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DARTNet Institute

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About DARTNet Institute

DARTNet Institute is a non-profit organization conducts research, supports collaboration among health care providers and organizations, and hosts data sets of health information for quality improvement and research. The company is based in Aurora, Colorado.

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12635 East Montview Blvd Suite 129

Aurora, Colorado, 80045,

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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

Apr 29, 2022

COPD, acute bronchitis, LRTI, other lower respiratory code or influenza code with prescribed oral corticosteroid (OCS) and/or respiratory specific antibiotic. Uncoded exacerbation with prescribed OCS and/or respiratory specific antibiotic (without other reason). Patient-reported COPD exacerbations are received from patient questionnaires. Data Collection Aggregated baseline demographic and clinical EHR data were collected from June 2019 to September 2020. Longitudinal patient data extended to earlier years at each site: January 2002 (Texas), January 2010 (Ohio), March 2007 (Ohio), August 2001 (New York), and July 2009 (N. Carolina). EHR data were extracted remotely by the DARTNet Institute, a non-profit organization that hosts data sets of health information for quality improvement and research ( http://www.dartnet.info/ ). EHR data was standardized using the Observational Medical Outcomes Partnership (OMOP) common data model (v6), allowing for the analysis of data from disparate sources. To ensure anonymity, all patients were assigned a unique Registry ID using a one-way hashing algorithm prior to storage in the database. PRIO data were collected by paper questionnaires or PEERS (a HIPAA compliant, browser-based study management, and PRIO data collection system) between Dec 2019 and November 2020. These data were integrated into the OMOP DARTNet database and reconciled with respective patient EHR data. Paired PRIO data were stored as a single data set per patient and assigned a unique registry ID using a one-way hashing algorithm. EHR and PRIO data quality were enhanced through a series of programmed data quality checks that automatically detect out-of-range or anomalous data. The APEX COPD databases are hosted in the US on an Amazon Web Service (AWS) firewall-firewall-protected server. This server is part of the HIPAA 1996 compliant DARTNet server environment maintained by AWS. OPC Global acts as data custodians, but each site/patient continues to own the patient-level data contributed. Statistical Analysis Stata version 14 (College Station, TX, US) and R version 3.6 (Vienna, Austria) were used to conduct all statistical analyses and data handling. Descriptive statistics were computed for all demographic, clinical, and PRIO variables. All available (non-missing) data were summarized. Categorical variables were presented as number (%) and numerical variables as mean (standard deviation). Results Patient Population A total of 17,192 patients were identified to be eligible for analysis from the APEX COPD Registry from healthcare organizations located in Texas (n=811), Ohio (n=8722), Colorado (n=472), New York (n=1149), and North Carolina (n=6038). Supplementary PRIO data was available from 63, 565, 62, 79, and 585 patients respectively from Texas, Ohio, Colorado, New York, and North Carolina. Baseline Characteristics Sex distribution was similar at each site with a slight female majority (>54%) ( Table 1 ). The 65–74 years age-group had the highest proportion of patients in Texas (36.3%), Colorado (28.4%), and North Carolina (34.2%). Meanwhile, in Ohio and New York, the age-group with the highest proportion of patients was the 55–64 years group (35.7% and 38.6% respectively). BMI distribution was also similar across sites with 62–73% of patients being overweight or obese (BMI ≥25). Colorado had the lowest proportion of overweight to obese patients and the lowest mean BMI (mean (SD) = 