ACO Working Group ERS 2017

1. Asthma-COPD
Overlap (ACO)
Working Group
Meeting
CHAIR: Marc Miravitlles
DATE: Saturday 9th September 2017
TIME: 12.30–13.10
VENUE: Melia Milano Hotel, Via Masaccio 19,
Milan, Italy

2. Agenda
1) Update on current project ‘ACOS proof of concept study- Comparability
of different population-definitions of ACOS within a UK database’.
2) Ideas for future projects.
3) Any new project ideas?
4) Prioritisation of projects and work plan to move projects forward.

3. 1) Update on current projects
ACOS proof of concept study

4. Background / rationale
• 2014, GINA and GOLD published their first joint statement on
Asthma-COPD Overlap Syndrome (ACOS)1
• Current thinking now recommends reference to ACO rather than
ACOS based on the clinical implications of the term “syndrome”2
• Various criteria for diagnosis of ACO have been proposed2-4
• The lack of a gold standard definition is a barrier to ACO research
and to understanding the biology of the condition and optimum
management approaches5
1. GINA-GOLD Diagnosis of disease of chronic airflow limitation: Asthma, COPD and asthma-COPD overlap
syndrome (ACOS), 2014; 2. Barnes PJ. Asthma-COPD Overlap. Chest. 2016;149:7-8; 3. Miravitlles M, et al.
Arch Bronconeumol 2014; 4.Koblizek V, et al. Pap Med Fac Univ Palacky Olomouc Czech Repub 2013; 5.
Kankaanranta H, et al. Basic Clin Pharmacol Toxicol. 2015;6. Postma DS, Rabe KF. NEJM 2015

5. Proof of Concept Study
Aim:
Explore the influence of the definition on the prevalence and clinical
presentation of ACO in databases used for observational research, in order
to inform (a) standard definition(s) for future studies and clinical trials.
Study design:
• Historical cohort study using the UK’s Optimum Patient
Care Research Database which contains >2.9 million
patients from >576 primary care practices across the UK
• Patients with 2 years of continuous records within the observation period
1 January 2014-31 December 2015

6. Population Definition Summary
Clinical diagnosis of
COPD
N=1,017
Aged ≥40 years
N=1,015
Evidence of smoking
N=940
Airflow Obstruction
(Post BD FEV1 per cent predicted or
FEV1/FVC <0.7)
N=750
Airflow Reversibility
(≥12% and ≥200mL increase in post-
bronchodilator FEV1)
N=208
Subgroup A3
n= 2
(Patient is <40 years)
n= 75
(No evidence of smoking –
current or ex)
n=190
(No airflow obstruction)
n= 542
(No airflow reversibility)
Subgroup A2
Subgroup A1
Subgroup A
POPULATION A
Clinical diagnosis of
Asthma & COPD
N=398
Aged ≥40 years
N=395
Evidence of smoking
N=330
Airflow Obstruction
(Post BD FEV1 per cent predicted or
FEV1/FVC <0.7)
N=244
Airflow Reversibility
(≥12% and ≥200mL increase in post-
bronchodilator FEV1)
N=127
Subgroup B3
Subgroup B2
Subgroup B1
Subgroup B
POPULATION B
n= 3
(Patient is <40 years)
n= 65
(No evidence of smoking –
current or ex)
n= 86
(No airflow obstruction)
n= 117
(No airflow reversibility)

7. Population Definition Summary
Clinical diagnosis
of Asthma
N=857
Aged ≥40 years
N=755
Evidence of smoking
N=429
Airflow Obstruction
(Post BD FEV1 per cent predicted or
FEV1/FVC <0.7)
N=157
Airflow Reversibility
(≥12% and ≥200mL increase in post-
bronchodilator FEV1)
N=109
Subgroup C3
Subgroup C2
Subgroup C1
Subgroup C
POPULATION C
n= 102
(Patient is <40 years)
n= 326
(No evidence of smoking –
current or ex)
n= 272
(No airflow obstruction)
n= 48
(No airflow reversibility)

8. ACO prevalence in the clinical populations
Population A
Clinical diagnosis
of COPD only
Population B
Clinical diagnosis of
Asthma & COPD
Population C
Clinical diagnosis
of Asthma only
ACO prevalence 20.5%
(208/1,015)
32.1%
(127/395)
14.4%
(109/755)
p-value compared to
asthma and COPD*
p<0.001 Reference p<0.001
*Chi-squared test

10. Summary: ACO prevalence using EMR
o Approach has strengths and weaknesses
o ACO prevalence varies depending on source population
– 20%* if clinical dx COPD only
– 32%* if clinical dx asthma + COPD
– 20%* if clinical dx asthma + COPD AND Asthma diagnosed when patients ≤40 years
of age
– 14%* if clinical dx asthma only
– 8%* if neither dx
*ACO definition requires airflow reversibility
o Future studies
– Add cross-sectional analyses to examine how patterns of comorbid conditions
vary depending on the source clinical population
– Compare results with similar cross-sectional analyses in different population-
based databases
– Cohort studies to evaluate outcomes using different ACO definitions

11. 2) Current ideas for future projects
(Prioritisation and work plan)
Phase 1 Repetition of the analyses in other national
databases to evaluate the ACO definitions.
Phase 2 Implications of a mixed asthma-COPD
phenotype vs COPD alone on patient outcomes.

