Atrial fibrillation in adult cardiac patients undergoing follow-up at Adama Hospital Medical College: using a log-binomial regression model

doi:10.21203/rs.3.rs-4968078/v1

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Atrial fibrillation in adult cardiac patients undergoing follow-up at Adama Hospital Medical College: using a log-binomial regression model

https://doi.org/10.21203/rs.3.rs-4968078/v1

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Introduction:

Atrial fibrillation (AF) is a prevalent arrhythmia, that is frequently associated with increased morbidity and mortality. Studies on atrial fibrillation among patients with cardiovascular disease in Ethiopia are currently scarce. Therefore, understanding the prevalence and associated risk factors for AF is crucial for guiding appropriate management and preventive strategies.

Methods

A facility-based cross-sectional study was conducted from January 1 to December 30, 2023. A simple random sampling method was used to select the study participants. A structured checklist was used to collect the data. The study applied a log-binomial regression model to assess the associations between atrial fibrillation and independent variables, with 95% confidence intervals (CIs) and relative risks (RRs). All analyses were performed using STATA (version 17) software.

Results

A total of 312 participants were included in this study. Overall, the prevalence of atrial fibrillation was 19.6% (95% CI: 15.2–24.0). The majority had longstanding AF (80.3%), followed by persistent AF (16.4%) and paroxysmal AF (3.2%). According the adjusted model, history of valvular heart disease (RR: 3.05; 95% CI: 2.13–4.374), stroke (RR: 3.28; 95% CI: 2.65–4.09), khat chewing (RR: 4.35; 95% CI: 2.70–7.024), CKD (RR: 2.74; 95% CI: 2.20–3.40), cardiomyopathy (RR: 1.99; 95% CI: 1.02–3.89), and IHD (RR: 0.20; 95% CI: 0.07–0.56) were significantly associated with atrial fibrillation among adult cardiac patients.

Conclusion

Overall, the prevalence of atrial fibrillation (AF) was 19.6% among cardiac patients in this study. Valvular heart disease (VHD), a history of stroke, and khat chewing were independently associated with AF in this study population. These findings emphasize the multifactorial nature of AF and provide valuable insights into potential risk factors for cardiac patients.

Atrial fibrillation

Cardiac patients

Cardiovascular disease

Prevalence

Ethiopia

Atrial fibrillation (AF) is the most common clinical arrhythmia and is associated with increased morbidity and mortality [1]. It is a significant contributor to adverse health outcomes, including stroke, heart failure, sudden death, and overall cardiovascular morbidity. It elevates the risk of thromboembolic stroke primarily through stasis in the left atrium, leading to potential embolization of the brain [2]. In 2010, approximately 33.5 million men and 12.6 million women worldwide were estimated to have atrial fibrillation (AF) [3]. Estimates indicate that the prevalence of atrial fibrillation is approximately 3% among adults aged 20 and older [4]. Studies show that it is projected to double in the next 50 years, particularly in Europe and North America [5, 6]. The burden of AF in developing countries is expected to increase by approximately 150% over the next 20 years as it shifts from an infectious disease to a chronic disorder [7].

The growing burden of atrial fibrillation (AF) is linked to the increase in chronic cardiovascular risk factors that contribute to AF development and progression. Having multiple cardiac risk factors can worsen adverse atrial remodeling and increase the risk of AF [8]. Atrial fibrillation frequently results in a diminished quality of life, is often asymptomatic, and is commonly detected only after severe outcomes such as stroke manifest [9, 10]. Unmanaged AF results in frequent hospital visits and a lower quality of life. To lessen the impact of complications, it is crucial to control AF effectively. Understanding current practices in AF management is key to achieving this goal [10].