27.9 [7.2]). Racial and ethnicity distribution was highly variable. Patients from Colorado (94.8%) and North Carolina (83.7%) were predominantly Caucasian. Ohio also had a majority (58.9%) Caucasian patients but also a sizable proportion of African American patients (31.9%). Texas had 45.9% Hispanic patients while New York consisted of mostly African American patients. The majority (83.7–100.0%) of patients were current or ex-smokers, except in Texas where 54.0% had never smoked. EHR recorded prescription of pharmacological intervention for smoking cessation was rare, with approximately half the number of current smokers across all sites having received intervention, the lowest being in in New York (7.7%) which has the highest proportion of current smokers (71.5%). The distribution of baseline characteristics for patients who provided supplementary PRIO data is presented in Supplementary Table 5 . Table 1 Demographic Characteristics of COPD Patients in the APEX COPD Registry Clinical Characteristics Overall, 34.7% to 42.2% of patients experienced an exacerbation in the previous year ( Table 2 ). Colorado had the fewest number of exacerbations with the highest proportion of no exacerbation (65.3%) and the smallest proportion of 3+ exacerbations (1.5%). Patients from Colorado also experienced the lowest average number of exacerbations (mean [SD] = 0.5 [0.7]) while New York and Ohio experienced the highest (mean [SD] = 0.9 [1.6] and 0.9 [1.5] respectively). Table 2 Disease Monitoring Characteristics of COPD Patient from the APEX COPD Registry Steady-state (no exacerbation in the 2 weeks pre- and post-measurement) total eosinophil count varied across the sites. Patients from Ohio had the lowest mean steady-state total eosinophil count (mean [SD] = 196.6 [181.8]) while North Carolina had a much higher total eosinophil count (mean [SD] = 244.4 [400.6]). ( Table 2 ). The proportion of patients with >300 cells/µL total eosinophil count, the cut-off for the greatest benefit for ICS treatment, was highest in Texas (23.1%) followed by North Carolina (21.9%). Results for exacerbations and total eosinophil count in patients who provided supplementary PRIO data are presented in Supplementary Table 6 . Co-Morbidities Co-morbidities were common across all sites ( Table 3 ). Hypertension was the most common co-morbidity at all sites (75.0–87.9%), except in Colorado where depression was more common (86.9%). A much higher rate of asthma (61.6%) and heart failure (72.3%) were observed in New York compared to the other sites (<41% and <26% respectively). In contrast, obstructive sleep apnea was much less common in New York (7.8%) than in other sites (>27%). Pneumonia in the last 24 months was observed in 11.6–16.9% of patients within Texas, Ohio, and Colorado. Almost none (1.5%) in North Carolina and none in New York had recorded pneumonia. The prevalence of co-morbidities in patients who provided supplementary PRIO data is presented in Supplementary Table 7 . Table 3 Differential Diagnosis and Comorbidities of COPD Patients from the APEX COPD Registry Treatment Only a small proportion of patients (<9%) were not on any therapy for COPD except in Colorado (28.4%) ( Table 4 ). A minority (4.9–14.9%) were given only short-acting bronchodilator therapy across all 5 sites. Among controller therapies given, with or without short-acting bronchodilator inhalers, inhaled corticosteroid (ICS) with long-acting beta-agonist (LABA) was the most common treatment combination (26.1–45.6%), except in Ohio where triple therapy with ICS, LABA, and Long-acting muscarinic antagonist (LAMA) was more common (32.9%). Triple therapy was given in roughly 1 in 5 patients at other sites except in Colorado with only 4.7% of patients receiving triple therapy. Prescribing of ICS monotherapy was most common in Colorado (12.9%) but was much less common at other sites (1.4–5.7%). Vaccination rates were similar across all sites with 19.3–34.7% and 56.8–78.0% of patients having received influenza vaccine within the past 12 months and pneumococcal vaccines within the past 10 