Skip to main content

Main menu

  • Home
  • Content
    • Latest
    • Archive
    • home
  • Info for
    • Authors
    • Reviewers
    • Subscribers
    • Institutions
    • Advertisers
    • Join SMJ
  • About Us
    • About Us
    • Editorial Office
    • Editorial Board
  • More
    • Advertising
    • Alerts
    • Feedback
    • Folders
    • Help
  • Other Publications
    • NeuroSciences Journal

User menu

  • My alerts
  • Log in
  • Log out

Search

  • Advanced search
Saudi Medical Journal
  • Other Publications
    • NeuroSciences Journal
  • My alerts
  • Log in
  • Log out
Saudi Medical Journal

Advanced Search

  • Home
  • Content
    • Latest
    • Archive
    • home
  • Info for
    • Authors
    • Reviewers
    • Subscribers
    • Institutions
    • Advertisers
    • Join SMJ
  • About Us
    • About Us
    • Editorial Office
    • Editorial Board
  • More
    • Advertising
    • Alerts
    • Feedback
    • Folders
    • Help
  • Follow psmmc on Twitter
  • Visit psmmc on Facebook
  • RSS
Research ArticleOriginal Article
Open Access

Incidence, clinical predictors, and clinical effect of new-onset atrial fibrillation in myocardial infarction patients

A retrospective cohort study

Fayez Elshaer, Abdulelah H. Alsaeed, Sultan N. Alfehaid, Abdulaziz S. Alshahrani, Abdulrahman H. Alduhayyim and Ayman M. Alsaleh
Saudi Medical Journal August 2022, 43 (8) 933-940; DOI: https://doi.org/10.15537/smj.2022.43.8.20220349
Fayez Elshaer
From the Department of Cardiac Sciences (Elshaer, Alsaleh); from the College of Medicine (Elshaer, Alsaeed, Alfehaid, Alshahrani, Alduhayyim), King Saud University Medical City, King Saud University, Riyadh, Kingdom of Saudi Arabia; and from the Department of Cardiology (Elshaer), National Heart Institute, Cairo, Egypt.
MSc, PhD
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • For correspondence: [email protected]
Abdulelah H. Alsaeed
From the Department of Cardiac Sciences (Elshaer, Alsaleh); from the College of Medicine (Elshaer, Alsaeed, Alfehaid, Alshahrani, Alduhayyim), King Saud University Medical City, King Saud University, Riyadh, Kingdom of Saudi Arabia; and from the Department of Cardiology (Elshaer), National Heart Institute, Cairo, Egypt.
MD
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Sultan N. Alfehaid
From the Department of Cardiac Sciences (Elshaer, Alsaleh); from the College of Medicine (Elshaer, Alsaeed, Alfehaid, Alshahrani, Alduhayyim), King Saud University Medical City, King Saud University, Riyadh, Kingdom of Saudi Arabia; and from the Department of Cardiology (Elshaer), National Heart Institute, Cairo, Egypt.
MD
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Abdulaziz S. Alshahrani
From the Department of Cardiac Sciences (Elshaer, Alsaleh); from the College of Medicine (Elshaer, Alsaeed, Alfehaid, Alshahrani, Alduhayyim), King Saud University Medical City, King Saud University, Riyadh, Kingdom of Saudi Arabia; and from the Department of Cardiology (Elshaer), National Heart Institute, Cairo, Egypt.
MD
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Abdulrahman H. Alduhayyim
From the Department of Cardiac Sciences (Elshaer, Alsaleh); from the College of Medicine (Elshaer, Alsaeed, Alfehaid, Alshahrani, Alduhayyim), King Saud University Medical City, King Saud University, Riyadh, Kingdom of Saudi Arabia; and from the Department of Cardiology (Elshaer), National Heart Institute, Cairo, Egypt.
MD
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Ayman M. Alsaleh
From the Department of Cardiac Sciences (Elshaer, Alsaleh); from the College of Medicine (Elshaer, Alsaeed, Alfehaid, Alshahrani, Alduhayyim), King Saud University Medical City, King Saud University, Riyadh, Kingdom of Saudi Arabia; and from the Department of Cardiology (Elshaer), National Heart Institute, Cairo, Egypt.
MSc, FRCP
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • Article
  • Figures & Data
  • eLetters
  • Info & Metrics
  • References
  • PDF
Loading

Abstract

Objectives: To calculate the incidence of new-onset atrial fibrillation (NOAF) in myocardial infarction (MI) patients and examine associated predictors and clinical outcomes of NOAF patients.

