Variation in Demographic and Clinical Characteristics of Patients with COPD Receiving Care in US Primary Care: Data from the Advancing the Patient EXperience (APEX) in COPD Registry
Bibliographic record
Abstract
Chester Fox,1,2 Wilson Pace,1,3 Elias Brandt,1 Victoria Carter,4,5 Ku-Lang Chang,6 Chelsea Edwards,7 Alexander Evans,4 Gabriela Gaona,1 MeiLan K Han,8 Alan Kaplan,5,9,10 Rachel Kent,1 Janwillem WH Kocks,5,11– 13 Maja Kruszyk,5,7 Le Lievre Chantal,5,7 Tessa LiVoti,4,5 Cathy Mahle,14 Barry Make,15 Amanda Ratigan,1 Asif Shaikh,14 Neil Skolnik,16,17 Brooklyn Stanley,4 Barbara P Yawn,17 David B Price4,5,18 1DARTNet Institute, Aurora, CO, USA; 2University at Buffalo, Buffalo, NY, USA; 3University of Colorado, Denver, CO, USA; 4Optimum Patient Care, Cambridge, UK; 5Observational and Pragmatic Research Institute, Singapore; 6University of Florida College of Medicine, Gainesville, FL, USA; 7Optimum Patient Care, Brisbane, Queensland, Australia; 8University of Michigan, Ann Arbor, MI, USA; 9Family Physician Airways Group of Canada, Stouffville, Ontario, Canada; 10University of Toronto, Toronto, Canada; 11General Practitioners Research Institute, Groningen, the Netherlands; 12Groningen Research Institute Asthma and COPD (GRIAC), University of Groningen, University Medical Center Groningen, Groningen, the Netherlands; 13Department of Pulmonology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands; 14Boehringer Ingelheim, Ridgefield, CT, USA; 15Department of Medicine, NJH, Denver, CO, USA; 16Abington Jefferson Health, Jenkintown, PA, USA; 17University of Minnesota, Minneapolis, MN, USA; 18Centre of Academic Primary Care, Division of Applied Health Sciences, University of Aberdeen, Aberdeen, UKCorrespondence: David B Price, Centre of Academic Primary Care, Division of Applied Health Sciences, University of Aberdeen, Polwarth Building, Foresterhill, Aberdeen, AB25 2ZD, UK, Tel +65 3105 1489, Email dprice@opri.sgIntroduction: Little is known about the variability in chronic obstructive pulmonary disease (COPD) management and how it may be affected by patient characteristics across different healthcare systems in the US. This study aims to describe demographic and clinical characteristics of people with COPD and compare management across five primary care medical groups in the US.Methods: This is a retrospective observational registry study utilizing electronic health records stored in the Advancing the Patient Experience (APEX) COPD registry. The APEX registry contains data from five US healthcare organizations located in Texas, Ohio, Colorado, New York, and North Carolina. Data on demographic and clinical characteristics of primary care patients with COPD between December 2019 and January 2020 were extracted and compared.Results: A total of 17,192 patients with COPD were included in analysis: Texas (n = 811), Ohio (n = 8722), Colorado (n = 472), New York (n = 1149) and North Carolina (n = 6038). The majority of patients at each location were female (> 54%) and overweight/obese (> 60%). Inter-location variabilities were noted in terms of age, race/ethnicity, exacerbation frequency, treatment pattern, and prevalence of comorbid conditions. Patients from the Colorado site experienced the lowest number of exacerbations per year while those from the New York site reported the highest number. Hypertension was the most common co-morbidity at 4 of 5 sites with the highest prevalence in New York. Depression was the most common co-morbidity in Ohio. Treatment patterns also varied by site; Colorado had the highest proportion of patients not on any treatment. ICS/LABA was the most commonly prescribed treatment except in Ohio, where ICS/LABA/LAMA was most common.Conclusions and Relevance: Our data show heterogeneity in demographic, clinical, and treatment characteristics of patients diagnosed with COPD who are managed in primary care across different healthcare organizations in the US.Keywords: quality of care, observational study, research database, patient-reported outcomes
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".