scholarly journals 4541 Harnessing the Power of the Electronic Medical Record in Interstitial Lung Disease

2020 ◽  
Vol 4 (s1) ◽  
pp. 48-49
Author(s):  
Erica Farrand ◽  
Eric Vittinghoff ◽  
Harold Collard
Keyword(s):  
Interstitial Lung Disease ◽  
Clinical Research ◽  
Lung Disease ◽  
Clinical Information ◽  
Demographic Variables ◽  
Data Capture ◽  
Research Quality ◽  
Quality Data ◽  
Research Database ◽  
Consensus Statements

OBJECTIVES/GOALS: Harnessing the EHR to support clinical research is critical for the study of rare diseases such as interstitial lung disease (ILD). However no studies have compared EHR and research-quality data in the ILD population. Our objectives were to (1) identify ILD patients and extract clinical data from an EHR system and (2) assess the performance of ILD data capture. METHODS/STUDY POPULATION: Case validated algorithms were implemented to identify patients from the University of California San Francisco EHR and extract key ILD clinical information including, demographic variables, process measures and patient outcomes. Key clinical information were defined based on consensus statements and ILD clinical trials. A subset of ILD patients, had variables recorded in both the EHR and a separate ILD longitudinal research database. The completeness of EHR data capture and level of agreement were compared between three data collection methods: (1) data manually and systematically collected for an ILD research database (gold standard), (2) data automatically extracted from structured fields in the EHR, and (3) data extracted from unstructured data sources. RESULTS/ANTICIPATED RESULTS: We identified 5857 ILD patients in the EHR, of which 2100 patients had data available in the both the EHR and research database. Baseline demographic variables, co-morbidities, use of diagnostic testing, pharmacotherapy were accurately extracted from structured fields. Outcome measures, including lung physiology, radiographic patterns, pathology results, and health related quality of life (HRQoL) were unevenly extracted from structured fields alone. With the exception of HRQoL, these measures were accurately captured in unstructured EHR sources. Notably, certain metrics were better defined in the EHR, including health care resource utilization metrics, acute exacerbations, medication side effects, supplemental oxygen use and specialty care referrals (rheumatology, lung transplant, palliative care, etc). DISCUSSION/SIGNIFICANCE OF IMPACT: A large real-world ILD cohort can be algorithmically extracted from the EHR along with key clinical variables with accuracy comparable to protocol-driven research databases. Rigorous assessment of the types of disease-specific variables that are present in EHR-derived data will inform future interventions to improve the fidelity, accessibility and use of the EHR in clinical research.

scholarly journals The identification and management of interstitial lung disease in systemic sclerosis: evidence-based European consensus statements

2020 ◽  
Vol 2 (2) ◽  
pp. e71-e83 ◽  
Author(s):  
Anna-Maria Hoffmann-Vold ◽  
Toby M Maher ◽  
Edward E Philpot ◽  
Ali Ashrafzadeh ◽  
Rafic Barake ◽  
...  
Keyword(s):  
Systemic Sclerosis ◽  
Interstitial Lung Disease ◽  
Lung Disease ◽  
Evidence Based ◽  
European Consensus ◽  
Consensus Statements

scholarly journals Seasonal and residential clustering at disease onset of anti-MDA5-associated interstitial lung disease

RMD Open ◽  
2020 ◽  
Vol 6 (2) ◽  
pp. e001202
Author(s):  
Naoshi Nishina ◽  
Shinji Sato ◽  
Kenichi Masui ◽  
Takahisa Gono ◽  
Masataka Kuwana
Keyword(s):  
Interstitial Lung Disease ◽  
Lung Disease ◽  
Environmental Influence ◽  
Disease Onset ◽  
Clinical Information ◽  
Trna Synthetase ◽  
Rayleigh Test ◽  
Wilcoxon Rank Sum Test ◽  
Patient Groups ◽  
Test Distance