Methods: A retrospective cohort study was used to carry out this study. All MI patients admitted to King Khaled University Hospital, Riyadh, Saudi Arabia, between January 2015 to 2020 were eligible for inclusion. The study excluded those with a previous diagnosis of atrial fibrillation and patients who died at presentation.

Results: A total of 281 patients were analyzed with a mean age of 58.7±12.7. Incidence of NOAF was 7.8%. Significant predictors identified by multivariate logistic regression analysis included older age (p=0.004), history of MI (p=0.012), and undergoing coronary artery bypass graft surgery (CABG) as treatment (p=0.016). New-onset atrial fibrillation was associated with higher odds of major adverse cardiovascular event (p=0.039), ventricular tachycardia (p=0.001), and mortality (p=0.031).

Conclusion: New-onset atrial fibrillation is a relatively common complication of MI, and in our study, it was associated with higher odds of further complications including death. Therefore, identification of MI patients at risk of developing NOAF is crucial. Our study suggests that older age, a previous history of MI, and undergoing CABG are significant predictors of NOAF development.

Keywords:
  • atrial fibrillation
  • myocardial infarction
  • predictors

New-onset atrial fibrillation (NOAF) is a common complication of myocardial infarction (MI). Reported incidence rates ranged between 6% and 21%.1 Development of NOAF in an MI patient is associated with other complications and worse outcomes including heart failure, stroke, and cardiogenic shock.2 Moreover, NOAF patients have a higher odds of mortality with an odds ratio (OR) of 1.37 according to a systematic review.2,3 As a result, determining NOAF predictors can assist with early detection and potentially improve outcomes. Known factors that increase the risk of developing NOAF after MI include older age, congestive heart failure, hypertension, and high heart rate at admission.1,2,4-9 In regard to the predictors of NOAF, older age is one of the most consistent predictors reported regardless of the study design or region.4,5,7,8 However, not all predictors are as well established as older age. For example, the use of gender as a predictor of NOAF produced opposite results in different studies. Some studies found that male gender was a predictor of NOAF, while other studies found that gender was not a predictor when multivariate regression analysis was carried out.4,5,7 Instead, they found female gender to be associated with the development of NOAF.4,7 This shows the importance of further studies to investigate previously studied predictors in order to provide more data regarding the topic.

Recent studies have found new associated factors. A 2017 study found that cardiogenic shock is a predictor of NOAF.4 Another example is a study by Parashar et al5 which reported a significant association between NOAF and high levels of C-reactive protein and N-terminal-pro-B-type natriuretic peptide (BNP). Further studies are needed to investigate these factors in populations that have not been previously studied, such as people in Saudi Arabia or other Arab nations. This study aimed to calculate the incidence of NOAF and examine associated predictors and clinical outcomes in a cohort of patients from Saudi Arabia with MI.

Methods

The study was carried out as a retrospective cohort study from September 2020 to December 2021. Before making the study design, we carried out a review of the existing literature. A search of MEDLINE and Google scholar was carried out with the MeSH terms “atrial fibrillation” and “myocardial infraction”. We then filtered out the results to include studies carried out regarding NOAF only.

Patients who were admitted to King Khaled University Hospital (KKUH) (a tertiary care hospital), Riyadh, Saudi Arabia, with a diagnosis of MI between January 2015 and 2020 were eligible for inclusion. Data were collected from the medical records. Patients were identified using the International Classification of Diseases-10 codes for MI (I121). We then removed the cases that failed to meet the inclusion criteria (incorrect coding, old cases, and MI patients transferred only for percutaneous coronary intervention [PCI] from other hospitals). We excluded patients who had a confirmed pervious diagnosis of atrial fibrillation before the development of MI and patients who died at presentation. Patients were retrospectively followed up for one year. A study flow chart of the data collection pathway is shown in Figure 1.