Recent studies indicate that thorough interventions targeting AF risk factors and underlying conditions can lower AF recurrence and burden, and enhance overall health. [1]. To our understanding, the preceding two community-based cross-sectional studies in Ethiopia focused on the general population, yet they did not specifically address high-risk patients with cardiovascular disease. Exploring AF risk factors in cardiac patients is crucial for improving patient care, optimizing treatment strategies, and ultimately enhancing health outcomes for this vulnerable population than the general population. Therefore, understanding the prevalence and associated factors of AF among adult cardiac patients at follow-up at the Adama Hospital Medical College is crucial for guiding appropriate management and preventive strategies.

Study Area and Period

A facility-based cross-sectional study was conducted at Adama Hospital Medical College from January 1 to December 30, 2023, serving a catchment population exceeding five million individuals from the Oromia, Amhara, Afar, and Somalia regional states. The hospital offers comprehensive inpatient and outpatient services, alongside educational programs for undergraduate and postgraduate students in medicine and health sciences.

Study participants

The study population consisted of adult cardiac patients aged 15 years and older who were confirmed with echocardiography (ECG) and 12-lead ECG, and who were receiving follow-up care at the AHMC Medical Referral Clinic during the designated period. Patients with incomplete clinical records, those who did not have a confirmed diagnosis of atrial fibrillation (AF), and individuals with inconclusive ECG results were excluded from the analysis.

Sample size determination

The single population proportion formula was used to determine the sample size. Using a single population proportion formula, the sample size was calculated as 312 by taking the proportion of AF = 0.5, since no study has been performed on the prevalence of AF among cardiac patients, with a 95% confidence interval and a 5% margin of error (precision). Precision (d): A precision of 5% is recommended if the prevalence of the disease is between 10% and 90%. Since the calculated sample size was greater than 5% of the study population, a finite population correction was used to calculate the final sample size.

$$\:n=\frac{{Z}^{2}\text{p}(1-\text{p})}{{d}^{2}}$$

The dependent variable in this study was atrial fibrillation (yes, no). The independent variables were sociodemographic factors such as age, sex, residency, and occupation; behavioral factors such as smoking cigarettes, khat chewing, and alcohol intake; comorbidities such as hypertension, heart failure, thyroid disorder, diabetes, and chronic kidney disease (CKD); and structural heart diseases such as valvular heart disease (VHD), dilated cardiomyopathy (DCMP), hypertensive heart disease (HHD), ischemic heart disease (IHD), congenital heart disease (CHD), and chronic obstructive pulmonary disease (COPD).

Methods of case ascertainment

Atrial fibrillation: ECG findings of absence of discrete P waves and occurrence of associated f-waves in the presence of an irregular ventricular response. Cardiovascular disease is a pathologic process affecting the entire arterial circulation and comprises one or more of the following: heart disease, arrhythmia, hypertension, stroke, peripheral arterial disease, transient ischemia attack, and carotid atherosclerotic disease.

Ischemia stroke refers to a focal neurologic deficit resulting from poor blood flow to the brain lasting more than 24 hours. Heart failure: is a complex clinical syndrome with typical symptoms including shortness of breath, body swelling, and fatigue that can occur at rest or on effort and is characterized by objective evidence of an underlying structural abnormality or cardiac dysfunction. Hypertension was defined as a systolic blood pressure ≥ 140 mm Hg and/or diastolic pressure of ≥ 90 mm Hg or using antihypertensive medication or a recorded diagnosis of hypertension. Anticoagulation: treatment with anticoagulant drugs to reduce the risk of blood clot formation.

Data Collection Tools and Procedure

The data were collected using a structured checklist consisting of three parts. The first part included socio-demographic characteristics, while the second part included clinical and laboratory characteristics. The third part focused on cardiovascular and other conditions associated with atrial fibrillation. This data abstraction checklist was prepared in English, as patient card data are predominantly documented in English. The data collection process involved identifying the medical record number from the Health Management Information System logbook, which was then used to trace and access individual patient medical charts.

Data quality assurance

Trained internal medicine residents and general practitioners collected the data, and preliminary ECG readings were obtained. To assure data quality, senior internal medicine residents or the primary investigator double-checked ECG records that did not have written reports from internists. Training was provided to the data collectors on a variety of data collection topics. Under the supervision of the lead investigator, all patient data were collected and used. To guarantee accuracy, consistency, and completeness, the lead investigator also oversaw the data collectors' extraction of information.

