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Risk of heart failure in elderly patients with atrial fibrillation and diabetes taking different oral anticoagulants: a nationwide cohort study

Cardiovascular Diabetology volume 22, Article number: 1 (2023) Cite this article

Abstract

Background

Heart failure (HF) is a critical complication in elderly patients with atrial fibrillation (AF) and diabetes mellitus (DM). Recent preclinical studies suggested that non-vitamin K antagonist oral anticoagulants (NOACs) can potentially suppress the progression of cardiac fibrosis and ischemic cardiomyopathy. Whether different oral anticoagulants influence the risk of HF in older adults with AF and DM is unknown. This study aimed to evaluate the risk of HF in elderly patients with AF and DM who were administered NOACs or warfarin.

Methods

A nationwide retrospective cohort study was conducted based on claims data from the entire Taiwanese population. Target trial emulation design was applied to strengthen causal inference using observational data. Patients aged  ≥ 65 years with AF and DM on NOAC or warfarin treatment between 2012 and 2019 were included and followed up until 2020. The primary outcome was newly diagnosed HF. Propensity score-based fine stratification weightings were used to balance patient characteristics between NOAC and warfarin groups. Hazard ratios (HRs) were estimated using Cox proportional hazard models.

Results

The study included a total of 24,835 individuals (19,710 NOAC and 5,125 warfarin users). Patients taking NOACs had a significantly lower risk of HF than those taking warfarin (HR = 0.80, 95% CI 0.74–0.86, p < 0.001). Subgroup analyses for individual NOACs suggested that dabigatran (HR = 0.86, 95% CI 0.80–0.93, p < 0.001), rivaroxaban (HR = 0.80, 95% CI 0.74–0.86, p < 0.001), apixaban (HR = 0.78, 95% CI 0.68–0.90, p < 0.001), and edoxaban (HR = 0.72, 95% CI 0.60–0.86, p < 0.001) were associated with lower risks of HF than warfarin. The findings were consistent regardless of age and sex subgroups and were more prominent in those with high medication possession ratios. Several sensitivity analyses further supported the robustness of our findings.

Conclusions

This nationwide cohort study demonstrated that elderly patients with AF and DM taking NOACs had a lower risk of incident HF than those taking warfarin. Our findings suggested that NOACs may be the preferred oral anticoagulant treatment when considering the prevention of heart failure in this vulnerable population. Future research is warranted to elucidate causation and investigate the underlying mechanisms.

Background

In the elderly population, atrial fibrillation (AF) and diabetes mellitus (DM) are both global epidemics and important public health problems [1, 2]. Due to their high prevalence and incidence, these two chronic conditions commonly coexist. Heart failure (HF), another global epidemic, affects at least 26 million people worldwide and is one of the leading causes of morbidity, hospitalization, and mortality in older adults, placing a huge financial burden on the health care system [3, 4]. Current evidence indicates that HF is a critical complication in patients with AF and DM. Hyperglycemia, insulin resistance, and hyperinsulinemia in DM can trigger a cascade of deleterious effects contributing to development of HF and effort intolerance [5,6,7]. The tachycardia, irregularity, loss of atrial systole, and cardiac fibrosis in patients with AF also contribute to HF development [4, 8]. Since AF, DM, and aging are all major risk factors of HF [4, 5, 9] and concomitant HF in elderly patients with AF and DM could increase their risk of stroke, worsen patient prognoses, and increase the healthcare cost burden [5, 10, 11], the prevention of HF development in the elderly population with AF and DM is crucial.

Long-term oral anticoagulant treatment is an essential medication for stroke prevention in elderly patients with AF and DM [12, 13]. Warfarin, a vitamin K antagonist, has been used to prevent stroke for decades. Non-vitamin K antagonist oral anticoagulants (NOACs) have been approved as another choice of oral anticoagulants and have been found to offer comparable efficacy and safety for stroke prevention [14]. In addition to its anticoagulation effect, recent studies have suggested that NOACs, compared with warfarin, were linked to better glycemic control and lower diabetes complication risks [15,16,17]. Additionally, recent preclinical studies suggested that NOACs have potential anti-inflammatory effects and may suppress the progression of cardiac fibrosis and ischemic cardiomyopathy, all of which are related to the pathophysiology of HF [18,19,20]. Thus, it is reasonable to suppose that NOACs may have a beneficial effect on preventing HF compared with warfarin in patients with AF and DM. As HF is also an independent and potent risk factor for stroke development [11], of which oral anticoagulants are mainly prescribed for prevention, choosing appropriate oral anticoagulant types to decrease incident HF risks is crucial.

However, to date, the evidence comparing the risk of HF between NOAC and warfarin use is still lacking, even though this issue is critical for improving patient prognosis in elderly adults already with AF and DM. Therefore, we used nationwide cohort data to investigate the risk of HF development in elderly patients with AF and DM taking NOAC versus warfarin.

Methods

Data sources

We conducted a nationwide retrospective cohort study using data from the National Health Insurance Research Database (NHIRD) in Taiwan. The National Health Insurance program, a mandatory single-payer program administered by Taiwan’s government, covered more than 99% of the entire population in Taiwan (approximately 23.6 million individuals) [21, 22]. The NHIRD contains patient demographic information and medical claims for all inpatient, outpatient, and emergency care services in Taiwan. The diagnostic and procedure codes in NHIRD were derived using the International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) codes before 2016 and the International Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM) codes since 2016. Information on mortality was obtained by cross-referencing the NHIRD with the Taiwan National Register of Deaths. The NHIRD is maintained by the Health and Welfare Data Science Center, Ministry of Health and Welfare, Taiwan, and the anonymized data has been made available for research purposes by formal application. Our study was approved by the Research Ethics Committee of Hualien Tzu Chi Hospital (REC No: IRB107-152-C); the requirement for informed consent was waived due to the retrospective use of anonymized data. This study was conducted in accordance with the World Medical Association Declaration of Helsinki.

Study population

We conducted the observational study with target trial emulation to strengthen causal inference [23, 24]; the details of how we emulated a target trial are described in Additional file 1: Table S1. We applied similar selection criteria to those of the target trial to include all adults aged  ≥ 65 years with diagnoses of both AF and DM who had been treated with oral anticoagulants between 2012 and 2019 in NHIRD. The ICD-9-CM code 427.31 and ICD-10-CM codes I48.0, I48.1, I48.2, and I48.91 were used to identify AF diagnosis; the ICD-9-CM code 250 or ICD-10-CM codes E08-E13 were used for DM diagnosis. Both diagnoses should be made at least once in an inpatient service or twice in outpatient services. We restricted our study population to patients aged  ≥ 65 years because both AF and HF developed mainly in older adults.

We excluded patients without AF and DM diagnoses at baseline. We excluded patients with end-stage renal disease (ESRD), rheumatic heart disease, congenital heart disease, or having valve replacement surgery before the index date because those patients are more likely to receive warfarin over NOACs [25], and their exclusion helped minimize a potential confounding-by-indication bias. To apply the new-user design, those with a prescription of any oral anticoagulants in 2011 were excluded, enhancing the likelihood of identifying new oral anticoagulant users since 2012 when NOACs were introduced in Taiwan’s National Health Insurance program. We excluded those with index dates in 2020, ensuring at least 1-year follow-up for each patient. Finally, we excluded patients with any prior HF diagnoses before the index date (Additional file 1: Figure S1).

Exposures, outcomes, and follow-up

To emulate a target trial with intention-to-treat analysis, we used an as-started design that divided patients into NOAC and warfarin groups according to their first oral anticoagulant use regardless of subsequent prescriptions [24]. The index date (time zero of follow-up) was defined as the date of initiation of oral anticoagulant treatment, and follow-up began since then.

The primary outcome was the incident HF diagnosed in an inpatient service or at least three times in an outpatient service (ICD-9-CM code: 428; ICD-10-CM code: I50). The date of the first HF diagnosis was assigned as the date of event occurrence. We followed up with each patient from their index date until an occurrence of the outcome event, death, or December 31, 2020 (the last date in our database), whichever came first.

In our main analyses, we compared HF risk between overall NOACs versus warfarin. We further performed subgroup analyses that subclassified NOACs into four subgroups (dabigatran, rivaroxaban, apixaban, and edoxaban) and compared each with warfarin. We also performed analyses stratified by age (65–74 and  ≥ 75 years), sex, and hospital levels.

Covariates and confounders

Pre-existing comorbidity was defined as a condition diagnosed at least once on an inpatient basis or twice on an outpatient basis within the year prior to the index date. The Charlson comorbidity index was calculated to quantify the overall comorbidity status [26]. We also calculated the CHA2DS2-VASc score, which is used to predict stroke risk and determine whether an oral anticoagulant should be used in clinical practice [27, 28]. We defined baseline antidiabetic drugs based on the treatment prescribed within 1 month prior to the index date; the number of diabetes medication types were also calculated. Other baseline medications were defined as a drug prescribed for at least 30 days within the year prior to the index date. The duration of AF and DM were defined as the period from the date of first diagnosis of AF or DM to the date of initiating oral anticoagulants (index date). The index year, monthly income (derived from income-related insurance premiums), physician’s medical specialty, and the hospital level of oral anticoagulant initiation were also retrieved as covariates [17].

Propensity score-based fine stratification weighting

We calculated the propensity score for each patient to estimate the probability of initiating NOACs using multivariable logistic regression models, including all covariates shown in Table 1. We used fine stratification weights based on propensity scores to create more exchangeable groups with balanced characteristics for comparisons. Two fine stratification weighting methods were applied to cover both targets of inference: estimation of the average treatment effect in the whole population (ATE) and estimation of the average treatment effect among the treated population (ATT) [29]. The individuals were stratified into 50 strata by the propensity score distribution; how the weights were calculated in each stratum is described elsewhere [29]. The propensity score-based fine stratification weighting was conducted individually for each comparison set, including that of overall analyses, subgroup analyses, stratified analyses, or sensitivity analyses.

Table 1 Baseline characteristics of older patients with atrial fibrillation and diabetes receiving NOAC or warfarin after propensity score-based fine stratification weighting
Full size table

Statistical analyses

The difference in baseline characteristics was determined by standardized difference, with a value of  < 0.1 considered negligible. The standardized difference is preferred to significance testing of covariates between study groups because it is not confounded by sample sizes or the statistical power [30]. We estimated the cumulative incidences and cause-specific hazard ratios (HRs) of HF using cause-specific Cox proportional hazard models with death treated as a censoring event [31]. To address the potential cluster effect and variation from each different hospital or clinic (where oral anticoagulant treatment was initiated), we included shared frailty, estimating the cluster random effect of hospital/clinic, into the regression model [32, 33]. A two-tailed probability (p) value < 0.05 was considered statistically significant. We managed data and performed statistical analyses using SAS software, version 9.4 (SAS Institute, Inc., Cary, NC, USA) and STATA, version 15 (Stata Corporation LLC, College Station, TX, USA).

Sensitivity analyses

Various sensitivity analyses were conducted to determine the robustness of our study results. First, to consider the treatment adherence during follow-up, we applied the on-treatment design (analog of per-protocol design in clinical trials) in which the follow-up would be censored when the oral anticoagulant type was switched or discontinued. Discontinuation was defined as patients without a refilled prescription of the index oral anticoagulant 90 days after the last prescription. Second, we restricted our analysis to those taking the index oral anticoagulant with a high medication possession ratio, defined as  ≥ 80%. The medication possession ratio was calculated by dividing the number of days with prescription of oral anticoagulants by the days of the follow-up period [34]. Third, we excluded patients with any diagnoses of chronic kidney disease (CKD) before the index date since we could not obtain individuals’ renal function data, which may influence the choice of NOACs. Fourth, to determine whether potential variations between physicians who initiated the NOAC or warfarin prescription influenced our results, we performed a sensitivity analysis that included shared frailty, estimating the cluster random effect of different physicians, into the regression model. Additionally, we performed two sensitivity analyses with different statistical designs. One sensitivity analysis applied propensity score matching (rather than fine stratification) was performed to balance patient characteristics between groups; the matching was based on the nearest-neighbor matching algorithm without replacement, with a caliper width equal to 0.2 standard deviation of the logit of the propensity score [35, 36]. Another sensitivity analysis estimated the adjusted HRs by multivariable Cox regression models based on the original cohort without applying propensity score methods.

Results

Patient characteristics

We initially included 24,835 patients (19,710 NOAC and 5,125 warfarin users) after applying the inclusion and exclusion criteria; the patient characteristics in the original cohort are shown in Additional file 1: Table S2. For further analyses, we constructed pseudo-populations containing 19,591 NOAC and 5,117 warfarin users after applying propensity score-based fine stratification weighting. The patient characteristics in the weighted population for ATE and ATT estimation are presented in Table 1. The mean age was approximately 76.6 years, and female patients accounted for 47% of all participants. The mean follow-up duration was 3.0 years. Patient characteristics were balanced appropriately between groups after fine stratification weighting, with standardized differences  < 0.1. The flowchart of patient selection is presented in Additional file 1: Figure S1.

Risk of incident HF

In the analysis with propensity score-based fine stratification weighting for ATE estimation, NOAC use was significantly associated with a lower risk of developing HF than warfarin use (HR = 0.80, 95% confidence interval CI 0.74–0.86, p < 0.001). In the ATT estimation analysis, a similar result of lower HF risk in NOAC users was observed (HR = 0.77, 95% CI 0.70–0.84, p < 0.001) (Table 2). Figure 1 illustrates the curves for cumulative HF incidences in patients taking NOACs and those taking warfarin; a lower cumulative HF incidence was observed in NOAC users. The curves for estimating ATE and ATT are shown in Fig. 1A and B, respectively.

Table 2 Risk of heart failure in older patients with atrial fibrillation and diabetes receiving NOAC versus warfarin
Full size table
Fig. 1
figure 1

The cumulative incidence curves of HF in elderly patients with AF and DM taking NOACs and those taking warfarin. The curves were estimated according to the pseudo-populations constructed by A propensity score-based fine stratification weighting estimating ATE and B that estimating ATT. AF atrial fibrillation, ATE average treatment effect in the whole population, ATT average treatment effect among the treated population, DM diabetes mellitus, NOAC non-vitamin K antagonist oral anticoagulant, HF heart failure

Full size image

In the ATE estimation analyses for each NOAC, dabigatran (HR = 0.86, 95% CI 0.80–0.93, p < 0.001), rivaroxaban (HR = 0.80, 95% CI 0.74–0.86, p < 0.001), apixaban (HR = 0.78, 95% CI 0.68–0.90, p < 0.001), and edoxaban (HR = 0.72, 95% CI 0.60–0.86, p < 0.001) were all associated with a lower HF risk when compared with warfarin (Table 3). The ATT estimation analyses demonstrated similar findings.

Table 3 Risk of heart failure in older patients with atrial fibrillation and diabetes receiving each NOAC versus warfarin
Full size table

In the analyses stratified by age, sex, and hospital levels, consistent findings were observed; the significantly lower HF risk associated with NOAC use was observed in all stratified groups, regardless of age, sex, or hospital levels (Table 4).

Table 4 Risk of heart failure in older patients with atrial fibrillation and diabetes receiving NOAC versus warfarin, stratified for age, sex, and hospital levels
Full size table

Results of sensitivity analyses

With the application of an on-treatment design, NOAC users still demonstrated a lower HF risk than warfarin users (HR = 0.67, 95% CI 0.60–0.75, p < 0.001) in the ATE estimation analysis (Table 5). In the analysis restricted to only patients with a high medication possession ratio (≥ 80%), a more remarkable association between NOAC use and lower HF risk was observed (HR = 0.47, 95% CI 0.40–0.56, p < 0.001) (Table 5). In the analysis that excluded patients with CKD, a similar result of a lower HF risk in NOAC users was observed (HR = 0.79, 95% CI 0.72–0.87, p < 0.001) (Table 5). Additionally, the analysis including shared frailty to address the potential cluster random effect of different physicians also demonstrated a similar result (HR = 0.80, 95% CI 0.74–0.86, p < 0.001). The above sensitivity analyses for ATT estimation demonstrated consistent results. In the analysis applying propensity score matching or using multivariable regression models to adjust for covariates without propensity score methods, NOAC users still had a lower HF risk than warfarin users (Additional file 1: Table S3). The baseline patient characteristics in the analysis applying propensity score matching are shown in Additional file 1: Table S4; the patient characteristics in the analysis using multivariable regression models only are shown in Additional file 1: Table S2. Overall, all the sensitivity analyses generated comparable results as our primary analyses, further supporting the robustness of our findings.

Table 5 Risk of heart failure in older patients with atrial fibrillation and diabetes receiving NOAC versus warfarin in the sensitivity analysis applying on-treatment design, that restricting patients with MPR  ≥ 80%, that excluding patients with CKD, and that considering cluster effects of different physicians
Full size table

Discussion

This nationwide retrospective cohort study demonstrated that elderly adults with AF and DM taking NOACs had an approximately 20% lower risk of incident HF than those taking warfarin. The association between NOAC use and decreased HF risk was consistent, regardless of age, sex, hospital-level subgroups, or the estimations for ATE or ATT. The findings were further supported by several sensitivity analyses. Notably, the lower risk of HF associated with NOAC use versus warfarin use was more remarkable in patients taking oral anticoagulants with a high medication possession ratio and when applying the on-treatment design to the analysis, implying the robust association between oral anticoagulant choices and HF risk.

Although the exact mechanisms of lower HF risk in NOAC users could not be determined in our study, several hypotheses could help explain our findings. Previous preclinical evidence has suggested that both factor Xa and thrombin have activities beyond coagulation, including involvement in inflammation, atherosclerotic plaque progression, atherothrombosis, vascular remodeling, and tissue fibrosis [18,19,20]. Among NOACs, rivaroxaban, apixaban, and edoxaban are factor Xa inhibitors, and dabigatran is a direct thrombin inhibitor; the inhibition of factor Xa or thrombin theoretically not only affects the function of coagulation but also the aforementioned activities. Recent preclinical and clinical studies have further supported that NOACs have potential anti-inflammatory effects, reduce atherosclerosis, help prevent ischemic heart disease, and suppress the progression of cardiac fibrosis and ischemic cardiomyopathy [18,19,20, 37, 38], all of which may restrain the pathophysiology of cardiac dysfunction and HF, further decreasing the risk of developing HF. In addition, previous studies have indicated that poor diabetes control increases the risk of developing HF [5, 39, 40]. Hyperglycemia, insulin resistance, and hyperinsulinemia could trigger a cascade of deleterious effects, such as inflammation, dyslipidemia, endothelial dysfunction, activation of the renin–angiotensin–aldosterone system, autonomic dysfunction, and cardiac fibrosis, which further cause both ischemic cardiomyopathy and diabetic cardiomyopathy, predisposing HF development [5]. Previous studies have found a beneficial role of vitamin K in improving insulin sensitivity and glucose tolerance and reducing insulin resistance through several mechanisms [41,42,43]. In recent real-world studies, better blood glucose and diabetes control were suggested in patients taking NOACs than in those taking warfarin due to the presence or absence of their mechanisms of antagonizing vitamin K [15,16,17]. It is therefore plausible to support that one of the explanations for NOACs being associated with lower HF risk than warfarin may be via their beneficial effects on glycemic and diabetes control.

Some existing studies have evaluated the efficacy and safety of NOACs versus those of warfarin for stroke prevention in AF patients already coexisting with HF [44, 45]. However, to our knowledge, evidence regarding the risk of incident HF in those treated with NOACs versus those treated with warfarin is still lacking. Our study focused on elderly AF patients with DM, a vulnerable population prone to HF, and demonstrated that NOACs were associated with a decreased risk of incident HF compared with warfarin. Such findings have important clinical impacts because HF coexisting with AF and DM could increase the risk of stroke, for which oral anticoagulants are mainly prescribed for prevention, and substantially deteriorate patient prognosis and quality of life [5, 10, 11]. Our results suggested that NOACs are the preferred oral anticoagulant treatment among elderly AF patients with DM when considering the prevention of HF development in this vulnerable population.