Figure 1
  • Download figure
  • Open in new tab
  • Download powerpoint
Figure 1

- Data collection pathway. *Inclusion criteria: I) King Khaled University Hospital (KKUH) patients who were admitted for primary treatment of MI as inpatients; II) admission between January 2015 to January 2020. **Exclusion criteria: I) patients who had a confirmed pervious diagnosis of AF before the development of MI; II) patients who died at presentation. ICD: International Classification of Diseases, MI: myocardial infarction. ER: Emergency room, PCI: percutaneous coronary intervention, AF: atrial fibrillation

The variables examined in the study included demographics (age, gender, body mass index [BMI], and smoking status), comorbidities and past medical history (prior MI, hypertension, diabetes, dyslipidemia, congestive heart failure, structural heart disease, chronic lung disease, asthma, mental disorders, dementia, and any other illnesses). Moreover, the study also investigated the baseline clinical admission data (heart rate, systolic blood pressure, diastolic blood pressure, type of MI, Killip class, and presentation timing). Late presentation was defined as >6 hours between MI symptom onset and hospital arrival. Biomarker and electrolyte levels at presentation were also investigated (C-reactive protein, BNP, troponin I, creatinine, sodium, potassium, and urea). This was detected using the lab results of the blood sample obtained on arrival. Details regarding the management were investigated including medications on arrival and during admission, type of treatment (PCI, coronary artery bypass graft [CABG], and medical therapy). Lastly, the heart rate and systolic and diastolic blood pressure were recorded at the 6- and 12-month follow-ups. The outcome variable was development of NOAF. Post MI complications were also recorded for up to 12 months.

The study was approved by the King Saud University Institutional Review Board (Research ID: E-20-5141). All of the steps carried out in this study were in accordance with the Helsinki Declaration.

Statistical analysis

Statistical analyses were carried out using the Statistical Package for the Social Sciences, version 25.0 (IBM Corp., Armonk, NY, USA). The study had a subgroup sample for patients who completed a full 6- and 12- months follow-up. The data were first analyzed using Chi-square (for categorical data) and independent t-test (for the quantitative data) for the analysis of the baseline categorical and quantitative clinical and demographic characteristics. This analysis included the following variables: demographics, baseline clinical admission data, biomarkers, electrolytes at presentation, and follow-up data. Quantitative variables were expressed as means with standard deviation (SD), while categorical variables were expressed as numbers or frequencies with percentage. Afterwards, the study carried out a multivariant logistic regression analysis of the patients’ clinical characteristics to identify the independent predictors. This analysis included variables from all the previous categories. The variables chosen for this analysis were the variables that were significant in the Chi-square analysis, and the ones that were significant in previous literature. Lastly, the study then ran a backward stepwise elimination algorithm on the comorbidities of the patients, complications developed after the MI and the medications given on arrival and during treatment. This was carried out to reduce the variables until only the significant predictors (p<0.05) remained in the multivariate logistic regression. Missing values in this analysis were addressed using multiple imputation. The missing values were the following: 5 patients with missing BMI during admission; 3 missing location and type of MI; 2 missing presenting heart rate and troponin; and one patient missing systolic and diastolic blood pressure. The major missing variable was BNP, with 32 patients. This is most likely because it is not part of the routine investigations of acute coronary syndrome in the emergency room in KKUH, Riyadh, Saudi Arabia. A p-value of <0.05 and 95% confidence interval (CI) were used to report the statistical significance and precision of results in all steps of the analysis.

The STROBE cohort checklist was used to ensure proper reporting of the study.10

Results

A total of 296 MI patients were eligible for study inclusion. Ten patients were excluded because of a history of atrial fibrillation and 5 for death at presentation. Finally, 281 patients were included for analysis. Among these, 119 patients completed 6- and 12-month follow-up visits (follow-up subgroup cohort). Overall, mean age was 58.7±12.7 years, and 232 (82.6%) patients were men. Prevalence of comorbidities was as follows: diabetes, 176 patients (62.6%); hypertension, 160 (56.9%); obesity (BMI >30 kg/m2), 87 (31.0%), and smoking, 106 (37.7%). The incidence of NOAF was 22 (95% CI: [5.2-11.7]). Mean age in patients who developed NOAF was 66.6±12.0 years and 19 (86.4%) were men.

Table 1 shows baseline categorical clinical and demographic data in patients grouped according to development of NOAF. Older age category (age >55) patients had a statistically significant higher proportion of NOAF (p=0.01). Also, a significantly higher proportion of patients in the NOAF group died (p=0.031).

View this table:
  • View inline
  • View popup
Table 1

- Categorical baseline clinical and demographic characteristics.

Table 2 shows the quantitative baseline clinical and demographic data in patients grouped according to development of NOAF for both the entire cohort and the follow-up cohort. In the entire cohort, age was significantly older in the NOAF group (p=0.002); in addition, both BNP (p=0.046) and urea (p=0.047) levels at presentation were found to be significantly associated with NOAF. In the follow-up cohort, none of the variables significantly differed between the NOAF group and non-NOAF group.