years respectively. Vaccination data were not available from the New York site. Treatment and vaccination data for patients who provided supplementary PRIO data are presented in Supplementary Table 8 . Table 4 Treatment Patterns of COPD Patients in the Last 24 Months from the APEX COPD Registry Patient-Reported Information/Outcomes (PRIO) Among the 1354 patients who provided additional PRIO data, the majority (68–74%) reported a CAT score of 10–30. ( Table 5 ). Texas had the highest proportion of patients who reported Grade 0 or 1 mMRC-rated breathlessness (66.2%) and patients who were categorized as GOLD group A (21.0%). New York reported the highest proportion of mMRC at >2 (54.6%); however, Colorado reported the highest proportion of patients at the highest level 4 (11.9%). Table 5 Additional Patient Reported Information/Outcome Data from the APEX COPD Registry In Ohio and Colorado, 57% of the patients reported no exacerbation in the past 12 months. In contrast, >60% of patients from the Texas site reported having at least one exacerbation in the previous year. Texas also had the highest proportion of patients reporting 3 or more exacerbations (25.4%), compared to the lowest in New York (14.1%). Most patients reported not being hospitalized in the past 12 months (78.5–88.5%) despite more than 10% of patients in Ohio reported having had 3 or more hospitalizations. Discussion Summary of Findings This study demonstrates the extent of the inter-site heterogeneity in both the demographic, clinical, and treatment characteristics, as well as patient-reported outcomes of COPD in patients with COPD managed in different healthcare organizations across 5 states in the US The study also provides an up-to-date refresh of COPD population demographics and clinical characteristics in varying situations within US primary care at a specific cross-sectional point, which can be utilized by future studies. Slight variations in age and BMI were observed across the states, however, a wider variation was observed in patient ethnicity. Ethnicity has been suggested to play a role in the development and severity of COPD, 26 and may also impact access and quality of healthcare. 3 Data on the distribution of demographic characteristics may be useful for tailoring health policies according to the needs of individual sites. Tobacco smoking remains a top risk factor for COPD and its co-morbidities. 27 Smoking cessation is a key intervention for the improvement of COPD and the GOLD recommendations strongly support treatment of tobacco dependence. 1 Counselling and pharmacological intervention are effective in helping patients to cease smoking. 28–31 The low uptake of documented prescription pharmacological intervention to assist smoking cessation across all sites suggests a care gap that could be readily addressed. One possible approach is by promoting comprehensive and accessible insurance coverage for smoking cessation interventions. 32 Interestingly, the Texas site recorded low smoking rate which may be due to smoking data not being entirely present within primary care EHR or stored separately within the system. Both EHR-recorded and patient-reported exacerbations were collected and analyzed in this study. Regardless of the inter-site variation, we observed a higher number of patient-reported exacerbations compared to EHR-recorded exacerbations across all sites. The difference between EHR-recorded and patient-reported prevalence of exacerbation ranged from 1% in Ohio up to 24% in North Carolina. This may indicate COPD exacerbations for which the patients did not seek appointments with primary care, either self-managing or attending urgent care or other sites outside of the usual EHR system, and was thus unrecorded. Underreporting of COPD exacerbations has been observed in previous studies and is likely to be common. 33 , 34 Since the frequency of exacerbations is important in determining appropriate pharmacotherapy, support for enhanced patient interactions with their primary care site appears to be important. Tools such as COPD Action Plans may facilitate better interactions for COPD exacerbation identification and management. 