Statistical analysis

In the descriptive analysis, the mean (standard deviation) was used for normally distributed continuous variables, and the frequency (percentage) was used for categorical variables. The logistic regression method is often utilized to estimate the odds ratio (OR) when the prevalence of the outcome is high (> 10%); however, the OR is no longer an appropriate estimate of the relative risk (RR) [11]. In medical research, it is still common to equate the odds ratio (OR) with the relative risk (RR), resulting in an inflation of study findings [12, 13]. The extent of overstatement hinges on the outcome rate or prevalence, with a higher rate leading to greater exaggeration. Consequently, in our study, we opted for a log-binomial regression model rather than the ordinary logistic model. This approach models the probability of the outcome using the binomial distribution and employs the logarithm of the probability as the link function within a generalized linear model. [14, 15]. Multivariate log-binomial regression analyses were conducted to identify factors strongly associated with the dependent variable, and the statistical significance was judged based on a p-value < 0.05 and a 95% confidence interval (CI) of the risk ratio (RR). All analyses were performed using STATA (version 17) software.

Sociodemographic characteristics of the participants

Among the 312 patients in this study, 182 (58.3%) were male and 130 (41.7%) were female. Of the total participants, 70 (22.4%) were under 40 years old, 157 (50.3%) were aged between 40 and 60 years, and 85 (27.2%) were over 60 years old. Two hundred (64.1%) were urban residents, 13 (4.2%) were alcohol users, and 12 (3.8%) were khat chewers(Table 1).

Table 1

Sociodemographic characteristics of the cardiac patients at Adama Hospital Medical College from January 1 to December 30, 2023 (n = 312)
Variables	Categories	Frequency	Percent (%)
Sex	Males	182	58.3
Sex	Females	130	41.7
Age	< 40	70	22.4
	40–60	157	50.3
	> 60	85	27.2
Place of resident	Urban	200	64.1
Place of resident	Rural	112	35.9
Alcohol use	Yes	13	4.2
Alcohol use	No	299	95.8
Khat chewing	Yes	12	3.8
Khat chewing	No	300	96.2

Comorbidities and related variables in cardiac patients

Concerning comorbidities, hypertension (HTN) was diagnosed in 115 study subjects (36.9%), while heart failure was present in 79 patients (25.3%). Diabetes mellitus was reported in 40 patients (12.8%), with a history of stroke noted in 31 study participants (9.9%). Chronic kidney disease was identified in 14 participants (4.5%), and chronic obstructive pulmonary disease (COPD) was observed in 5 patients (1.6%) (Table 2).

Table 2

Comorbidities and related variables of cardiac patients at Adama Hospital Medical College from January 1 to December 30, 2023 (n = 312)
Comorbidities (yes)	Value
Diabetes mellitus (n, %)	40 (12.8)
Chronic Kidney Disease (n, %)	14 (4.5)
chronic obstructive pulmonary disease (n, %)	5 (1.6)
Hypertension (n, %)	115 (36.9)
Heart failure (n, %)	79 (25.3)
History of Stroke (n, %)	31 (9.9)

The most common structural abnormalities identified on echocardiography (ECG) were ischemic heart disease (28.3%), valvular heart disease (VHD) (including rheumatic and degenerative components) 19%, and hypertensive heart disease 17.5%. Among those within VH had rheumatic heart disease, and 3.5% degenerative heart disease. Additionally, 16.1% of patients exhibited at least one of the following conditions: dilated cardiomyopathy, hypertrophic cardiomyopathy, congenital heart disease, or Cor pulmonale (Fig. 1).