View this table:
  • View inline
  • View popup
Table 2

- Quantitative baseline clinical and demographic characteristics.

Multivariate regression analyses were carried out for the entire cohort, with the results shown in Table 3. Older age was found to be a significant predictor of the development of NOAF (OR=1.088, 95% CI: [1.028-1.152]; p=0.004). Compared to patients who underwent PCI, the odds of NOAF were almost 8 times higher in those who underwent CABG (OR=7.950; 95% CI: [1.473-42.897]; p=0.016). Patients who developed a major adverse cardiovascular event (MACE) during PCI had 12 times higher odds of developing NOAF (OR=12.15; 95% CI: [1.133-130.220]; p=0.039). Multivariate logistic regression analyses were then carried out in the follow-up cohort, which is shown in Table 4, and it included follow-up variables. In this cohort, older age was significantly associated with development of NOAF (OR=1.083; 95% CI: [1.009-1.164]; p=0.028).

View this table:
  • View inline
  • View popup
Table 3

- Logistic regression analysis for demographics, baseline clinical admission data, biomarkers, electrolytes at presentation.

View this table:
  • View inline
  • View popup
Table 4

- Logistic regression analysis for demographics, baseline clinical admission data, biomarkers, electrolytes at presentation and follow-up data.

Backward stepwise elimination was then carried out using patient comorbidities, complications, and medications administered (Table 5). This was carried out to reduce the variables until only the significant predictors remained in the multivariate logistic regression (p<0.05). The overall model was statistically significant (χ2=27.713, p<0.05). Patients who had a history of prior MI were associated with a higher OR of developing NOAF. New-onset atrial fibrillation patients were associated with higher odds of developing ventricular tachycardia after the MI and were more likely to receive unfractionated heparin and low molecular weight heparin. Moreover, they had lower odds of receiving clopidogrel, ticagrelor or an oral hypoglycaemic agent.

View this table:
  • View inline
  • View popup
Table 5

- Logistic regression analysis using backward stepwise elimination for comorbidities, complications, and medications administered.

Discussion

In this study, NOAF developed in 7.8% of MI patients, which was similar to the incidence reported in previous studies.1 Older age, certain biochemical markers, previous history of MI, type of treatment, and medication received were significant predictors of NOAF. Patients with NOAF had higher odds of developing MACE, ventricular tachycardia, and a higher mortality.

Older age has been reported as a significant predictor of NOAF in almost all previous NOAF studies.1,2,4-8 Our study found similar results. Degenerative age-related cardiac changes such as fibrosis are established risk factors for atrial fibrilation.11 Moreover, older age is associated with increased risk of MI complications; therefore, higher incidence of NOAF would be expected in older patients.12 Our study also found that history of MI was significantly associated with development of NOAF. Similar to age, MI is associated with degenerative cardiac changes such as fibrosis and an increased risk of complications from any subsequent MI.13 Furthermore, patients with multiple MIs are more likely to be older and have multiple comorbidities, which puts them at higher risk for NOAF.13

We also investigated levels of biochemical markers, including troponin I, BNP, creatinine, urea, and electrolytes. However, only BNP and urea levels significantly differed between the NOAF and non-NOAF groups. N-terminal-pro-B-type natriuretic peptide is released by the ventricles because of muscular stretching. Elevated BNP level in the acute setting is an important marker of congestive heart failure.14 Previous studies have reported that elevated BNP is an independent predictor of NOAF, which was confirmed in a recent large prospective study.5,15,16 While studies investigating the association of NOAF after MI and urea level are very limited, our study found that urea level significantly differed between MI patients who did and did not develop NOAF. However, a similar finding has been reported in coronavirus disease 2019 patients.17 Urea is elevated in patients with decreased glomerular filtration rate (suggestive of kidney failure), congestive heart failure, and dehydration.18 In our study, BNP (a marker of heart failure) was also significantly higher in patients who developed NOAF; therefore, urea and BNP may have been elevated together in patients with heart failure.

