Presentation made during the EISBM workshop, 13-15 June 2012 by Chris Newby ( AirProm ).

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'