12. Phase I
Repetition of the analyses in
other national databases to
evaluate the ACO definitions.

13. Database eligibility criteria
Inclusion:
• Must be “population-based”, requiring them to be largely representative of the broad,
heterogeneous population treated within everyday routine care in their respective country
of origin. The following types of population-based databases may be eligible:
o Clinical databases (e.g. primary care databases)
o Administrative/billing-based (e.g. insurance claims records)
• Have at least two continuous years of “recent” (within the last 10 years: 2006-2015)
clinical data
• Have produced at least one publication in a peer reviewed journal
• Include variables permitting:
o Evaluation of patient age (i.e. patient age or date/year of birth)
o Evidence of current or past smoking (e.g. smoking status, pack years, prescription of smoking
cessation therapy/advice).
Exclusion:
• To maximise the external validity of the study findings and avoid biasing outcomes by
working within pre-selected populations unrepresentative of the diversity of patients
managed in routine clinical practice, the following will not be eligible for inclusion in the
initial phase of this study:
o Clinical trials databases
o Case series of patients

14. Which databases should be
included in the protocol?
DATABASE Time for completion of Stage 1 Cost for completion of Stage 1
1. Dutch ASTHMA / COPD Service 8 weeks
EUR 10,000
(~2 months post-doc salary)
2. Adelphi Respiratory Disease
Specific Programme
≤ 4 weeks £0
3. Optimum Patient Care Research
Database (OPCRD)
4-6 weeks £10,000
4. SIDIAP 6 weeks EUR 1,500
5. MAJORICA TBC TBC
6. PCORnet Common Data Model
Data available Sept 2016;
analysis estimate ?
TBC
7. HealthCore 3 weeks
$4,167
(if manual programming required)
8. MarketScan "1 day" ?
9. Optum Humedica "1 day" ?
?
✓
X Valuable for repeat analysis and validation when available

15. Phase II
Implications of a mixed asthma-COPD
phenotype vs COPD alone on patient
outcomes

16. Implications of a mixed asthma-
COPD phenotype vs COPD alone
on patient outcomes
Aims:
• To identify the prevalence and incidence of patients diagnosed as
having ACO
• To identify the burden and cost of ACO compared with COPD and
asthma populations
• To assess respiratory and cardiovascular outcomes in ACO,
COPD, asthma treated with ICS, ICS/LABA and LABA.
• Characterising ACO patients to develop a diagnostic tool

17. Clinical Outcomes
• Presence of atopy, defined as ≥1 of the following:
o Physician diagnosis of eczema
o Physician diagnosis of allergic rhinitis
o Eosinophilia (cut off >200/μl; REG COPD blood eosinophilia study used ≥450μl)
o Positive skin prick test
o Positive to ≥1 allergen
• Smoking history:
o Pack years, where available
o Duration of smoking, defined as:
– For ex-smokers: years between first current smoking / active smoking code and non-
smoker or smoking cessation code
– For current smokers: years between first current smoking record and year of study /
cross sectional analysis
• Historical “onset” of disease:
o Duration of asthma, defined as years between first recorded asthma diagnosis / encounter
and year of study / cross-sectional analysis
o Duration of COPD, defined as years between first recorded COPD diagnosis / encounter
and year of study / cross-sectional analysis
o Time between first recorded asthma diagnosis/encounter and first COPD
diagnosis/encounter

18. Clinical Outcomes
• COPD severity: in terms of GOLD status (where evaluable)
• Comorbidities:
o Cardiovascular disease
o Other chronic respiratory conditions
o Diabetes
o Gastroesophageal reflux disease (GERD)
o Charleson Comorbidity Index
o Lung Cancer
• Respiratory treatment: Current management (i.e. during the phase 1 24-month cross-
sectional analysis period), records (prescriptions for / claims data) for the following, and
combinations of the following, will be examined: SABA, SAMA, LABA, LAMA, ICS,
theophylline, LTRA, Roflumilast, chronic azithromycin.
• Exacerbations: Functional consequences of different definitions, (i) proportion of patients
and (ii) annualised rate of respiratory-related exacerbations over the phase 1 24-month
evaluation period, where a respiratory-related event is defined as any of the following:
o Physician diagnosis of asthma exacerbation;
o Physician diagnosis COPD exacerbation;
o Accident & Emergency / Emergency Room attendance with a lower respiratory code
o Hospital admission with a lower respiratory code
o A course of prednisolone
o A course of systemic antibiotics coded for a lower respiratory tract infection

19. REG projects with an ACO component
Clinical and Cost implications of OLDOSA
The term “OLDOSA syndrome” has been
proposed1, which refers to the coexistence
of OLD (obstructive lung disease: COPD
and asthma) and OSA
1. Ioachimescu OC, et al. Respirology. 2013;18:421-31
AIMS:
Evaluate the impact of (i) continuous positive airway pressure (CPAP) therapy
(ii) a sleep breathing disorder diagnosis (as a proxy for CPAP treatment)
(iii) an OSA diagnosis
on clinical outcomes and healthcare resource utilisation in UK patients with comorbid OLD
Obstructive sleep apnoea WG are looking for anyone interested in being involved

20. 3) Any new ideas for projects

21. • Are these projects still:
o Relevant?
o Feasible?
o Valid?
o A priority?
• How do we set priorities in ACO research?
• How to we ensure these priorities are pursued?
• What are the two most important projects to push forwards?
Prioritisation

22. • Develop a work plan-
Will it be possible to share the data? So one person can do all the analysis
Or will different people need to analyse the different databases?
Should be possible to share analysis scripts to minimize analysis time.
• Need to secure - funding
- database access
- analytical support
Next steps for Phase I study-
Evaluation of ACO definition in other national databases