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Unveiling Cardiac Arrhythmias: Influencing Factors and Prevalence in Portugal

Explore the prevalence of cardiac arrhythmias in Portugal due to factors like age, obesity, hyperthyroidism, hypertension, CKD, DM, and hyperlipidemia. Understand the impact on cardiovascular health and associated risks.

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Unveiling Cardiac Arrhythmias: Influencing Factors and Prevalence in Portugal

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  1. Feeling the Portuguese pulse: unveilingthehospitalisation-leadingcardiacarrhythmias Class 22 Introdução à Medicina II May 2012 ALBERTO,M ANDRADE,T CARDOSO,S CORREIA,C MAGALHÃES,D MEDEIROS,N NEVES,A SANTOS,J TELES,A VIEIRA,B

  2. Introduction:background Background Justification and Aim • Cardiac arrhythmias are a large group of conditions in which there is not a normal sinus rhythm and normal atrioventricular (AV) conduction.[1] • Some of the most common arrhythmias:[1] • Atrioventricular(AV) block; • Atrial premature beats (APB); • Ventricular premature beats (VPB); • Sinus bradycardia; • Atrial fibrillation (AF). Methods Results Discussion Aknowledgments References [1] Lévy S, et al. Arrhythmia management for the primary clinician [Internet]. UpToDate; 2010 May [cited 2011 Oct 27]. Available from: http://www.uptodate.com/contents/arrhythmia-management-for-the-primary-care-clinician?source=preview&anchor=H4&selectedTitle=1~150#H4

  3. Influencing factors Background Justification and Aim AGE Atrial fibrillation (AF), which affects approximately 0.4% of the global population,[2]doubles its prevalence every ten years beyond the 50 year benchmark.[3] In the USA, roughly 70% of individuals with AF are between 65 and 85 years of age.[4] The prevalence of AF in Portugal is higher than in other countries where similar data is available, when focusing on the population aged 40 and onwards.[5] Methods Results Discussion Aknowledgments References [2] Benjamin EJ. et al. Independent risk factors for atrial fibrillation in a population-based cohort. The Framingham Heart Study. JAMA 1994;271:840–4 [3] KannelWB. et al. Prevalence, incidence, prognosis, and predisposing conditions for atrial fibrillation: population-based estimates. Am J Cardiol 1998; 82(8A):2N–9N [4] Charlemagne A. et al. Epidemiology of atrial fibrillation in France: extrapolation of international epidemiological data to France and analysis of French hospitalisation data. Archivesof Cardiovascular Diseases. 2011 Feb; 104(2):115-24 [5]BonhorstD. etal.Prevalence of atrial fibrillation in the Portuguese population aged 40 and over: the FAMA study. Revista Portuguesa de Cardiologia. 2010 Mar; 29(3):331-50

  4. Influencing factors Background Justification and Aim Fatal arrhythmias are pointed as the most frequent cause of death among obese patients.[6] Associated to greater levels of cardiovascular morbidity, including angina, myocardial infarction and arrhythmias.[8] Hyperthyroidism  increase of cholesterol synthesis[8] OBESITY Methods Results Discussion Aknowledgments Fig. 1: Prevalence of obesity in Portugal by NUT II regions, 2006[7] References HYPERTHYROIDISM [6] Mathew B. et al. Obesity: effects on cardiovascular disease and its diagnosis. J Am Board Fam Med. 2008 Novâ Dec; 21(6): 562–568 [7]Alves C. et al. Epidemiological data on obesity in Portugal [Internet]. 10º Congresso Português de Obesidade – Porto [2006 November]. Available from: http://www.eurotrials.com/contents/files/publicacao_ficheiro_68_1.pdf [8] Neves C. etal.Doenças da tiróide, dislipidemia, e Patologia Cardiovascular; Rev. PortCardiol 2008; 27(10): 1211-1236

