Identifier CONFIDENTIAL GlaxoSmithKline group of companies WWEpi Project number: PRJ3116 1 TITLE PAGE Division: Worldwide Development Information Type: Worldwide Epidemiology Study Protocol Title: Medications containing inhaled corticosteroids (ICS), blood eosinophils and decline in lung function (FEV1) in a cohort of patients with Chronic Obstructive Pulmonary Disease (COPD) identified in the Clinical Practice Research Datalink (CPRD) database Compound Number: Development Phase IV Effective Date: December 15, 2017 Subject: COPD, ICS, eosinophils, lung function, FEV1 Author(s): Copyright 2017 the GlaxoSmithKline group of companies. All rights reserved. Unauthorised copying or use of this information is prohibited PPD
Transcript
Identifier CONFIDENTIAL GlaxoSmithKline group of companies WWEpi Project number: PRJ3116
1
TITLE PAGE
Division: Worldwide Development Information Type: Worldwide Epidemiology Study Protocol
Title: Medications containing inhaled corticosteroids (ICS), blood eosinophils and decline in lung function (FEV1) in a cohort of patients with Chronic Obstructive Pulmonary Disease (COPD) identified in the Clinical Practice Research Datalink (CPRD) database
7. RESEARCH QUESTION AND OBJECTIVE(S) ....................................................... 13
8. RESEARCH METHODS .......................................................................................... 14 8.1. Study Design ................................................................................................ 14 8.2. Study Population and Setting ....................................................................... 16 8.2.1 Setting and data source ........................................................................... 16 8.2.2 Study population........................................................................................ 17 8.3. Variables ...................................................................................................... 17
8.4. Data sources ................................................................................................ 22 8.5. Study size ..................................................................................................... 24 8.6. Data management ........................................................................................ 25
8.7.2. Exploratory analysis ...................................................................... 27 8.7.3. Diagnostic for Models ................................................................... 28 8.7.4. General considerations for data analyses ..................................... 28
8.8. Quality control and Quality Assurance ......................................................... 28 8.9. Limitations of the research methods ............................................................ 29 8.10. Other aspects ............................................................................................... 30
9. PROTECTION OF HUMAN SUBJECTS .................................................................. 30 9.1. Ethical approval and subject consent ........................................................... 30 9.2. Subject confidentiality................................................................................... 30
10. PLANS FOR DISSEMINATING AND COMMUNICATING STUDY RESULTS ................................................................................................................ 31
GlaxoSmithKline Research & Development Limited 980 Great West Road Brentford Middlesex, TW8 9GS UK
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GlaxoSmithKline Research & Development Limited 980 Great West Road Brentford Middlesex, TW8 9GS UK
Sponsor Contact Address
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In some countries, the clinical trial sponsor may be the local GlaxoSmithKline affiliate company (or designee). Where applicable, the details of the Sponsor and contact person will be provided to the relevant regulatory authority as part of the clinical trial submission.
• I confirm agreement to conduct the study in compliance with the protocol. • I acknowledge that I am responsible for overall study conduct. I agree to
personally conduct or supervise the described clinical study.
• I agree to ensure that all associates, colleagues and employees assisting in the conduct of the study are informed about their obligations. Mechanisms are in place to ensure that site staff receives the appropriate information throughout the study.
1. LIST OF ABBREVIATIONSCOPD Chronic Obstructive Pulmonary Disease CPRD Clinical Practice Research Datalink ICS Inhaled Corticosteroids EOS Blood Eosinophils FEV1 Forced Expiratory Volume in One Second FVC Forced Vital Capacity GOLD Global Initiative for Chronic Obstructive Lung Disease LABA Long-acting beta agonist SABA Short-acting beta agonist CVD Cardiovascular disease CAD Coronary artery disease PVD Peripheral vascular disease MI Myocardial infarction HER Electronic health records HES Hospital Episode Statistics ICD-10 International Classification of Diseases AECOPD Exacerbations of COPD LRTI Lower respiratory tract infection OCS Oral corticosteroids ONS Office for National Statistics
Title: Medications containing inhaled corticosteroids (ICS), blood eosinophils and decline in lung function (FEV1) in a cohort of patients with Chronic Obstructive Pulmonary Disease (COPD) identified in the Clinical Practice Research Datalink (CPRD) database
Rationale and background: People with COPD are commonly treated with short or long-acting bronchodilator inhalers. If symptoms persist or patients experience exacerbations of COPD they are put onto medications that contain inhaled corticosteroid (ICS). However, the risks and benefits of ICS in the treatment of COPD patients have long been debated. Randomized clinical and observational studies have shown that while the use of ICS in COPD patients does reduce rehospitalisation and exacerbations, it can also increase the chances of respiratory infections. Blood eosinophils may represent a marker of a patient phenotype with an increased response to treatment with ICS. This study aims to assess the relationship between ICS and lung function decline, particularly FEV1, over a 10 year period in a primary care COPD population with pre-defined high or low blood eosinophil levels.
Research question and objectives: The aim of this study is to describe and compare the rate of decline in lung function in COPD patients on ICS-containing therapy vs. those not on ICS-containing therapy stratified by high and low blood eosinophil levels.
Specifically, we will, among COPD patients on or not on ICS with high or low blood eosinophil levels:
(1) Describe baseline characteristics in terms of demographics, smoking history, COPD severity, AECOPD, comorbidities, COPD treatment, blood eosinophil count, COPD symptoms and FEV1;
(2) Estimate crude rates of FEV1 decline and rates adjusted for covariates; and,
(3) Estimate rates of FEV1 decline in sub-groups of interest, e.g. male vs. female
Study design: This will be a retrospective cohort study using electronic medical records (EMR) linked with other electronic healthcare data sources providing information on hospital admission, mortality and socioeconomic status. Patients with records of confirmed COPD diagnoses will be identified, and if they meet the inclusion criteria, will be grouped based on eosinophil blood count. Patients will be followed from date of first FEV1 measurement until the 29th February 2016 as this is the end of linked data availability, or earlier if they die, leave their GP practice, or their GP practice stops contributing data. The main analysis will be conducted using a prevalent ICS user cohort; further, a sensitivity sub-population of incident ICS users will be created.
Population: The study population will include all people within CPRD over the age of 35 with a validated diagnosis of COPD from 01/01/2004, who are smokers or ex-smokers, and who have spirometry data (FEV1) recorded more than once in their record at least 6 months apart, have a valid blood eosinophil count value, and do not have a
history of asthma. End of study will be the first of: date of death, transfer out of GP date, last collection date, or 29/02/16 as this is the end of linked data availability. We are aware that patients are only eligible for linkage if they (i) registered at a participating English practice prior to the transfer of identifiers to the trusted third party for matching (ii) had a valid identifier for linkage (either NHS number or postcode), (iii) had not opted out or dissented from CPRD or the linkage scheme.
