The document discusses efforts to harmonize data from two asthma cohorts - the BTS severe asthma registry and the UBioPred cohort. Over 80 variables were identified that could potentially be harmonized between the two datasets. Factor and cluster analysis of the BTS data identified four clusters of patients with different characteristics and outcomes over time. A cluster classifier was developed that can predict which cluster a patient belongs to based on a subset of variables, correctly classifying 97% of patients. Harmonizing the data from the two cohorts would increase statistical power to further explore heterogeneity in severe asthma.

1. Supported by
Prominent international speakers from
h"p://workshop.eisbm.eu1

2. AirProm'Harmonisa,on'and'
Sta,s,cal'Analysis'
Chris'Newby'
Research'Associate'
Data'2'Knowledge'
Health'Sciences'&'Respiratory'Medicine'
University'of'Leicester'
UK'

3. Harmonisa,on'of'BTS'severe'asthma'
registry'and'UBioPred'
• BTS'severe'asthma'registry'variables'based'on'
informa,on'obtained'from'pa,ent'records'
imputed'by'research'Nurses'
'
• What'variables'can'be'obtained'from'both'
cohorts'so'that'variable'defini,ons'remain'the'
same'and'power'is'increased''

4. Data'Shaper'methodology''
Aim:%
To%provide%a%template%to%facilitate%
harmoniza;on%between%pre>exis;ng%
Cohort%2% cohorts%and%support%the%design%of%
emerging%ones.%
Cohort%3%
Cohort%1%
common% Data%
Cohort%4% Pool%%
Cohort%5%

5. Three'steps'to'harmonisa,on'
Identify core sets of information to be shared
(selection and definition of the variables)
DATA SCHEMA
Assess potential to share the core set of information between a group of
cohorts
HARMONIZATION PLATFORM
Achieve processing and pooling of information
(Real Data)
Processing and pooling platform

6. Data'Schema'Structure:'
a"nested"hierarchical"structure"
Modules% M1%

7. Data'Schemer'
Around'80'variables'poten,ally'can'be'
harmonised'between'BTS'data'and'UBioPred'
Harmonised'Variable'
Module' Theme' Domain' Harmonised'Variable'Name'
Descrip,on'
Clinical'Assessment' Individual'Disease'History' Asthma'Disease'History' Age'of'onset'of'symptoms' age'of'onset'of'symptoms'
severity'stage'determined'by'
Clinical'Assessment' Individual'Disease'History' Asthma'Disease'History' ATS/ERS'severity'stage'
ATS/ERS'guidelines'
severity'stage'determined'by'
Clinical'Assessment' Individual'Disease'History' Asthma'Disease'History' GINA'severity'stage'
GINA'guidelines'
Physical'Measures' Body'Composi,on'Measures' Body'Mass'Index' BMI' height/weight'
Physical'Measures' Body'Composi,on'Measures' Obesity' Obese' BMI>=30'
Physical'Measures' Lung'Func,on'Measures' Unstandardized' Pre'Bronchodilator'FEV1' PreBronchodilatorFev1'
Physical'Measures' Lung'Func,on'Measures' Unstandardized' Post'Bronchodilator'FEV1' PostBronchodilatorFev1'

8. Three'steps'to'harmonisa,on'
Identify core sets of information to be shared
(selection and definition of the variables)
DATA SCHEMA
Assess potential to share the core set of information between a group of
cohorts
HARMONIZATION PLATFORM
Achieve processing and pooling of information
(Real Data)
Processing and pooling platform

9. BTS'Severe'asthma'analysis'
• To'explore'the'heterogeneity'of'Severe'
asthma'through'data'collected'in'registry'
• Factor'analysis'carried'out'to'explore'variable'
heterogeneity'
• Cluster'analysis'carried'out'to'explore'pa,ent'
heterogeneity'
• Clusters'followed'over',me'to'determine'
medical'outcomes'''

10. Factor'analysis'
'' Component'
1' 2' 3' 4'
Lung'Func,on' BMI/Atopy' Treatment' Blood'
Eosinophil'
BMI' .045' .677' .215' .026'
Pre_bronchodilator'FVC'%' .767' _.340' .086' .246'
predicted'
Pre_bronchodilator'FEV1'(%)' .986' .029' _.110' _.023'
predicted'
Pre_bronchodilator'FEV1/FVC'' .674' .372' _.207' _.266'
Blood'eosinophil'x109/L' .010' .033' _.033' .907'
Total'IgE'kU/L' .030' _.520' _.137' .073'
BDP'equivalent'(mcg/day)' _.070' .079' .710' .114'
Age'at'onset'of'symptoms' _.041' .593' _.397' .254'
Oral'Steroid'dose'(mg)' _.059' .134' .641' _.130'

11. 4'Clusters'found'using'mixture'
modelling''
• Cluster'1,'severe'airflow'obstruc,on'
'
• Cluster'2,'non_obese,'minimal'airflow'
Obstruc,on'
• Cluster'3,'obese,'minimal'airflow'obstruc,on'
• Cluster'4,'moderate'airflow'obstruc,on'with'
higher'number'of'exacerba,ons'and'on'larger'
amounts'of'treatment'

