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Record W4389281912

The Long-Term Burden of COPD Exacerbations During Maintenance Therapy and Lung Function Decline

2020· article· en· W4389281912 on OpenAlexaboutno aff
M Kerkhof, Jaco Voorham, Paul Dorinsky, C Cabrera, Patrick Darken, Mohsen Sadatsafavi, Sin DD, V Carter, Price DB

Bibliographic record

VenueDOAJ (DOAJ: Directory of Open Access Journals) · 2020
Typearticle
Languageen
FieldMedicine
TopicChronic Obstructive Pulmonary Disease (COPD) Research
Canadian institutionsnot available
Fundersnot available
KeywordsLung functionCOPDMedicineTerm (time)Intensive care medicineLungInternal medicine
DOInot available

Abstract

fetched live from OpenAlex

Marjan Kerkhof,1 Jaco Voorham,1 Paul Dorinsky,2 Claudia Cabrera,3,4 Patrick Darken,5 Janwillem WH Kocks,1,6 Mohsen Sadatsafavi,7,8 Don D Sin,7,8 Victoria Carter,1 David B Price1,9 1Observational & Pragmatic Research Institute Pte Ltd, Singapore, Singapore; 2AstraZeneca, Durham, NC, USA; 3AstraZeneca, Mölndal, Sweden; 4Department of Medical Epidemiology and Biostatistics, Karolinska Institute, Stockholm, Sweden; 5AstraZeneca, Morristown, NJ, USA; 6General Practitioners Research Institute, Groningen, The Netherlands; 7Respiratory Evaluation Sciences Program, Collaboration for Outcomes Research and Evaluation, University of British Columbia, Vancouver, BC, Canada; 8Centre for Heart Lung Innovation, St. Paul’s Hospital, Vancouver, BC, Canada; 9Academic Primary Care, University of Aberdeen, Aberdeen, UKCorrespondence: David B Price Polwarth Building, Foresterhill, Aberdeen AB25 2ZD, UKTel +65 6962 3627Email dprice@opri.sgIntroduction: Early identification of preventable risk factors of COPD progression is important. Whether exacerbations have a negative impact on disease progression is largely unknown. We investigated whether the long-term occurrence of exacerbations is associated with lung function decline at early stages of COPD.Methods: Patients diagnosed with mild/moderate COPD (obstruction and FEV1% predicted 50– 90%), aged ≥ 35 years, and a smoking history, who had ≥ 6 years of UK electronic medical records after initiation of maintenance therapy were studied. Multilevel mixed-effect linear regression was performed to determine the association between the count of any year in which the patient had ≥ 1 exacerbation over a 6-year period and FEV1 decline, adjusted for sex, age, anthropometrics and smoking habits. Exacerbations were defined as any prescription for an acute oral corticosteroid course and/or lower respiratory-related antibiotics and/or any COPD-related emergency or inpatient hospitalization.Results: Of 11,337 patients included (mean age 65 years; 49% female) 31.6%, 23.3%, 16.6%, 11.6%, 8.1%, 5.3% and 3.4% had 0, 1, 2, 3, 4, 5 and 6 years with ≥ 1 exacerbation. The mean annual FEV1 decline accelerated by 1.50 mL/year (95% Confidence Interval 1.02; 1.98) with every additional year with ≥ 1 exacerbation from 31.0 mL/year in subjects without any exacerbation to 40.0 mL/year in patients experiencing ≥ 1 exacerbation every year. Patients with more years with ≥ 1 exacerbation had a lower mean FEV1 at first diagnosis: 14.7 mL (11.7; 17.8) lower with every additional year with exacerbations. When counting years with ≥ 2 exacerbations, greater effects were observed (2.19 [1.50; 2.88] mL/year excess decline per year with ≥ 2 exacerbations; 16.5 mL [12.1; 20.8] lower FEV1 at diagnosis).Conclusion: Patients who experienced a greater exacerbation burden after initiation of maintenance therapy had worse lung function at diagnosis and a more rapid lung function decline thereafter, which emphasizes the need for better treatment strategies.Keywords: COPD, exacerbations, spirometry, inhalation therapy, observational study

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.022
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.022
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0040.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.158
GPT teacher head0.496
Teacher spread0.338 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations1
Published2020
Admission routes1
Has abstractyes

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