Variables: GP confirmed diagnosis of COPD will be determined through the published COPD code list (see annex 1). Average yearly FEV1 rate of decline in mL/year will be determined by recorded spirometry values. This is obtained from the Additional file. Where more than one record is available on the same day, we will use the highest value. Patients will be grouped on whether they receive ICS-containing medications in the first year prior to their follow-up start, which will be determined through ICS prescription dates and on level of last eosinophil blood count also prior to their follow-up start.
Other variables included in this study will be: sex, BMI, ethnicity, socioeconomic status (SES; using the index of multiple deprivation (IMD)), co-morbid conditions such as heart failure, COPD severity such as GOLD grades, neutrophil level, history of AECOPD frequency, prior use of COPD medications other than those containing ICS including LABA, SABA, oral prednisolone, short and long acting anti-cholinergics, theophyllines, combination SABA and short acting anti-cholinergics, and nebulised therapy.
Data sources: We will use electronic health records database: the Clinical Practice Research Datalink (CPRD GOLD) dataset, which consists of detailed primary care data from GPs across the UK. Linked CPRD data from Hospital Episode Statistics (HES) and Office of National Statistics (ONS) in England will also be used.
Study size: Initial feasibility calculations performed have confirmed that this study is adequately powered. The cohort studied will comprise 61,104 identified eligible COPD patients aged 35 or older, who are smokers or ex-smokers, have at least 2 spirometry measurements in their data at least 6 months apart, and have at least one eosinophil measurement recorded in their data. The median follow up time is 5.4 years. To evaluate a difference of 2.5 mL/year between the mean rates of decline in two groups of ICS or non-ICS users 10,593 and 8,410 patients are required.
Data analysis: Analyses and data management will be performed in STATA and R. Annual rates of FEV1 decline in COPD patients with and without prevalent ICS therapy and with high or low blood eosinophil counts will be estimated using linear mixed models (and broken stick regression if necessary) among patients with at least 2 FEV1 measurements at least 6 months apart, primarily using an intention to treat (ITT) analysis. Analyses will additionally be stratified by GOLD grades of airflow limitation, and adjusted for age, sex, height, smoking status, and other necessary covariates. Further, analyses will be stratified by important patient characteristics including gender, age, AECOPD frequency, BMI, co-morbid conditions, ethnicity, and SES. All crude and adjusted rates will be compared by ratios between those with high eosinophils and ICS therapy and i) those with high eosinophils and no ICS therapy, and ii) those with low eosinophils and ICS therapy and also between those with low eosinophils and ICS therapy and those with low eosinophils and no ICS therapy.
Milestones: Protocol will be finalized in November 2017 when the ISAC CPRD approval is also expected. The interim analysis will be available in January 2018 in time for a decision on progressing an ERS abstract. The study analysis should close around May 2018.
Milestone Planned date Protocol development August-October 2017 Protocol sign off December 2017 Analysis start December 2017 Analysis complete May 2018 Final report of study results September 2018
6. RATIONAL AND BACKGROUND
6.1. Background
Chronic obstructive pulmonary disease (COPD) is a progressive disease characterised by the chronic obstruction of airflow in the airways and lungs. It is associated with pathological changes in the lungs, co-morbidities such as cardiovascular disease, diabetes, and musculoskeletal disorders, as well as extra-pulmonary manifestations such as systemic inflammation (Gosker, Langen et al. 2009, Vogelmeier, Criner et al. 2017). In addition, it is well known that people with COPD have a faster decline in their lung function (FEV1) than people without COPD (Fletcher and Peto 1977).
In the UK it is estimated that 3 million people are living with COPD: however, only one third of this population are currently diagnosed. COPD was ranked as the ninth leading cause of death worldwide in 2016 (Collaborators 2017) and is projected to be not only the third leading cause of death globally by 2020 but the seventh leading cause of disability adjusted life years (DALYs) lost worldwide by 2030, and is one of the commonest causes of hospital admission (Vogelmeier, Criner et al. 2017).
People with COPD are commonly treated with short or long-acting bronchodilator inhalers in the first instance, and inhaled corticosteroids (ICS) are added if symptoms persist or if patients experience acute exacerbations of COPD (AECOPD). It has been
extensively debated whether the harms of ICS outweigh the benefits and if specific subgroups of people with COPD may be more likely to benefit from them than others.
Studies have shown that ICS-containing therapy reduces the rate of FEV1 decline and hospitalisation in COPD patients. The Study to Understand Mortality and Morbidity (SUMMIT) trial showed that the rate of moderate/severe AECOPD was 29% (95% CI: 22%-35%) lower in COPD patients on combined ICS therapy compared to the placebo group. In addition, the rate of hospitalisation was 27% (95% CI: 13%-39%) lower and annual rate of FEV1 decline was slower (-38ml/year (SE: 2.4) vs. -46ml/year (SE: 2.5) in COPD patients on combined ICS therapy compared to those on placebo (Vestbo, Anderson et al. 2016, Martinez, Vestbo et al. 2017). Further, a meta-analysis of published randomized clinical trials, the Inhaled Steroids Effect Evaluation in COPD (ISEEC), found that compared to those on placebo, the mean decline of FEV1 was 2.42% (SE 0.19%) higher in COPD patients on ICS (Soriano, Sin et al. 2007). However, the difference in FEV1 decline between those on ICS and those on placebo became non-significant after 6 months of ICS therapy.
On the other hand, studies have also shown that ICS therapy has little or no effect on mortality and can increase the rate of pneumonia and upper respiratory tract infections. Results from the SUMMIT trial and other clinical trials show that there was no significant difference between those on ICS therapy and those on the placebo in terms of mortality (Drummond, Dasenbrook et al. 2008, Vestbo, Anderson et al. 2016). Furthermore, in an observational study, COPD patients on ICS had a rate of first hospitalisation or death due to pneumonia (termed “serious pneumonia”) 1.69 (95% CI: 1.63-1.75) times higher than those on placebo. Patients who withdrew from ICS had a rate of “serious pneumonia” 1.08 (95% CI: 0.99-1.17) times higher than the placebo group, illustrating the reversibility of the effect of ICS after withdrawal (Suissa, Patenaude et al. 2013). Similarly, a meta-analysis of 14 clinical trials found that the odds of upper respiratory tract infection (URTI) in COPD patients on ICS compared to those not on ICS was 1.16 (95% CI: 1.05-1.29) times higher. They additionally investigated the effect of high and low dose ICS on risk of URTI and found that high dose ICS increased this risk significantly (Yang, Chen et al. 2017).