35 Blood eosinophil levels are associated with a patient’s response to ICS therapy. 1 In this study, the steady state total eosinophil counts varied by site. However, only half or fewer of the patients at each site had eosinophil count data. In addition, the current observation does not suggest any clear pattern between eosinophil count and ICS prescription, either as monotherapy or in combination, across the locations. This may indicate an opportunity for improved treatment selection based on appropriate biomarkers, specifically on recommendations for eosinophil measurement to direct treatment decisions. Variation in the maintenance therapy prescribing patterns was present and might indicate differential uptake of the GOLD recommendation. Colorado had the highest proportion without maintenance treatment and also the lowest rates of EHR-derived and patient-reported exacerbations relative to the other sites. This may suggest that individuals in Colorado are being diagnosed at an early stage of COPD. This warrants further investigation as diagnosis of COPD is often delayed across the US. 34 Influenza and pneumococcal vaccination are recommended for all patients with COPD. 1 The varying and suboptimal uptake of vaccination may represent another opportunity for optimization of patient management. Strengths and Limitations This study was conducted from a registry of 17,000 primary care patients in the US across 5 healthcare organizations that collected a predefined set of data with an analysis protocol. Standardization of data collected across the sites also facilitated unbiased comparison. The study’s list of variables was selected through voting by a panel of experts, ensuring that clinically relevant variables are extracted and compared. Data within the registry was also enhanced with PRIO data from over 1000 patients, providing additional insight into the burden of COPD on patients’ lives beyond that reported in EHR data. There are several limitations to the current study. The data in this study was originally stored for routine patient care instead of research purposes. Consequently, there is missing and incomplete data within the registry; in particular, vaccination data are not stored on-site in New York but are instead stored within a statewide database which was not accessible by the APEX team. Therefore, vaccination within New York could not be included in the current study. Due to the nature of the study, the COPD status of patients within the registry are not directly confirmed. COPD diagnosis code was used as selection criteria instead of confirmation by spirometry. This was decided as spirometry is often not completed during diagnosis, or poorly recorded in primary care EHR. 36 Therefore, no COPD-confirming result, ie FEV1/FVC ratio of >70%, was required. Additionally, due to the lack of consensus in asthma/COPD definition, lack of accuracy in diagnosis within EHR especially in the absence of confirmatory spirometry, and the ambition to gather the most representable COPD population for observation, patients with asthma-COPD overlap were included. Patients were excluded however if they were classified as an active asthma patient (visit with an asthma code within the previous 2 years). The current study is also limited to providing descriptive analyses of the variation across healthcare systems based on all available patient EHR. Follow-up data collection and analyses would need to be conducted to generate a more solid conclusion on the reasons behind these differences between healthcare systems and their impact on COPD patient outcomes. The difference of treatment pattern in this study also do not take into account the differences in patient characteristics within each site. Deeper analyses into the differences in appropriateness of COPD therapy relative to the GOLD recommendation across the sites are also warranted. Conclusion This study shows the heterogeneity in the demographic and clinical characteristics and treatment of