Dataset Information

Patterns and characterization of COPD exacerbations using real-time data collection.

ABSTRACT: Patients with chronic obstructive pulmonary disease often experience exacerbations. These events are important as they are a major cause of morbidity and mortality. Recently, it has been increasingly recognized that patients may experience symptoms suggestive of an exacerbation but do not seek treatment, which are referred to as unreported or untreated exacerbations. Symptom diaries used in clinical trials have the benefit of identifying both treated and untreated exacerbation events.The Kamada study was a multicenter, double-blind randomized controlled trial of inhaled augmentation therapy in alpha-1 antitrypsin deficiency (AATD). A retrospective review of daily electronic symptom diary cards was undertaken from the two leading centers to identify symptomatic episodes consistent with a definition of an exacerbation. The aims were to explore the relationship between exacerbation events and classical "Anthonisen" symptoms and to characterize treated and untreated episodes.Forty-six AATD patients with airflow obstruction and history of exacerbations were included in the analysis. Two hundred thirty-three exacerbation episodes were identified: 103 untreated and 130 treated. Untreated episodes were significantly shorter (median 6 days; interquartile range [IQR] 3-10 days) than the treated episodes (median 10 days; IQR 5-18.25 days: P<0.001). Using logistic regression analysis, Anthonisen type and length of dyspnea were significant predictors of the treatment of an exacerbation event.Real-time electronic diary cards provide valuable information about the characterization of exacerbations. Untreated episodes are common and are significantly shorter in duration than the treated episodes. Dyspnea is the most important single Anthonisen symptom in the prediction and/or driver of treatment.

SUBMITTER: Ejiofor SI

PROVIDER: S-EPMC5279955 | biostudies-literature | 2017

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Patterns and characterization of COPD exacerbations using real-time data collection.

Ejiofor Stanley I SI Stolk Jan J Fernandez Pablo P Stockley Robert A RA

International journal of chronic obstructive pulmonary disease 20170125

<h4>Introduction</h4>Patients with chronic obstructive pulmonary disease often experience exacerbations. These events are important as they are a major cause of morbidity and mortality. Recently, it has been increasingly recognized that patients may experience symptoms suggestive of an exacerbation but do not seek treatment, which are referred to as unreported or untreated exacerbations. Symptom diaries used in clinical trials have the benefit of identifying both treated and untreated exacerbati ...[more]

PMID: 28182151

Similar Datasets

Project description:BackgroundHealth care and public health professionals rely on accurate, real-time monitoring of infectious diseases for outbreak preparedness and response. Early detection of outbreaks is improved by systems that are comprehensive and specific with respect to the pathogen but are rapid in reporting the data. It has proven difficult to implement these requirements on a large scale while maintaining patient privacy.ObjectiveThe aim of this study was to demonstrate the automated export, aggregation, and analysis of infectious disease diagnostic test results from clinical laboratories across the United States in a manner that protects patient confidentiality. We hypothesized that such a system could aid in monitoring the seasonal occurrence of respiratory pathogens and may have advantages with regard to scope and ease of reporting compared with existing surveillance systems.MethodsWe describe a system, BioFire Syndromic Trends, for rapid disease reporting that is syndrome-based but pathogen-specific. Deidentified patient test results from the BioFire FilmArray multiplex molecular diagnostic system are sent directly to a cloud database. Summaries of these data are displayed in near real time on the Syndromic Trends public website. We studied this dataset for the prevalence, seasonality, and coinfections of the 20 respiratory pathogens detected in over 362,000 patient samples acquired as a standard-of-care testing over the last 4 years from 20 clinical laboratories in the United States.ResultsThe majority of pathogens show influenza-like seasonality, rhinovirus has fall and spring peaks, and adenovirus and the bacterial pathogens show constant detection over the year. The dataset can also be considered in an ecological framework; the viruses and bacteria detected by this test are parasites of a host (the human patient). Interestingly, the rate of pathogen codetections, on average 7.94% (28,741/362,101), matches predictions based on the relative abundance of organisms present.ConclusionsSyndromic Trends preserves patient privacy by removing or obfuscating patient identifiers while still collecting much useful information about the bacterial and viral pathogens that they harbor. Test results are uploaded to the database within a few hours of completion compared with delays of up to 10 days for other diagnostic-based reporting systems. This work shows that the barriers to establishing epidemiology systems are no longer scientific and technical but rather administrative, involving questions of patient privacy and data ownership. We have demonstrated here that these barriers can be overcome. This first look at the resulting data stream suggests that Syndromic Trends will be able to provide high-resolution analysis of circulating respiratory pathogens and may aid in the detection of new outbreaks.