Blood eosinophils were recently identified as a potential biomarker of response to interventions with ICS-containing medications among patients with COPD (Pascoe, Locantore et al. 2015, Barnes, Sharma et al. 2016, Pavord, Lettis et al. 2016). Most epidemiology work on blood eosinophil levels in COPD has focused on their distribution and the association of blood eosinophils and AECOPD risk (Kerkhof, Sonnappa et al. 2017) and only few studies have investigated the role of eosinophils on lung function decline, specifically FEV1 decline. One randomised control study found that COPD patients with high eosinophils had a slower rate of FEV1 decline when on ICS compared to the placebo and those with low eosinophils had a similar rate of FEV1 decline when on ICS compared to the placebo (Barnes, Sharma et al. 2016).
In summary, evidence on the effect of ICS on lung function decline in COPD patients is conflicting, and the majority of these studies are randomized clinical trials. It is therefore important to address this evidence gap using “real world” data that is representative of the general patient population. This is because patients identified through “real world” data
do not always meet the inclusion criteria for clinical trials and thus results of clinical trials can often only be generalizable to specific patients.
6.2. Rationale
The recent randomized clinical trial, the Study to Understand Mortality and Morbidity (SUMMIT), was designed to investigate the relationship between the combined ICS fluticasone furoate/vilanterol on mortality in COPD patients with or at risk of cardiovascular disease (CVD). It followed patients from 2011 to 2015. Findings show that although there was no effect of combined ICS on mortality or survival, it reduced lung function decline and exacerbations rate in patients on ICS compared to patients on placebo (Drummond, Dasenbrook et al. 2008, Vestbo, Anderson et al. 2016, Martinez, Vestbo et al. 2017). On the other hand, studies have found that COPD patients on ICS therapy are more at risk of respiratory infections, including LRTI and pneumonia (Suissa, Patenaude et al. 2013, Yang, Chen et al. 2017) and it is therefore debated whether the harms of ICS outweigh the benefits. However, people eligible for clinical trials are not always similar to the general patient population and such therefore results generated by clinical studies may not be generalizable. This proposed study will therefore evaluate whether ICS-containing medications may modify lung function decline in a population generalizable to the UK and whether blood eosinophils, which have been shown to be stable over 2 years in COPD patients (Landis, Suruki et al. 2017), can be used to guide management of COPD.
7. RESEARCH QUESTION AND OBJECTIVE(S)
The aim of this study is to describe and compare the changes in the rate of decline in lung function in a population-based representative cohort of COPD patients on ICS-containing medications and not on ICS-containing medications, and to investigate the differences in lung function decline in specific groups of COPD patients, characterised by high or low eosinophil counts. This will allow us to investigate whether COPD patients overall may benefit from ICS treatment, in terms of lung function decline, and whether there are sub-populations of COPD patients with a specific blood eosinophil count who may benefit.
To address this aim we will use a general population of COPD patients with a validated diagnosis of COPD recorded in their data, who are aged 35 years or older, who are smokers or ex-smokers, do not have a history of asthma, have 2 or more FEV1 measurements at least 6 months apart, and have at least 1 blood eosinophil count prior to follow-up start..
Objectives are to:
1. Describe baseline characteristics of patients who are eligible and not eligible for the study in terms of demographics, COPD severity, comorbidities, AECOPD,
COPD treatment including treatment with ICS containing medications (hereafter denoted as ‘ICS’), blood eosinophil count and FEV1.
2. To investigate the rate of FEV1 decline in COPD patients, overall and stratified by GOLD grades of airflow limitation, with high blood eosinophil counts and on ICS, compared to: - COPD patients with high blood eosinophils not on ICS - COPD patients with low blood eosinophils on ICS
3. To investigate the rate of FEV1 decline in COPD patients, overall and stratified by
GOLD grades of airflow limitation, with low blood eosinophils and on ICS, compared to: - COPD patients with low blood eosinophils not on ICS - COPD patients with high blood eosinophils on ICS
8. RESEARCH METHODS
8.1. Study Design
This will be a retrospective cohort study using routine collected primary care electronic health records (EHR) from the Clinical Practice Research Datalink (CPRD) and linked data from HES. The nature of this design will allow us to follow FEV1 longitudinally to investigate the rate of FEV1 decline in COPD patients. Since 2004 as part of the QOF guidelines lung function should be measured every 15 months in COPD patients, which makes CPRD-GOLD a reliable source of observational data for investigating lung function decline. Through using linked CPRD data we can obtain a bigger picture of the decline in lung function by using hospital admission data and mortality data.
People with COPD will be identified based on a validated diagnosis of COPD (Quint, Mullerova et al. 2014) from the 1st of January 2004 to the 29th of February 2016. COPD patients will be included if they are aged 35 or over, have a current registration date, an up to standard GP date, are smokers or ex-smokers, do not have a history of asthma, have FEV1 measurements recorded twice or more in their data and are at least 6 months apart and have a blood eosinophil value prior to index date. The first FEV1 measurement that meets all these requirements will be the index date. Patients will consequently be included if they have at least 1 blood eosinophil count prior to follow up, within 12 months prior to index date. End of follow up will be earliest of the end of database (expected as 29th February 2016), transfer out of GP, last date of data collection, or date of death.
In this study we will compare the rates of FEV1 decline in patients on ICS and not on ICS with high and low blood eosinophil counts. To do this we will group COPD patients based on their prevalent ICS use over 12 months prior to index date and their eosinophil count, creating four sub-cohorts:
- Eligible patients with high eosinophils and on ICS
- Eligible patients with high eosinophils and not on ICS
- Eligible patients with low eosinophils and on ICS
- Eligible patients with low eosinophils and not on ICS
Patients with a recording of a prescription for an ICS-containing medication one year prior to their index date will be defined as patients on ICS. This includes patients who have been prescribed 1 or more ICS or ICS combination inhaler. COPD patients who have no recorded data on ICS prescription in the year prior to their index date will be defined as not on ICS. In primary analysis, this prevalent ICS use assignment at index date will persist throughout the follow-up independent on treatment changes, i.e. patients taking on or off ICS (ITT approach).