patients diagnosed with COPD who are managed in primary care in the US These differences could stem from both real inter-location differences in the patient and disease characteristics, but may also be due to differences in uptake of guideline recommendations. Data from this study is hoped to facilitate further investigations of the differences to enable improvement and standardization of the quality of care in primary care for patients with COPD across the US. Abbreviations AATD, Alpha-1 anti-trypsin deficiency; APEX, advancing the patient experience; CAT, COPD Assessment Test; COPD, chronic obstructive pulmonary disease; EHR, electronic health record; GOLD: Global Initiative for Chronic Obstructive Lung Disease; GERD, gastroesophageal reflux disease; ICS, inhaled corticosteroids; LABA, long-acting beta-agonist; LAMA, long-acting muscarinic antagonist; LRTI, lower respiratory tract infection; MMRC, Modified Medical Research Council; OSA, obstructive sleep apnoea; OCS, oral corticosteroid; PRIO, patient reported information/outcomes; SABA, short-acting beta-agonist; SAMA, short-acting muscarinic antagonist; SD, standard deviation. Data Sharing Statement The dataset supporting the conclusions of this article was derived from the APEX COPD Registry. Anonymized Data Ethics Protocols and Transparency committee (ADEPT0520), the American Academy of Family Physicians and the APEX Steering Committee. Central ethics (Institutional Review Board, IRB) approval was obtained from the American Academy of Family Physicians for most sites (AAFP; IRB reference number: 19-349). The authors do not have permission to give public access to the study dataset; researchers may request access to APEX Registry data for their own purposes via the APEX COPD website ( https://www.apexcopd.org/ ) or via the enquiries email [email protected] . Ethics Approval Patient EHR data were shared in accordance with the local regulatory/ethics requirements. Informed consent was obtained from patients via an online portal to allow data sharing for ethically approved research purposes as well as recruitment for future studies. Patients were permitted to opt out of data sharing. This study was designed, implemented, and reported in accordance with the European Network Centres for Pharmacoepidemiology and Pharmacovigilance (study reference number: EUPAS29401); http://www.encepp.eu/encepp/viewResource.htm?id=29348 and performed in compliance with all applicable local laws and regulations. Governance was provided by Optimum Patient Care (OPC) Global, the Respiratory Effectiveness Group, the Anonymized Data Ethics Protocols and Transparency Committee (ADEPT0520), the American Academy of Family Physicians, and the APEX Steering Committee. Central ethics (Institutional Review Board, IRB) approval was obtained from the American Academy of Family Physicians for most sites (AAFP; IRB reference number: 19-349). Secondary ethics approval was obtained for 1 site which has their own ethics approval board. Acknowledgments Medical writing support was provided by Dr. Antony Hardjojo of Jaya Medical Writing Pte Ltd, Singapore. Writing, editorial support, and/or formatting assistance in the development of this manuscript was provided by Andrea Teh, BSc (Hons) and Shilpa Suresh, MSc of the Observational and Pragmatic Research Institute, Singapore. We wish to acknowledge Kidane Gebremariam for his contribution to protocol development. BIPI was given the opportunity to review the manuscript for medical and scientific accuracy as well as intellectual property considerations. Author Contributions The authors meet criteria for authorship as recommended by the International Committee of Medical Journal Editors. All authors made a significant contribution to the work reported, whether that is in the conception, study design, execution, acquisition of data, analysis, and interpretation, or in all these areas. All authors took part in drafting, revising or critically reviewing the article. All authors gave final approval of the version to be published. All authors have agreed on the