Figure 1: Structural abnormalities of cardiac patients identified by echocardiography at Adama Hospital Medical College from January 1 to December 30, 2023

Magnitude of atrial fibrillation

Overall, the prevalence of atrial fibrillation (AF) in this study was 19.6% (95% CI: 15.2, 24.0) (Fig. 2). Among patients with AF, 80.3% had longstanding AF (diagnosed more than 1 year ago), 16.4% had persistent AF (diagnosed within 7 days to 1 year), and 3.2% had paroxysmal AF (diagnosed less than 7 days ago) (Fig. 3).

Figure 2: The magnitude of atrial fibrillation among cardiac patients at Adama Hospital Medical College from January 1 to December 30, 2023

Figure 3: Types of atrial fibrillation based on duration since the first diagnosis of AF at Adama Hospital Medical College from January 1 to December 30, 2023

Factors associated with atrial fibrillation

Table 3 shows the multivariable log-binomial regression analysis of the association between atrial fibrillation and its associated factors. According to the multivariable log-binomial model, after adjusting for potential confounding effects, sex, alcohol use, khat chewing status, valvular heart disease status, history of stroke, chronic kidney disease, cardiomyopathy, and ischemic heart disease were significantly associated with AF.

Table 3

Factors associated with atrial fibrillation among cardiac patients at Adama Hospital Medical College from January 1 to December 30, 2023 (n = 312)
Variables	Categories	Atrial fibrillation		aRR (95% CI)	P value
Variables	Categories	Yes n (%)	No n (%)	aRR (95% CI)	P value
Sex	Male	36 (59)	146 (58.2)	1.02 (1.02, 1.03)	0.000
Sex	Female	25(41)	105 (41.8)	1
Alcohol use	Yes	4(6.6)	9(3.6)	2.62 (1.75, 3.87)	0.000
Alcohol use	No	57(93.4)	242(96.4)	1
Khat chewing	Yes	10(16.4)	21(8.4)	3.29 (2.65, 4.09)	0.000
Khat chewing	No	51(83.6)	230(91.6)	1
Valvular hear disease	Yes	25(41)	40(15.9)	3.05 (2.13, 4.37)	0.000
Valvular hear disease	No	36(59)	211(84.1)	1
History of stroke	Yes	10(16.4)	21(8.4)	3.29 (2.65, 4.09)	0.000
History of stroke	No	51(83.6)	230(91.6)	1
Hypertension	Yes	14(23)	101(40.2)	0.65 (0.42, 1.01)	0.053
Hypertension	No	47(77)	150(59.8)	1
Diabetes Mellitus	Yes	4(6.6))	63(26.6)	0.99 (0.33, 2.98)	0.984
Diabetes Mellitus	No	57(93.4)	215(85.7)	1
Chronic Kidney disease	Yes	5(8.2)	9(3.6%)	2.74 (2.20, 3.40)	0.000
Chronic Kidney disease	No	56(91.6)	242(96.4)	1
Cardiomyopathy	Yes	10(16.4)	28(11.2)	1.99 (1.02, 3.88)	0.044
Cardiomyopathy	No	53(83.6)	223 (88.8)	1
Hypertensive heart disease	Yes	8(13.1)	52(20.7)	0.93 (0.44, 1.96)	0.847
Hypertensive heart disease	No	53(86.9)	199(79)	1
Ischemic heart disease	Yes	6(9.8)	91(36.3)	0.20 (0.07, 0.56)	0.002
Ischemic heart disease	No	55(90.2)	160(63.7)	1
aRR: adjusted relative risk; CI: Confidence interval; Statistically significant variables are highlighted in bold.

Accordingly, males were 1.02 times higher relative risk of developing atrial fibrillation (AF) than females. (Adjusted relative risk (aRR): 1.02; 95% CI: 1.01–1.03). The relative risk of developing AF was approximately threefold greater among alcohol users (aRR: 2.62; 95% CI: 1.75–3.87) than among non-drinkers. The relative risk of developing AF among khat chewers was 3.29-fold greater than that among their counterparts (aRR: 3.29; 95% CI: 2.65, 4.09). Patients with structural abnormalities of valvular heart disease had a 3.05-fold greater risk of developing AF than did their counterparts (aRR: 3.05; 95% CI: 2.65, 4.09).