ObjectivesTo investigate whether the onset of polymyositis (PM)/dermatomyositis (DM)-associated interstitial lung disease (ILD) is influenced by season and residence in the context of myositis-specific autoantibodies.MethodsFor patients with PM/DM-associated ILD enrolled in a multicentre cohort, 365 and 481 patients were eligible for seasonal and geographical analysis, respectively, based on the availability of reliable clinical information. The patients were divided into three groups: (1) anti-melanoma differentiation-associated gene 5 (MDA5) antibody-positive patients, (2) anti-aminoacyl tRNA synthetase (anti-ARS) antibody-positive patients and (3) patients negative for those antibodies. Seasonality was assessed by the Rayleigh test. Distance from residence to the nearest waterfront was measured on Google Map and was compared between groups by the exact Wilcoxon rank-sum test.ResultsIn anti-MDA5-positive patients, the disease developed more frequently in October–March (p=0.03), whereas a seasonal relationship was not found in the remaining two patient groups. Residence at disease onset in anti-MDA5-positive patients was significantly closer to the waterfront, especially to freshwater, compared with that in anti-ARS-positive or anti-MDA5-/ARS-negative patients (p=0.003 and 0.006, respectively).ConclusionsAnti-MDA5-associated ILD occurred predominantly from October to March in individuals residing near freshwater, suggesting an environmental influence on the onset of this disease subset.

scholarly journals Time-dependent propensity-matched general population study of the effects of statin use on cancer risk in an interstitial lung disease and pulmonary fibrosis cohort

BMJ Open ◽  
2021 ◽  
Vol 11 (10) ◽  
pp. e047039
Author(s):  
Jun-Jun Yeh ◽  
Jung-Nien Lai ◽  
Cheng-Li Lin ◽  
Chung-Y Hsu ◽  
Chia-Hung Kao
Keyword(s):  
Interstitial Lung Disease ◽  
Pulmonary Fibrosis ◽  
Lung Disease ◽  
Inhaled Corticosteroids ◽  
Time Dependent ◽  
Research Database ◽  
Cox Models ◽  
General Population Study ◽  
Risk Of Cancer ◽  
Statin Use

ObjectiveTo determine the effect of statins on risk of cancer in patients with interstitial lung disease (ILD) and pulmonary fibrosis.SettingWe retrospectively enrolled patients with ILD and pulmonary fibrosis and divided them into two cohorts by statin use (statin users (n=10 036) and statin non-users (n=10 036)).ParticipantsWe selected patients with ILD and pulmonary fibrosis (N=53 862) from Taiwan’s National Health Insurance Research Database. Time-dependent Cox models were used to compare risk of cancer of propensity-matched statin users and non-users. Cumulative cancer incidence was analysed through Cox proportional regression. We calculated adjusted HRs (aHRs) and their 95% CIs for cancer after adjusting for sex, age, comorbidities, and use of inhaled corticosteroids, oral steroids and statins.ResultsCompared with statin non-users, the aHRs (95% CIs) for statin users were 0.60 (0.55 to 0.65) for cancer, 0.52 (0.35 to 0.78) for haematological malignancy, 0.52 (0.38 to 0.72) for cancer of the head and neck, 0.73 (0.59 to 0.89) for colorectal cancer, 0.34 (0.26 to 0.43) for liver cancer, 0.39 (0.23 to 0.67) for pancreatic cancer, 0.40 (0.17 to 0.96) for skin cancer, 0.67 (0.52 to 0.87) for breast cancer, 0.27 (0.14 to 0.54) for cervical cancer, 0.37 (0.30 to 0.46) for other immunological cancers, 0.73 (0.54 to 0.98) for bladder/kidney cancer and 0.88 (0.71 to 1.09) for lung cancer.ConclusionStatin use is associated with lower risk of cancer in the ILD and pulmonary fibrosis cohort.

European consensus statements for interstitial lung disease in systemic sclerosis

2020 ◽  
Vol 2 (6) ◽  
pp. e317-e318
Author(s):  
Michael S Putman ◽  
Paul Sufka ◽  
Samuel Whittle ◽  
Philip C Robinson
Keyword(s):  
Systemic Sclerosis ◽  
Interstitial Lung Disease ◽  
Lung Disease ◽  
European Consensus ◽  
Consensus Statements

Clinical characteristics of cancer-associated myositis complicated by interstitial lung disease: a large-scale multicentre cohort study

