The Dutch Multicentre Study Into Opportunistically Screening Geriatric Patients for Atrial Fibrillation Using a PPG Smartphone App; the Dutch-GERAF Study
Conditions: Atrial Fibrillation, Frailty, Major Bleed
Sponsor: Dijklander Ziekenhuis

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The Dutch multicentre study into opportunistically screening Geriatric 
patients for Atrial Fibrillation using a PPG smartphone App; the Dutch-GERAF 
Study 
 
Outline of the healthcare innovation project 
 
Background of the project:  
The latest European Cardiology Society’s guideline (1. Kirchhof et al.) on the management of atrial 
fibrillation (AF) recommends opportunistic screening for people 65 years and older, and systematic 
screening might be considered in high risk population. The geriatric population that visits the 
outpatient service is such a population. Our previous work (2. Zwart et al., 3. Zwart et al.) showed 
that geriatric patients have a high rate of AF, and furthermore a very high rate of newly detected AF 
can be found when repeated screening is applied.  
Since AF in elderly people often exists without symptoms, paroxysmal AF can easily remain 
undetected, and therefore untreated (2. Zwart et al.). Because geriatric patients have a very high risk 
of risk it is very relevant to identify cases of silent AF. To know the rate of paroxysmal AF helps to 
estimate the magnitude of the problem, and what the yield of repeated screening for AF might be in 
this population. 
However, repeated screening for AF is not the common geriatric practise at this moment. It is very 
relevant to determine if comparable results are found when repeated screening is applied in multiple 
geriatric practises, and with a different method of detection.  The primary goal of this innovation 
project is to assess the rate of newly detected AF when using a smartphone-based PPG detection 
app, and to determine if repeated screening for AF should become standard geriatric practise. This 
innovation project seeks to address those questions.  
Photo-plethysmography (PPG) is a promising technology used in smartphones and smartwatches 
for heartrate and rhythm assessment. (6) To retrieve PPG measurements a photo-emitter of infrared 
light is coupled to a photo-receiver. The amount of infrared light absorbed or reflected by blood 
represents the systolic and diastolic phase. This technology has low costs and does not require 
additional hardware. Although easy to use and widely available, PPG measurements require good 
signal quality which may be affected by poorly perfused tissue or tremors. Especially in AF detection 
proper signal-noise assessment is important because of the irregular depolarization of the ventricles. 
To overcome these previous issues, we developed an automated PPG based diagnostic algorithm for 
the smartphone to detect AF. The test characteristics of this technique are robust and described 
elsewhere. (7)  
 
Principal investigators and steering committee: 
Dr. M.E.W. Hemels, cardiologist, Rijnstate hospital Arnhem 
Dr. R.W.M.M. Jansen, geriatrician, North West Clinics, Alkmaar 
Dr. R. Pisters, cardiologist Rijnstate hospital, Arnhem 
Dr. R.K. Riezebos, cardiologist, OLVG, Amsterdam 
Dr T. Germans, cardiologist, North West Clinics, Alkmaar 
 
Participating centers: 
Dijklander Hospital, Hoorn. Drs. Lennaert A.R. Zwart, geriatrician. 

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Meander Medical Centre, Amersfoort. Drs. Johan F.H. Wold, geriatrician. 
North West Clinics, Alkmaar. Dr. René W.M.M. Jansen, geriatrician. 
Rijnstate Hospital, Arnhem. Dr. Diane G. Taekema, geriatrician. 
Onze Lieve Vrouwe Gasthuis, Amsterdam. Dr. Robert K. Riezebos, cardiologist. 
 
Aims of the project: 
a. To determine the rate of newly detected AF with repeated screening using a PPG based 
smartphone AF detection algorithm during follow-up visits at a geriatric outpatient center. 
b. To determine the rate of (paroxysmal) AF during follow-up visits. 
c. To evaluate the predictive value of a single measurement of NTproBNP at the baseline 
evaluation for the development of new AF. 
d. To evaluate the predictive value of the ABC Bleeding Risk Score, compared to the ATRIA 
Bleeding Risk Score, ORBIT Bleeding Risk Score, and HASBLED score. 
e. To evaluate adverse events such as major bleeding, stroke, death and cognitive disorders 
(Mild Cognitive Impairment or dementia), on the long term, namely: 3 years. 
 