  5. Influencing factors Background Justification and Aim HYPERTENSION Hypertension facilitates development and progression of cardiac diseases such as left ventricular hypertrophy (LVH), coronary artery disease (CAD), arrhythmia and heart failure.[9] A 2007 Portuguese study (subjects aged 18 to 90 years old) pointed North as the region with the lowest prevalence of hypertension (33,4%), and Alentejo with the highest (49,5%).[10] CKDaffects up to 10% of adults [11]and carries a high risk for cardiovascular disease, including AF.[12] Methods Results Discussion Aknowledgments References CHRONIC KIDNEY DISEASE (CKD) [9] Ishimitsu T, et al. Hypertension complicated with heart disease.Nihon Rinsho. 2011 November; 69(11):2007-14 [10]Macedo M, etal.Prevalência, Conhecimento, Tratamento e Controlo da Hipertensão em Portugal. Estudo PAP. Revista Portuguesa de Cardiologia. 2007; 26(1):21-39 [11]Coresh J, et al. Prevalence of chronic kidney disease in the United States. JAMA. 2007; 298: 2038-2047 [12] Soliman EZ, et al. Chronic kidney disease and prevalent atrial fibrillation: the Chronic Renal Insufficiency Cohort (CRIC). Am Heart J. 2010; 159:1102-1107

  6. Influencing factors Background Justification and Aim DMincreases the incidence of cardiac arrhythmias.[13] Individuals with DM had one third greater risk of incident AF compared with those without diabetes after adjustment with no evidence of interactions with race or gender.[14] Hyperlipidemia, an important risk factor for cardiovascular disease, may be associatedwith AF.[15] DIABETES MELLITUS (DM) Methods Results Discussion Aknowledgments References HYPERLIPIDEMIA [13]Aubin MC, et al. A high-fat diet increases risk of ventricular arrhythmia in female rats: enhanced arrhythmic risk in the absence of obesity or hyperlipidemia. J Appl Physiol. 2010; 108: 933–940 [14]Huxley R, et al. Type 2 diabetes, glucose homeostasis and incident atrial fibrillation: the Atherosclerosis Risk in Communities Study. Heart. 2012 January; 98(2): 133–138 [15]Watanabe H, et al. Association Between Lipid Profile and Risk of Atrial Fibrillation. Official Journal of the Japanese Circulation Society.

  7. Justification and Aim Background Justification and Aim • Current challenge: swiftly manage growing numbers of patients with cardiac arrhythmias. Methods • Main goal: to find out whether there is or not an asymmetrical distribution in hospitalisations due to cardiac arrhythmias in Portugal, and to provide a possible explanation for those findings. Results Discussion Aknowledgments References • AnalysePortuguese arrhythmia-caused hospitalisations from 2000 to 2008, dividing it by NUT II regions and age groups; • Resort to population age, Hypertension, Diabetes Mellitus, Hyperthyroidism, Obesity, Chronic Kidney Disease and Hyperlipidemia to try and explain our findings; • Study the evolution of the arrhythmias and associated factors.

  8. Methods: participants Background Justification and Aim Methods Patient’s age ranged from 0 to 108 years. Results Discussion Number of episodes 113 631 Aknowledgments References

  9. Methods: Portugal by NUT II regions Background Justification and Aim Mainland Portugal is currently divided into fiveNUT II regions: Methods North Results Discussion Centre Aknowledgments References Alentejo Lisbon Fig. 2: Portugal map by NUT II regions [16] Algarve [16] INE – Instituto Nacional de Estatística. Available from: http://www.ine.pt/xportal/xmain?xpid=INE&xpgid=ine_princindic

  10. Methods: study design Background Justification and Aim • This study is both descriptive and relational.[17] • Covers a nine-year period (2000-2008). • Each episode was analysed only once: cross-sectional study.[17] • Readmissions were considered independent (new) episodes. • Should be regarded as an epidemiologic study. Methods Results Discussion Aknowledgments References [17]Trochim W. Time inresearch [Internet]. Research Methods Knowledge Base; 2006 November [cited 2011 Dec 2]. Available from: http://www.socialresearchmethods.net/kb/timedim.php