journal to which the article has been submitted and agree to be accountable for all aspects of the work. All authors have given approval for the submission of this article. The authors received no direct compensation related to the development of the manuscript. Funding APEX COPD is established and maintained by Optimum Patient Care (OPC) Global Limited; and research was conducted by the Observational & Pragmatic Research Institute Pte Ltd (OPRI). The establishment of the APEX registry was co-funded by OPC Global and Boehringer Ingelheim Pharmaceuticals, Inc. (BIPI). OPC Global retains intellectual property rights to the APEX registry. Disclosure Chester Fox declares no conflict of interest. Wilson Pace is on the advisory board for Mylan; stock from Eli Lilly, Novo Nordisk, Pfizer, Novartis, Johnson & Johnson, Stryker, Amgen, Gilead, and Sanofi. Elias Brandt, Gabriela Gaona, and Rachel Kent are employees of the DARTNet Institute. Amanda Ratigan is a consultant to Boehringer-Ingelheim, but at the time of this disclosure had received no funding directly or through any organization she works for from Boehringer-Ingelheim. Victoria Carter, Alexander Evans, Maja Kruszyk, Chantal Le Lievre, and Brooklyn Stanley are employees of Optimum Patient Care, a co-founder of the APEX COPD initiative. Chelsea Edwards was an employee of Optimum Patient Care Australia at the time that this study was conducted. Ku-Lang Chang declares no conflict of interest. MeiLan K Han reports personal fees from GlaxoSmithKline, AstraZeneca, Boehringer Ingelheim, Cipla, Chiesi, Novartis, Pulmonx, Teva, Verona, Merck, Mylan, Sanofi, DevPro, Aerogen, Polarian, Regeneron, United Therapeutics, UpToDate, Altesa Biopharma, Medscape and Integrity. She has received either in kind research support or funds paid to the institution from the NIH, Novartis, Sunovion, Nuvaira, Sanofi, AstraZeneca, Boehringer Ingelheim, Gala Therapeutics, Biodesix, the COPD Foundation and the American Lung Association. She has participated in Data Safety Monitoring Boards for Novartis and Medtronic with funds paid to the institution. She has received stock options from Meissa Vaccines and Altesa Biopharma. Alan Kaplan is a member of the advisory board of, or speakers bureau for, AstraZeneca, Behring, Boehringer Ingelheim, Covis, Cipla, Grifols, GlaxoSmithKline, Merck Frosst, Novo Nordisk, Novartis, Pfizer, Purdue, Sanofi, Teva, and Trudel. Janwillem Kocks reports grants, personal fees and non-financial support from AstraZeneca, grants, personal fees and non-financial support from Boehringer Ingelheim, grants and personal fees from Chiesi Pharmaceuticals, grants, personal fees and non-financial support from GSK, grants and non-financial support from Mundi Pharma, grants and personal fees from TEVA, grants and personal fees from Novartis, personal fees from MSD, personal fees from COVIS Pharma, grants from Valneva outside the submitted work; and Janwillem Kocks holds <5% shares of Lothar Medtec GmbH and 72.5% of shares in the General Practitioners Research Institute. Tessa Li Voti was an employee of Optimum Patient Care at the time this study was conducted. Cathy Mahle and Asif Shaikh are employees of Boehringer Ingelheim, a co-founder of the APEX COPD initiative. Barry Make reports funding from the NHLBI for the COPDGene study; grants and medical advisory boards from Boehringer Ingelheim, GlaxoSmithKline, AstraZeneca, and Sunovion; personal fees for DSMB from Spiration and Shire/Baxalta; CME personal fees from WebMD, National Jewish Health, American College of Chest Physicians, Projects in Knowledge, Hybrid Communications, SPIRE Learning, Ultimate Medical Academy, Catamount Medical, Eastern Pulmonary Society, Catamount Medical Communications Medscape, Eastern VA Medical Center, Academy Continued Healthcare Learning, and Mt. Sinai Medical Center; royalites from Up-To-Date; medical advisory boards from Novartis, Phillips, Third Pole, Science 24/7, and Verona; grants from Pearl; outside the submitted work. Neil Skolnik is on advisory boards for AstraZeneca, Teva, Lilly, Boehringer