Introduction

Atrial fibrillation (AF) is the commonest supraventricular rhythm disorder worldwide leading to various cardiovascular complications such as heart failure, stroke and death through thromboembolic mechanism, cardiomyopathy, or atrial asystole.

In 2009 AF affected 3 to 5 million people in the United States of America and its prevalence is expected to reach 8 million by 2050 partly because of population aging [1]. About 8.8 million people suffer of AF in Europe with a projection to 18 million affected persons by 2060 [2].

Data gathered in 2019 indicate that AF affected no less than 60 million people worldwide which is an increase by more than 111% in comparison to 1990. The deaths imputable to AF had reached 0.32 million inhabitants whilst the burden of disability consecutive to AF was estimated to 8.4 million people. These rates represent a respective increase by 169.4% and 126.6% in comparison to figures reported in 1990 [4]. The same study revealed an incidence of 16,185 new cases in 2019, i.e. 32.2/100,000 inhabitants in subSahara central Africa.

AF if grafted high morbidity and mortality and places a heavy economic burden on the society. Given the ensuing physical disability, it considerably reduces the professional productivity of the affected persons. The overall annual cost of managing AF and its complications is high. It is estimated between 2000 and 14200 US dollars per patient in the United States of America, and between 450 and 3000 euros in Europe [3].

The gold standard diagnosis of AF is 12-lead ECG recording. Since the examination is difficult to perform in mass field surveys, several tools have been validated for screening with convincing reliability [5-14]. We used Alivecor Kardia software embedded on a smartphone with the aim to explore the epidemiological profile of AF a poorly recognized disease among the general Congolese partly because such information is not yet available.

Methods

Study design and participant sampling

From August 1 to September 30, 2022, the present crosssectional survey targeted for analytical purposes a random sample of the general population of the city of Kinshasa, DRC. The sample size was calculated using the basic formula for cross-sectional studies:

n ≥(Z^2 x(p)(1-p))/d^2   where n = sample size, z = 1.96 (confidence coefficient), p = previous prevalence, d = 0.02 (margin of error or range of imprecision reflecting the desired degree of absolute precision).

Since the prevalence of AF in DRC is yet unknown, we opted for 50% a median rate which insures a better distribution of the phenomenon.

The calculated sample size was n ≥ (1.96)2*0.5*0.5/ (0.02)2=2401. By incorporating a 10% for non-respondents, we obtained 2641 people to be interviewed.

To accrue participants in the study we used the technique of Lot Quality Assurance Sampling (LQAS) which combines the principles of stratified and simple random sampling. Specifically, the sampling units were made up of the communes of the cityprovince of Kinshasa at the first degree; the districts of the commune at the second degree; the streets at the third degree; the inhabited plots at the fourth degree and the households at the fifth degree.

A trained medical team visited at home the selected households and invited people aged 45 years and over to participate in the study by informed written or oral consent. However, those who did not complete two-thirds of the survey questionnaire were excluded from the analysis.

Procedures

The data collection procedure consisted of a semi-structured questionnaire incorporated into a KOBO collect program. The first part of the procedure focused on socio-demographic characteristics (gender, age, marital status, religion, level of education, family composition, socioeconomic level of households and monthly income); the second on the clinical and anthropometric data (family and personal medical history, weight, height, body mass index, heart rate, blood pressure) and the third part on screening for AF by Alivecor Kardia software embedded on a smartphone.

Body weight measurement was taken using a removable Seca brand scale and height using a measuring rod. Body mass index (BMI) was calculated as the ratio of weight (Kg) to the square of height (m). Three blood pressure records and heart rate obtained using an Omron Hem 7 BP monitor with appropriate cuff secured on the upper arm. The mean of these records was used in the analysis. Subjects with AF positive test on Alivecor Kardia Cardia were invited for a 12-lead ECG.

Operational definitions

In the present work diabetes was defined as a known history of diabetes mellitus or use of antidiabetic treatment. Hypertension was BP ≥140/90mmHg, known history of high BP and/or use of antihypertensive drugs. Participants reporting statin use within three months of the present survey were considered dyslipidaemias. Overweight/obesity was a BMI ≥25kg/m [2]. Heart failure refers to a known history of hospitalization for episode of cardiac insufficiency. Monthly income was considered as low, middle, and high respectively when it was less than USD 200, between USD 200-500, and more than USD 500. Use of traditional drugs was any mention of having been treated by traditional practitioners within the last six months.

