

STUDY DESIGN AND METHODS

A prospective cohort of 128 stable patients with bronchiectasis were recruited across three centers in three different countries (Spain, Scotland, and Italy). A two-step cluster strategy was used to stratify patients according to levels of lactoferrin, lysozyme, LL-37, and secretory leukocyte protease inhibitor in sputum. Measurements of inflammation (IL-8, tumor growth factor β, and IL-6), tissue remodeling and damage (glycosaminoglycan, matrix metallopeptidase 9, neutrophil elastase, and total and bacterial DNA), and neutrophil chemotaxis were assessed.

RESULTS

Three clusters of patients were defined according to distinct airway profiles of AMPs. They represented groups of patients with gradually distinct airway infection and disease severity. Each cluster was associated with an airway profile of inflammation, tissue remodeling, and tissue damage. The relationships between soluble mediators also were distinct between clusters. This analysis allowed the identification of the cluster with the most deregulated local innate immune response. During follow-up, each cluster showed different risk of three or more exacerbations occurring (P = .03) and different times to first exacerbations (P = .03).

INTERPRETATION

Bronchiectasis patients can be stratified in different clusters according to profiles of airway AMPs, inflammation, tissue remodeling, and tissue damage. The combination of these immunologic variables shows a relationship with disease severity and future risk of exacerbations.

Prediction of breakthrough pain during labour neuraxial analgesia: comparison of machine learning and multivariable regression approaches

INTRODUCTION

Risk-prediction models for breakthrough pain facilitate interventions to forestall inadequate labour analgesia, but limited work has used machine learning to identify predictive factors. We compared the performance of machine learning and regression techniques in identifying parturients at increased risk of breakthrough pain during labour epidural analgesia.

METHODS

A single-centre retrospective study involved parturients receiving patient-controlled epidural analgesia. The primary outcome was breakthrough pain. We randomly selected 80% of the cohort (training cohort) to develop three prediction models using random forest, XGBoost, and logistic regression, followed by validation against the remaining 20% of the cohort (validation cohort). Area-under-the-receiver operating characteristic curve (AUC), sensitivity, specificity, and positive and negative predictive values (PPV and NPV) were used to assess model performance.

RESULTS

Data from 20 716 parturients were analysed. The incidence of breakthrough pain was 14.2%. Of 31 candidate variables, random forest, XGBoost and logistic regression models included 30, 23, and 15 variables, respectively. Unintended venous puncture, post-neuraxial analgesia highest pain score, number of dinoprostone suppositories, neuraxial technique, number of neuraxial attempts, depth to epidural space, body mass index, pre-neuraxial analgesia oxytocin infusion rate, maternal age, pre-neuraxial analgesia cervical dilation, anaesthesiologist rank, and multiparity, were identified in all three models. All three models performed similarly, with AUC 0.763-0.772, sensitivity 67.0-69.4%, specificity 70.9-76.2%, PPV 28.3-31.8%, and NPV 93.3-93.5%.

CONCLUSIONS

Machine learning did not improve the prediction of breakthrough pain compared with multivariable regression. Larger population-wide studies are needed to improve predictive ability.

Bronchiectasis phenotypes

PURPOSE OF REVIEW

Bronchiectasis is a chronic respiratory disease with heterogeneous clinical manifestations and outcomes. Identifying clinical phenotypes could help in managing bronchiectasis patients and hopefully improve disease prognosis by adopting personalized treatment. We review the current literature on clinical phenotypes of bronchiectasis and try to highlight priorities for future research.

RECENT FINDINGS

Different studies have tried to stratify bronchiectasis patients according to cause, microbiology, or associated conditions. In consideration of the huge heterogeneity of bronchiectasis different cluster analyses in bronchiectasis have also been performed. Unfortunately classification by cause is clinically meaningful only in a few conditions in which specific treatment is available or a specific management is recommended. This is the case, for instance, of allergic bronchopulmonary aspergillosis, immunodeficiencies or chronic airways comorbidities of bronchiectasis, such as chronic obstructive pulmonary disease and asthma. From a clinical perspective, the microbiological cluster stratification proposed by Aliberti et al. seems the most profitable one but still so a number of host-related factors have an unpredictable effect on clinical manifestations and outcomes of bronchiectasis.