Figure 2: Cohort selection diagram from initial feasibility data review
8.2. Study Population and Setting
8.2.1 Setting and data source
The study will be performed using only CRPD-GOLD data with linkage to secondary care data from hospital admission through the HES data. The linkage to secondary care data will allow more complete information on COPD exacerbations compared to using
All patients registered with the National Health Service
Patients available in CPRD-GOLD
Patients with a COPD diagnosis, age 35 or older, and current/ex-smoker smoking status with linked data to
HES and ONS: N=111,157
Patients with FEV1 measurements during follow-up: N=87,439
Patients with ≥2 FEV1 measurements during follow-
up: N=64,289
Patients with ≥2 FEV1 measurements at least 6 months
apart: N=61,077
Patients on ICS at baseline: N=33,583
Patients not on ICS at baseline: N=23,660
Patients with at least one eosinophil measurement prior to
only primary care data. CRPD-GOLD data includes information on patients registered with the NHS at over 600 GPs. The data represents about 7% of the UK population (Herrett, Gallagher et al. 2015). For this study we will use data from the 1st of January 2004 up until the 29th February 2016. CPRD has been used extensively in epidemiological studies involving COPD and is a reliable source of longitudinal data.
8.2.2 Study population
The source population will include all men and women who are registered with a GP in the UK and have data available in the CPRD-GOLD database. Patients will be included in the study if they meet the following eligibility criteria:
- are aged 35 or over
- have been diagnosed with COPD
- have FEV1 measurements recorded twice or more in their data and at least 6 months apart
- are ex or current smokers
- have up-to-standard (UTS) data available in CPRD-GOLD and are currently registered at a GP
- do not have a history of asthma
- have a valid eosinophil measurement 24 months prior to index date
Patients will also be included irrespective of their country of origin, any comorbidities, or COPD severity.
Patients will be split into two groups depending on whether they are being prescribed with at least one prescription for ICS-containing therapy [ICS] within a period of 12 months prior to their index date. Patients who have no recorded ICS-containing medication prescription date will be categorised as not using ICS therapy at follow-up start.
The study follow-up period will start at the first FEV1 measurement after the patient’s first COPD diagnosis, UTS data and current registration date and will end on the 29th February 2016 or before if the patient died or transferred out of the GP.
8.3. Variables
8.3.1. Exposure definitions
The primary exposures of interest are COPD diagnosis (the COPD code list is published (Quint, Mullerova et al. 2014)), ICS use and blood eosinophil count.
GP diagnosed COPD will be used to define COPD. COPD has previously been validated in CPRD-GOLD (Quint, Mullerova et al. 2014) and the code list has been published (see annex 2). First GP-diagnosed COPD will be used.
ICS-containing medications
Patients will be grouped based on recordings for ICS-containing medications (see annex 2 for code list) use within 12 months prior to follow up. Patients with at least one ICS or combined ICS prescription one year prior will be classified as patients on ICS. Patients with no ICS or combined ICS prescriptions recorded in their data one year prior to follow up will be classified as patients not on ICS. ICS prescription dates will also allow us to identify when and how long patients were on ICS for. Type of ICS-containing medication use and dose will also be determined.
As an exploratory analysis, we will consider several scenarios including one where patients who are defined as not using ICS therapy at baseline will additionally be censored by their first ICS prescription date and are therefore grouped by original assignment. The majority of patients will end up on an ICS at some point in their follow-up and therefore by censoring these patients we can more definitively say that patients assigned as not on ICS are not exposed to ICS throughout their follow up period. Further, we will calculate dosages using a budesonide dose equivalence to allow us to examine a potential effect of increasing the dose of ICS-containing medication on rate of FEV1 decline.
An additional exploratory analysis will use an incident ICS cohort. Incident use of ICS-containing medication will be determined by using patients who had no ICS-containing medications at baseline. During the patient’s first year of follow-up ICS use will be identified to categorise patients with incident ICS use. This differs to the main analysis whereby all patients are included and categorised on use of ICS-containing medication in the year prior to the patient’s start of follow-up. Patients using at least one ICS-containing medication will be categorised as being on ICS and those not on any ICS-containing medication will be categorised as receiving no ICS. These patients will be further grouped as having high or low blood eosinophils.
Eosinophils
Patients will be grouped based on their blood eosinophil levels (see annex 2 for code list). Patients must have at least one eosinophil measurement 2 years prior to study start. Eosinophil levels will be reported in cell count per microliter. In addition, eosinophils will be reported as percentages of total white blood cell count. If eosinophils are recorded in units other than percentages, such as cells per microliter, then total white blood cell count will be used to convert eosinophils into percentages. Eosinophil measurements will be dropped if they are within 4 weeks of a prescribed oral prednisolone or an AECOPD.
High and low blood eosinophils will be defined as having ≥150cells/µL and <150cells/ µL respectively. This cut off is common in epidemiological studies using eosinophils (Barnes, Sharma et al. 2016, Kerkhof, Sonnappa et al. 2017). Sensitivity analyses will look at different eosinophil cut offs inclusive of 150, 300, and 500cells/µl. In addition, we will test whether the relationship between blood eosinophil level and lung function decline is linear. If not, more than 2 cut offs will be needed to reflect a potential U-curve shape relationship. Eosinophil counts will also be identified during follow-up.
8.3.2. Outcome definitions
FEV1 decline
The primary outcome of interest will be lung function decline, defined by FEV1 decline. In this study we will include patients who have a diagnosis of COPD and have at least 2 FEV1 measurements of which at least 2 have an interval of at least 6 months. Including patients with at least 2 FEV1 allows us to investigate the rate of FEV1 decline in the population.
Since 2004 as part of QOF GPs are encouraged to record lung volume function of COPD patients every 15 months and therefore using FEV1 as a measure of lung function decline in our study (Quint, Mullerova et al. 2014).
8.3.3. Confounders and effect modifiers
The variables defined in the table below will be used to describe the study population. These variables may be potential confounders or effect modifiers. Code lists for these variables can be found in annex 2. Patients with missing values for any of the covariates will be reported. To control for potential confounders and effect modifiers we will include them in the final regression model if necessary.
Table 1: Potential confounders and effect modifiers
Variable Definition
Demographics
Age* Age at baseline in years as a continuous variable.
Sex* Male, female
Socioecomic status (SES) * Using linked data from the Index of Multiple Deprivation, which is a measure of area deprivation for 32,482 areas in the UK ranging from 1 (most deprived) to 32,482 (least deprived). This is commonly split into 5, 10 or 20 groups. We will use
quintiles of IMD where 1 indicates most deprived and 5 indicates most deprived.
Ethnicity* Self-reported categorical variable derived from HES date.
BMI * The nearest BMI value within the last 3 years prior to the index date will be used. BMI will be calculated using patients’ weight in kilograms divided by their height in meters squared. This is a continuous variable however, BMI will also be categorized following the WHO classification: Underweight (Below 18.5), Normal (18.5 - 24.9), Overweight (25.0 - 29.9), and Obese (30.0 and greater).
Smoking status * The most recent smoking status prior to patients’ index date will be used. This is a binary variable: ex-smoker, current smoker. Non-smokers will not be included as the inclusion criteria states that only ex-smokers and current smokers will be included in the study population.