Ingelheim, Sanofi, Janssen Pharmaceuticals, Intarcia, Mylan, and GlaxoSmithKline; Payment for lectures/speaking engagements from AstraZeneca and Boehringer Ingelheim; Research Support from Sanofi, AstraZeneca, Boehringer Ingelheim, and GlaxoSmithKline. Barbara P Yawn has served on COPD-related advisory boards for GlaxoSmithKline, AstraZeneca, Novartis, and Boehringer Ingelheim, Teva, receives consultancy fees from ndd Medical Technology and received COPD-related investigator-initiated research funds from GlaxoSmithKline, Boehringer Ingelheim, AstraZeneca, and Novartis. David Price has advisory board membership with AstraZeneca, Boehringer Ingelheim, Chiesi, Mylan, Novartis, Regeneron Pharmaceuticals, Sanofi Genzyme, Thermo Fisher; consultancy agreements with Airway Vista Secretariat, AstraZeneca, Boehringer Ingelheim, Chiesi, EPG Communication Holdings Ltd, FIECON Ltd, Fieldwork International, GlaxoSmithKline, Mylan, Mundipharma, Novartis, OM Pharma SA, PeerVoice, Phadia AB, Spirosure Inc, Strategic North Limited, Synapse Research Management Partners S.L., Talos Health Solutions, Theravance and WebMD Global LLC; grants and unrestricted funding for investigator-initiated studies (conducted through Observational and Pragmatic Research Institute Pte Ltd) from AstraZeneca, Boehringer Ingelheim, Chiesi, Mylan, Novartis, Regeneron Pharmaceuticals, Respiratory Effectiveness Group, Sanofi Genzyme, Theravance, British Lung Foundation, and UK National Health Service; payment for lectures/speaking engagements from AstraZeneca, Boehringer Ingelheim, Chiesi, Cipla, GlaxoSmithKline, Kyorin, Mylan, Mundipharma, Novartis, Regeneron Pharmaceuticals and Sanofi Genzyme; payment for travel/accommodation/meeting expenses from AstraZeneca, Boehringer Ingelheim, Mundipharma, Mylan, Novartis, Thermo Fisher; stock/stock options from AKL Research and Development Ltd which produces phytopharmaceuticals; owns 74% of the social enterprise Optimum Patient Care Ltd (Australia and UK) and 92.61% of Observational and Pragmatic Research Institute Pte Ltd (Singapore); 5% shareholding in Timestamp which develops adherence monitoring technology; is peer reviewer for grant committees of the UK Efficacy and Mechanism Evaluation programme, and Health Technology Assessment; and was an expert witness for GlaxoSmithKline. References 1. Global Initiative for Chronic Obstructive Lung Disease. Global strategy for the diagnosis, management, and prevention of chronic obstructive pulmonary disease. 2021 report. Available from: https://goldcopd.org/wp-content/uploads/2020/11/GOLD-REPORT-2021-v1.1-25Nov20_WMV.pdf . Accessed . 5. Ford ES, Murphy LB, Khavjou O, Giles WH, Holt JB, Croft JB. Total and state-specific medical and absenteeism costs of COPD among adults aged ≥ 18 years in the United States for 2010 and projections through 2020. Chest. 2015;147(1):31–45. doi:10.1378/chest.14-0972 6. May SM, Li JT. Burden of chronic obstructive pulmonary disease: healthcare costs and beyond. Allergy Asthma Proc. 2015;36(1):4–10. doi:10.2500/aap.2015.36.3812 7. Huybrechts KF, Bateman BT, Pawar A, et al. Maternal and fetal outcomes following exposure to duloxetine in pregnancy: Cohort Study. BMJ;2020. m237. doi:10.1136/bmj.m237 8. Hill K, Goldstein RS, Guyatt GH, et al. Prevalence and underdiagnosis of chronic obstructive pulmonary disease among patients at risk in primary care. Can Med Assoc J. 2010;182(7):673–678. doi:10.1503/cmaj.091784 9. Tinkelman DG, Price D, Nordyke RJ, Halbert RJ. COPD screening efforts in primary care: what is the yield? Prim Care Respir J. 2007;16(1):41–48. doi:10.3132/pcrj.2007.00009 10. Johns DP, Walters JAE, Walters EH. Diagnosis and early detection of COPD using spirometry. J Thorac Dis. 2014;6(11):1557–1569. doi:10.3978/j.issn.2072-1439.2014.08.18 11. Albitar HAH, Iyer VN. Adherence to Global Initiative for Chronic Obstructive Lung Disease guidelines in the real world: current understanding, barriers, and solutions. Curr Opin Pulm Med. 2020;26(2):149–154. doi:10.1097/MCP.0000000000000655 12. van Schayck CP, Chavannes NH. Detection of asthma and chronic obstructive pulmonary disease in primary care. Eur Respir J. 2003;21(Supplement 39):16S–22S. doi:10.1183/09031936.03.00040403 13. Jones RCM, Price D, Ryan D, et al. Opportunities to diagnose chronic obstructive pulmonary disease in routine care in the UK: a retrospective study of a clinical cohort. Lancet Respir Med. 2014;2(4):267–276. doi:10.1016/S2213-2600(14)70008-6 14. Calverley PM. COPD: early detection and intervention. Chest. 