Development of MENARI plus (Self Pulse Assessment and Clinical

Scoring) for Detecting Atrial Fibrillation in High-risk Population

Muhamad Rizki Fadlan

1,2

, Monika Sitio

1,2

, Diah Ivanasari

1,2

, Astrid Pramudya

1,2

Ardani Galih Prakosa

1,2

, Dea Arie Kurniawan

1,2

, Ardian Rizal

1,2

and Mohammad Saifur Rohman

1,2

Department of Cardiology and Vascular Medicine, Faculty of Medicine, Brawijaya University,

Dr. Saiful Anwar General Hospital, Malang, East Java, Indonesia

Brawijaya Cardiovascular Research Center, Brawijaya University, Malang, East Java, Indonesia

{deaariekurniawan, ardianrizal}@gmail.com, ippoenk@yahoo.com

Keywords: Atrial Fibrillation, Scoring System, Pulse Palpation.

Abstract: This study was intended to examine the accuracy of MENARI Plus in atrial fibrillation detection. We collected

data from a total of 476 subjects who were considered to be high-risk patients at Saiful Anwar General

Hospital, a member of Yayasan Jantung Indonesia in Banyuwangi and Batu, East Java, Indonesia. We

identified independent risk factors for detecting atrial fibrillation (AF) with multiple regressions. Every

participant underwent a self-pulse radial check and electrocardiography examination. AF was diagnosed by a

cardiologist. In this study, the mean age of these patients was 58,9, ± 10,9 years. We found 25% of patients

had AF, and 74,4% of our subjects were female. This tools had an area under the receiver operating curve

(AUC) of 0.83 (95% CI 0.84 to 0.92) with a sensitivity of 0.84 (95% CI 0.82 to 0.94) and a specificity of 0.80

(95% CI 0.79 to 0.84) at a cut-off score of 7 on the scale. In this study, the sensitivity of MENARI Plus was

high; however, it had low specificity for atrial fibrillation. Therefore, it is applicable for ruling out atrial

fibrillation, and it may also become an effective screening tool in earlier detection of atrial fibrillation. In

addition, MENARI Plus can be opportunistically used by the doctors to analyse patients with previously

undetected atrial fibrillation.

1 INTRODUCTION

The prevalence of AF was 1–2% in the general

Indonesian population; it is therefore important to

understand the risk factors associated with AF. Early

diagnoses are needed for stroke prevention in

40–60% of asymptomatic AF patients. Oral

anticoagulant is the most important medical measure

that can be taken to avoid unnecessary strokes in AF

patients (Rizal and Yuniadi, 2019). AF can be

identified by

pulse irregularity, which means that

pulse palpation is a prospective approach in

identifying undiagnosed AF patients.

The 2016 European Society of Cardiology

guidelines explained that opportunistic pulse

palpation and ECG record are applied when the

patients with a minimum age of 65 years experienced

pulse irregularity (Kirchhof et al., 2016). A

worldwide AF-awareness campaign is held annually.

The purpose is to increase public awareness of this

condition.

Indonesia started a national campaign to increase

awareness of atrial fibrillation in 2016 by celebrating

AF Campaign Day every September. Indonesia

promotes national screening program using the theme

of ‘AYO MENARI,’ which translates into English as,

‘Let’s Go Dancing’. MENARI stands for MEraba

NAdi sendiRI (translated into English as a self-pulse

check). Self-pulse checks are commonly used to

measure heart rate, and the measure is being

increasingly advocated as a means self-checking for

atrial fibrillation, of which an ‘irregularly irregular’

pulse is a characteristic feature. From a recent meta-

analysis, the self-pulse check has quite high

sensitivity and specificity compared to 12-lead ECG

recordings (0.98 and 0.92, respectively) (Taggar et

al., 2016).

In Indonesia, it is surprising that the effectiveness

of self-pulse checking for detecting atrial fibrillation

in the general population is unknown. This study was

intended to assess the effectiveness of our national

screening program ‘MENARI’ (self-pulse check) and

to try to increase the diagnosis yield by adding some

Fadlan, M., Sitio, M., Ivanasari, D., Pramudya, A., Prakosa, A., Kurniawan, D., Rizal, A. and Rohman, M.

Development of MENARI plus (Self Pulse Assessment and Clinical Scoring) for Detecting Atrial Fibrillation in High-risk Population.

DOI: 10.5220/0009427300570061

In Proceedings of the 4th Annual International Conference and Exhibition on Indonesian Medical Education and Research Institute (The 4th ICE on IMERI 2019), pages 57-61

ISBN: 978-989-758-433-6

clinical conditions to MENARI. We ultimately

attempted to build a simple scoring system that can be

used by the general population to detect AF.

2 MATERIAL AND METHODS

This was an observational analytic study with a cross-

sectional design. This research was conducted at the

Cardiology Outpatient Centre of Saiful Anwar

General Hospital Malang, with assistance from

Lavalette Hospital Malang, Indonesian Heart

Association and the geriatric association in Malang,

Indonesia. Our study samples included subjects

between the ages of 50–75 years old, since the

registry in Yayasan Jantung Indonesia, Malang Raya,

mostly found AF in patients within this age range.