Comorbidities * Selected comorbidities that ever occurred in the patient’s history or occurred within the last 3 years prior to their index date will be used. These comorbidities will be used due to previous associations found between these variables and COPD. The comorbidities defined as ever having occurred prior to the patient’s index date include:
- Myocardial infarction - Stroke - Lung cancer - Bronchiectasis
The comorbidities defined as the nearest event date to the index date 3 years prior include:
COPD Medications ** In this study the following COPD medications will be flagged: inhaled corticosteroids, combination inhaled corticosteroids and long acting beta agonists, long and short acting beta agonists, oral prednisolone, short and long acting anti-
cholinergics, theophyllines, combination short acting beta agonists and short acting anti-cholinergics, nebulised therapy.
These will be flagged 1 year prior to the patient’s index date and throughout follow up.
COPD Severity
Medical Research Council (MRC) dyspnea scale *
MRC dyspnea scale is a measure of breathlessness from exercise. It is part of the Quality Outcomes Framework and consists of 5 categories:
- Grade 1: not troubled by breathless except on strenuous exercise
- Grade 2: Short of breath when hurrying on the level or walking up a slight hill
- Grade 3: Walks slower than most people on the level, stops after a mile or so, or stops after 15 minutes on level ground
- Grade 4: Stops for breath after walking about 100 yards or after a few minutes on level ground
- Grade 5: Too breathless to leave the house, or breathless when undressing
MRC dyspnea will be flagged at the nearest date before the index date in the patient’s records and patient’s will be categorized into groups defined as MRC<grade 3 and MRC≥grade 3
White blood cell count* White blood cell counts in absolute white blood cell count (109L) will be used. This will additionally be used to convert absolute eosinophil counts into percentages of total white blood cell counts.
Neutrophil count * Neutrophil count and percentages will be used. These will be flagged at the closest date prior to the patient’s index date. Both absolute neutrophil counts and percentage of WBC count will be flagged. Where there are missing values for either absolute count or relative percentages, white blood cell count will be used to calculate the missing value. An eosinophil vs neutrophil ratio will be calculated. This will be calculated by dividing absolute blood eosinophils by absolute neutrophil count (Negewo, McDonald et al. 2016).
GOLD grade ** The closest GOLD grade of airflow limitation measurement to the index date will be used. Gold grades are defined as having a validated diagnosis of COPD and:
Moderate/Severe AECOPD * AECOPD frequency will be determined using an algorithm that takes into account AECOPD codes from CPRD for moderate events (LRTI codes, symptoms of AECOPD (cough, dyspnea, sputum), and OCS and antibiotic use) and linked HES data for severe (hospitalized) events, AECOPD will be flagged 12 months prior to patient’s index date and in the first year after the patient’s index date and categorized into:
Variables in table 1 marked with a single asterisk indicate potential confounders. Those marked with double asterisk indicate effect modifiers.
8.4. Data sources
Primary care data CPRD-GOLD, extracted from Vision Primary Care EMR systems, will be the primary care database used in the study. Data in CPRD-GOLD has been collected since 1987 and is a large longitudinal primary care database that contains medical records of patients registered at participating GPs in England, Wales, Scotland, and Northern Ireland. As of July 2016 CRPD-GOLD data includes information on 14.2 million patients registered with the NHS at 701 GPs and represents about 8.5% of the UK population. CPRD contains information on patients and all care events, which are continuously recorded to support ongoing clinical care and management of patients. Patient information includes demographic information (age, sex, height etc.), records of clinical events through medical diagnoses, records of prescriptions and
immunisations/vaccinations, diagnostic tests (including laboratory blood tests), referrals to secondary care or specialists, and lifestyle information (smoking, alcohol etc.). The NHS dictionary of medicines and devices (dm+d) is used as a dictionary containing unique identifiers (codes) and associated textual descriptions for representing medicines and medical devices in information systems and electronic communications (http://www.dmd.nhs.uk/). CPRD-GOLD contains data from the Vision EMR system which uses Read codes - specifically the Unified 5-byte Version 2 Read code set - as the basic means to record patient findings and procedures, and other relevant information.
Linked secondary care data CPRD-GOLD is linked to other secondary care patient datasets, such as HES. A third party (the Health and Social Care Information Centre) carries out data linkage of identifiable data. Patient identifiers including NHS number, gender, date of birth, and postcode of residence are sent to the trusted third party by the GP. CPRD holds only a local patient identifier which is meaningful only at the patients’ registered general practice. This identifier is pseudonymised a second time before being made available to researchers and analysts with access to the database. Linked CPRD data is available for approximately 57% of contributing CPRD GPs in the UK. Linked HES data contains information on patient’s hospital admissions at NHS health care providers, which is recorded during a patient’s visit. Available information includes inpatient episodes of care, outpatient appointments, A&E attendances, and discharge information. ICD-10 codes are used to code information about diagnoses, and OPCS-4 codes are used to code information on procedures. Whilst HES contains extensive information about appointment dates and times, patient demographics, specialities, hospital admission, diagnoses, and procedures, there is significantly less coded clinical information. Other linked CPRD datasets include ONS data. The ONS contains data on businesses and trade, the economy, employment, and the general population such as the registry of births and deaths in the UK. ONS data will be used in this study to identify deaths. Linked CPRD-GOLD data will be used to identify exacerbations, deaths, and other patient characteristics. ONS data will be used to identify patient deaths. HES data will be used to identify AECOPD, ethnicity, and IMD. Patients will be included in this study regardless of their eligibility for linkage however, only patients who are eligible for linkage will be included in analyses involving exacerbations as we will require linked data. We are aware that patients are only eligible for linkage if they (i) registered at a participating English practice prior to the transfer of identifiers to the trusted third party for matching (ii) had a valid identifier for linkage (either NHS number or postcode), (iii) had not opted out or dissented from CPRD or the linkage scheme.
Patients in CPRD-GOLD are eligible for this study if they meet the following inclusion criteria:
- Have a validated COPD diagnoses from 2004 - Are aged 35 years or more at the start of follow up - Are current or ex-smokers - Have at least 2 FEV1 measurements at least 6 months apart - Have at least 1 eosinophil measurement recorded in their data prior to follow up
In the ECLIPSE study (GSK study 104960), an observational cohort set up to investigate the rate of FEV1 decline in COPD patients and ICS, researchers used a formula (Schlesselman 1973) to determine sample size in longitudinal study with repeated measures. The following formulae (Schlesselman 1973)were used in the ECLIPSE study to compare two groups:
In this formula Zα/2 and Zβ refer to the unit normal deviates for errors type I and type II, which for an alpha value of 0.05 and a power beta value of 0.8 give a Zα/2 and Zβ of 1.96 and 0.84 respectively. σ2β is the estimate of the variance associated with the rate of FEV1 decline, σ2 is the estimate of the within subject variance, P is the number of measurements in the study, D is the duration of the study, and ∆ is the unit difference between groups. To calculate the number of people in each group a further calculation is performed using the following formula:
, where and r is an estimate of the rate of FEV1 decline in those on ICS divided by the rate of FEV1 decline in those not on ICS.