Regarding CKD, the relative risk of developing AF was approximately threefold greater among cardiac patients who were diagnosed with CKD than among those who were not diagnosed with CKD (aRR, 2.74; 95% CI: 2.20–3.40). Similarly, patients with cardiomyopathy had a 99% greater risk of developing AF than did non-cardiomyopathy cardiac patients (aRR 1.99; 95% CI: 1.02–3.88). On the other hand, cardiac patients with structural abnormalities of ischemic heart disease were 0.2 times less likely to develop AF compared to their counterparts (aRR: 0.20; 95% CI: 0.07, 0.56).

This study aimed to assess the prevalence and associated factors of AF among adult cardiac patients at follow-up at the Adama Hospital Medical College. Our findings revealed that the prevalence of atrial fibrillation (AF) among adult cardiac patients was 19.6%. Similarly, our findings were in line with the findings of studies conducted in Sub-Saharan Africa, which reported rates ranging between 4.6% and 20.8% [16]. However, this finding was higher than the prevalence of 1–2% found in the general population [17]. These findings suggest that cardiac patients are at a higher risk of developing AF than the general population, which is consistent with expectations given their underlying cardiac conditions.

In this study, sociodemographic and behavioral factors were associated with atrial fibrillation. We found that compared with female sex, male sex was significantly associated with a greater risk of developing atrial fibrillation. This result was in line with the findings of a study conducted in Ethiopia [16], which revealed that males have a greater risk of atrial fibrillation (AF) due to various factors. This may include biological differences, lifestyle factors, and discrepancies in healthcare-seeking behavior. Similarly, the study revealed that individuals who chewed khat were a fourfold greater risk of atrial fibrillation (AF) than non-chewers. The observed association might be explained by the effects of khat increasing the risk of cardiovascular-related disease, including increased heart rate, blood pressure, and sympathetic activity [18]. Khat has the potential to worsen pre-existing cardiac conditions [19, 20]. Furthermore, our study identified alcohol consumption as a risk factor for atrial fibrillation (AF) among cardiac patients. This finding was in line with the findings of a cohort study conducted in Norway [21]. Further investigation into the specific mechanisms and dose-response relationships between alcohol consumption and AF is crucial for developing personalized risk assessment and prevention strategies.

This study revealed that valvular heart disease (VHD) is significantly associated with atrial fibrillation (AF). This finding was consistent with the previous findings [22, 23]. This may be due to heart valve abnormalities in VHD causing fibrosis and scarring, affecting valve function and creating arrhythmogenic substrates in the atria, increasing susceptibility to arrhythmia [24]. Moreover, VHD disrupts heart chamber blood flow, promoting stasis and thrombus formation, which can embolize the systemic circulation, potentially leading to stroke or other thromboembolic events [25]. Similarly, we found that patients with cardiomyopathy had a significant association with atrial fibrillation (AF). The risk of developing AF among patients who had cardiomyopathy was nearly two times greater than that of patients without this condition. To date, there is no literature available on the relationship between AF and cardiomyopathy. Therefore, further research involving a larger, multicenter cohort is required to gain a comprehensive understanding of these factors.

Accordingly, our study revealed a robust association between a history of stroke and a relative risk of atrial fibrillation (AF). Patients with a previous history of stroke faced nearly triple the risk of experiencing AF compared to their counterparts. This finding agreed with prior research indicating a substantial increase in AF likelihood among individuals with a history of stroke [3]. Evidence shows that approximately 20–30% of stroke patients are diagnosed with AF before their cerebrovascular incident, while up to 24% of stroke patients may experience AF solely through intensive cardiac monitoring [26]. This may be due to the bidirectional nature of the association between AF and stroke. Common underlying risk factors for AF and stroke include hypertension, diabetes, obesity, and advanced age, which contribute to their development and can exacerbate their interrelationship [27]. Moreover, changes in the autonomic nervous system resulting from stroke may create conditions favorable to cardiac arrhythmias, particularly atrial fibrillation, via mechanisms such as autonomic dysregulation. [28].

We found that chronic kidney disease (CKD) was significantly associated with atrial fibrillation among cardiac patients. Patients with CKD had a nearly threefold greater risk of developing atrial fibrillation than patients without CKD. These findings were supported by a previous study conducted in Japan [29]. This can be explained through several physiological mechanisms. First, CKD often leads to fluid and electrolyte imbalances, including potassium and magnesium imbalances, which can disrupt heart electrical activity, increasing the risk of developing atrial fibrillation [30, 31]. Second, CKD is linked to inflammation and oxidative stress, which can damage the heart's structure and function, potentially leading to the development of atrial fibrillation [32]. Third, CKD activates the renin-angiotensin-aldosterone system (RAAS), promoting cardiac remodeling, fibrosis, and atrial enlargement, and predisposing individuals to atrial fibrillation [33].

This study revealed that among patients, those with ischemic heart disease (IHD) had a lower risk of atrial fibrillation. Our findings contradict previous study findings [34–36]. The complex interplay between AF and IHD has been acknowledged [35]. The potential explanations for this finding include undiagnosed AF patients within the IHD group, survivor bias, variations in IHD severity, and differences in treatment strategies. Further research is necessary to elucidate the intricate relationship between IHD and AF.

While previous studies have established hypertension and diabetes mellitus (DM) as risk factors for AF, this study did not find statistically significant associations with these comorbidities. This discrepancy might be attributed to the relatively small sample size or the specific characteristics of the study cohort. Further research with larger sample sizes and longitudinal designs is needed to clarify the relationship between these comorbidities and AF in the Ethiopian context.

The limitation of the study

There are several limitations in this study. First, due to the absence of advanced diagnostic tools such as Holter and event monitors in the study area, the diagnosis of atrial fibrillation was based solely on traditional 12-lead ECG criteria. Second, this study is a single-center study, which may limit the applicability of the results beyond a specific hospital setting, potentially limiting their representativeness to the broader Ethiopian population. Third, this study included a relatively small sample size, which may lightly affect the precision level of estimation. Finally, the cross-sectional study design precludes establishing causal relationships between risk factors and atrial fibrillation (AF). Additionally, data on certain factors such as weight, height, and lifestyle habits were not consistently recorded for all patients. This lack of uniformity in data recording may result in inaccuracies in assessing both exposure and risk factors.

This study aimed to assess the prevalence and associated factors of atrial fibrillation (AF) among adult cardiac patients at follow-up at the Adama Hospital Medical College. The findings revealed that the prevalence of atrial fibrillation was 19.6%. Independent variables associated with AF included sex, khat chewing, alcohol consumption, valvular heart disease, history of stroke, chronic kidney disease, cardiomyopathy, and ischemic heart disease. Healthcare providers should be engaged in the prevention, early detection, and effective management of atrial fibrillation among adult cardiac patients, ultimately improving patient outcomes and reducing its burden.

atrial fibrillation

CHD

Congenital Heart Disease

CHF

Congestive Heart Failure

Confidence Interval

COPD

Chronic Obstructive Pulmonary Disease

Cardiovascular

DCMP

Dilated Cardiomyopathy

ECG

Electrocardiography

HHD

Hypertensive Heart Disease

IHD

Ischemic heart disease:OR:Odds Ratio

RAAS

Renin-angiotensin-aldosterone system

RR – Risk Ratio

VHD

Valvular Heart Disease

WHO – World health organization

Author contributions

EY and TGC designed the study’s intellectual content and wrote the initial manuscript. EY and TGC participated in the original database construction and data cleaning. EY and TGC conducted the statistical analysis and contributed to manuscript preparation. EY , DT, TC, and TGC interpreted the results and created the tables and figures. All authors have read and approved the manuscript.

Data availability statement

The data analyzed in this study are available from the corresponding author upon reasonable request.

Guarantor: Not applicable.

