1. Chung-Wah Siu2, Chi-Chung Choy1, Chi-Kin Chan3
Effectiveness of community atrial fibrillation screening in over 10,000 citizens using smartphone electrocardiogram- The AFinder program
Ngai-Yin Chan1,
Chung-Wah Siu2, Chi-Chung Choy1, Chi-Kin Chan3
Department of Medicine & Geriatrics, Princess Margaret Hospital, Hong Kong
Department of Medicine, Queen Mary Hospital, Hong Kong
Department of Medicine, United Christian Hospital, Hong Kong

2. - I have nothing to declare

3. Funding
The AFinder program was financially supported by Bayer. Bayer has no influence on the design, implementation, data analysis and preparation of report of this study.

4. Is AF Suitable for Screening ?
It is an important health problem with an accepted treatment
There are facilities for diagnosis and treatment
There is a latent and symptomatic stage
The natural history is understood
There is an agreed policy on whom to treat
The cost of finding is economically balanced with overall health
The case finding is a continuous process
The screening test should be suitable and acceptable to the population
Wilson J, Junger G. Principles and practice of screening for disease. In: Public Health paper No. 34. Edn. Geneva: World Health Organization, 1968.

5. Current Recommendation for AF Screening
Kirchhof P et al ESC guidelines for the management of AF developed in collaboration with EACTS. EHJ ;37:

6. AF Screening in the Pharmacy Setting
Lowres N et al. Feasibility and cost- effectiveness of stroke prevention through community screening for atrial fibrillation using iPhone ECG in pharmacies. The SEARCH-AF study.
Thromb Haemost 2014;111:

7. Community AF Screening
In a community AF screening study on 13,122 Hong Kong citizens, 0.8% detection rate (number-needed-to-screen=125) for newly diagnosed AF was observed.1
In STROKESTOP study, a detection rate of 3% (NNS=33) for newly diagnosed AF was observed with intermittent ECG recordings over 2 weeks in 7,173 citizens in Sweden.2
Chan NY et al. Screening for atrial fibrillation in 13,122 Hong Kong citizens with smartphone electrocardiogram. Heart 2017;103:24-31.
Svennberg E et al. Mass screening for untreated atrial fibrillation. Circulation 2015;131:

8. AF Screening in the Primary Care Setting
Chan PH et al. Diagnostic performance of a smartphone-based photoplethysmographic application for atrial fibrillation screening in a primary care setting. JAHA 2016;5(7). pii:e003428

9. AF Screening With Automatic BP Measuring Device
Sensitivity = 80.6%
Specificity = 98.7% Positive predictive value = 42.4% Negative predictive value = 99.8%
Chan PH et al. Diagnostic performance of an automatic blood pressure measurement device, Microlife WatchBP Home A, for atrial fibrillation screening in a real-world primary care setting. BMJ Open 2017;7(6). e

10. AFinder Program – Objective and Outcome Measures
We aim to examine the effectiveness of a non-governmental organization-led, community-based AF screening program carried out by trained layperson volunteers.
The primary outcome measures are NNS for one newly diagnosed AF and NNS for one appropriately treated newly diagnosed AF.
The secondary outcome measures are proportion of under-treated known AF and the diagnostic performance of the automated detection algorithm of the smartphone app in detecting AF.

11. AFinder Program – Methodology
A territory-wide, community-based, systematic AF screening program
Initiated by a panel of 4 cardiologists
Developed, organized and led by a non-governmental organization, named the Hong Kong Council of Social Service (HKCSS)
First medical program under the World Health Organization Network for Age- friendly Cities and Communities in Hong Kong

12. Engagement and Empowerment of Elderly Citizens
A focus group discussion with elderly citizens was held to contribute to the design of the program
84 layperson volunteers aged over 50 were recruited and trained to use the Kardia mobile device to perform smartphone-based single- lead ECG for community AF screening

13. AFinder Program- A Resourceful Website

14. AFinder Program-Screened Population, Screening Activities and Procedures
The screening program was publicized via different channels including media promotion and placement of posters in community centres by different NGO’s in Hong Kong
All citizens aged 50 or above were eligible for participation
118 screening sessions in community centres were conducted in the period between November to September

15. AFinder Program-Screened Population, Screening Activities and Procedures
For every participant, his/her sex and age were recorded and a single 30-second smartphone ECG was performed
ECG’s were downloaded by a call centre weekly and sent to a cardiologist for review
Citizens with AF were phone contacted for completing a baseline and 9-month FU questionnaire
The ECG reports were mailed to those participants with AF and they were advised to seek medical attention

16. AFinder Program- Baseline and 9-month FU Questionnaires
Baseline questionnaire :
-Previous history of AF
-Symptoms of AF
-Medical conditions
9-month questionnaire :
-Whether medical attention had been sought
-Whether oral anticoagulant, Aspirin or Clopidogrel was prescribed
-Level of drug compliance (very good=compliant all the time, fair=compliant > 50% of time, unsatisfactory=compliant <50% of time, poor=not taking the drug at all)

17. AFinder Program- Results
839 (7.2%) smartphone ECG’s were uninterpretable
NNS for 1 newly diagnosed AF=145

18. AFinder Program- Results
25 (0.23%) participants did not seek medical attention
30 (0.28%) participants were not prescribed oral anticoagulation (17 were given Aspirin, 1 was given Clopidogrel and 12 were not given any antithrombotic drug)
NNS for 1 newly diagnosed AF receiving appropriate oral anticoagulation=671