Hypothesis: 
a. We expect to find a rate of known AF of approximately 20%, and to find newly detected AF in 
4 to 5% of the screened population. 
b. We expect to find a rate of paroxysmal AF of 45 to 60%. 
c. We expect that the baseline level of NTproBNP or BNP will be predictive of the development 
of new AF, especially when combined with the findings of the comprehensive geriatric 
assessment (CGA) and CHA2DS2Vasc score. 
d. We expect a rate of major bleeding of 3-5% per year. 
e. We expect that the ABC bleeding score, using age, GDF-15 and high sensitive Troponin T, will 
be predictive of major bleeding in the geriatric population. 
 
Design: 
a. Prospective multicentre cohort study 
b. We aim to include 1250 geriatric ambulatory patients 
c. Inclusion period will last for 6 months, with a consecutive follow up period of 6 months, at 3 
years patient files will be accessed for adverse events  
d. Scheduled commencement of the study is October 2020 
e. Blinding: none 
f. Randomization: none 
 
Ethical board: 
a. The Commission Human Bound Research region Arnhem-Nijmegen, The Netherlands, has 
exempted the protocol as a ‘niet WMO-plichtig onderzoek’. 
b. The record number of this decision is: 2019-5889. 
 
Methods: 
a. The PPG measurements will be performed with a specially designed app, this research app 
will be developed by Happitech. Every participating center will receive a smartphone in order 
to install the application. 

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b. All newly referred patients of 65 years and older that visit geriatric outpatient services are 
eligible (including Fall clinic, Memory clinic, Pre-operative geriatric assessment clinic, or any 
other geriatric outpatient clinics that are led by a geriatrician).  
c. Patients will be sent printed informative material about the project together with their 
appointment details. This material includes the informed consent form. They are asked to 
read the material, and fill out the informed consent form at home. At arrival to the 
outpatient clinic remaining questions will be answered, and the informed consent form 
collected, scanned, and added to their electronic medical file.  
d. Inclusion criteria: 
a. The patient is able and willing to give written informed consent or allows a 
representative to do so in their name. 
b. The paper copy will remain at the outpatient clinic to serve as a backup. 
e. Exclusion criteria: 
a. The patient has a pacemaker or implantable cardioverter defibrillator. 
b. The patient is known with a severe dementia, MoCA ≤ 15 points. 
c. The patient has a severe tremor, from whatever cause, and thus is unable to use the 
PPG based smartphone AF detection algorithm. 
f. Exclusion from analysis: 
a. Less than three measurements with the PPG based smartphone AF detection are 
performed. 
g. Procedure (See also Figure 1, on page 5): 
a. All participating patients will be allocated a study number. The allocation of study 
numbers will be coordinated by the research nurse, in collaboration with the local 
investigator and secretary. To ensure patient privacy, it is not allowed to use the 
hospital’s patient number as the study number. Predefined study numbers will be 
provided to each centre.  
b. The study number will be communicated with the physician by the secretary, and 
added to the electronic hospital files. 
c. A CGA will be performed at baseline. 
d. A 12 lead ECG will be performed at study entry to assess the baseline heart rhythm. 
e. At every visit to the outpatient service one measurement with the PPG based 
smartphone AF detection algorithm will be performed. 
f. The application will be made available for patients to install on their own smartphone, 
or their caretaker’s. 
g. Before each measurement the physician/patient/caretaker enters the patient’s study 
number into the PPG app, which allows the investigators to register which 
measurement belongs to which participant, without accessing their medical files. 
h. Coded, raw data will be analysed by the app, and results automatically sent by email 
to the principle investigator (Lennaert A.R. Zwart). 
i. 
The smartphone shows the measurement in real time. In case of artefacts due to 
movement or otherwise, the PPG based smartphone AF detection algorithm will 
reveal this and a repeat measurement will be performed.  
j. 
In case the PPG based smartphone AF detection algorithm detects AF, a 12 lead ECG is 
performed to confirm and document the diagnosis of AF and appropriate treatment 
will be initiated by the treating physician. 