Type of treatment and medications received also significantly differed between patients who developed NOAF and those who did not. The odds of NOAF were much higher in MI patients who underwent CABG compared to those who underwent PCI. This was consistent with what was reported by a previous study based on the Global Registry of Acute Coronary Events.19 This association might be due to the fact that patients who underwent CABG instead of PCI are generally older and have more comorbidities and more advanced cardiovascular disease, which are characteristics associated with NOAF development.19,20 New-onset atrial fibrillation patients were more likely to receive unfractionated heparin and low molecular weight heparin and less likely to receive clopidogrel and ticagrelor. This finding was consistent with those of other studies and was likely due to the concern of increased risk of bleeding.7,19

In our study, odds of mortality were higher in patients who developed NOAF. A previous systematic review reported that NOAF was associated with a higher risk of mortality in MI patients even after adjusting for confounders.3 We also found that MI patients with NOAF were more likely to experience a MACE after PCI than those who did not develop NOAF. Moreover, they were more likely to develop ventricular tachycardia. The association between NOAF and the development of ventricular tachycardia was also found by other studies.7,19 However, the number of patients in our study who experienced a MACE or ventricular tachycardia were limited. Further studies are warranted to confirm our findings.

Study limitations

Because of its retrospective and observational nature, cause and effect cannot be demonstrated. The study was carried out in a single center, so the sample size was limited. Furthermore, our findings may not necessarily be generalizable to patients with characteristics that differ from our study population, such as patients with pre-existing atrial fibrillation or NOAF patients without MI.

In conclusion, the incidence of NOAF in MI patients was 7.8%. Older age, history of MI, and undergoing CABG were predictors of NOAF in multivariate analysis. New-onset atrial fibrillation patients were more likely to experience a MACE or ventricular tachycardia and had a higher mortality. This study provided data regarding NOAF patients in an Arabic population which had limited data regarding the topic. The study’s finding of the association between the high levels of urea and the development of NOAF was a novel finding in the context of NOAF post-MI. The study also confirms the dangers of NOAF and how it leads to higher rates of mortality and complications. This shows the importance of the early detection and treatment of NOAF. Further large-scale prospective studies are needed, especially in the Arabic region, to confirm our findings. Also, clinical trials would be better suited at investigating the role of medications in NOAF development.

Acknowledgment

The authors gratefully acknowledge Cambridge Proofreading (www.proofreading.org) for English language editing.

Footnotes

  • Disclosure. Authors have no conflict of interests, and the work was not supported or funded by any drug company.

  • Received May 8, 2022.
  • Accepted July 7, 2022.
  • Copyright: © Saudi Medical Journal

This is an Open Access journal and articles published are distributed under the terms of the Creative Commons Attribution-NonCommercial License (CC BY-NC). Readers may copy, distribute, and display the work for non-commercial purposes with the proper citation of the original work.