In this study the median number of follow-up years is 5.4 years, the median number of FEV1 measurements is 4, the standard error of the rate of FEV1 decline is 0.32ml/year, and the within-subject variation in FEV1 is 350.6ml. In addition, the ratio of the rate of FEV1 decline in those not on ICS (-19.38ml/year) compared to those on ICS (-15.85ml/year) is 1.24. The number of patients required in our sample, according to the formulae above, using these estimates, with enough power to detect a unit difference of ∆ is shown in the table below. To evaluate a difference of 2.5 mL/year between the mean rates of decline in two groups of ICS or non-ICS users 10,593 and 8,410 patients are required.
CPRD-GOLD data will be extracted by and linked HES-APC data will be provided by CPRD. Data will be managed using STATA 15.
8.6.1. Data handling conventions
Following CPRD policy on potential disclosure, no data will be reported where cell counts are greater than zero and less than 5. Variable definitions and data handling conventions are described in other sections. Code lists for study variables are found in the annex.
8.6.2. Resourcing needs
The study is overseen by a senior PhD level Epidemiologist at and a senior PhD level and clinical professional at Biostatistical and methodological issues will be addressed by the epidemiologist responsible for the study in consultation with a senior level statistician at with expertise in observational data. A senior level statistician has contributed to the development of the draft protocol and will continue to provide expertise and skills as needed during the conduct of the study.
8.7. Data analysis
8.7.1. Essential analysis
Flow charts (CONSORT diagrams) will be constructed to illustrate the pathway from all available CPRD patients to our included population for this study.
8.7.1.1. Descriptive analyses
Baseline characteristics will be explored and compared between: i) eligible and non-eligible patients; ii) patients with at least 2 FEV1 measurements during follow up and patients with at least 3 FEV1 measurements; and iii) patients with high EOS and on ICS, patients with high EOS and not on ICS, patients with low EOS and on ICS, and patients with low EOS and not on ICS. Examined baseline characteristics include gender, age, ethnicity, SES, smoking status (current/ex), comorbidities, BMI, GOLD grades at start of follow up and other markers of COPD severity, COPD medications, AECOPD, and mean
follow up times. Descriptions of these variables have been given in section 8.3.3. These characteristics will be reported using counts and percentages for categorical variables, mean (SD) and median (IQR) for continuous variables, and rates for count variables (e.g. exacerbations). Appropriate statistical testing will be used to highlight any statistically significant differences between these groups. Any significant differences will be reported.
8.7.1.2. Statistical Models
The main outcome of this study is annual rate of FEV1 decline. The main analysis of this study will examine the rate of FEV1 decline in our eligible population stratified into 4 groups:
1) Patients with high EOS and on ICS 2) Patients with high EOS and not on ICS 3) Patients with low EOS and on ICS 4) Patients with low EOS and not on ICS
To do this a mixed linear regression model will be used due to the nature of the study using repeated measures of FEV1 within patients. This model will be fitted for repeated measures of FEV1 within patients. Mixed linear regression models will be constructed to investigate the rate of FEV1 decline for each eosinophil and ICS group compared to the reference eosinophil and ICS group. The fixed part of the model will include FEV1 as the dependent variable, time from 1st FEV1 as the independent variable, eosinophil/ICS group (4 categories), and an interaction term between time and eosinophil/ICS group. The random effects part of the model will include patient ID as the level 2 variable and time from first FEV1.This will allow patients to have different intercepts and difference rates of FEV1 decline. The reference categories will be (a) high blood eosinophils and ICS use and (b) low blood eosinophils and ICS use. Rate ratios (and 95% confidence intervals) will be estimated in eligible patients to compare the rates of FEV1 decline. Rates of FEV1 decline in ml/year will be derived from the coefficient for time and the interaction of time and blood eosinophil/ICS group in the models. These coefficients illustrate the mean rate of FEV1 decline in each group. Rate ratios will consequently be calculated by dividing the rate of each blood eosinophil/ICS group over the reference blood eosinophil/ICS group. Rates of FEV1 decline can then be interpreted as being x times faster for a particular group compared to the reference group. Rate ratios will be calculated for each of the comparisons listed below. First, (objective 2) patients with high eosinophils and on ICS will be compared to those with:
- High eosinophils and no ICS - Low eosinophils and ICS
Secondly, (objective 3) patients with low eosinophils and on ICS will be compared to those with:
- Low eosinophils and no ICS - High eosinophils and ICS
To investigate strata by GOLD grades, we will repeat the previous analysis with populations split into the four GOLD Grades. Rates of FEV1 decline in the above groups
will be measured over time, defined as time from last GOLD grade reached, as patients newly diagnosed with a certain GOLD grade will differ from patients who have remained in the same GOLD grade for a period of time. This will help account for the natural progression of COPD. Analyses will initially be performed with no adjustment for potential confounders, before adding them into the model. If necessary, non-linear regression modelling with additionally be used as this method will allow for variability in FEV1 decline over time. This model will be used if rate of FEV1 decline is not found to be linear and varies over time. Similar to the multi-level model, the analysis will initially be performed without adjusting for potential confounders, before adding them into the model. Potential confounders will be identified and pre-specified for all models and will include only selected variables from the list below:
- Age - Gender - SES - Smoking status (current/ex) - Comorbidities (heart failure, MI, stroke, lung cancer, bronchiectasis, GORD,
anxiety, depression) - BMI - Neutrophils - AECOPD - COPD medication (other than ICS or combined ICS)
Missing data will be addressed using multiple imputation when using multi-level models if we are able to assume that observations are missing at random.
8.7.2. Exploratory analysis
Exploratory analyses will assess the sensitivity and robustness of our models. Firstly, analyses for objectives 2-4 will be repeated using different thresholds of blood eosinophils. Initially we will define high and low eosinophils as ≥150cells/µL and <150cells/µL respectively. Other thresholds for eosinophil count will be explored such as using a cut-off of 300cells/µL and 500 cells/µL. Patients will be categorised into the 4 groups and statistical analyses will be performed following objectives 2-4 and compared to results using blood eosinophil threshold of 150cells/µL. Further exploratory analyses will also include eosinophil count as a continuous measure.