After seeking and obtaining informed consent from

each participant, subjects of this study completed a

structured questionnaire and provided information on

gender, age, smoking status, medical history, physical

exercise, profession and level of education. We used

the patients’ medical records to collect medical

history, medication history and lifestyle factors. We

used standard methods for measuring blood pressure,

weight, height and waist circumference

Patients were able to assess their self-pulse. The

patients implemented the Mini-Mental State

Examination (MMSE). They receive an explanation

of the benefit of pulse palpation. The patients also

obtained knowledge of self-assessment of heart

regularity, especially the technique of radial artery

pulse palpation. Every participant performed self-

pulse palpation and electrocardiography.

2.1 Statistical Analyses

The mean with SD are presented for continuous

variables and several patients (%) for the categorical

variables. We used a multivariate regression to

develop clinical risk factors. The stepwise model

selection procedure was performed to develop clinical

scoring. The pool of variables was also confirmed by a

removal approach with a P-value threshold of 0.25.

Testing for interactions was applied for risk factors in

the final model; however, no potential advance

progression could be accomplished by involving these

interactions. The area under the curve (AUC) was used

to evaluate the performance of this score.

2.2 Ethics

This study received approval from the Medical Ethics

Committee at Saiful Anwar General Hospital Malang

(Ethical Clearance No. 400/122/K3/302/2019). All

subjects of this study signed and were given informed

consents.

3 RESULT

3.1 Clinical Characteristics

A total of 476 subjects participated in the study; 321

(65.6%) of the subjects were female. The mean age of

the population was 53,6, ±11,06 Years old. Mean

SBP and DBP were 125, ±17,5; and 79,7, ±11,5

mmHg; and the prevalence of subjects with atrial

fibrillation was found to be 23,5%. The baseline

characteristics of the subject are described in Table 1.

Table 1: Baseline Characteristic.

Characteristics Total n=476

Age 53,6 ± 11,06

Female 65,6%

SBP 125±17,5

DBP 79,7±11,5

AF 23,5%

Heart rate 83,5±12,5

Weight 61,57±9,7

BMI 26,27±6,6

MMSE score 28,6±0,5

Diabetes 9,8%

Hypertension 41%

History of TIA/Stroke 5,5%

3.2 Clinical Characteristics of

Participants by ECG

Derivation of patients’ characteristics are provided in

Table 2. A total of 461 subjects were analysed. As

many as 43 subjects with developed AF were

compared with other patients without AF. The

patients with developed AF were older (P < .03),

The 4th ICE on IMERI 2019 - The annual International Conference and Exhibition on Indonesian Medical Education and Research Institute

Table 2: Univariate Analysis.

Variables AF Non-AF P

Age >65 43.4% 26.8% 0.03

BMI > 26 9.3% 8.8% 0.45

MENARI (irregular) 66,6% 30,9% 0.000

Hypertension 66.1% 32.2% 0.000

Diabetes Mellitus 5.2% 9.8% 0.26

Stroke/TIA 11.1% 4.3% 0.138

Hyperthyroid 5.7% 0% 0.00

Dyspnoea 8.5% 6.5% 0.54

Palpitation 66% 32.5% 0.000

Chest pain 1.9% 5.3% 0.43

Syncope 2.2% 5.7 0.46

Dizziness 22.6% 11.4% 0.089

Fatigue 34% 17.9% 0.02

more frequently men (P < .001), and experienced

higher rates of hypertension (P < .000), hyperthyroid

(P < .000) and stroke (P < .138). Clinical

manifestation in AF patients were palpitations

(p < .000), fatigue (P < .02) dizziness (P< .089).

Risk Factors for incidences of AF and Score

Development Univariate Cox regression analysis is

shown in Table 3. On multivariable analyses, age was

>65 years, Irregular Self Pulse palpation (MENARI),

Hypertension, Palpitation, and fatigue were

independent risk factors for incident AF (Table 3).

Given the significant association with diagnosed AF,

a combination of independent risk factors was

identified as the new simple MENARI Plus score

(Table 3): Irregular self-pulse palpation (MENARI)

(4 point each); P: palpitation (2 points); L: oLd (age

> 65 years, 1 point); U: fatigUe (2 points); and H:

hypertenSion (2 points). The total score ranged from

0 to 11 points.

The score presented a good discrimination with an

area under the receiver operating curve (AUC) of 0.83

(95% CI 0.84 to 0.92) with a sensitivity of 0.84 (95%

CI 0.82 to 0.94) and a specificity of 0.80 (95% CI 0.79

to 0.84). A bootstrap sampling procedure was used

internally to validate the score results, which gave an

AUC of the validation (AUC 0.87 [95% CI 0.80 to

0.95).

Figure 1: AUC of Derivation Set.