Human Ethics and Consent to Participate declarations

This study adhered to the principles outlined in the Declaration of Helsinki. Ethical approval was obtained from the Institutional Review Board Ethical Committee of Adama Hospital Medical College. Written informed consent was obtained from each participant.

Clinical trial number: Not applicable.

Funding:

No funding was received for this research.

Acknowledgments

The authors would like to acknowledge the study participants, data collectors, colleagues, and the Adama Hospital Medical College.

Conflict of interest: The authors declare that no conflicts of interest exist.

Brandes A, et al. Risk Factor Management in Atrial Fibrillation. Arrhythm Electrophysiol Rev. 2018;7(2):118–27.
Essa H, Hill AM, Lip GYH. Atrial Fibrillation and Stroke. Card Electrophysiol Clin. 2021;13(1):243–55.
Chugh SS, et al. Worldwide epidemiology of atrial fibrillation: a Global Burden of Disease 2010 Study. Circulation. 2014;129(8):837–47.
Mathew J, et al. Incidence, predictive factors, and prognostic significance of supraventricular tachyarrhythmias in congestive heart failure. Chest. 2000;118(4):914–22.
Newell A, Haynes J, Smith R. Evaluation of asymptomatic atrial fibrillation. Am Fam Physician, 2012. 86(6): p. Online.
Page RL et al. 2015 ACC/AHA/HRS Guideline for the Management of Adult Patients With Supraventricular Tachycardia: Executive Summary: A Report of the American College of Cardiology/American Heart Association Task Force on Clinical Practice Guidelines and the Heart Rhythm Society. Circulation, 2016. 133(14): pp. e471-505.
Ntep-Gweth M, et al. Atrial fibrillation in Africa: clinical characteristics, prognosis, and adherence to guidelines in Cameroon. EP Europace. 2010;12(4):482–7.
Linz D, et al. Atrial fibrillation in sub-Saharan Africa: The knowns and unknowns? Int J Cardiol Heart Vasc. 2019;22:212–3.
Brieger D, et al. National Heart Foundation of Australia and the Cardiac Society of Australia and New Zealand: Australian Clinical Guidelines for the Diagnosis and Management of Atrial Fibrillation 2018. Heart Lung Circ. 2018;27(10):1209–66.
Narasimhan C, et al. Cardiovascular risk profile and management of atrial fibrillation in India: Real world data from RealiseAF survey. Indian Heart J. 2016;68(5):663–70.
Greenland S. Interpretation and choice of effect measures in epidemiologic analyses. Am J Epidemiol. 1987;125(5):761–8.
Agrawal D. Inappropriate interpretation of the odds ratio: oddly not that uncommon. Pediatrics. 2005;116(6):1612–3.
Knol MJ, et al. Overestimation of risk ratios by odds ratios in trials and cohort studies: alternatives to logistic regression. CMAJ. 2012;184(8):895–9.
Skov T, et al. Prevalence proportion ratios: estimation and hypothesis testing. Int J Epidemiol. 1998;27(1):91–5.
Wacholder S. Binomial regression in GLIM: estimating risk ratios and risk differences. Am J Epidemiol. 1986;123(1):174–84.
Tegene E, et al. Prevalence and risk factors for atrial fibrillation and its anticoagulant requirement in adults aged ≥ 40 in Jimma Town, Southwest Ethiopia: A community based cross-sectional study. Int J Cardiol Heart Vasc. 2019;22:199–204.
Kornej J, et al. Epidemiology of Atrial Fibrillation in the 21st Century: Novel Methods and New Insights. Circ Res. 2020;127(1):4–20.
Al-Motarreb A, Al-Habori M, Broadley KJ. Khat chewing, cardiovascular diseases and other internal medical problems: the current situation and directions for future research. J Ethnopharmacol. 2010;132(3):540–8.
Choi SE, et al. Atrial fibrillation and stroke. Expert Rev Cardiovasc Ther. 2023;21(1):35–56.