SUMMARY

Although numerous attempts to identify clinical phenotypes in bronchiectasis have been performed, currently there is need to further investigate the host-related factors (endotypes) that surely play a determinant role in disease severity.

Distinct "Immunoallertypes" of Disease and High Frequencies of Sensitization in Non-Cystic Fibrosis Bronchiectasis

RATIONALE

Allergic sensitization is associated with poor clinical outcomes in asthma, chronic obstructive pulmonary disease, and cystic fibrosis; however, its presence, frequency, and clinical significance in non-cystic fibrosis bronchiectasis remain unclear.

OBJECTIVES

To determine the frequency and geographic variability that exists in a sensitization pattern to common and specific allergens, including house dust mite and fungi, and to correlate such patterns to airway immune-inflammatory status and clinical outcomes in bronchiectasis.

METHODS

Patients with bronchiectasis were recruited in Asia (Singapore and Malaysia) and the United Kingdom (Scotland) (n = 238), forming the Cohort of Asian and Matched European Bronchiectasis, which matched recruited patients on age, sex, and bronchiectasis severity. Specific IgE response against a range of common allergens was determined, combined with airway immune-inflammatory status and correlated to clinical outcomes. Clinically relevant patient clusters, based on sensitization pattern and airway immune profiles ("immunoallertypes"), were determined.

MEASUREMENTS AND MAIN RESULTS

A high frequency of sensitization to multiple allergens was detected in bronchiectasis, exceeding that in a comparator cohort with allergic rhinitis (n = 149). Sensitization was associated with poor clinical outcomes, including decreased pulmonary function and more severe disease. "Sensitized bronchiectasis" was classified into two immunoallertypes: one fungal driven and proinflammatory, the other house dust mite driven and chemokine dominant, with the former demonstrating poorer clinical outcome.

CONCLUSIONS

Allergic sensitization occurs at high frequency in patients with bronchiectasis recruited from different global centers. Improving endophenotyping of sensitized bronchiectasis, a clinically significant state, and a "treatable trait" permits therapeutic intervention in appropriate patients, and may allow improved stratification in future bronchiectasis research and clinical trials.

The biology of pulmonary exacerbations in bronchiectasis

Bronchiectasis is a heterogeneous chronic disease. Heterogeneity characterises bronchiectasis not only in the stable state but also during exacerbations, despite evidence on clinical and biological aspects of bronchiectasis, exacerbations still remain poorly understood.Although the scientific community recognises that bacterial infection is a cornerstone in the development of bronchiectasis, there is a lack of data regarding other trigger factors for exacerbations. In addition, a huge amount of data suggest a primary role of neutrophils in the stable state and exacerbation of bronchiectasis, but the inflammatory reaction involves many other additional pathways. Cole's vicious cycle hypothesis illustrates how airway dysfunction, airway inflammation, infection and structural damage are linked. The introduction of the concept of a "vicious vortex" stresses the complexity of the relationships between the components of the cycle. In this model of disease, exacerbations work as a catalyst, accelerating the progression of disease. The roles of microbiology and inflammation need to be considered as closely linked and will need to be investigated in different ways to collect samples. Clinical and translational research is of paramount importance to achieve a better comprehension of the pathophysiology of bronchiectasis, microbiology and inflammation both in the stable state and during exacerbations.

Contemporary management of bronchiectasis in children

Introduction: Bronchiectasis is increasingly recognized as a major cause of morbidity and mortality worldwide. It affects children of all ethnicities and socioeconomic backgrounds and represents a far greater burden than cystic fibrosis (CF). Bronchiectasis often begins in childhood and the radiological changes can be reversed, when mild, with optimal management. As there are limited pediatric studies in this field, current treatment approaches in children are based largely upon adult and/or CF studies. The recent establishment of bronchiectasis registries will improve understanding of pediatric bronchiectasis and increase capacity for large-scale research studies in the future. Areas covered: This review summarizes the current management of bronchiectasis in children and highlights important knowledge gaps and areas for future research. Current treatment approaches are based largely on consensus guidelines from international experts in the field. Studies were identified through searching Medline via the Ovid interface and Pubmed using the search terms 'bronchiectasis' and 'children' or 'pediatric' and 'management' or 'treatments'. Expert opinion: Bronchiectasis is heterogeneous in nature and a one-size-fits-all approach has limitations. Future research should focus on advancing our understanding of the aetiopathogenesis of bronchiectasis. This approach will facilitate development of targetted therapeutic interventions to slow, halt or even reverse bronchiectasis in childhood.