In addition, we will censor patients who are defined as not using ICS therapy by their first ICS prescription date and are therefore grouped by original assignment. By censoring these patients we can more definitively say that patients assigned as not on ICS are not exposed to ICS throughout their follow up period. Further, we would like to explore the rate of FEV1 decline in patients who are not on ICS-containing medication in the year prior to their index but who are prescribed ICS-containing medication during their follow up compared to those never on ICS-containing medication and those on ICS-containing medication in the year prior to the index date.
An incident ICS-containing medication cohort will also be explored in which those on ICS-containing medication in the year following the index date will be classified as being on ICS and those who are not prescribed any ICS-containing medication will be classified as not on ICS. We will compare the incident ICS cohort with the prevalent ICS cohort to investigate the rates of FEV1 decline in both these populations with high and low blood eosinophils. Incident and prevalent ICS users will also be compared to ever non-users in terms of rate of FEV1 decline in those with high and low blood eosinophils. In addition, we will investigate the effect of ICS initiation on FEV1 decline by using broken stick regression modelling and compares rates of FEV1 decline before and after ICS initiation.
Analyses investigating a potential dose-response relationship between ICS-containing medication and rate of FEV1 decline will be explored in both incident and prevalent ICS populations separately. ICS-containing medication prescription will be compared using budesonide dose equivalence of ICS-containing medication. In addition, the number of ICS-containing medications and lung function decline will be explored.
Furthermore, when testing multiple correlations a problem of multiple testing can arise. This can give rise to statistically significant results that may only be significant by chance. It is important to be mindful of this when performing analyses and presenting results.
8.7.3. Diagnostic for Models
Diagnostic tests will be performed for all models to test model assumptions. Tests will include plotting residuals against fitted values, QQ plots, and other tests to assess the model fit, homoscedasticity, and normality of level 1 error terms. Models that violate assumptions will be altered, for example, by transforming variables to natural logs or squares, to adhere to model assumptions.
8.7.4. General considerations for data analyses
Following CPRD policy on potential disclosure, no data will be reported where cell counts are greater than zero and less than 5. Incompletely recorded covariates will result in missing values in the data and therefore may require the use of missing-value methods such as multiple imputation.
8.8. Quality control and Quality Assurance
The results produced by the analyst will be reviewed consistently and all code used will be reviewed by a senior level analyst. Any differences in results or code will be reconciled. The independent analysis approach by the primary analyst is in place to ensure that the protocol is interpreted and operationalised consistently and that the results of the analysis are constant across the analyses, thus ensuring reliability of the study findings.
In addition, to protect the data, it will be stored and used only on software and cannot be used outside of property.
Initial feasibility counts have been explored and this study has enough power to be carried out. 61,104 COPD patients are aged 35 or older, are smokers or ex-smokers, have at least 2 FEV1 measurements in their data at least 6 months apart, and have at least one eosinophil measurement recorded in their data prior to their index date. The mean follow up time is 6.5 years.
8.9. Limitations of the research methods
Observational studies allow understanding of disease progression and associations using real world data sources, in this case electronic health data. However, strengths and limitations are associated with this study design.
CPRD-GOLD is a source of electronic health data, which contains extensive information on patients and GPs and can be linked to other secondary care databases to allow in depth research over long follow up periods. Whilst CPRD is a commonly used source of data in epidemiological studies, there are limitations in terms of representativeness, data quality and coding of data.
Firstly, it is important to note that although CRPD is broadly representative of the UK population in terms of age, sex, and socio-economic status, it may not be representative of all GPs across the UK (Herrett, Gallagher et al. 2015). Selection of patients into the study will be based on prospectively recorded diagnoses of COPD, FEV1 measurement, and eosinophil measurements and will be representative of COPD patients in the UK. However, it is important to note that studies using CPRD cannot prove causality, and merely find results that are consistent with casual hypotheses backed up by trials. Even when accounting for many confounders, there can always be residual confounding.
Algorithms used to identify patients with COPD have high sensitivity and PPV (Quint, Mullerova et al. 2014): however, it could be possible that GPs misdiagnose asthma as COPD or COPD as asthma, particularly in patients aged over forty years (Tinkelman, Price et al. 2006). To avoid misclassification of these diseases we did not include COPD patients with asthma in the study cohort. Nevertheless, it may also be possible that not all COPD patients were included in the study. In addition, CPRD only contains diagnosed COPD and symptomatic patients without a diagnosis of COPD would thus not be included. Furthermore, AECOPD frequency may be misclassified as those who have worse disease severity may meet with their GP more often and there would be more opportunity to report milder or self-managed exacerbations. To avoid this, the algorithm does not include patients who have self-managed an exacerbation using a rescue pack. Similarly, eosinophil counts from blood tests would have been performed if patients had seen their GP for reasons other than for COPD diagnosis. Patients would have had an eosinophil blood count if they developed an infection or an AECOPD. Thus, eosinophil counts may be higher than usual and may bias the analysis and confuse the categorisation of “high” and “low” eosinophils. To eliminate any bias, eosinophil counts will not be used if they are within 4 weeks of an AECOPD or a lower respiratory tract infection.
In this study eosinophil counts have been categorised into high or low blood eosinophil counts and may not be representative of patients especially those with long follow-up periods. Exploratory analyses will use eosinophil counts as a continuous variable in the model. It is also assumed that the patient’s eosinophil count level will be the same throughout follow-up and we acknowledge this limitation.
In addition, data quality between GPs may vary. Despite the implementation of the Quality and Outcomes Framework (QOF) in 2004, which encouraged GPs to record key data at a high level and lung function in COPD patients every 15 months, there is still some variation between GPs in terms of coding variables and recording data as free text, which researchers may miss. This therefore means not all available data is or can be used.
8.10. Other aspects
All aspects of the study have been covered in previous sections.
9. PROTECTION OF HUMAN SUBJECTS
9.1. Ethical approval and subject consent
CPRD and other similar EMR systems are databases of pseudonymised EMRs. Our approach to the study is naturalistic; we will not be conducting further diagnostic tests, alter disease management strategies, or collect data in addition to or above routine medical care.
Linkage of the primary care databases to other datasets such as HES is undertaken by a trusted third party. The identifiers (date of birth, gender, NHS number, postcode of residence) required for linkage are sent directly from the originating general practice to the trusted third party. CPRD holds only a local patient identifier which is meaningful only at the patients’ registered general practice. This identifier is pseudonymised a second time before being made available to researchers and analysts with access to the database.
CPRD’s processes have been reviewed by the Confidentiality Advisory Group (CAG) and approved by the Health Research Authority (HRA) and Secretary of State to process patient identifiable information without consent under Regulation 5 of the Health Service (Control of Patient Information) Regulations 2002. This effectively removes the obligation to obtain patient consent for the use of confidential patient information for conducting purely observational research using CPRD databases, and associated linked datasets. This approval is conditional on approval of a study protocol by the CPRD Independent Scientific Advisory Committee (ISAC).