References

  1. 1.↵
    1. Schmitt J,
    2. Duray G,
    3. Gersh BJ,
    4. Hohnloser SH
    . Atrial fibrillation in acute myocardial infarction: a systematic review of the incidence, clinical features and prognostic implications. Eur Heart J 2009; 30: 1038–1045.
    OpenUrlCrossRefPubMed
  2. 2.↵
    1. Kundu A,
    2. O’Day K,
    3. Shaikh AY,
    4. Lessard DM,
    5. Saczynski JS,
    6. Yarzebski J, et al.
    Relation of atrial fibrillation in acute myocardial infarction to in-hospital complications and early hospital readmission. Am J Cardiol 2016; 117: 1213–1218.
    OpenUrl
  3. 3.↵
    1. Jabre P,
    2. Roger VL,
    3. Murad MH,
    4. Chamberlain AM,
    5. Prokop L,
    6. Adnet F, et al.
    Mortality associated with atrial fibrillation in patients with myocardial infarction: a systematic review and meta-analysis. Circulation 2011; 123: 1587–1593.
    OpenUrlAbstract/FREE Full Text
  4. 4.↵
    1. Rhyou HI,
    2. Park TH,
    3. Cho YR,
    4. Park K,
    5. Park JS,
    6. Kim MH, et al.
    Clinical factors associated with the development of atrial fibrillation in the year following STEMI treated by primary PCI. J Cardiol 2018; 71: 125–128.
    OpenUrl
  5. 5.↵
    1. Parashar S,
    2. Kella D,
    3. Reid KJ,
    4. Spertus JA,
    5. Tang F,
    6. Langberg J, et al.
    New-onset atrial fibrillation after acute myocardial infarction and its relation to admission biomarkers (from the TRIUMPH registry). Am J Cardiol 2013; 112: 1390–1395.
    OpenUrlCrossRefPubMed
  6. 6.
    1. Iqbal Z,
    2. Mengal MN,
    3. Badini A,
    4. Karim M
    . New-onset atrial fibrillation in patients presenting with acute myocardial infarction. Cureus 2019; 11: e4483.
    OpenUrl
  7. 7.↵
    1. Congo KH,
    2. Belo A,
    3. Carvalho J,
    4. Neves D,
    5. Guerreiro R,
    6. Pais JA, et al.
    New-onset atrial fibrillation in ST-segment elevation myocardial infarction: predictors and impact on therapy and mortality. Arq Bras Cardiol 2019; 113: 948–957.
    OpenUrl
  8. 8.↵
    1. He J,
    2. Yang Y,
    3. Zhang G,
    4. Lu XH
    . Clinical risk factors for new-onset atrial fibrillation in acute myocardial infarction: a systematic review and meta-analysis. Medicine (Baltimore) 2019; 98: e15960.
    OpenUrl
  9. 9.↵
    1. Ogunsua A,
    2. Vaze A,
    3. Kassas I,
    4. Hansra B,
    5. Nagy A,
    6. Elhag R, et al.
    TCT-150 risk factors for new-onset atrial fibrillation after percutaneous coronary intervention among patients with non-ST elevation myocardial infarction. J Am Coll Cardiol 2017; 70: B65.
    OpenUrlCrossRef
  10. 10.↵
    1. Von Elm E,
    2. Altman DG,
    3. Egger M,
    4. Pocock SJ,
    5. Gøtzsche PC,
    6. Vandenbroucke JP
    . The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement: guidelines for reporting observational studies. Lancet 2007; 370: 1453–1457.
    OpenUrlCrossRefPubMed
  11. 11.↵
    1. Go AS,
    2. Hylek EM,
    3. Phillips KA,
    4. Chang Y,
    5. Henault LE,
    6. Selby JV, et al.
    Prevalence of diagnosed atrial fibrillation in adults: national implications for rhythm management and stroke prevention: the AnTicoagulation and Risk Factors in Atrial Fibrillation (ATRIA) Study. JAMA 2001; 285: 2370–2375.
    OpenUrlCrossRefPubMed
  12. 12.↵
    1. Carro A,
    2. Kaski JC
    . Myocardial infarction in the elderly. Aging Dis 2011; 2: 116–137.
    OpenUrlPubMed
  13. 13.↵
    1. Motivala AA,
    2. Tamhane U,
    3. Ramanath VS,
    4. Saab F,
    5. Montgomery DG,
    6. Fang J, et al.
    A prior myocardial infarction: how does it affect management and outcomes in recurrent acute coronary syndromes? Clin Cardiol 2008; 31: 590–596.
    OpenUrlCrossRefPubMed
  14. 14.↵
    1. Durak-Nalbantić A,
    2. Džubur A,
    3. Dilić M,
    4. Pozderac Z,
    5. Mujanović-Narančić A,
    6. Kulić M, et al.
    Brain natriuretic peptide release in acute myocardial infarction. Bosn J Basic Med Sci 2012; 12: 164–168.
    OpenUrlPubMed
  15. 15.↵
    1. Lloyd-Jones DM,
    2. Wang TJ,
    3. Leip EP,
    4. Larson MG,
    5. Levy D,
    6. Vasan RS, et al.
    Lifetime risk for development of atrial fibrillation: the Framingham Heart Study. Circulation 2004; 110: 1042–1046.
    OpenUrlAbstract/FREE Full Text
  16. 16.↵
    1. Patton KK,
    2. Ellinor PT,
    3. Heckbert SR,
    4. Christenson RH,
    5. DeFilippi C,
    6. Gottdiener JS, et al.
    N-terminal pro-B-type natriuretic peptide is a major predictor of the development of atrial fibrillation: the cardiovascular health study. Circulation 2009; 120: 1768–1774.
    OpenUrlAbstract/FREE Full Text
  17. 17.↵
    1. Ergün B,
    2. Ergan B,
    3. Sözmen MK,
    4. Küçük M,
    5. Yakar MN,
    6. Cömert B, et al.
    New-onset atrial fibrillation in critically ill patients with coronavirus disease 2019 (COVID-19). J Arrhythm 2021; 37: 1196–1204.
    OpenUrl
  18. 18.↵
    1. Walker HK,
    2. Hall WD,
    3. Hurst JW
    1. Hosten AO
    . BUN and creatinine. In: Walker HK, Hall WD, Hurst JW, editors. Clinical methods: the history, physical, and laboratory examinations (3rd ed). Butterworths 1990: Chapter 193.
  19. 19.↵
    1. McManus DD,
    2. Huang W,
    3. Domakonda KV,
    4. Ward J,
    5. Saczysnki JS,
    6. Gore JM, et al.
    Trends in atrial fibrillation in patients hospitalized with an acute coronary syndrome. Am J Med 2012; 125: 1076–1084.
    OpenUrlPubMed
  20. 20.↵
    1. Alkhouli M,
    2. Alqahtani F,
    3. Kalra A,
    4. Gafoor S,
    5. Alhajji M,
    6. Alreshidan M, et al.
    Trends in characteristics and outcomes of patients undergoing coronary revascularization in the United States, 2003-2016. JAMA Netw Open 2020; 3: e1921326.
PreviousNext
Back to top

In this issue

Saudi Medical Journal: 43 (8)
Saudi Medical Journal
Vol. 43, Issue 8
1 Aug 2022
  • Table of Contents
  • Cover (PDF)
  • Index by author
Print
Download PDF
Email Article

Thank you for your interest in spreading the word on Saudi Medical Journal.

NOTE: We only request your email address so that the person you are recommending the page to knows that you wanted them to see it, and that it is not junk mail. We do not capture any email address.

Enter multiple addresses on separate lines or separate them with commas.
Incidence, clinical predictors, and clinical effect of new-onset atrial fibrillation in myocardial infarction patients
(Your Name) has sent you a message from Saudi Medical Journal
(Your Name) thought you would like to see the Saudi Medical Journal web site.
Citation Tools
Incidence, clinical predictors, and clinical effect of new-onset atrial fibrillation in myocardial infarction patients
Fayez Elshaer, Abdulelah H. Alsaeed, Sultan N. Alfehaid, Abdulaziz S. Alshahrani, Abdulrahman H. Alduhayyim, Ayman M. Alsaleh
Saudi Medical Journal Aug 2022, 43 (8) 933-940; DOI: 10.15537/smj.2022.43.8.20220349

Citation Manager Formats

  • BibTeX
  • Bookends
  • EasyBib
  • EndNote (tagged)
  • EndNote 8 (xml)
  • Medlars
  • Mendeley
  • Papers
  • RefWorks Tagged
  • Ref Manager
  • RIS
  • Zotero
Share
Incidence, clinical predictors, and clinical effect of new-onset atrial fibrillation in myocardial infarction patients
Fayez Elshaer, Abdulelah H. Alsaeed, Sultan N. Alfehaid, Abdulaziz S. Alshahrani, Abdulrahman H. Alduhayyim, Ayman M. Alsaleh
Saudi Medical Journal Aug 2022, 43 (8) 933-940; DOI: 10.15537/smj.2022.43.8.20220349
Twitter logo Facebook logo Mendeley logo
  • Tweet Widget
  • Facebook Like
  • Google Plus One
Bookmark this article

Jump to section

  • Article
    • Abstract
    • Methods
    • Results
    • Discussion
    • Acknowledgment
    • Footnotes
    • References
  • Figures & Data
  • eLetters
  • References
  • Info & Metrics
  • PDF

Related Articles

  • No related articles found.
  • PubMed
  • Google Scholar

Cited By...

  • Patch angioplasty carotid endarterectomy versus eversion carotid endarterectomy
  • Google Scholar

More in this TOC Section

  • Psychological stress and its association with bronchial asthma in Saudi Arabia
  • The factors affecting comfort and the comfort levels of patients hospitalized in the coronary intensive care unit
  • Exploring communication challenges with children and parents among pharmacists in Saudi Arabia
Show more Original Article

Similar Articles

Keywords

  • atrial fibrillation
  • myocardial infarction
  • predictors

CONTENT

  • home

JOURNAL

  • home

AUTHORS

  • home
Saudi Medical Journal

© 2025 Saudi Medical Journal Saudi Medical Journal is copyright under the Berne Convention and the International Copyright Convention.  Saudi Medical Journal is an Open Access journal and articles published are distributed under the terms of the Creative Commons Attribution-NonCommercial License (CC BY-NC). Readers may copy, distribute, and display the work for non-commercial purposes with the proper citation of the original work. Electronic ISSN 1658-3175. Print ISSN 0379-5284.

Powered by HighWire