  11. Methods: data collection Background Justification and Aim • Database was provided by Department of Health Information and Decision Sciences, Faculty of Medicine, University of Porto. • Papers about cardiac arrhythmias: • to relate them with age, gender, demographic or geographic data, hyperthyroidism, obesity, hypertension, chronic kidney disease, diabetes mellitus and hyperlipidemia. • ICD-9-CM arrhythmia diagnosis codes based on Quan H. et al.[18] • 426 Conduction disorders • 427 Cardiac dysrhythmias • Main data collection method: on-line research on Pubmed. Methods Results Discussion Aknowledgments References [18] Quan H. et al. Coding algorithms for defining comorbidities in ICD-9-CM and ICD-10 administrative data. Medical Care. 2005 Nov; 43(11):1130-9

  12. Methods: Variables description Background • Principal diagnosis Justification and Aim 426 and 427 ICD-9-CM codes Methods Results • Secondary diagnosis Discussion Aknowledgments Chosen based on frequency References 10

  13. Methods: Variables description Background Justification and Aim • Demographicvariables: Methods Gender Results • Ageing Index Discussion • Patient’s age group Aknowledgments • Patient’s residence by NUT II regions References • Portuguese population data Obtained from INE[16] [16] INE – Instituto Nacional de Estatística. Available from: http://www.ine.pt/xportal/xmain?xpid=INE&xpgid=ine_princindic

  14. Methods: Statistical analysis Background Justification and Aim • IBM SPSS Statistics v20® and Microsoft Office Excel 2010® • Unpaired, two-tail t-tests andWelch tests • 95% CI (confidence intervals) • Frequencies • Logistic regression Methods Results Discussion Aknowledgments References

  15. Results Background Justification and Aim Methods Results Discussion Aknowledgments References Table 1 – Characteristics of the patients hospitalised due to cardiac arrhythmias, from 2000 to 2008, in mainland Portugal. Note: Readmissions were considered independent episodes. • Gender and agedistribution were very similar across all NUT II regions, as expected. • In particular, all five regions registered their greatest number of hospitalisations in patients within the 75-79 years old range.

  16. Results Background Justification and Aim Methods Results Discussion Aknowledgments References Table 2 – Number of Hospitalisations (NOH) per a hundred thousand inhabitants, by year and NUT II region. Note: Results were obtained by dividing the NOH of each region for its total population, times a hundred thousand.

  17. Results Background Justification and Aim Methods Results Discussion Aknowledgments References Chart 1 – Number of Hospitalisations (NOH) per a hundred thousand inhabitants, by year and NUT II region. Note: Results were obtained by dividing the NOH of each region for its total population, times a hundred thousand.

  18. Results Background Justification and Aim Methods Results Discussion Aknowledgments References Table 3 – p-values* for comparisons between the values of the NUT II regions. Note: *Obtained through t-tests. • There was a general trend of increasing hospitalisationsper year. Lisbon is the sole exception, showing an inversion to negative evolution between 2005 and 2006. • Regarding NOH per a hundred thousand inhabitants, Northstands clearly apart from all other regions.

  19. Results Background Justification and Aim Methods Results Discussion Aknowledgments References Table 4 – Ageing Index (AgIdx) by NUT II regions, from 2000 to 2008. Notes: Ageing Index: quotient between the number of people of 65 years-old or more and the number of those of 14 or less years-old. It is expressed in number of elders by 100 youngsters; *Obtained through Welch test. • The AgeingIndexrose steadily in North and Centre, while it hovered around the same values in the remaining regions. • Northis, concerning general population, by far the youngest region, incontrast with Alentejo.

  20. Results Background Justification and Aim Methods Results Discussion Aknowledgments References Chart 2 – Number of Hospitalisations (NOH) per a hundred thousand inhabitants, by age group and NUT II region. Notes: Results were obtained by dividing the NOH of each region for its total population by age group, times a hundred thousand. A nine year average was used for values of NOH and population by age group.

  21. Results Background Justification and Aim Methods Results Discussion Aknowledgments References Chart 3 – Most frequent secondary diagnoses registered on patients at time of admission. • Hypertension was the most registered disease, having being diagnosed to 29,1% of patients. • Hyperthyroidism, despite not appearing in this chart, is a well-known arrhythmia-potentiating factor, so was included for analysis.

  22. Results Background Justification and Aim Methods Results Discussion Aknowledgments Table 5 – Number of hospitalisations with Hypertension, Obesity, Hyperlipidemia, Chronic Kidney Disease (CKD), Hyperthyroidism or Diabetes Mellitus (DM) as secondary diagnoses, per a hundred thousand hospitalisations due to cardiac arrhythmias and per NUT II region. References

  23. Results Background Justification and Aim Methods Results Discussion Aknowledgments References Table 6 – Odds ratio for secondary diagnoses and demographic variables on the hospitalisations motivated by cardiac arrhythmias. Notes: All hospitalisations in mainland Portugal from 2000 to 2008 were included in this logistic regression. The dependent variable was the principal diagnosis leading to the hospitalisation: 1 – cardiac arrhythmia; 0 – other. * For secondary diagnoses: number of hospitalisations featuring that disease as secondary diagnosis. † For demographic variables: number of hospitalisations in that region. ** Base category for odds ratio determination.

  24. Discussion Background Justification and Aim • Ageis a risk factor in the emergence of cardiac arrhythmias (AOR = 1,04). • All co-morbidities (secondary diagnoses excluding heart-related diseases), bar CKD and DM, obtained significant adjusted odds ratio values above 1 for hospitalisations due to arrhythmias. • DM apparently turned out being a protective factor, in contrast with what is described. • Hyperthyroidism deserves a special attention, since it is the most important factor for the appearance of cardiac arrhythmias: individuals with hyperthyroidismare 3,45times likelier to develop arrhythmias than those without this condition. Methods Results Discussion Aknowledgments References

  25. Discussion Background Justification and Aim • There is no significant difference between Centre, Alentejo and Algarve regarding NOH per a 100 000 inhabitants, nor when eliminating the factorage. • This proved to be strange, because Centre features a lower NOHwith any co-morbiditiesthan Alentejo or Algarve and a lower AOR. • A counterbalance between age influence and co-morbidities could explain chart 1, but age was shown to be irrelevant for comparing these three regions (chart 2). Methods Results Discussion Aknowledgments References

  26. Discussion Background Justification and Aim • Chart1 brings all attention to North, which registered the lowest NOH per a 100 000 inhabitants. • Lisbon does not stand apart from Centre, Alentejo and Algarve. • However, when the age groups are included, age’s influence is eliminated and significantdifferences are found for both North and Lisbon in comparison with the other three regions and between themselves (chart 2), but while North still has the lowest ratios, Lisbon holds the worst scenario. • Since North and Lisbon are the youngest regions, some conclusions can be withdrawn: • regarding North, a young population means more protection(other factors also contribute to a low NOH/100 000 inhabitants); • concerning Lisbon, age influence is offsetting co-morbidities, resulting in an outcome on the level of Centre, Alentejo and Algarve. Without age’s protective effect, hospitalisations in Lisbon rose sharply. • In fact, when accounting all the factors, Lisbon has a AOR of 1,56 in comparison with North. Methods Results Discussion Aknowledgments References

  27. Discussion Background Justification and Aim • North presents higher values of NOHwith Obesity, Hyperlipidemia, CKD and DM per a 100 000 hospitalisations due to arrhythmias than Lisbon. • Obesity and Hyperlipidemia increase the odds of being hospitalised due to arrhythmias (Adjusted Odds Ratio (AOR) = 1,17 and AOR=1,14) and are higher in North, which seems to be a contradiction. • However, Hypertension (AOR = 1,30) and Hyperthyroidism (AOR = 3,45) are both morefrequent in Lisbon and are more relevant (higher AOR) than Obesityand Hyperlipidemia. • As such, we suppose that hypertension and hyperthyroidism are at the root of the differences found in chart 2. • Nonetheless, we believe they are not the unique reasons for such glaring disparities. Methods Results Discussion Aknowledgments References