2000;117(5 Suppl 2):365s–371s. doi:10.1378/chest.117.5_suppl_2.365S 15. Larsson K, Janson C, Ställberg B, et al. Impact of COPD diagnosis timing on clinical and economic outcomes: the Arctic Observational Cohort Study. Int J Chron Obstruct Pulmon Dis. 2019;14:995–1008. doi:10.2147/COPD.S195382 16. Ferrone M, Masciantonio MG, Malus N, et al. The impact of integrated disease management in high-risk COPD patients in primary care. NPJ Prim Care Respir Med. 2019;29(1):8. doi:10.1038/s41533-019-0119-9 17. Rennard S, Thomashow B, Crapo J, et al. Introducing the COPD Foundation Guide for Diagnosis and Management of COPD, recommendations of the COPD Foundation. COPD. 2013;10(3):378–389. doi:10.3109/15412555.2013.801309 18. Yawn BB, Thomashaw B, Mannino DM, et al. The 2017 update to the COPD Foundation COPD pocket consultant guide. COPD. 2017;4(3):177–185. doi:10.15326/jcopdf.4.3.2017.0136 19. Qaseem A, Wilt TJ, Weinberger SE, et al. Diagnosis and management of stable chronic obstructive pulmonary disease: a clinical practice guideline update from the American College of Physicians, American College of Chest Physicians, American Thoracic Society, and European Respiratory Society. Ann Intern Med. 2011;155(3):179–191. doi:10.7326/0003-4819-155-3-201108020-00008 20. Criner GJ, Bourbeau J, Diekemper RL, et al. Prevention of acute exacerbations of COPD: American College of Chest Physicians and Canadian Thoracic Society Guideline. Chest. 2015;147(4):894–942. doi:10.1378/chest.14-1676 21. Department of Defense, D. of V. A. VA/DoD. Clinical practice guideline for the management of Chronic Obstructive Pulmonary Disease; 2014. Available from: https://www.healthquality.va.gov/guidelines/CD/copd/VADoDCOPDCPG.pdf . Accessed . 24. Edwards CL, Kaplan AG, Yawn BP, et al. Development of the advancing the patient experience in COPD registry: a Modified Delphi Study. COPD. 2021;8(1):135–151. doi:10.15326/jcopdf.2020.0154 25. Halpin DMG, de Jong HJI, Carter V, Skinner D, Price D. Distribution, temporal stability and appropriateness of therapy of patients with COPD in the UK in relation to GOLD 2019. EClinicalMedicine. 2019;14:32–41. doi:10.1016/j.eclinm.2019.07.003 26. Gilkes A, Ashworth M, Schofield P, et al. Does COPD risk vary by ethnicity? A retrospective cross-sectional study. Int J Chron Obstruct Pulmon Dis. 2016;11:739–746. doi:10.2147/COPD.S96391 27. Chatila WM, Thomashow BM, Minai OA, Criner GJ, Make BJ. Comorbidities in chronic obstructive pulmonary disease. Proc Am Thorac Soc. 2008;5(4):549–555. doi:10.1513/pats.200709-148ET 28. Wagena EJ, Knipschild PG, Huibers MJH, Wouters EFM, van Schayck CP. Efficacy of bupropion and nortriptyline for smoking cessation among people at risk for or with Chronic Obstructive Pulmonary Disease. Arch Intern Med. 2005;165(19):2286–2292. doi:10.1001/archinte.165.19.2286 29. Tønnesen P. Smoking cessation and COPD. Eur Respir Rev. 2013;22(127):37. doi:10.1183/09059180.00007212 30. Hoogendoorn M, Feenstra TL, Hoogenveen RT, Rutten-van Mölken MPMH. Long-term effectiveness and cost-effectiveness of smoking cessation interventions in patients with COPD. Thorax. 2010;65(8):711. doi:10.1136/thx.2009.131631 31. Lemmens V, Oenema A, Knut IK, Brug J. Effectiveness of smoking cessation interventions among adults: a systematic review of reviews. Eur J Cancer Prev. 2008;17(6):535–544. doi:10.1097/CEJ.0b013e3282f75e48 32. Centers for Disease Control and Prevention. Coverage for Tobacco use cessation treatments. Content last reviewed on June 2, 2021. Available from: https://www.cdc.gov/tobacco/quit_smoking/cessation/coverage/index.htm . Accessed

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