Project description:BackgroundThe American Society for Clinical Oncology recently launched the minimal common oncology data elements project to facilitate cancer data interoperability. However, clinical data are often unrecorded in an organized way, and converting them into a structured format can be time-consuming. Clinical Data Warehouse (CDW) is a database that consolidates data from different clinical sources. However, the clinical data extracted from this database include not only structured data but also natural language generated during clinical practice. Therefore, applying these data to a clinical study is challenging because they are unstructured, and unformatted to allow essential content to be found. This study determined how best to organize a huge amount of clinical data to evaluate the upper aerodigestive tract cancers' clinical features and outcomes, including cancer of the head and neck, esophagus, lung, thymus, and mesothelioma.MethodsThe Real-time autOmatically updated data warehOuse in healThcare (ROOT) uses six main regions to describe the journey of cancer patients. This study, developed an algorithm optimized for each disease category using natural language processing of unstructured data and data capture of structured data. Data from patients diagnosed at the Samsung Medical Center from 2008-2020 were used.ResultsComprehensive clinical data for 67,617 patients across six tumor types: 28,954 with non-small-cell lung cancer, 2,540 with small-cell lung cancer, 30,035 with head and neck cancer, 4,950 with esophageal cancer, 966 with thymic cancer, and 172 with mesothelioma were collected. Additionally, the results of a longitudinal molecular study, including epidermal growth factor receptor (EGFR) mutations, anaplastic lymphoma kinase (ALK) tests, and next-generation sequencing (NGS), were included. Scattered information was integrated and automatically built up to match the cohort, allowing users to capture the most updated test results and treatment outcomes.ConclusionsThis landmark study documented the successful construction of a real-time updating system for medical big data, based on the CDW program.

Project description:RationaleAcute exacerbations of chronic obstructive pulmonary disease (AECOPD) are caused by a variety of different etiologic agents. Our aim was to phenotype COPD exacerbations using imaging (chest X-ray [CXR] and computed tomography [CT]) and to determine the possible role of the blood tests (C-reactive protein [CRP], the N-terminal prohormone brain natriuretic peptide [NT-proBNP]) as diagnostic biomarkers.Materials and methodsSubjects who were hospitalized with a primary diagnosis of AECOPD and who had had CXRs, CT scans, and blood collection for CRP and NT-proBNP were assessed in this study. Radiologist blinded to the clinical and laboratory characteristics of the subjects interpreted their CXRs and CT images. ANOVA and Spearman's correlation were performed to test for associations between these imaging parameters and the blood-based biomarkers NT-proBNP and CRP; logistic regression models were used to assess the performance of these biomarkers in predicting the radiological parameters.ResultsA total of 309 subjects were examined for this study. Subjects had a mean age of 65.6±11.1 years, 66.7% of them were males, and 62.4% were current smokers, with a mean FEV1 54.4%±21.5% of predicted. Blood NT-proBNP concentrations were associated with cardiac enlargement (area under the curve [AUC] =0.72, P<0.001), pulmonary edema (AUC =0.63, P=0.009), and pleural effusion on CXR (AUC =0.64, P=0.01); whereas on CT images, NT-proBNP concentrations were associated with pleural effusion (AUC =0.71, P=0.002). Serum CRP concentrations, on the other hand, were associated with consolidation on CT images (AUC =0.75, P<0.001), ground glass opacities (AUC =0.64, P=0.028), and pleural effusion (AUC =0.72, P<0.001) on CT images. A serum CRP sensitivity-oriented cutoff point of 11.5 mg/L was selected for the presence of consolidation on CT images in subjects admitted as cases of AECOPD, which has a sensitivity of 91% and a specificity of 53% (P<0.001).ConclusionElevated CRP may indicate the presence of pneumonia, while elevated NT-proBNP may indicate cardiac dysfunction. These readily available blood-based biomarkers may provide more accurate phenotyping of AECOPD and enable the discovery of more precise therapies.

Dataset Information

Patterns and characterization of COPD exacerbations using real-time data collection.

Publications

Patterns and characterization of COPD exacerbations using real-time data collection.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets