Effects of prescribed antithrombotics and other cardiovascular pharmacotherapies on all-cause mortality in patients with diabetes and atrial fibrillation – a cohort study from Sweden using propensity score analyses
© Wändell et al.; licensee BioMed Central Ltd. 2014
Received: 13 August 2013
Accepted: 20 December 2013
Published: 7 January 2014
To study mortality rates among patients with diabetes and concomitant atrial fibrillation (AF), prescribed different cardiovascular drugs in primary health care.
Study population consisted of men (n = 1319) and women (n = 1094) aged ≥45 years from a database including 75 primary care centres in Sweden. Cox regression analysis, with hazard ratios (HRs), 95% confidence interval (95% CIs) and mortality (years to death) as outcome, and Laplace regression, with difference in time to first 10% mortality (with 95% CI), were performed. Independent variables were prescribed cardiovascular drugs. Regression models were adjusted for a propensity score calculated separately for each prescribed drug class (comprising age, cardiovascular co-morbidities, education, marital status and pharmacotherapy).
Overall mortality was lower in the whole sample for anticoagulants vs no treatment (HR 0.45; 95% CI 0.26-0.77); and among patients < 80 years for anticoagulants vs. antiplatelets (HR 0.44; 95% CI 0.25-0.78); while among individuals aged ≥80 years, antiplatelets (HR 0.47; 95% CI 0.26-0.87) and anticoagulants (HR 0.49; 95% CI 0.24-1.00) vs. no treatment were equally effective. Statins were associated with lower mortality among those <80 years (HR 0.45; 95% CI 0.29-0.71). Laplace regression models in the whole sample, with years to first 10% of total mortality as outcome, were significant for: among patients < 80 years anticoagulants vs. no treatment 2.70 years (95% CI 0.04-5.37), anticoagulants vs. antiplatelets 2.31 years (95% CI 0.84-3.79), and those ≥80 antiplatelets vs. no treatment 1.78 years (95% CI 1.04-2.52).
Our findings suggest that antiplatelets could exert a beneficial effect among those above 80 years.
KeywordsAntithrombotic drugs Statins Pharmacotherapy Mortality Follow-up
Diabetes is a chronic disease affecting around 4.5% of the Swedish population . Diabetes is associated with a 34% higher risk of developing atrial fibrillation (AF), the mechanisms of which are currently unclear .
The overall lifetime risk of developing AF from the age of 40 years has been estimated at 25%, irrespective of sex . However, previous studies have shown a 1.5-fold greater risk of AF among men than among women , and that men develop AF five years earlier than women .
In contrast, women with AF have a higher excess mortality risk than in men with AF , and women also have a higher risk of stroke and different types of arrhythmias when prescribed antiarrhythmic drugs than do men .
The risk of stroke is markedly increased in AF patients, and anticoagulants are preferred to antiplatelets due to their superiority in stroke prevention . However, anticoagulants are also effective in preventing myocardial infarctions , although their effect on mortality has not been adequately addressed . As regards vascular and all-cause mortality, antiplatelets are just as effective as anticoagulants according to a Cochrane review .
In terms of pharmacological treatment and prevention of macrovascular complications in patients with diabetes, multifactorial treatment has been found to be effective , in particular treatment with statins , and with anti-hypertensive drugs . It would be of interest to investigate whether these treatment strategies would be effective in reducing mortality in diabetic patients with AF, and if there are any gender and age differences. We have earlier described changes between 2002 and 2007 in the drugs prescribed to AF patients in primary care, with increased use of selective beta blockers, anticoagulant therapy and lipid-lowering . Moreover, we have also found different effects of cardio-vascular drug classes on all-cause mortality in AF patients, with positive effects by statins among men and women < 80 years, anticoagulants among men and women, but not in women ≥ 80 years, and by antiplatelets among men ≥ 80 years . Interestingly, an increased mortality was found among men < 80 years prescribed digitalis.
The primary aim of the present study was to compare the effects of antiplatelets and anticoagulants on mortality in men and women with diabetes and AF. A further aim was to understand the effects on mortality of cardiovascular drugs that are commonly prescribed to these patients.
The study used individual-level patient data from 75 primary health care centers (PHCCs), 48 of which were located in Stockholm County. Individuals attending any of the participating PHCCs between 2001 and 2008 were included in the study. We used Extractor software (http://www.slso.sll.se/SLPOtemplates/SLPOPage1____10400.aspx; accessed September 19, 2010) to extract individual electronic patient records (EPRs). National identification numbers were replaced with new unique serial numbers to ensure anonymity. The files were linked to a database constructed using the Total Population Register, the Inpatient Register and the Swedish Cause of Death Register , which contains individual-level data on age, gender, education and hospital admissions for all residents registered in Sweden. Thus, a new research database containing clinical data and information on socioeconomic status on the 1,098,420 primary care-seeking individuals registered at the 75 PHCCs was created. Data from the Cause of Death Register, which has been shown to be 99.8% complete , were used for the follow-up.
Ethical approvals were obtained from regional ethical boards at Karolinska Institutet and the Lund University.
All persons diagnosed with both diabetes and AF who attended the 75 PHCCs between January 1st 2001 and December 31st 2007 were included in this study. They were identified by the presence of the ICD-10 (10th version of the WHO’s International Classification of Diseases) codes for AF (I48) and diabetes mellitus (E10-14) in the medical records. In total, 2,413 individuals, 1,319 men and 1,094 women, who were ≥45 years of age at the time of AF diagnosis were identified and included .
Time to death in the period from registration of AF diagnoses until December 31st 2007.
Drugs prescribed to individuals in the study population during the assessment period were recorded by Anatomic Therapeutic Chemical (ATC) Classification. With regard to anti-thrombotic drugs (B01A), patients were divided into three groups: no treatment, anti-platelet treatment with no anticoagulant treatment (B01AC), including acetylsalicylic acid (ASA; B01AC6, B01AC30), anticoagulant treatment with no anti-platelet treatment (B01AA), and finally a group who had received both anticoagulant and anti-platelet treatment. Diuretic drugs (C03) were recorded as thiazides or related agents, and were also registered when in combination with other drugs (C03A, C03B, C03E, C09B C09DA), loop diuretics (C03C) or potassium-saving diuretics (C03D), including aldosterone antagonists (C03DA). Furthermore, the following cardiovascular agents were recorded: heart-active drugs (C01), beta blockers (C07), calcium receptor-blocking agents (C08), and RAS-blocking agents (C09). Lipid-lowering drugs (C10A), including statins (C10AA), were also recorded.
Demographic and socio-economic variables
Gender: Men and women.
Individuals were divided into the following age groups 45–54, 55–64, 65–74, 75–84 and ≥85 years. Individuals younger than 45 years were excluded.
Educational level was categorized as ≤9 years (partial or complete compulsory schooling), 10–12 years (partial or complete secondary schooling) and >12 years (college and/or university studies).
Marital status was classified as married, unmarried, divorced or widowed.
We identified the following cardiovascular co-morbidities from the EPRs among individuals in the study population: hypertension (I10-15), coronary heart disease (CHD; I20-25), heart failure (HF; I50 and I110), non-rheumatic valvular diseases (I34-38), cardiomyopathy (I42) and cerebrovascular diseases (CVDs; I60-69), including intracranial bleeding (I60-62).
Differences in means and distributions between men and women were compared by Student’s t-test, chi-square analysis and Fisher’s exact test.
Follow-up analyses were performed using Cox regression with hazard ratios (HRs) and 95% confidence interval (95% CI), using time to death as the outcome. Laplace regression was used to calculate the difference in years until any given percentage of the participants prescribed pharmacotherapies vs. those without these particular drugs die [19, 20]. We calculated the difference in years until 10% of the participants died for those prescribed a particular therapy vs. those not prescribed. The 10th percentile was chosen, as the results are more reliable in Laplace regression if both exposed and unexposed individuals have a sufficient number of fatalities (events) during the follow-up. Laplace regression also uses bootstrap technique in the estimation of confidence intervals. Since different distributions and mathematical calculations are used to obtain results in Cox and Laplace regression, respectively, putting emphasis on findings significant with both methods reduces the risk of chance findings.
Models were tested for interaction between sex and each pharmacotherapy. Analyses were performed for individuals <80 and ≥80 years of age. 80 years has commonly been referred to as a cut-off age for stroke prophylaxis , as well as the beginning of a period of increased prevalence and incidence of AF . Furthermore, there were too few women aged <75 when they died (n = 18) to provide reliable results.
In the regression models, prescription of antithrombotic drugs (anti-platelets or anticoagulants), digitalis, diuretics (thiazides, loop diuretics and aldosterone antagonists), beta blockers, RAS-blocking agents, calcium receptor-blocking agents and statins were used as independent factors in the analyses. For antithrombotic drugs we used three groups: no antithrombotic treatment, anti-platelets only and anticoagulants only. We excluded the group with both anti-platelets and anticoagulants, as it would be hard to interpret the effects seen. We excluded specific anti-arrhythmic agents, potassium-saving agents other than aldosterone antagonists and lipid-lowering agents other than statins due to their low prescription rates. We did not divide RAS-blocking agents into ACE inhibitors and ARB, or calcium receptor-blockers into those with vessel- and heart-specific effects. Adjustment was made for a propensity score comprising age group, cardiovascular co-morbidities (hypertension, CHD, HF and CVDs), educational level, marital status, and all pharmacotherapies . A unique propensity score was constructed for each drug class to balance all other cardiovascular drug classes, as well as co-morbidities, age, sex (in sex-adjusted models) and socio-economic factors. We excluded valvular diseases and cardiomyopathy from the analyses due to the low numbers of cases.
A p-value for two-sided tests of <0.01 was considered statistically significant. This cut-off was used due to the multiple comparisons between men and women. A p-value of <0.05 was considered statistically significant for variables in the Cox and Laplace regression analyses, using 95% confidence intervals.
Baseline data for patients aged ≥45 years with diagnoses of AF and diabetes mellitus ( n = 2,413) in primary care attending the 75 PHCCs between January 1 st 2001 and December 31 st 2007
Men n = 1,319
Women n = 1,094
Number of patients
Age (years), mean (SD)
College and/or university studies
Coronary heart disease
Chronic heart failure
Prescription of drug classes for patients aged ≥45 years with diagnoses of AF and diabetes mellitus ( n = 2,413) in primary care attending the 75 PHCCs between January 1 st 2001 and December 31 st 2007
Men n = 1,319
Women n = 1,094
All antithrombotic agents (B01A)
Anticoagulant agents (B01AA)
Antiplatelet agents (B01AC)
ASA (B01AC6, B01AC30)
Antithrombotic treatment group:
No antithrombotic agent
Both antiplatelets and anticoagulants
Specific anti-arrhythmic agents (C01B)
Any diuretic treatment (C03)
Loop diuretics (C03C)
Thiazides (C03A, C03B, C03E, C09B C09DA)
Any potassium-saving agent (C03D)
Aldosterone antagonists (C03DA)
All beta blockers (C07)
Beta1-selective agents (C07AB, C07F)
Non-selective beta blockers (C07AA)
Calcium receptor-blocking agents (C08)
RAS-blocking agents (C09)
ACE inhibitors (C09A, C09B)
ARB (C09C, C09D)
All lipid-lowering drugs (C10A)
Number of cardiovascular drugs
Cox regression models with total mortality as outcome for patients for patients aged ≥45 years with diagnoses of AF and diabetes mellitus ( n = 2,413) attending the 75 PHCCs between January 1 st 2001 and December 31 st 2007, stratified by age: 45–79 years (n = 1,704) and 80–104 years (n = 709)
Patients aged <80 years
Patients aged ≥80 years
HR (95% CI)
HR (95% CI)
HR (95% CI)
Antiplatelets vs. no treatment
Anticoagulants vs. no treatment
Anticoagulants vs. antiplatelets
Calcium receptor-blocking agents
Laplace regression models with years to first 10% of total mortality as outcome for patients aged ≥45 years with diagnoses of AF and diabetes mellitus ( n = 2,413) attending the 75 PHCCs between January 1 st 2001 and December 31 st 2007, stratified by age: 45–79 years (n = 1,704) and 80–104 years (n = 709)
Patients aged <80 years
Patients aged ≥80 years
HR (95% CI)
HR (95% CI)
HR (95% CI)
Antiplatelets vs. no treatment
0.81 (-1.24; 2.87)
1.78 (1.04; 2.52)
1.02 (-0.34; 2.39)
Anticoagulants vs. no treatment
2.70 (0.04; 5.37)
1.22 (-0.06; 2.51)
2.20 (0.79; 3.62)
Anticoagulants vs. antiplatelets
2.31 (0.84; 3.79)
0.23 (-1.10; 1.55)
1.21 (-0.03; 2.45)
1.02 (-0.26; 2.30)
0.25 (-2.35; 2.85)
0.58 (-0.28; 1.45)
-2.44 (-3.79; -1.08)
0.69 (-0.89; 2.26)
-1.22 (-2.79; 0.34)
0.18 (-1.34; 1.70)
1.09 (0.10; 2.07)
0.35 (-0.56; 1.24)
-0.17 (-1.23; 0.88)
-0.17 (-0.89; 0.56)
-0.07 (-0.90; 0.76)
1.28 (-0.13; 2.69)
0.29 (-0.47; 1.06)
1.18 (0.32; 2.04)
-0.18 (-1.40; 1.04)
-0.19 (-0.74 (0.36)
0.23 (-0.57; 1.02)
Calcium receptor-blocking agents
-0.02 (-1.20; 1.16)
-0.07 (-0.86; 0.72)
-0.54 (-1.61; 0.53)
2.11 (0.88; 3.35)
0.38 (-1.37; 2.14)
1.24 (0.38; 2.10)
Significant interactions were found regarding sex and digitalis (p = 0.002), as well as sex and loop diuretics (p = 0.003). For digitalis, in men HR was 1.13 (95% CI 0.76-1.68) and in women 0.54 (95% CI 0.35-0.82), and for loop diuretics, in men HR was 2.51 (95% CI 1.39-4.53) and in women 0.91 (95% CI 0.54-1.53).
The main findings of this study was that anticoagulants were superior in reducing mortality among individuals with diabetes and AF below 80 years of age, while antiplatelet therapy seemed equally effective as anticoagulants among individuals 80 years and above. An unexpected finding was that mortality was reduced in women who were prescribed digitalis, but not in men. In addition, mortality was increased in men prescribed loop diuretics. Furthermore, we found that mortality was reduced in patients aged below 80 years prescribed statins, and also among those below 80 years prescribed beta blockers.
One might have expected that all prescribed pharmacotherapies would decrease mortality among the population in this study. However, the benefits of a drug could be offset by side effects, as in the case of disopyramide, quinidine and sotalol, which are known to be associated with increased mortality . The studied subgroup of subjects, i.e. AF patients with diabetes, could show a risk pattern different to what could be expected in AF patients in general.
An interesting finding was the equally positive effects of antiplatelets and anticoagulants on mortality among individuals ≥80 years of age, making findings from a Cochrane review accountable also for patients with diabetes and AF . Although anticoagulants are superior to antiplatelets in stroke prevention of AF patients , the current study indicates that among elderly individuals antiplatelets could be useful for those who do not tolerate anticoagulants because of equal effects on all-cause mortality.
Prescription of digitalis was associated with decreased mortality only among women. It seems, according to a review, that beneficial effects of digitalis are obtained at low digoxin concentrations, while high serum levels were associated with increased mortality . Positive effects of digitalis in AF and CHF seem to be related to attenuation of sympathetic activation and neurohumoral alterations. Diabetes is associated with an impaired myocardial energy production affecting myocyte contraction and diastolic function , leading to diastolic heart dysfunction, and little is known about the optimal treatment of this condition . The female myocardium could possibly benefit more from the effects of digitalis than the male. Another explanation could be that the gender differences might reflect different prescription patterns in men and women with AF. The previously reported higher mortality among men < 80 years  could be due to more pronounced CHF , and consequently higher digitalis doses.
The finding of an association between prescription of loop diuretics with an increased mortality among men could be explained by concomitant presence of CHF, as CHF in AF patients do increase mortality . Prescribed loop diuretics may be a marker of more advanced CHF, but this is contradicted by the fact that other possible markers of this, i.e. digitalis , and aldosterone antagonists , were not associated with significantly increased mortality. Another possibility is that loop diuretics could have increased arrhythmia risks due to potassium depletion [32, 33].
The reduced mortality associated with prescription of statins is in line with evidence of positive effect of statins on diabetic patients . The mortality reduction associated with statins was more pronounced compared to a meta-analysis of randomized trials , as is commonly the case in observational studies using propensity scores . Statins have been shown to have anti-arrhythmic properties , which could be relevant for patients with AF. However, it cannot be excluded that the positive finding for survival could be due to confounding by indication, i.e., severely ill patients not being prescribed statins to the same extent.
Crude mortality was higher in women than in men, in agreement with others’ findings . In an earlier Swedish study, we showed that men with AF had a relative mortality risk of 1.3 and women with AF a relative mortality risk of 1.9 when they were compared to men and women in the general population . The women in this study were on average five years older than the men (mean age 76.3 vs. 71.7), and could consequently be expected to have 11% higher mortality rate than men (data from Statistics Sweden). However, we found a 46% higher mortality rate in women compared to men, which could be explained by the excess mortality risk in women due to both diabetes , as well as to AF , or the co-morbidity.
A limitation of this study was the small sample size, which prevented us from detecting small differences, even though the analyses included almost 9,000 person-years at risk. Since this was an observational study of the association between prescription of drugs and mortality, the findings may have been subject to confounding by indication [41, 42]. Furthermore, drugs prescribed by other caregivers were not included in the patient records, which may have weakened the associations between prescription of certain medications and mortality in this study. We did not have access to doses of the prescribed drugs. Severity of CHF and CHD were not classified in the patient records. Moreover, AF could not be classified as paroxysmal, persistent or permanent and heart rhythm could not be classified as sinus rhythm or fibrillation rhythm. We had not access to data on renal disease, and besides not of non-cardiovascular diseases or pharmacotherapies. Thus, we cannot exclude the possibility that residual confounding may explain at least partly some of the results.
A major strength of this study was that we were able to link clinical data from individual EPRs to data from national demographic and socioeconomic registers with less than 1% of information missing. In addition, an earlier study of the diagnoses recorded in the EPRs showed that less than 2% of all diagnoses per individual were missing . The use of a propensity score with covariates relevant to mortality as the outcome was an additional strength of this study as it enabled adjustment with a low risk of over-fitting regression models . Besides, we used Laplace regression to confirm results of Cox regression in the entire sample.
In conclusion, we found significantly positive effects of both antiplatelets and anticoagulants on survival among individuals aged ≥80 years, which could imply that antiplatelets despite little effect in stroke prevention could be beneficial on survival in elderly diabetes patients with AF when anticoagulants cannot be prescribed. We also found gender differences, indicating a positive effect by digitalis on survival among women, and a negative effect by loop diuretics among men, both needing further attention. The decreased mortality risk associated with prescription of statins underlines the positive effect of statins in diabetes patients with AF, and treatment by statins could be as important as antithrombotics in these patients. More studies focusing on diabetes patients with AF are needed to confirm or reject our findings.
This work was supported by grants to Dr Kristina Sundquist from the Swedish Research Council (K2009-70X-15428-05-3; K2012-70X-15428-08-3), to Dr Jan Sundquist from the Swedish Freemasons Foundation as well as ALF funding from Region Skåne awarded to Jan Sundquist and Kristina Sundquist.
- Wirehn AB, Karlsson HM, Carstensen JM: Estimating disease prevalence using a population-based administrative healthcare database. Scand J Public Health. 2007, 35 (4): 424-431. 10.1080/14034940701195230.View ArticlePubMedGoogle Scholar
- Huxley RR, Filion KB, Konety S, Alonso A: Meta-analysis of cohort and case–control studies of type 2 diabetes mellitus and risk of atrial fibrillation. Am J Cardiol. 2011, 108 (1): 56-62. 10.1016/j.amjcard.2011.03.004.PubMed CentralView ArticlePubMedGoogle Scholar
- Bajpai A, Savelieva I, Camm AJ: Treatment of atrial fibrillation. Br Med Bull. 2008, 88 (1): 75-94. 10.1093/bmb/ldn046.View ArticlePubMedGoogle Scholar
- Kannel WB, Wolf PA, Benjamin EJ, Levy D: Prevalence, incidence, prognosis, and predisposing conditions for atrial fibrillation: population-based estimates. Am J Cardiol. 1998, 82 (8A): 2N-9N.View ArticlePubMedGoogle Scholar
- Humphries KH, Kerr CR, Connolly SJ, Klein G, Boone JA, Green M, Sheldon R, Talajic M, Dorian P, Newman D: New-onset atrial fibrillation: sex differences in presentation, treatment, and outcome. Circulation. 2001, 103 (19): 2365-2370. 10.1161/01.CIR.103.19.2365.View ArticlePubMedGoogle Scholar
- Michelena HI, Powell BD, Brady PA, Friedman PA, Ezekowitz MD: Gender in atrial fibrillation: ten years later. Gend Med. 2010, 7 (3): 206-217. 10.1016/j.genm.2010.06.001.View ArticlePubMedGoogle Scholar
- Hart RG, Pearce LA, Aguilar MI: Meta-analysis: antithrombotic therapy to prevent stroke in patients who have nonvalvular atrial fibrillation. Ann Intern Med. 2007, 146 (12): 857-867. 10.7326/0003-4819-146-12-200706190-00007.View ArticlePubMedGoogle Scholar
- Lip GY, Lane DA: Does warfarin for stroke thromboprophylaxis protect against MI in atrial fibrillation patients?. Am J Med. 2010, 123 (9): 785-789. 10.1016/j.amjmed.2010.01.031.View ArticlePubMedGoogle Scholar
- Lane DA, Lip GY: Atrial fibrillation and mortality: the impact of antithrombotic therapy. Eur Heart J. 2010, 31 (17): 2075-2076. 10.1093/eurheartj/ehp486.View ArticlePubMedGoogle Scholar
- Aguilar MI, Hart R, Pearce LA: Oral anticoagulants versus antiplatelet therapy for preventing stroke in patients with non-valvular atrial fibrillation and no history of stroke or transient ischemic attacks. Cochrane Database Syst Rev. 2007, 3: CD006186Google Scholar
- Gaede P, Lund-Andersen H, Parving HH, Pedersen O: Effect of a multifactorial intervention on mortality in type 2 diabetes. N Engl J Med. 2008, 358 (6): 580-591. 10.1056/NEJMoa0706245.View ArticlePubMedGoogle Scholar
- Kearney PM, Blackwell L, Collins R, Keech A, Simes J, Peto R, Armitage J, Baigent C: Efficacy of cholesterol-lowering therapy in 18,686 people with diabetes in 14 randomised trials of statins: a meta-analysis. Lancet. 2008, 371 (9607): 117-125. 10.1016/S0140-6736(08)60104-X.View ArticlePubMedGoogle Scholar
- Stratton IM, Cull CA, Adler AI, Matthews DR, Neil HA, Holman RR: Additive effects of glycaemia and blood pressure exposure on risk of complications in type 2 diabetes: a prospective observational study (UKPDS 75). Diabetologia. 2006, 49 (8): 1761-1769. 10.1007/s00125-006-0297-1.View ArticlePubMedGoogle Scholar
- Carlsson AC, Wandell P, Sundquist K, Johansson SE, Sundquist J: Differences and time trends in drug treatment of atrial fibrillation in men and women and doctors’ adherence to warfarin therapy recommendations: a Swedish study of prescribed drugs in primary care in 2002 and 2007. Eur J Clin Pharmacol. 2013, 69 (2): 245-253. 10.1007/s00228-012-1322-6.View ArticlePubMedGoogle Scholar
- Wandell P, Carlsson AC, Sundquist K, Johansson SE, Sundquist J: Effect of cardiovascular drug classes on all-cause mortality among atrial fibrillation patients treated in primary care in Sweden: a cohort study. Eur J Clin Pharmacol. 2013, 69 (2): 279-287. 10.1007/s00228-012-1395-2.View ArticlePubMedGoogle Scholar
- Sundquist K, Li X: Coronary heart disease risks in first- and second-generation immigrants in Sweden: a follow-up study. J Intern Med. 2006, 259 (4): 418-427. 10.1111/j.1365-2796.2006.01630.x.View ArticlePubMedGoogle Scholar
- Almgren T, Wilhelmsen L, Samuelsson O, Himmelmann A, Rosengren A, Andersson OK: Diabetes in treated hypertension is common and carries a high cardiovascular risk: results from a 28-year follow-up. J Hypertens. 2007, 25 (6): 1311-1317. 10.1097/HJH.0b013e328122dd58.View ArticlePubMedGoogle Scholar
- Wandell PE, Carlsson AC, Sundquist K, Johansson SE, Sundquist J: Total mortality among levothyroxine-treated women with atrial fibrillation in Swedish primary health care. Int J Cardiol. 2011, 152 (1): 147-148. 10.1016/j.ijcard.2011.07.066.View ArticlePubMedGoogle Scholar
- Bottai M, Zhang J: Laplace regression with censored data. Biom J. 2010, 52 (4): 487-503. 10.1002/bimj.200900310.View ArticlePubMedGoogle Scholar
- Orsini N, Wolk A, Bottai M: Evaluating percentiles of survival. Epidemiology. 2012, 23 (5): 770-771. 10.1097/EDE.0b013e3182625eff.View ArticlePubMedGoogle Scholar
- Friberg L, Hammar N, Ringh M, Pettersson H, Rosenqvist M: Stroke prophylaxis in atrial fibrillation: who gets it and who does not? Report from the Stockholm Cohort-study on Atrial Fibrillation (SCAF-study). Eur Heart J. 2006, 27 (16): 1954-1964. 10.1093/eurheartj/ehl146.View ArticlePubMedGoogle Scholar
- Fuster V, Ryden LE, Cannom DS, Crijns HJ, Curtis AB, Ellenbogen KA, Halperin JL, Le Heuzey JY, Kay GN, Lowe JE, et al: ACC/AHA/ESC 2006 guidelines for the management of patients with atrial fibrillation: full text: a report of the American College of Cardiology/American Heart Association Task Force on practice guidelines and the European Society of Cardiology Committee for Practice Guidelines (Writing Committee to Revise the 2001 guidelines for the management of patients with atrial fibrillation) developed in collaboration with the European Heart Rhythm Association and the Heart Rhythm Society. Europace. 2006, 8 (9): 651-745.View ArticlePubMedGoogle Scholar
- Dahabreh IJ, Sheldrick RC, Paulus JK, Chung M, Varvarigou V, Jafri H, Rassen JA, Trikalinos TA, Kitsios GD: Do observational studies using propensity score methods agree with randomized trials? A systematic comparison of studies on acute coronary syndromes. Eur Heart J. 2012, 33 (15): 1893-1901. 10.1093/eurheartj/ehs114.PubMed CentralView ArticlePubMedGoogle Scholar
- Lafuente-Lafuente C, Longas-Tejero MA, Bergmann JF, Belmin J: Antiarrhythmics for maintaining sinus rhythm after cardioversion of atrial fibrillation. Cochrane Database Syst Rev. 2012, 5: CD005049Google Scholar
- Hoppe UC, Erdmann E: Digitalis in heart failure! Still applicable?. Z Kardiol. 2005, 94 (5): 307-311. 10.1007/s00392-005-0219-0.View ArticlePubMedGoogle Scholar
- Kota SK, Jammula S, Panda S, Modi KD: Effect of diabetes on alteration of metabolism in cardiac myocytes: therapeutic implications. Diabetes Technol Ther. 2011, 13 (11): 1155-1160. 10.1089/dia.2011.0120.View ArticlePubMedGoogle Scholar
- Piccini JP, Klein L, Gheorghiade M, Bonow RO: New insights into diastolic heart failure: role of diabetes mellitus. Am J Med. 2004, 116 (Suppl 5A): 64S-75S.View ArticlePubMedGoogle Scholar
- Jimenez-Navarro MF, Ramirez-Marrero MA, Anguita-Sanchez M, Castillo JC: Influence of gender on long-term prognosis of patients with chronic heart failure seen in heart failure clinics. Clin Cardiol. 2010, 33 (3): E13-18. 10.1002/clc.20476.View ArticlePubMedGoogle Scholar
- Seiler J, Stevenson WG: Atrial fibrillation in congestive heart failure. Cardiol Rev. 2010, 18 (1): 38-50. 10.1097/CRD.0b013e3181c21cff.View ArticlePubMedGoogle Scholar
- Gheorghiade M, Harinstein ME, Filippatos GS: Digoxin for the treatment of chronic and acute heart failure syndromes. Acute Card Care. 2009, 11 (2): 83-87. 10.1080/17482940902883246.View ArticlePubMedGoogle Scholar
- Schrier RW, Masoumi A, Elhassan E: Aldosterone: role in edematous disorders, hypertension, chronic renal failure, and metabolic syndrome. Clin J Am Soc Nephrol. 2010, 5 (6): 1132-1140. 10.2215/CJN.01410210.View ArticlePubMedGoogle Scholar
- Cooper HA, Dries DL, Davis CE, Shen YL, Domanski MJ: Diuretics and risk of arrhythmic death in patients with left ventricular dysfunction. Circulation. 1999, 100 (12): 1311-1315. 10.1161/01.CIR.100.12.1311.View ArticlePubMedGoogle Scholar
- Domanski M, Norman J, Pitt B, Haigney M, Hanlon S, Peyster E: Diuretic use, progressive heart failure, and death in patients in the Studies Of Left Ventricular Dysfunction (SOLVD). J Am Coll Cardiol. 2003, 42 (4): 705-708. 10.1016/S0735-1097(03)00765-4.View ArticlePubMedGoogle Scholar
- Pesaro AE, de Matos SA, Serrano CV, Giraldez RR, Ladeira RT, Nicolau JC: Effect of beta-blockers on the risk of atrial fibrillation in patients with acute myocardial infarction. Clinics (Sao Paulo). 2010, 65 (3): 265-270. 10.1590/S1807-59322010000300005.View ArticleGoogle Scholar
- CIBIS-II Investigators and Committees: The Cardiac Insufficiency Bisoprolol Study II (CIBIS-II): a randomised trial. Lancet. 1999, 353 (9146): 9-13.View ArticleGoogle Scholar
- MERIT-HF Study Group: Effect of metoprolol CR/XL in chronic heart failure: Metoprolol CR/XL Randomised Intervention Trial in Congestive Heart Failure (MERIT-HF). Lancet. 1999, 353 (9169): 2001-2007.View ArticleGoogle Scholar
- Bachmann JM, Majmudar M, Tompkins C, Blumenthal RS, Marine JE: Lipid-altering therapy and atrial fibrillation. Cardiol Rev. 2008, 16 (4): 197-204. 10.1097/CRD.0b013e318177271d.View ArticlePubMedGoogle Scholar
- Friberg J, Scharling H, Gadsboll N, Truelsen T, Jensen GB: Comparison of the impact of atrial fibrillation on the risk of stroke and cardiovascular death in women versus men (The Copenhagen City Heart Study). Am J Cardiol. 2004, 94 (7): 889-894. 10.1016/j.amjcard.2004.06.023.View ArticlePubMedGoogle Scholar
- Wandell PE: Five-year mortality in men and women with atrial fibrillation. Scand J Prim Health Care. 2001, 19 (2): 112-116. 10.1080/028134301750235358.View ArticlePubMedGoogle Scholar
- Lee C, Joseph L, Colosimo A, Dasgupta K: Mortality in diabetes compared with previous cardiovascular disease: a gender-specific meta-analysis. Diabetes Metab. 2012, 38 (5): 420-427. 10.1016/j.diabet.2012.04.002.View ArticlePubMedGoogle Scholar
- de Koning JS, Klazinga NS, Koudstaal PJ, Prins A, Borsboom GJ, Mackenbach JP: The role of ‘confounding by indication’ in assessing the effect of quality of care on disease outcomes in general practice: results of a case–control study. BMC Health Serv Res. 2005, 5 (1): 10-10.1186/1472-6963-5-10.PubMed CentralView ArticlePubMedGoogle Scholar
- Grobbee DE, Hoes AW: Confounding and indication for treatment in evaluation of drug treatment for hypertension. BMJ. 1997, 315 (7116): 1151-1154. 10.1136/bmj.315.7116.1151.PubMed CentralView ArticlePubMedGoogle Scholar
- Sundquist K, Chaikiat A, Leon VR, Johansson SE, Sundquist J: Country of birth, socioeconomic factors, and risk factor control in patients with type 2 diabetes: a Swedish study from 25 primary health-care centres. Diabetes Metab Res Rev. 2011, 27 (3): 244-254. 10.1002/dmrr.1161.View ArticlePubMedGoogle Scholar
- Senn S, Graf E, Caputo A: Stratification for the propensity score compared with linear regression techniques to assess the effect of treatment or exposure. Stat Med. 2007, 26 (30): 5529-5544. 10.1002/sim.3133.View ArticlePubMedGoogle Scholar
This article is published under license to BioMed Central Ltd. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.