Application of machine learning in rheumatic disease research

Over the past decade, there has been a paradigm shift in how clinical data are collected, processed and utilized. Machine learning and artificial intelligence, fueled by breakthroughs in high-performance computing, data availability and algorithmic innovations, are paving the way to effective analyses of large, multi-dimensional collections of patient histories, laboratory results, treatments, and outcomes. In the new era of machine learning and predictive analytics, the impact on clinical decision-making in all clinical areas, including rheumatology, will be unprecedented. Here we provide a critical review of the machine-learning methods currently used in the analysis of clinical data, the advantages and limitations of these methods, and how they can be leveraged within the field of rheumatology.

eDoctor: machine learning and the future of medicine

Machine learning (ML) is a burgeoning field of medicine with huge resources being applied to fuse computer science and statistics to medical problems. Proponents of ML extol its ability to deal with large, complex and disparate data, often found within medicine and feel that ML is the future for biomedical research, personalized medicine, computer-aided diagnosis to significantly advance global health care. However, the concepts of ML are unfamiliar to many medical professionals and there is untapped potential in the use of ML as a research tool. In this article, we provide an overview of the theory behind ML, explore the common ML algorithms used in medicine including their pitfalls and discuss the potential future of ML in medicine.

Peering Into the Black Box of Artificial Intelligence: Evaluation Metrics of Machine Learning Methods

OBJECTIVE

Machine learning (ML) and artificial intelligence (AI) are rapidly becoming the most talked about and controversial topics in radiology and medicine. Over the past few years, the numbers of ML- or AI-focused studies in the literature have increased almost exponentially, and ML has become a hot topic at academic and industry conferences. However, despite the increased awareness of ML as a tool, many medical professionals have a poor understanding of how ML works and how to critically appraise studies and tools that are presented to us. Thus, we present a brief overview of ML, explain the metrics used in ML and how to interpret them, and explain some of the technical jargon associated with the field so that readers with a medical background and basic knowledge of statistics can feel more comfortable when examining ML applications.

CONCLUSION

Attention to sample size, overfitting, underfitting, cross validation, as well as a broad knowledge of the metrics of machine learning, can help those with little or no technical knowledge begin to assess machine learning studies. However, transparency in methods and sharing of algorithms is vital to allow clinicians to assess these tools themselves.

Advances in bronchiectasis: endotyping, genetics, microbiome, and disease heterogeneity

Bronchiectasis is characterised by pathological dilation of the airways. More specifically, the radiographic demonstration of airway enlargement is the common feature of a heterogeneous set of conditions and clinical presentations. No approved therapies exist for the condition other than for bronchiectasis caused by cystic fibrosis. The heterogeneity of bronchiectasis is a major challenge in clinical practice and the main reason for difficulty in achieving endpoints in clinical trials. Recent observations of the past 2 years have improved the understanding of physicians regarding bronchiectasis, and have indicated that it might be more effective to classify patients in a different way. Patients could be categorised according to a heterogeneous group of endotypes (defined by a distinct functional or pathobiological mechanism) or by clinical phenotypes (defined by relevant and common features of the disease). In doing so, more specific therapies needed to effectively treat patients might finally be developed. Here, we describe some of the recent advances in endotyping, genetics, and disease heterogeneity of bronchiectasis including observations related to the microbiome.

When and how ruling out cystic fibrosis in adult patients with bronchiectasis

Background

Bronchiectasis is the final result of different processes and most of the guidelines advocate for a careful evaluation of those etiologies which might be treated or might change patients’ management, including cystic fibrosis (CF).

Main body

CFTR mutations have been reported with higher frequency in bronchiectasis population. Although ruling out CF is considered as a main step for etiological screening in bronchiectasis, CF testing lacks of a standardized approach both from a research and clinical point of view. In this review a list of most widely used tests in CF is provided.

Conclusions

Exclusion of CF is imperative for patients with bronchiectasis and CFTR testing should be implemented in usual screening for investigating bronchiectasis etiology. Physicians taking care of bronchiectasis patients should be aware of CFTR testing and its limitations in the adult population. Further studies on CFTR expression in human lung and translational research might elucidate the possible role of CFTR in the pathogenesis of bronchiectasis.