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k. If AF is detected at home, patients will be invited for the 12 lead ECG in the outpatient 
clinic. 
l. 
Only patients who are known with AF and have an ECG or Holter registration showing 
AF in their medical record will be classified as patients with ‘known AF’. 
m. Patients will be classified as ‘newly detected AF’ if AF is detected by the PPG device 
and consecutively confirmed on a 12 lead ECG. These ECGs will be periodically 
collected by the research nurse, in order to be reviewed to confirm the diagnosis for 
research purposes, by an independent cardiologist (Martin Hemels or Ron Pisters). 
n. All positive measurements with the PPG based smartphone AF detection algorithm, 
will be assessed by an independent cardiologist (Martin Hemels or Ron Pisters). 
o. Patients known with AF will also be screened with the device in the same fashion, 
making it possible to estimate the proportion of paroxysmal AF, but a confirmatory 
ECG will not be necessary as AF is already documented in those patients. 
p. In patients with newly detected AF, the algorithm will still be used in follow up visits, 
in order to determine the rate of paroxysmal AF. 
q. At study entry the baseline characteristics will be collected, including: 
i. Medical history and medication use (as part of the CGA). 
ii. MoCA. 
iii. Functional measures such as pattern of gait and muscle weakness (as part of 
the CGA) and basic measurements such as weight and blood pressure (as 
part of the CGA) 
iv. Standard blood samples including kidney function, blood count, electrolytes 
and INR if applicable. Blood will be taken during the routine blood sampling, 
to assess the HS Troponine T, NTproBNP and GDF-15 at an external location 
(Dijklander Hospital). A separate protocol can be found on page 6 of this 
protocol. 
r. Information about the study will be available in print at every participating centre. 
This information folder is added as an attachment. 
s. Of all patients at least 3 measurements with the PPG based smartphone AF detection 
algorithm will be collected, including the measurement at study entry, during a 6 
months follow-up period. After 3 years adverse events will be accessed.  
t. A designated research nurse will have access to the code list (patient identifier and 
study code) at each participating centre collect participating centres, collect data on   
the baseline characteristics, and the measurements with the PPG based smartphone 
AF detection algorithm. 
h. Frailty will be calculated as a Frailty Index, based on the accumulation of deficits model. It 
will incorporate a combination factors: functional, cognitive, physical and social. As proposed 
in literature (4. Searle et al., 5. Rockwood et al.), a number between 30 and 40 factors will be 
used to calculate the index. A detailed list of the variables and their definition is added as an 
attachment. 
i. 
A detailed plan for the statistical analysis can be found on page 7 of this protocol. 
j. 
Statistical analysis will be performed by Lennaert A.R. Zwart, geriatrician at the Dijklander 
Hospital, and PhD student at the Free University in Amsterdam, under supervision of the 
principal investigators. 

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k. First draft of the manuscript will be written by Lennaert A.R. Zwart, geriatrician at the 
Dijklander Hospital, and PhD student at the Free University in Amsterdam, in collaboration 
with the principal investigators. 
l. 
Privacy: To protect patient privacy patients will be assigned a unique study code. The key to 
the code will be stored at a secure location at each hospital with limited access.  Only coded 
data will be used for any data processing. The Board of Directors of the participating 
hospitals will be asked for approval the protocol. In addition, they will also be asked to 
appoint the research nurse for 0 fte (without salary) in their hospital. 
m. After 3 years, all patient files will be checked for the occurrence of: incident major bleeding, 
stroke, death or cognitive disorders (mild cognitive impairment or dementia + subtypes). 
 
 

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Figure 1. Study protocol 
 
 
 
 
 
Baseline 
Follow up 1 
Follow up 2 
CGA 
PPG 
screening 
PPG 
screening 
PPG 
screening 
ECG 
Laboratory 
If positive 
If positive 
Confirmatory ECG 
Extra Follow up  
PPG 
screening 
If positive 

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Laboratory Protocol 
 
For the Dutch GERAF health care innovation project 
 
As part of the Comprehensive Geriatric Assessment blood samples are taken. The number of 
measurements may differ between the participating centres, for example in which vitamins are 
analysed.  
 