Statistical analysis

The data obtained on the KOBO collect program, were cracked, exported to Excel 2010 software for cleaning and transferred for analysis to SPSS Version 21 for Windows. We presented categorical variables as absolute and relative frequency and summarized quantitative variables by measures of central tendency and dispersion. Student’s t test was used to compare means of two groups, and Pearson’s Chi-square test for comparison of proportions. The correlates of AF were assessed using logistic regression analysis in a bivariate and multivariate model. The calculated adjusted ORs helped to estimate the degree of association between the dependent and the independent variables.

Results

General characteristics of the participants

The study enrolled 2562 subjects, mean age 59,6±8,9 years, 44.3% males. Age group 50 to 59 years was the most represented (46.4%). As regard the educational level, most participants had reached high school (48,5%) and (58,3%) had a professional occupation. People of the Congo descent were the most represented ethnic group (60.6%) and a low monthly income was observed in nearly half of the subjects. Hip circumference and body mass index were significantly greater among females than males. Hypertension (35.5%), alcohol intake (30.8%), diabetes mellitus (18.6%) and obesity (18.4%) were the commonest cardiovascular risk factors. Heart failure and heart palpitations were reported by 3.4% and 37,5% of participants respectively.

Prevalence and determinants of atrial fibrillation

AF was suspected by use of the KARDIA device in 118 participants (4.6%). The condition was confirmed on a 12-lead ECG recording in 108 subjects (4.2%). As an AF diagnostic tool, the Alivecor Kardia device demonstrated 99,6% of specificity, 100% of sensitivity and negative predictive value and 91,5% of positive predictive value when compared with 12 lead-ECG. (Figure 1) illustrates the prevalence of AF by use of the two diagnostic tools whilst Figure 2 highlights AF repartition according to gender, age categories, ethnic groups, and participant monthly income. 

Figure 1: Prevalence of atrial fibrillation in the general Congolese.

Atrial fibrillation in various participant categories

The frequency of AF was higher (p=0.018) in males than females (Figure 2A) and significantly (p<0.001) increased with age (Figure 2B) from 1.1% below age 50 years to 9.7% in participants aged 70 years and over. It was also greater (22.4%) in high economic income participants (Figure 2D). The Luba (6.2%) and Ngala (6.1%) ethnic groups were more affected (p=0.016) compared to the Congo (3.4%) and the Swahili (2.9%) groups (Figure 2C).

Figure 2: Specific prevalence of atrial fibrillation by gender (A), age group (B), linguistic space (C) and income category (D).

(Table 1) summarizes the characteristics of participants with and without AF. On average participants with AF were older (64.4±11.0 years vs 59.4±8.7 years) with men being more affected than women. Among the subjects with AF, 35.2% were unemployed, 39.8% had a university educational level and 73.1% were married. Difference in BP, body mass index and HC were not significant.

Table 1: Characteristics of participants according to AF status

Cardiovascular risk factors, diseases and symptoms encountered among subjects with and without AF are highlighted in (Table 2). Arterial hypertension (58,2 vs39.4 %), dyslipidaemia (22,2 vs 2.9%), history of heart failure (36,1 vs 1.9%), stroke (13 vs 3.3 %), previous myocardial infarction (2.8 vs 0.6%) and heart palpitations (64.8 vs 36.3%) predominated among participants with AF whilst the trend was just not significant for history of gout. Diabetes mellitus (11.1 vs 18.9%) and tobacco use (4.6 vs 9.8%) were less frequent in participants with than those without AF. The difference between the two groups in the other variables was not significant.

Table 2: Cardiovascular risk factors and history according to AF status

Determinants of atrial fibrillation

In bivariate analysis, the high socio-economic level, obesity, arterial hypertension, heart failure, dyslipidaemia and history of ischemic stroke stand out distinctly as determinants of AF. On the other hand, in multivariate analysis only overweight/obesity and previous heart failure emerged as the independent determinants of AF. (Table 3)

Table 3: Determinants of AF in the study population