19. Results- Characteristics of Newly Diagnosed AF
All participants with AF (n=244)
Newly diagnosed AF (n=74)
Known AF (n=133)
p-value
Age
79.5±7.9
81.1±7.3
78.1±8.1
0.007
Sex (F), n(%)
172 (70.5)
51 (68.9)
97 (72.9)
0.542
Medical conditions
Heart failure, n(%)
17 (7.0)
6 (8.1)
9 (6.8)
0.738
Hypertension, n(%)
50 (67.6)
95 (71.4)
0.569
Diabetes, n(%)
63 (25.8)
19 (25.7)
34 (25.6)
0.973
Stroke, n(%)
40 (16.4)
9 (12.2)
22 (16.5)
0.0004
Coronary artery disease, n(%)
25 (10.2)
7 (9.5)
12 (9.0)
0.920
Peripheral artery disease, n(%)
8 (3.3)
7 (5.3)
0.045

20. Results- Characteristics of Newly Diagnosed AF
All participants with AF (n=244)
Newly diagnosed AF (n=74)
Known AF (n=133)
p-value
CHA2D2VASc score
3.9±1.5
3.9±1.6
3.9±1.4
0.990
0 in men and 1 in women, n (%)
5 (2)
2 (2.7)
0.055
1 in men and 2 in women, n (%)
13 (5.3)
3 (4.1)
10 (7.5)
0.338
≥2 in men and ≥3 in women, n (%)
226 (92.6)
69 (93.2)
123 (92.5)
0.823

21. Results- Characteristics of Newly Diagnosed AF
All participants with AF (n=244)
Newly diagnosed AF (n=74)
Known AF (n=133)
p-value
Symptoms of AF
Palpitations, n (%)
18 (24.3)
Shortness of breath, n (%)
25 (33.8)
Decrease in exercise tolerance, n(%)
19 (25.7)
Dizziness, n(%)
16 (21.6)
Syncope, n(%)
7 (9.5)
Chest pain, n(%)
10 (13.5)
Asymptomatic, n(%)
36 (48)

22. Treatment Rates for Newly Diagnosed and Known AF
29.8%
250
200
33.8%
150
22.2%
100 50
Newly diagnosed and Newly diagnosed AF known AF
Known AF
Appropriately treated with oral anticoaguation
Not appropriately treated with oral anticoagulation

23. Results- Characteristics of Under-treated Known AF
All known AF (n=133)
Under-treated known AF (n=88)
Treated known AF (n=45)
p-value
Age
78.1±8.1
78.2±7.6
77.8±9.1
0.8
Sex (F), n(%)
97 (72.9)
67 (76.1)
30 (66.7)
0.248
Medical conditions
Heart failure, n(%)
9 (6.8)
7 (8.0)
2 (4.4)
0.464
Hypertension, n(%)
95 (71.4)
64 (72.7)
33 (73.3)
0.934
Diabetes, n(%)
34 (25.6)
22 (25)
12 (26.7)
0.834
Stroke, n(%)
22 (16.5)
8 (9.1)
14 (31.1)
0.001
Coronary artery disease, n(%)
12 (9.0)
5 (5.7)
7 (15.6)
0.064
Peripheral artery disease, n(%)
7 (5.3)
2 (2.3)
5 (11.1)
0.033

24. Results- Characteristics of Under-treated Known AF
CHA2D2VASc score
All known AF (n=133)
3.9±1.4
Under-treated known AF (n=88)
3.7±1.3
Treated known AF (n=45)
4.3±1.5
p-value
0.042
0 in men and 1 in women, n (%)
1 in men and 2 in women, n (%)
10 (7.5)
8 (9.1)
2 (4.4)
0.331
≥2 in men and ≥3 in women, n (%)
123 (92.5)
80 (90.9)
43 (95.6)

25. Results- Characteristics of Under-treated Known AF
Among 88 patients with under-treated known AF, 23 (26.1%; 95% CI: ; 22 on Aspirin and 1 on Clopidogrel) were on antiplatelet drugs

26. Results- Diagnostic Performance of Automated Detection Algorithm for AF
Sensitivity = 75%; Specificity = 98.2%
Positive predictive value = 64.9%; negative predictive value = 99.5%

27. Conclusions
A new format and approach of AF screening in the community by an NGO-led program carried out by trained layperson volunteers is feasible and capable of detecting citizens with newly diagnosed AF with an NNS similar to other programs
The effectiveness of the program in subsequently leading participants with newly diagnosed AF or under-treated known AF to receive appropriate oral anticoagulation therapy is weakened by the lack of a structured downstream management pathway
The specificity and negative predictive value of the automated AF detection algorithm of the Kardia mobile device are excellent but both the sensitivity and positive predictive value are still suboptimal; the sensitivity of the algorithm has to be further improved before it can be considered as a tool for auto-screening

28. Limitations
The participants self-reported their history of AF, other comorbid conditions, health
-seeking behaviour, drug history and compliance to treatment
The management outcomes in other domains like treatment of cardiovascular conditions, rate control and rhythm control were not asked during the 9-month follow-up survey
Smartphone single-lead ECG was used for the diagnosis of AF, conventional lead ECG was not performed as reference
The uninterpretable rate of smartphone ECG’s was relatively high
The cost-effectiveness of this program has not been evaluated
Whether this new approach of community AF screening program is applicable to other regions is unknown

29. Acknowledgement We sincerely thank all the elderly citizens who contributed to the design of the AFinder screening program, all the workers who conducted the community screening for AF and all the participants of the program. Last but not the least, we thank HKCSS for leading and implementing this large-scale community program