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      Novel antihyperglycaemic drugs and prevention of chronic obstructive pulmonary disease exacerbations among patients with type 2 diabetes: population based cohort study

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          Abstract

          Objective

          To determine whether the use of glucagon-like peptide 1 (GLP-1) receptor agonists, dipeptidyl peptidase 4 (DPP-4) inhibitors, and sodium-glucose co-transporter-2 (SGLT-2) inhibitors, separately, is associated with a decreased risk of exacerbations of chronic obstructive pulmonary disease among patients with chronic obstructive pulmonary disease and type 2 diabetes.

          Design

          Population based cohort study using an active comparator, new user design.

          Setting

          The United Kingdom Clinical Practice Research Datalink linked with the Hospital Episode Statistics Admitted Patient Care and Office for National Statistics databases.

          Participants

          Three active comparator, new user cohorts of patients starting the study drugs (GLP-1 receptor agonists, DPP-4 inhibitors, or SGLT-2 inhibitors) or sulfonylureas with a history of chronic obstructive pulmonary disease. The first cohort included 1252 patients starting GLP-1 receptor agonists and 14 259 starting sulfonylureas, the second cohort included 8731 patients starting DPP-4 inhibitors and 18 204 starting sulfonylureas, and the third cohort included 2956 patients starting SGLT-2 inhibitors and 10 841 starting sulfonylureas.

          Main outcome measures

          Cox proportional hazards models with propensity score fine stratification weighting were fitted to estimate hazard ratios and 95% confidence intervals of severe exacerbation of chronic obstructive pulmonary disease (defined as hospital admission for chronic obstructive pulmonary disease), separately for GLP-1 receptor agonists, DPP-4 inhibitors, and SGLT-2 inhibitors. Whether these drugs were associated with a decreased risk of moderate exacerbation (defined as a co-prescription of an oral corticosteroid and an antibiotic along with an outpatient diagnosis of acute chronic obstructive pulmonary disease exacerbation on the same day) was also assessed.

          Results

          Compared with sulfonylureas, GLP-1 receptor agonists were associated with a 30% decreased risk of severe exacerbation (3.5 v 5.0 events per 100 person years; hazard ratio 0.70, 95% confidence interval 0.49 to 0.99) and moderate exacerbation (0.63, 0.43 to 0.94). DPP-4 inhibitors were associated with a modestly decreased incidence of severe exacerbation (4.6 v. 5.1 events per 100 person years; hazard ratio 0.91, 0.82 to 1.02) and moderate exacerbation (0.93, 0.82 to 1.07), with confidence intervals including the null value. Finally, SGLT-2 inhibitors were associated with a 38% decreased risk of severe exacerbation (2.4 v 3.9 events per 100 person years; hazard ratio 0.62, 0.48 to 0.81) but not moderate exacerbation (1.02, 0.83 to 1.27).

          Conclusions

          In this population based study, GLP-1 receptor agonists and SGLT-2 inhibitors were associated with a reduced risk of severe exacerbations compared with sulfonylureas in patients with chronic obstructive pulmonary disease and type 2 diabetes. DPP-4 inhibitors were not clearly associated with a decreased risk of chronic obstructive pulmonary disease exacerbations.

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          Most cited references58

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          Balance diagnostics for comparing the distribution of baseline covariates between treatment groups in propensity-score matched samples

          The propensity score is a subject's probability of treatment, conditional on observed baseline covariates. Conditional on the true propensity score, treated and untreated subjects have similar distributions of observed baseline covariates. Propensity-score matching is a popular method of using the propensity score in the medical literature. Using this approach, matched sets of treated and untreated subjects with similar values of the propensity score are formed. Inferences about treatment effect made using propensity-score matching are valid only if, in the matched sample, treated and untreated subjects have similar distributions of measured baseline covariates. In this paper we discuss the following methods for assessing whether the propensity score model has been correctly specified: comparing means and prevalences of baseline characteristics using standardized differences; ratios comparing the variance of continuous covariates between treated and untreated subjects; comparison of higher order moments and interactions; five-number summaries; and graphical methods such as quantile–quantile plots, side-by-side boxplots, and non-parametric density plots for comparing the distribution of baseline covariates between treatment groups. We describe methods to determine the sampling distribution of the standardized difference when the true standardized difference is equal to zero, thereby allowing one to determine the range of standardized differences that are plausible with the propensity score model having been correctly specified. We highlight the limitations of some previously used methods for assessing the adequacy of the specification of the propensity-score model. In particular, methods based on comparing the distribution of the estimated propensity score between treated and untreated subjects are uninformative. Copyright © 2009 John Wiley & Sons, Ltd.
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            Multi-ethnic reference values for spirometry for the 3-95-yr age range: the global lung function 2012 equations.