Geographic variation in the aetiology, epidemiology and microbiology of bronchiectasis

Bronchiectasis is a disease associated with chronic progressive and irreversible dilatation of the bronchi and is characterised by chronic infection and associated inflammation. The prevalence of bronchiectasis is age-related and there is some geographical variation in incidence, prevalence and clinical features. Most bronchiectasis is reported to be idiopathic however post-infectious aetiologies dominate across Asia especially secondary to tuberculosis. Most focus to date has been on the study of airway bacteria, both as colonisers and causes of exacerbations. Modern molecular technologies including next generation sequencing (NGS) have become invaluable tools to identify microorganisms directly from sputum and which are difficult to culture using traditional agar based methods. These have provided important insight into our understanding of emerging pathogens in the airways of people with bronchiectasis and the geographical differences that occur. The contribution of the lung microbiome, its ethnic variation, and subsequent roles in disease progression and response to therapy across geographic regions warrant further investigation. This review summarises the known geographical differences in the aetiology, epidemiology and microbiology of bronchiectasis. Further, we highlight the opportunities offered by emerging molecular technologies such as -omics to further dissect out important ethnic differences in the prognosis and management of bronchiectasis.

Arterial stiffness in adults with steady-state bronchiectasis: association with clinical indices and disease severity

Background

Cardiovascular disease are common co-morbidities in bronchiectasis and contribute substantially to disease burden and mortality. Brachial-ankle pulse wave velocity (baPWV), a measure of arterial stiffness, has a strong predictive value for cardiovascular event. We hypothesized that baPWV would be increased in steady-state bronchiectasis patients, and correlates with the degree of systemic inflammation and disease severity assessed with Bronchiectasis Severity Index and FACED scores.

Methods

Eighty patients with steady-state bronchiectasis and 80 age- and sex-matched controls were enrolled. BaPWV was measured as an indicator of arterial stiffness. Demographic, clinical indices, radiology, spirometry, sputum bacteriology and systemic inflammatory mediators were also assessed.

Results

Bronchiectasis patients had significantly increased baPWV [median 1514 cm/s vs. 1352 cm/s, P = 0.0003] compared with control subjects. BaPWV significantly correlated with Bronchiectasis Severity Index (rho = 0.65, P < 0.001) and FACED (rho = 0.49, P < 0.001) scores. In multivariate regression analysis, age, Pseudomonas aeruginosa colonization, systolic blood pressure, body-mass index and exacerbation frequency in the last 12 months, but not systemic inflammatory markers, were independent factors influencing on baPWV in bronchiectasis patient after adjustment for other clinical variables. Reproducibility of baPWV measurement was good.

Conclusion

Bronchiectasis patients have increased arterial stiffness compared with control subjects, which correlates with disease severity, but not systemic inflammatory markers. Age, Pseudomonas aeruginosa colonization, systolic blood pressure, body-mass index and exacerbation frequency in last 12 months might independently predict the severity of arterial stiffness in bronchiectasis. Therefore, arterial stiffness might have contributed to the increased risks of developing cardiovascular diseases in bronchiectasis.

Electronic supplementary material

The online version of this article (10.1186/s12931-018-0790-3) contains supplementary material, which is available to authorized users.

Defining Phenotypes from Clinical Data to Drive Genomic Research

The rise in available longitudinal patient information in electronic health records (EHRs) and their coupling to DNA biobanks has resulted in a dramatic increase in genomic research using EHR data for phenotypic information. EHRs have the benefit of providing a deep and broad data source of health-related phenotypes, including drug response traits, expanding the phenome available to researchers for discovery. The earliest efforts at repurposing EHR data for research involved manual chart review of limited numbers of patients but now typically involve applications of rule-based and machine learning algorithms operating on sometimes huge corpora for both genome-wide and phenome-wide approaches. We highlight here the current methods, impact, challenges, and opportunities for repurposing clinical data to define patient phenotypes for genomics discovery. Use of EHR data has proven a powerful method for elucidation of genomic influences on diseases, traits, and drug-response phenotypes and will continue to have increasing applications in large cohort studies.