1. Introduction of a new IPCRG desktop helper
Esther I. Metting, Johannes C.C.M. in ’t Veen, P.N. Richard Dekhuijzen, Ellen van Heijst, Janwillem W.H. Kocks, Jacqueline B. Muilwijk-Kroes, Niels H. Chavannes, Thys van der Molen

2. Conflicts of interests
This study is partly funded by Novartis Pharma B.V.

3. Introduction desktop helper
Publication ERJ open research
Development of a decision trees to predict disease in patients with lung complaints in primary care

4. Its purpose
Predicting asthma, COPD, asthma/COPD overlap syndrome (ACOS)
In primary care patients suspected to have an obstructive lung disease
Along with an estimation of the accuracy
Based on a parsimonious set of predictors
To be used in daily clinical practice

5. Based on decision tree analysis
Visually hierarchical processes: easy to interpret, transparent
Can be used in clinical practice or in automatic
decision making
Can detect previously unknown interactions and relationships

7. Predictors and outcome
Based on a pulmonologists diagnosis in
an Asthma/COPD service for primary care
Derivation database (n=9297)
± 40 predictors
Spirometry
ACQ*, CCQ**
Patient characteristics
Patient history
* Asthma Control Questionnaire
** Clinical COPD Questionnaire

8. How the tool has been tested
The tool has been developed with the CHIAD analysis
Using the 10 fold cross validation technique
Repeated this procedure 10 times to test robustness
After that the tool was validated in an other primary care population (validation dataset, n=3215)

9. Derivation database (development)
Diagnosis predicted by decision support tool
Correct
ACOS*
32%
COPD
85%
Asthma
79%
Other**
54%
Total n (%)
69%
* Asthma/COPD overlap syndrome
** Diagnosis unclear, “indication of restriction” or “no disease”

10. Diagnosis predicted by decision support tool
Derivation database
Diagnosis predicted by decision support tool
ACOS* n
COPD
Asthma
Other**
Total
n (%)
Correct
225
355 98 33
711 (7.6)
225 (31.6)
135
1454 68 59
1716 (18.5)
1454 (84.7)
162
101
3253
609
4125 (44.4)
3252 (78.9) 28
128
1109
1480
2745 (29.5)
1480 (53.9)
Total n (%)
550 (5.9)
2038 (21.9)
3419 (48.7)
2181 (23.5)
9297 (100)
6412 (69.0)
* Asthma/COPD overlap syndrome
** Diagnosis unclear
*** “indication of restriction” or “no disease”

11. Diagnosis predicted by decision support tool
Validation database
Diagnosis predicted by decision support tool
ACOS* n
COPD
Asthma
Other**
Total
n (%)
Correct 59
152 35 2
247 (7.9)
59 (23.9) 53
452 36 6
555 (17.7)
452 (82.7) 42 32
532 78
685 (21.8)
532 (77.8)
Probable asthma 11 7
580
238
836 (26.6)
580 (69.4) 21 72
919
651
818 (26.0)
651 (50.5)
Total n (%)
175 (5.6)
708 (22.5)
1522 (48.5)
737 (23.5)
3141 (100.0)
2044 (65.1)
* Asthma/COPD overlap syndrome
** Diagnosis unclear
*** “indication of restriction” or “no disease”

12. Asthma/COPD-overlap syndrome
Derivation dataset: 32% correct
Validation dataset 24% correct
Based on FEV1/FVC, smoking, age of onset
Variation in parameters to predict diagnosis

14. The new IPCRG desktop helper

16. Overview of the different steps

17. Thresholds statistically determined

18. Several pathways leading to asthma (different “types” of asthmatics)

19. One way leading to COPD and ACOS

20. A few examples

21. Impact on real life practice
It can support clinicians in
Making the diagnosis
Control mechanism (extra check)
Giving more insight in importance of the different predictors. --> learning aspect
It can help patients
Faster diagnosis --> earlier treatment

22. Discussion How might this be used in your practice?
How might you share this more widely with colleagues
What challenges do you think people will have with it?
Accuracy ACOS
What to do if diagnosis is not clear?

23. Thank you for your attention