  28. Conclusions Background Justification and Aim Methods Results Discussion Aknowledgments References

  29. Conclusions Background Justification and Aim Methods Results Discussion Aknowledgments References

  30. Limitations Background Justification and Aim Methods Results Discussion Aknowledgments References

  31. Acknowledgments Background Justification and Aim • We would like to express thanks to: • Professor Doutor Alberto Freitas, for his continuous commitment, guidance and advice; • Professor DoutorAltamiro da Costa Pereira, for his constructive criticisms and sharp suggestions to improve our work; • Professor Fernando Lopes, for decisive orientation on a critical step of our work. • Special thanks go to supervisor Vasco Santos, whose knowledge and assistance was essential for the successful completion of this study. Methods Results Discussion Aknowledgments References

  32. References Background [1] LévyS. et al. Arrhythmia management for the primary clinician [Internet].UpToDate; 2010 May [cited 2011 Oct 27]. Available from: http://www.uptodate.com/contents/arrhythmia-management-for-the-primary-care-clinician?source=preview&anchor=H4&selectedTitle=1~150#H4 [2] Benjamin EJ. et al. Independent risk factors for atrial fibrillation in a population-based cohort. The Framingham Heart Study. JAMA 1994;271:840–4 [3] KannelWB. et al. Prevalence, incidence, prognosis, and predisposing conditions for atrial fibrillation: population-based estimates. Am J Cardiol 1998; 82(8A):2N–9N [4] Charlemagne A. et al. Epidemiology of atrial fibrillation in France: extrapolation of international epidemiological data to France and analysis of French hospitalisation data. Archivesof Cardiovascular Diseases. 2011 Feb; 104(2):115-24 [5]BonhorstD. etal.Prevalence of atrial fibrillation in the Portuguese population aged 40 and over: the FAMA study. Revista Portuguesa de Cardiologia. 2010 Mar; 29(3):331-50 [6] Mathew B. et al. Obesity: effects on cardiovascular disease and its diagnosis. J Am Board Fam Med. 2008 Novâ Dec; 21(6): 562–568 [7] Alves C. etal.Epidemiological data onobesityin Portugal [Internet]. 10º Congresso Português de Obesidade – Porto [2006 November]. Available from: http://www.eurotrials.com/contents/files/publicacao_ficheiro_68_1.pdf Justification and Aim Methods Results Discussion Aknowledgments References

  33. References Background [8] Neves C. etal.Doenças da tiróide, dislipidemia, e Patologia Cardiovascular; Rev. PortCardiol 2008; 27(10): 1211-1236 [9] Ishimitsu T, et al. Hypertension complicated with heart disease.Nihon Rinsho. 2011 November; 69(11): 2007-14 [10]Macedo M, etal.Prevalência, Conhecimento, Tratamento e Controlo da Hipertensão em Portugal. Estudo PAP. Revista Portuguesa de Cardiologia. 2007; 26(1): 21-39 [11]Coresh J, et al. Prevalence of chronic kidney disease in the United States. JAMA. 2007; 298: 2038-2047 [12]Soliman EZ, et al. Chronic kidney disease and prevalent atrial fibrillation: the Chronic Renal Insufficiency Cohort (CRIC). Am Heart J. 2010; 159: 1102-1107 [13]Aubin MC, et al. A high-fat diet increases risk of ventricular arrhythmia in female rats: enhanced arrhythmic risk in the absence of obesity or hyperlipidemia. J Appl Physiol. 2010; 108: 933–940 [14]Huxley R, et al. Type 2 diabetes, glucose homeostasis and incident atrial fibrillation: the Atherosclerosis Risk in Communities Study. Heart. 2012 January; 98(2): 133–138 [15]Watanabe H, et al. Association Between Lipid Profile and Risk of Atrial Fibrillation. Official Journal of the Japanese Circulation Society Justification and Aim Methods Results Discussion Aknowledgments References

  34. References Background Justification and Aim [16] INE – Instituto Nacional de Estatística. Available from: http://www.ine.pt/xportal/xmain?xpid=INE&xpgid=ine_princindic [17]Trochim W. Time inresearch[Internet]. Research Methods Knowledge Base; 2006 November [cited 2011 Dec 2]. Available from: http://www.socialresearchmethods.net/kb/timedim.php [18] Quan H. et al. Coding algorithms for defining comorbidities in ICD-9-CM and ICD-10 administrative data. Medical Care. 2005 Nov; 43(11): 1130-9 Methods Results Discussion Aknowledgments References

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