12. Variable% Cluster%1% Cluster%2% Cluster%3% Cluster%4% p>
(N=106)% (N=129)% (N=80)% (N=34)% value%
‘Severe% ‘Non>obese% ‘Obese% ‘Moderate%
airflow% minimal% minimal% airflow%
obstruc;on’% airflow% airflow% obstruc;on’%
obstruc;on’% obstruc;on’%
BMI% 28.7'(3.96)' 24.7'(3.76)' 36.8'(5.0)' 29.2'(6.44)' <0.001'
Atopy%(%)%% 47' 61' 61' 71' 0.057'
Reflux%history%%(%%yes)% 56' 67' 48' 35' 0.001'
HAD%Anxiety%score%% 8'(6)' 7'(5)' 9'(6.5)' 8.5'(5.75)' 0.042'
Pre>bronchodilator%FEV1% 43.4'(11.3)' 78.4'(20.5)' 78.3'(17.8)' 62.8'(23.3)' <0.001'
Predicted%(%)%
Pre%Bronchodilator%FVC% 67.2'(17)' 91.1'(17.1)' 88.4'(16.8)' 81.2'(18.8)' <0.001'
Predicted%(%)%
FEV1%FVC%Pre%Bronchodilator% 52.6'(13.9)' 68.1'(13.7)' 69.5'(10.3)' 61.1'(16.3)' <0.001'
(%)%
Blood%Eosinophil%count%109/l% 0.36'(0.39)' 0.29'(0.50)' 0.28'(0.41)' 0.42'(0.28)' 0.511'
Rescue%steroid%courses%in%last% 4'(5)' 3'(4)' 5'(4)' 5'(6)' 0.002'
year%(n)%
BDP%equivalent%inhaled% 1650'(501)' 1540'(647)' 1640'(541)' 4050'(1030)' 0.011'
steroid%dose%(mcg)%
Age%At%Onset%Of%Symptoms% 26'(22)' 26'(20)' 29'(17)' 19'(17)' 0.162'
(years)%

13. Cluster'Outcomes'over',me'
Cluster%1% Cluster%2% Cluster%3% Cluster%4%
‘Severe%airflow% ‘Non>Obese%minimal% ‘Obese%minimal% ‘Moderate%airflow%
obstruc;on’% airflow%obstruc;on’% airflow%obstruc;on’% obstruc;on’%
%
Difference' P_value' Difference' P_value' Difference' P_value' Difference' P_value'
BMI% 0.65'(2.34)' 0.011' 1.43'(2.51)' <0.001' 0.59'(3.44)' 0.152' 1.72'(2.56)' 0.001'
Pre%bronchodilator%%FEV1% 0.39'(0.58)' <0.001' 0.08'(0.60)' 0.201' 0.03'(0.52)' 0.628' 0.10'(0.72)' 0.488'
Pre>bronchodilator%%FEV1%%%predicted% 15'(22)' <0.001' 2'(27)' 0.593' 3'(21)' 0.318' 5''(25)' 0.308'
Blood%Eosinophil%count,%%x%109/l% _0.19'(0.46)' 0.001' _0.22'(0.72)' 0.011' _0.22'(0.46)' 0.004' _0.15'(2.79)' 0.11''
BDP%equivalent%(mcg/day)% 140'(713)' 0.051' 276'(839)' 0.001' 8'(798)' 0.931' _1576'(1576)' <0.001'
%%on%steroids%at%follow%up% 58%' 0.071' 50%' 0.002' 58%' 0.064' 74%' 0.096'
Rescue%steroid%courses%in%last%year%% 0'('5)' 0.005' 0'(6)' 0.021' _2'(4)' 0.001' _2'(5)' 0.004''

14. Cluster'Classifier'
• Predicts'which'cluster'a'pa,ent'belongs'to'at'a'
certain'point'in',me'
• Based'on'the'probability'of'being'in'
mul,variate'normal'distribu,on'of'subset'of'
variables'for'each'cluster'
• 97%'correct'cluster'membership'

15. Cluster'Classifier''
'
• At'follow'up'54%'of'pa,ents'remained'in'same'cluster'
%% Predicted%clusters%from%year%follow%up%data%
Baseline%cluster%% Cluster'1' Cluster'2' Cluster'3' Cluster'4'
1%% 34.6%' 32.7%' 23.1%' 9.6%'
2%%% 10.6%' 66.7%' 10.6%' 12.1%'
3%%% 11.4%' 0.0%' 80.0%' 8.6%'
4%%% 25.0%' 35.0%' 20.0%' 20.0%'

16. Medical'Systems'Biology'
Pre'Bron' Pre'Bron'
FEV1/'%' Pre'Bron' Post'Bron'
FVC/%'
FEV1/FVC' FEV1/FVC'
Predicted' Predicted'
Lung' Mucus'
Plugging' Bronchiectasis' Lung'
Restric,on'
Obstruc,on'
Bronchiole'wall'
Nasal'Polyps'
thickening'
%'eosinophils' Blood'
in'Sputum' eosinophil'
Total'IgE' Count'
blood'count'

17. BTS' Cohort'1' Cohort'2'
M'
Pre'Bron' Post'Bron'
Pre'Bron' Post'Bron'
FEV1/FVC' FEV1/FVC'
Pre'Bron' Post'Bron' E'
FEV1/FVC' FEV1/FVC' FEV1/FVC' FEV1/FVC'
T'
A'
'
Lung' Lung' Lung'
Obstruc,on' A'
Obstruc,on' Obstruc,on'
N'
A'
L'
%'eosinophils'in' %'eosinophils'in'
%'eosinophils'in'
Y'
Sputum' Sputum' S'
Sputum'
I'
S'
Blood'eosinophil' Blood'eosinophil' Blood'eosinophil'
Count' Count' Count'
Blood'eosinophil' Biomarker'
Count'in'Tissue' levels'

18. linking'with'other'cohorts'
• Meta'analysis'of'models'to'expand'and'verify'
cohort'specific'models''
'
• Harmonisa,on'of'data'so'a'combined'version'
of'data'exists'and'can'be'used'for'future'
greater'powered'analysis'
'
• Verifica,on'of'exis,ng'models'in'other'
cohorts'