For the Dutch GERAF health care innovation project the standard laboratory testing should include 
measurement of: 
Hemoglobine in mmol/L 
eGFR in mL/min 
TSH in mU/L 
FT4 in pmol/L 
Glucose in mmol/L 
HbA1C in mmol/mol 
INR 
 
For study purposes one extra Lithium Heparine tube should be drawn, during the standard blood 
sampling. 
This extra Heparine tube should be frozen at -20 degrees Celcius and stored after sampling at the 
participating centre. 
 
The following extra analyses will be performed, for research purposes only: 
GDF-15 in pg/mL 
NT-proBNP in pg/mL 
High sensitive Troponin T in ng/L 
 
All samples will be transported to the Dijklander Hospital and will be analysed there by the Diagnostic 
Centre West Friesland (DCWF).  
After analysis, Lennaert A.R. Zwart will collect the outcomes and add them to the anonymised 
dataset. 
Costs for the analysis and storage will be covered by Roche Diagnostics. 
 
 
 

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Statistical analysis plan 
 
1. Automated generation of variables, based on collected data: 
1. CHA2DS2VASc score 
2. HAS-BLED score 
3. ORBIT Bleeding Risk Score 
4. ABC Bleeding Score 
5. ATRIA Bleeding Risk Score 
6. Class of impaired renal function 
7. BMI 
8. Underweight 
9. Anemia 
10. Abnormal score on MoCA 
11. Frailty Index, based on accumulation of deficits model 
 
2.1 Univariate analysis: 
Comparison of groups at baseline  
1. Patients with sinus rhythm at baseline 
2. Patients with known AF at baseline 
 
2.2 Univariate analysis: 
Comparison of sensitivity and specificity of the bleeding risk assessment scores 
1. Comparing Area Under the ROC curve 
2. Comparing sensitivity, specificity and predictive values at the per score predefined cut-off 
values 
 
3.1 Univariate analysis 
Comparison of groups including follow up data 
1. Patients with sinus rhythm during entire study 
2. Patients with newly detected AF 
Correction for age if necessary  
 
3.2 Multivariate analysis 
Correction/multivariate analysis of significant differences found in the univariate analysis 3.1 
 
4. Multivariate analysis / Regression analysis 
1. Factors predicting newly detected AF 
2. NT-proBNP and/or GDF-15 predicting newly detected AF 
3. Factors predicting death, or major bleeding, or composite outcome (death + major bleeding) 
 
 
 
 
 

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References 
1. Kirchhof P, Benussi S, Kotecha D, et al. 2016 ESC Guidelines for the management of atrial 
fibrillation developed in collaboration with EACTS. EP Europace, Volume 18, Issue 11, 
November 2016, 1609-1678. 
 
2. Zwart LAR, Germans T, Simsek S, Ruiter JH, Jansen RWMM. Atrial fibrillation, anticoagulation 
and major bleeding in geriatric patients at risk of falling. OBM Geriatrics 2019, Volume 3, 
Issue 3, doi:10.21926/obm.geriatr.1903071 
 
3. Zwart LAR, Jansen RWMM, Germans T, Simsek S, Ruiter JH, Hemels MEW. Opportunistic 
screening for atrial fibrillation with a single lead device in geriatric patients. Journal of 
Geriatric Cardiology (2020) 17: 149-154. 
 
 
4. Searle SD, Mitnitski A, Gahbauer EA, Gill TM, Rockwood K. A standard procedure for creating 
a frailty index. BMC Geriatrics 2008, 8:24.  
 
5. Rockwood K and Mitnitski A. Frailty in relation to the accumulation of deficits. Journal of 
Gerontology 2007, Vol. 62A, No. 7, 722-727. 
 
6. Moraes JL, Rocha MX, Vasconcelos GG, Vasconcelos Filho JE, de Albuquerque VHC, 
Alexandria AR. Advances in Photopletysmography Signal Analysis for Biomedical Applications. 
Sensors (Basel, Switzerland). 2018;18(6). 
 
7. Riezebos K et al. Article in press in HRS Cardiovascular Digital Health Journal.