The main strengths of our study were the use of a real-world nationwide database representing Taiwan’s entire population, the target trial emulation design strengthening causal inference using observational data, the novel findings demonstrating the different risks of HF between different oral anticoagulant users, and the study robustness supported by various sensitivity analyses. However, some limitations should be acknowledged. First, we could not gather data on lifestyle, smoking and drinking history, and detailed laboratory examination results (e.g., blood glucose and renal function). Additionally, the indication for which the physicians had chosen warfarin over NOAC (or vice versa) for each patient could not be obtained from the claims-based dataset. Although we employed propensity score methods (including fine stratification weighting and matching) and multivariable regressions to exclude potential confounders, there may still be some unknown or unmeasured confounders. Second, we were unable to access patients’ comprehensive medical records to confirm diagnostic accuracy due to the patient anonymity policy in the NHIRD; therefore, potential misclassification errors may exist in the claims-based data. However, misclassifications among patients taking NOACs and those taking warfarin are non-differential, thereby pushing the estimates towards the null [46, 47]. Since we already observed a significant difference in the HF risk between NOACs and warfarin in our study, the true effect sizes may be larger than we observed. Third, some patients could alter the types of oral anticoagulants used during follow-up; hence, our main analysis with an as-started design (emulating intention-to-treat analysis) may underestimate the true effect sizes for differences in HF risk between NOAC and warfarin groups. In the sensitivity analysis with an on-treatment design (analog of per-protocol) and that limited to patients with a high medication possession ratio of index anticoagulant treatment, we further obtained larger effect sizes with more significant results. Such results implied that our findings of lower HF risk in NOAC users might be genuine and merits further confirmation in future studies. Fourth, our study focused on a vulnerable population, namely elderly patients with AF and DM; patient baseline characteristics revealed a significant comorbidity status in our study population. However, it remains unclear whether the observed lower HF risk among NOAC users can be generalized to younger or healthier patients; more research is required to answer this question.

Conclusions

In this nationwide retrospective cohort study, elderly patients with AF and DM taking NOACs had a lower risk of incident HF than those taking warfarin. Our findings suggest that NOACs may be the preferred oral anticoagulant treatment to reduce the risk of HF in elderly AF patients with DM. Future research is warranted to elucidate causation and investigate the underlying mechanisms of our findings.

Availability of data and materials

The dataset used in this study is managed by the Taiwan Ministry of Health and Welfare and thus cannot be made available publicly. Researchers interested in accessing this dataset can submit a formal application to the Ministry of Health and Welfare to request access (Taiwan Ministry of Health and Welfare, No. 488, Sect. 6, Zhongxiao E Rd, Nangang District, Taipei 115, Taiwan; website: https://dep.mohw.gov.tw/DOS/cp-2516-59203-113.html).

Abbreviations

HF:

Heart failure

AF:

Atrial fibrillation

DM:

Diabetes mellitus

NOAC:

Non-vitamin K antagonist oral anticoagulant

HR:

Hazard ratio

CI:

Confidence interval

NHIRD:

National Health Insurance Research Database

ESRD:

End-stage renal disease

ATE:

Average treatment effect in the whole population

ATT:

Average treatment effect in the treated population

CKD:

Chronic kidney disease

References

  1. Kirkman MS, Briscoe VJ, Clark N, Florez H, Haas LB, Halter JB, Huang ES, Korytkowski MT, Munshi MN, Odegard PS, et al. Diabetes in older adults. Diabetes Care. 2012;35(12):2650–64.

    Article  Google Scholar 

  2. Chugh SS, Havmoeller R, Narayanan K, Singh D, Rienstra M, Benjamin EJ, Gillum RF, Kim YH, McAnulty JH Jr, Zheng ZJ, et al. Worldwide epidemiology of atrial fibrillation: a Global Burden of Disease 2010 Study. Circulation. 2014;129(8):837–47.