El-Menyar A, et al. Khat use: history and heart failure. Oman Med J. 2015;30(2):77–82.
Voskoboinik A, et al. Alcohol and Atrial Fibrillation: A Sobering Review. J Am Coll Cardiol. 2016;68(23):2567–76.
Santhanakrishnan R, et al. Atrial Fibrillation Begets Heart Failure and Vice Versa: Temporal Associations and Differences in Preserved Versus Reduced Ejection Fraction. Circulation. 2016;133(5):484–92.
Zühlke L, et al. Characteristics, complications, and gaps in evidence-based interventions in rheumatic heart disease: the Global Rheumatic Heart Disease Registry (the REMEDY study). Eur Heart J. 2015;36(18):1115–a22.
Kubala M, et al. Arrhythmias in Patients With Valvular Heart Disease: Gaps in Knowledge and the Way Forward. Front Cardiovasc Med. 2022;9:792559.
Saksena D, et al. Follow-up and management of valvular heart disease patients with prosthetic valve: a clinical practice guideline for Indian scenario. Indian J Thorac Cardiovasc Surg. 2019;35(Suppl 1):3–44.
Sposato LA, et al. Atrial Fibrillation Detected After Stroke and Transient Ischemic Attack: A Novel Clinical Concept Challenging Current Views. Stroke. 2022;53(3):e94–103.
Čarná Z, Osmančík P. The effect of obesity, hypertension, diabetes mellitus, alcohol, and sleep apnea on the risk of atrial fibrillation. Physiol Res. 2021;70(Suppl4):S511–25.
Takahashi C, Hinson HE, Baguley IJ. Autonomic dysfunction syndromes after acute brain injury. Handb Clin Neurol. 2015;128:539–51.
Watanabe H, et al. Close bidirectional relationship between chronic kidney disease and atrial fibrillation: the Niigata preventive medicine study. Am Heart J. 2009;158(4):629–36.
Rafaqat S, et al. Electrolyte’s imbalance role in atrial fibrillation: Pharmacological management. Int J Arrhythmia. 2022;23(1):15.
Goette A, Honeycutt C, Langberg JJ. Electrical remodeling in atrial fibrillation. Time course and mechanisms. Circulation. 1996;94(11):2968–74.
Rapa SF et al. Inflammation and Oxidative Stress in Chronic Kidney Disease-Potential Therapeutic Role of Minerals, Vitamins and Plant-Derived Metabolites. Int J Mol Sci, 2019. 21(1).
Lee HC. Electrical remodeling in human atrial fibrillation. Chin Med J (Engl). 2013;126(12):2380–3.
Habbal AB, et al. Posttraumatic Stress Disorder (PTSD) and Instigation of Cardiovascular Events: Ischemic Heart Disease (IHD) and Atrial Fibrillation (AF). Cureus. 2022;14(10):e30583.
Volpe M, Gallo G. Atrial fibrillation and ischaemic heart disease: should we use acetylsalicylic acid beside anticoagulants? Eur Heart J Supplements. 2020;22(SupplementL):L166–9.
Mercer BN, et al. Ischemic Heart Disease Modifies the Association of Atrial Fibrillation With Mortality in Heart Failure With Reduced Ejection Fraction. J Am Heart Assoc. 2018;7(20):e009770.

No competing interests reported.

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Atrial fibrillation in adult cardiac patients undergoing follow-up at Adama Hospital Medical College: using a log-binomial regression model

Status:

Version 1

Abstract

Introduction:

Methods

Results

Conclusion

Figures

BACKGROUND

METHODS AND MATERIALS

Study Area and Period

Study participants

Sample size determination

Methods of case ascertainment

Data Collection Tools and Procedure

Data quality assurance

Statistical analysis

RESULTS

Sociodemographic characteristics of the participants

Comorbidities and related variables in cardiac patients

Magnitude of atrial fibrillation

Factors associated with atrial fibrillation

DISCUSSION

The limitation of the study

CONCLUSION

Abbreviations

Declarations

References

Additional Declarations

Status:

Version 1