Rheumatology ◽  
2019 ◽  
Vol 59 (1) ◽  
pp. 112-119 ◽  
Author(s):  
Yuko Kaneko ◽  
Takahiro Nunokawa ◽  
Yoshinori Taniguchi ◽  
Yukie Yamaguchi ◽  
Takahisa Gono ◽  
...  
Keyword(s):  
Interstitial Lung Disease ◽  
Lung Disease ◽  
Large Scale ◽  
Clinical Information ◽  
Trna Synthetase ◽  
Aminoacyl Trna Synthetase ◽  
Multicentre Cohort Study ◽  
Incidence Risk ◽  
Incident Cases ◽  
Multicentre Cohort

Abstract Objective To clarify the incidence, risk factors, and impact of malignancy in patients with PM/DM-associated interstitial lung disease (ILD). Methods This study used data from 497 patients with PM/DM-associated ILD enrolled in a multicentre, retrospective and prospective cohort of incident cases. Cancer-associated myositis (CAM) was defined as malignancy diagnosed within 3 years before or after PM/DM diagnosis. Demographic and clinical information was recorded at the time of diagnosis, and data about the occurrence of mortality and malignancy was collected. Results CAM was identified in 32 patients with PM/DM-associated ILD (6.4%). Patients with CAM were older (64 vs 55 years, P < 0.001), presented with arthritis less frequently (24% vs 49%, P = 0.01), and showed a lower level of serum Krebs von den Lungen-6 (687 vs 820 IU/l, P = 0.03) than those without CAM. The distribution of myositis-specific autoantibodies, including anti-melanoma differentiation–associated gene 5, anti-aminoacyl tRNA synthetase, and anti-transcriptional intermediary factor 1-γ antibodies, did not differ between the groups. Survival analysis demonstrated that CAM patients had a poorer survival than non-CAM patients (P = 0.006), primarily due to excess deaths by concomitant malignancy, while mortality due to ILD-related respiratory failure was similar between the groups (P = 0.51). Conclusion Concomitant malignancy can occur in patients with PM/DM-associated ILD, and has significant impact on mortality. Older age, lack of arthritis, and a lower level of serum Krebs von den Lungen-6 at diagnosis are predictors of concomitant malignancy.

scholarly journals Building a Centre of Excellence in Data Management in East Africa

2021 ◽  
Author(s):  
Betty Agwang ◽  
Yuka Manabe
Keyword(s):  
Data Collection ◽  
Infectious Diseases ◽  
Clinical Research ◽  
Data Management ◽  
The United States ◽  
Sub Saharan Africa ◽  
Research Quality ◽  
Data Management System ◽  
Quality Data ◽  
High Quality

Abstract Background: In resource-limited settings, there is a paucity of high quality data management systems for clinical research. The result is that data are often managed in high-income countries disadvantaging researchers at sites where the data are collected. An institutional data management system to address the data collection concerns of the collaborators and sponsors is a key institutional capacity element for high quality research. Our goal was to build a local data management center to streamline data collection and validation compliant with international regulatory bodies. Methods: Leveraging established collaborations between Office of Cyber Infrastructure and Computational Biology of the National Institutes of Health and the John Hopkins University School of Medicine in the United States, the Infectious Diseases Institute at Makerere University built a data management coordinating center. This included mentorship from the NIAID International Centers for Excellence in Research and training of key personnel in South Africa at a functioning data center. The number of studies, case report forms processed and the number of publications emanating from studies using the data management unit since its inception were tabulated. Results: The Infectious Diseases Institute data management core began processing data in 2009 with 3 personnel, hardware (network-enabled scanners, desktops, server held in Bethesda with nightly back up) and software licenses, in addition to on-site support from the NIH. In the last 10 years, 850,869 pages of data have been processed from 60 studies in Uganda, across sub-Saharan Africa, Asia and South America. Real-time data cleaning and data analysis occur routinely and enhance clinical research quality; a total of 212 publications from IDI investigators have been published over the past 10 years. Apart from the server back-up services provided by the NIH, the center is now self-sustaining from fees charged to individual studies. Conclusion: Collaborative partnership among research institutions enabled the IDI to build a core data management and coordination center to support clinical studies, build institutional research capacity, and to advance data quality and integrity for the investigators and sponsors.

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