9.2. Subject confidentiality
CPRD-GOLD and linked HES-APC data contain only fully de-identified patient data. No confidential information is available to the study team, and does not have any access to patient identifiers.
All data held and processed by CPRD is done so in compliance with the relevant legal obligations including the Data Protection Act 1998. Data are held on a secure computer network, with access restricted to authorised users.
This study will comply with all applicable laws regarding subject privacy. No direct subject contact or collection of additional subject data will occur. Study results will be in tabular form and aggregate analyses that omits subject identification. Any publications and reports will not include subject identifiers.
10. PLANS FOR DISSEMINATING AND COMMUNICATING STUDY RESULTS
Findings from this study will be shared internally with GSK stakeholders. The results will be disseminated through abstract submission to relevant conferences. We will also write a manuscript and submitting to a peer-reviewed journal.
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Negewo, N. A., V. M. McDonald, K. J. Baines, P. A. Wark, J. L. Simpson, P. W. Jones and P. G. Gibson (2016). "Peripheral blood eosinophils: a surrogate marker for airway eosinophilia in stable COPD." Int J Chron Obstruct Pulmon Dis 11: 1495-1504.
Pascoe, S., N. Locantore, M. T. Dransfield, N. C. Barnes and I. D. Pavord (2015). "Blood eosinophil counts, exacerbations, and response to the addition of inhaled fluticasone furoate to vilanterol in patients with chronic obstructive pulmonary disease: a secondary analysis of data from two parallel randomised controlled trials." The Lancet Respiratory Medicine 3(6): 435-442.
Pavord, I. D., S. Lettis, N. Locantore, S. Pascoe, P. W. Jones, J. A. Wedzicha and N. C. Barnes (2016). "Blood eosinophils and inhaled corticosteroid/long-acting beta-2 agonist efficacy in COPD." Thorax 71(2): 118-125.
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The study Tables and Figures are proposed to include the following:
Tables
Table 1: Descriptive baseline characteristics of eligible patients and non-eligible patients and test statistics.
Table 2: Descriptive baseline characteristics of eligible patients and patients with at least 3 FEV1 measurements and test statistics.
Table 3: Descriptive baseline characteristics for each ICS/blood eosinophil group and test statistics.
Table 4: Rates of FEV1 decline and rate ratios in each ICS/blood eosinophil group and stratified by GOLD grade.
Table 5: Exploratory analysis: Descriptive baseline characteristics of each ICS/blood eosinophil group at each exploratory blood eosinophil cut off and test statistics.
Table 6: Exploratory analysis: Rates of FEV1 decline and rate ratios in each ICS/blood eosinophil group and stratified by GOLD grade for each exploratory blood eosinophil cut off.
Table 7: Exploratory analysis: Descriptive baseline characteristics of each ICS/blood eosinophil group in the eligible censored population and test statistics.
Table 8: Exploratory analysis: Rates of FEV1 decline and rate ratios in each ICS/blood eosinophil group in the censored population and stratified by GOLD grade.
Table 9: Exploratory analysis: Descriptive baseline characteristics of each ICS/blood eosinophil group in the incident cohort compared to initial prevalent cohort and test differences.
Table 10: Exploratory analysis: Rates of FEV1 decline and rate ratios in each ICS/blood eosinophil group in the incident population and stratified by GOLD grade.
Table 11: Exploratory analysis: Descriptive baseline characteristics of each ICS/blood eosinophil group for the combined incident and prevalent cohorts compared to ever non-ICS users and test statistics.
Table 12: Exploratory analysis: Rates of FEV1 decline and rate ratios in each ICS/blood eosinophil group for the combined incident and prevalent cohorts and stratified by GOLD grade.
Table 13: Exploratory analysis: Table illustrating the dose and length of time on ICS-containing medication in the prevalent and incident cohorts.
Table 14: Exploratory analysis: Rates of FEV1 decline and rate ratios for increasing dose and length of time on ICS-containing medication.
Figures
Figure 1: Study design (similar style to that included in this protocol).
Figure 2: CONSORT diagram illustrating inclusion and exclusion criteria and eligible patients for study.
Figure 3a-e: Adjusted rates of FEV1 decline in: i) patients with high EOS and on ICS, ii) patients with high EOS and not on ICS, iii) patients with low EOS and on ICS, and iv) patients with low EOS and not on ICS and stratified by GOLD grade 1-4 (figures a-e respectively).
Figure 4a-e: Exploratory analysis: Adjusted rates of FEV1 decline in: i) patients with high EOS and on ICS, ii) patients with high EOS and not on ICS, iii) patients with low EOS and on ICS, and iv) patients with low EOS and not on ICS and stratified by GOLD grade 1-4 (figures a-e respectively) using a blood eosinophil cut off of 300cells/µl.
Figure 5a-e: Exploratory analysis: Adjusted rates of FEV1 decline in: i) patients with high EOS and on ICS, ii) patients with high EOS and not on ICS, iii) patients with low EOS and on ICS, and iv) patients with low EOS and not on ICS and stratified by GOLD grade 1-4 (figures a-e respectively) using a blood eosinophil cut off of 500cells/µl.
Figure 6a-e: Exploratory analysis: Adjusted rates of FEV1 decline in: i) patients with high EOS and on ICS, ii) patients with high EOS and not on ICS, iii) patients with low EOS and on ICS, and iv) patients with low EOS and not on ICS and stratified by GOLD grade 1-4 (figures a-e respectively) in the censored population.
Figure 7a-e: Exploratory analysis: Adjusted rates of FEV1 decline in: i) patients with high EOS and on ICS, ii) patients with high EOS and not on ICS, iii) patients with low EOS and on ICS, and iv) patients with low EOS and not on ICS and stratified by GOLD grade 1-4 (figures a-e respectively) in the incident ICS population.
An algorithm allows us to identify GP and hospital AECOPD events using LTRI, AECOPD, cough, sputum, annual review rescue pack, and breathlessness codes.
GP determined AECOPD is found according to the following definitions: 1) LTRI codes 2) AECOPD codes 3) symptoms definitions of AECOPD whereby a patient must have 2/3 of the following symptoms recorded on the same day: cough, sputum, breathlessness 4) abx and ocs prescriptions on the same day for 5-14 days
All above criteria must be met in order for a GP AECOPD to be determined. Events which are within 14 days of one another are dropped.
Hospital AECOPD is determined using linked HES data and ICD codes: J44.1, J44.0, J20, J22, and J44.9.
Our definition of AECOPD will use either GP or hospital admitted AECOPD.