            The aim of the Task Force was to derive continuous prediction equations and their lower limits of normal for spirometric indices, which are applicable globally. Over 160,000 data points from 72 centres in 33 countries were shared with the European Respiratory Society Global Lung Function Initiative. Eliminating data that could not be used (mostly missing ethnic group, some outliers) left 97,759 records of healthy nonsmokers (55.3% females) aged 2.5-95 yrs. Lung function data were collated and prediction equations derived using the LMS method, which allows simultaneous modelling of the mean (mu), the coefficient of variation (sigma) and skewness (lambda) of a distribution family. After discarding 23,572 records, mostly because they could not be combined with other ethnic or geographic groups, reference equations were derived for healthy individuals aged 3-95 yrs for Caucasians (n=57,395), African-Americans (n=3,545), and North (n=4,992) and South East Asians (n=8,255). Forced expiratory value in 1 s (FEV(1)) and forced vital capacity (FVC) between ethnic groups differed proportionally from that in Caucasians, such that FEV(1)/FVC remained virtually independent of ethnic group. For individuals not represented by these four groups, or of mixed ethnic origins, a composite equation taken as the average of the above equations is provided to facilitate interpretation until a more appropriate solution is developed. Spirometric prediction equations for the 3-95-age range are now available that include appropriate age-dependent lower limits of normal. They can be applied globally to different ethnic groups. Additional data from the Indian subcontinent and Arabic, Polynesian and Latin American countries, as well as Africa will further improve these equations in the future.
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              Data Resource Profile: Clinical Practice Research Datalink (CPRD)

              The Clinical Practice Research Datalink (CPRD) is an ongoing primary care database of anonymised medical records from general practitioners, with coverage of over 11.3 million patients from 674 practices in the UK. With 4.4 million active (alive, currently registered) patients meeting quality criteria, approximately 6.9% of the UK population are included and patients are broadly representative of the UK general population in terms of age, sex and ethnicity. General practitioners are the gatekeepers of primary care and specialist referrals in the UK. The CPRD primary care database is therefore a rich source of health data for research, including data on demographics, symptoms, tests, diagnoses, therapies, health-related behaviours and referrals to secondary care. For over half of patients, linkage with datasets from secondary care, disease-specific cohorts and mortality records enhance the range of data available for research. The CPRD is very widely used internationally for epidemiological research and has been used to produce over 1000 research studies, published in peer-reviewed journals across a broad range of health outcomes. However, researchers must be aware of the complexity of routinely collected electronic health records, including ways to manage variable completeness, misclassification and development of disease definitions for research.
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                Author and article information

                Contributors
                Role: doctoral student
                Role: masters student
                Role: statistician
                Role: endocrinologist and assistant professor
                Role: pulmonologist and professor
                Role: professor
                Role: associate professor
                Journal
                BMJ
                BMJ
                BMJ-UK
                bmj
                The BMJ
                BMJ Publishing Group Ltd.
                0959-8138
                1756-1833
                2022
                01 November 2022
                : 379
                : e071380
                Affiliations
                [1 ]Department of Epidemiology, Biostatistics, and Occupational Health, McGill University, Montreal, QC, Canada
                [2 ]Centre for Clinical Epidemiology, Lady Davis Institute, Jewish General Hospital, Montreal, QC, Canada
                [3 ]Division of Endocrinology, Jewish General Hospital, Montreal, QC, Canada
                [4 ]Department of Medicine, McGill University, Montreal, QC, Canada
                [5 ]Gerald Bronfman Department of Oncology, McGill University, Montreal, QC, Canada
                Author notes
                Correspondence to: L Azoulay laurent.azoulay@ 123456mcgill.ca (or @LaurentAzoulay0 on Twitter)
                Author information
                https://orcid.org/0000-0001-5162-3556
                Article
                bmj-2022-071380.R2 prar071380
                10.1136/bmj-2022-071380
                9623550
                36318979
                beb9307a-03e9-451c-a52d-69987de7c95e
                © Author(s) (or their employer(s)) 2019. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.

                This is an Open Access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/.

                History
                : 23 September 2022
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/501100000024, Canadian Institutes of Health Research;
                Categories
                Research

                Medicine
                Medicine

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