Development of MENARI plus (Self Pulse Assessment and Clinical Scoring) for Detecting Atrial Fibrillation in High-risk Population

Figure 2: AUC of Validation Set.

Table 3: MENARI Plus Score.

Predictors Multiplying Factor Value

Irregular MENARI +4 0.000

Palpitation +2 0.000

Age >65 +1 0.046

Hypertension +2 0.006

Fatigue +2 0.02

TOTAL POSSIBLE SCORE 11

4 DISCUSSION

4.1 Baseline Data

The prevalence of AF in our sample was high (23.5%

of the sample), compared to another study that ranged

between 6–9% of the population. This is likely due to

the fact that the sample was taken from a high-risk

population group. This study was performed with

patients between the ages 50–75 years old because

according to the registry from Yayasan Jantung

Indonesia, Malang Raya, incidences of atrial

fibrillation were mostly found in their age. It is often

associated with a higher prevalence of another

cardiovascular comorbidity, such as hypertension

(41%), the history of stroke or TIA (5.5%) and/or

diabetes mellitus (9.8%). Therefore, this number

cannot be generalised to the overall prevalence of AF

in Indonesia. AF is found more frequently in older

patients (43.4% vs. 26.8%, p = 0.03) and hypertensive

patients (66.1% vs. 32.2%, p = 0.00), compared to

younger and non-hypertensive patients. European

data revealed that incidences of AF are discovered in

3.7–4.2% of those aged 60–70 years of age; these

numbers increase to 10–17% in older ages.

Hypertension and diabetes mellitus were already

known to be risk factors for AF.

Although Self-Pulse Check / MENARI has been

widely used for screening purposes, its effectiveness

in Indonesia had never been evaluated. According to

a systematic review by Harris et al. conducted in

2012, the self-pulse check showed a high rate of

accuracy for detecting AF. The sensitivity and

specificity range were 87–97% and 70–81%,

respectively. This rate is high, but there still room for

improvement. A European Heart Rhythm Association

(EHRA) Consensus document from 2017 started to

divine another screening tool and technique with

higher diagnosis yield, such as a handled device and

smartphone.

The 4th ICE on IMERI 2019 - The annual International Conference and Exhibition on Indonesian Medical Education and Research Institute

Adding some clinical characteristics to increase

the accuracy of diagnosis has been widely used in

another diagnostic method, such as well-known

SYNTAX and Clinical Syntax for predicting clinical

outcome in patients who underwent percutaneous

coronary intervention. By adding these clinical

predictors, it can increase its accuracy; borrowing the

idea of adding simple clinical characteristics,

MENARI PLUS was built.

Several clinical conditions that were previously

known as symptoms and risk factors were analysed.

Statistically, some characteristics have proven to be

correlated to AF. MENARI Plus consists of self-pulse

check (MENARI / MEraba Nadi sendiRI in

Indonesian words); palpitation is the most common

symptom found in AF patients. More than half of all

patients with AF had an episode of palpitation as the

primary symptom; another symptom that has proven

to be significant is fatigue; and loss of atrial kick

during diastole, short diastolic filling time (in AF with

rapid ventricular response) and increased mitral and

tricuspid regurgitation are believed to be responsible

for the decrease in cardiac output.

The number of the patients admitted to the

hospital due to heart failure related to AF has

increased in the last decade. Current guidelines also

showed a strong correlation between heart failure and

atrial fibrillation. Catheter ablation of AF was shown

to reduce the morbidity and morbidity patient with

heart failure. Thus, indicated that fatigue and another

sign of heart failure are correlated with AF. As

previously mentioned, hypertension and older age are

strongly correlated with AF.

Table 4: Predictor for Atrial Fibrillation.

MENARI

Self-pulse check (MEraba NAdi

sendiRI in Indonesian word)

Palpitation

OLder Age (> 65 years old)

FatigUe

HypertenSion

5 CONCLUSION

We found that the specificity of MENARI Plus is low,

while its sensitivity is high for atrial fibrillation

identification in high-risk patients. This scoring

system can also rule out undetected atrial fibrillation.

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D., Casadei, B., … Vardas, P. (2016). 2016 ESC

Guidelines for the management of atrial fibrillation

developed in collaboration with EACTS. European

Heart Journal, 37(38), 2893–2962. http://doi.org/

10.1093/eurheartj/ehw210

Rizal, A., & Yuniadi, Y. (2019). Epigenetic implication in

atrial fibrillation: A potential biomarker? Journal of

Laboratory and Precision Medicine (JLPM), 4, 1–8.

http://doi.org/10.21037/jlpm.2019.09.02

Taggar, J. S., Coleman, T., Lewis, S., Heneghan, C., &

Jones, M. (2016). Accuracy of methods for detecting an

irregular pulse and suspected atrial fibrillation: A

systematic review and meta-analysis. European

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http://doi.org/10.1177/2047487315611347.

Development of MENARI plus (Self Pulse Assessment and Clinical Scoring) for Detecting Atrial Fibrillation in High-risk Population