    Article  Google Scholar 

  3. Savarese G, Lund LH. Global public health burden of heart failure. Card Fail Rev. 2017;3(1):7–11.

    Article  Google Scholar 

  4. Butrous H, Hummel SL. Heart failure in older adults. Can J Cardiol. 2016;32(9):1140–7.

    Article  Google Scholar 

  5. Dunlay SM, Givertz MM, Aguilar D, Allen LA, Chan M, Desai AS, Deswal A, Dickson VV, Kosiborod MN, Lekavich CL, et al. Type 2 diabetes mellitus and heart failure: a scientific statement from the american heart association and the heart failure society of America: this statement does not represent an update of the 2017 ACC/AHA/HFSA heart failure guideline update. Circulation. 2019;140(7):e294–324.

    Article  CAS  Google Scholar 

  6. Nesti L, Pugliese NR, Sciuto P, Natali A. Type 2 diabetes and reduced exercise tolerance: a review of the literature through an integrated physiology approach. Cardiovasc Diabetol. 2020;19(1):134.

    Article  CAS  Google Scholar 

  7. Nesti L, Pugliese NR, Sciuto P, De Biase N, Mazzola M, Fabiani I, Trico D, Masi S, Natali A. Mechanisms of reduced peak oxygen consumption in subjects with uncomplicated type 2 diabetes. Cardiovasc Diabetol. 2021;20(1):124.

    Article  CAS  Google Scholar 

  8. Sugumar H, Nanayakkara S, Prabhu S, Voskoboinik A, Kaye DM, Ling LH, Kistler PM. Pathophysiology of atrial fibrillation and heart failure: dangerous interactions. Cardiol Clin. 2019;37(2):131–8.

    Article  Google Scholar 

  9. Anter E, Jessup M, Callans DJ. Atrial fibrillation and heart failure: treatment considerations for a dual epidemic. Circulation. 2009;119(18):2516–25.

    Article  Google Scholar 

  10. Kotecha D, Piccini JP. Atrial fibrillation in heart failure: what should we do? Eur Heart J. 2015;36(46):3250–7.

    CAS  Google Scholar 

  11. Adelborg K, Szépligeti S, Sundbøll J, Horváth-Puhó E, Henderson VW, Ording A, Pedersen L, Sørensen HT. Risk of stroke in patients with heart failure: a population-based 30-year cohort study. Stroke. 2017;48(5):1161–8.

    Article  Google Scholar 

  12. Eckman MH, Singer DE, Rosand J, Greenberg SM. Moving the tipping point: the decision to anticoagulate patients with atrial fibrillation. Circ Cardiovasc Qual Outcomes. 2011;4(1):14–21.

    Article  Google Scholar 

  13. Chao TF, Lip GYH, Lin YJ, Chang SL, Lo LW, Hu YF, Tuan TC, Liao JN, Chung FP, Chen TJ, et al. Age threshold for the use of non-vitamin K antagonist oral anticoagulants for stroke prevention in patients with atrial fibrillation: insights into the optimal assessment of age and incident comorbidities. Eur Heart J. 2019;40(19):1504–14.

    Article  Google Scholar 

  14. Hindricks G, Potpara T, Dagres N, Arbelo E, Bax JJ, Blomström-Lundqvist C, Boriani G, Castella M, Dan GA, Dilaveris PE, et al. 2020 ESC guidelines for the diagnosis and management of atrial fibrillation developed in collaboration with the European Association for Cardio-Thoracic Surgery (EACTS): the task force for the diagnosis and management of atrial fibrillation of the European Society of Cardiology (ESC) Developed with the special contribution of the European Heart Rhythm Association (EHRA) of the ESC. Eur Heart J. 2021;42(5):373–498.

    Article  Google Scholar 

  15. Huang HK, Liu PP, Lin SM, Hsu JY, Peng CC, Munir KM, Wu TY, Yeh JI, Loh CH, Tu YK. Risk of developing diabetes in patients with atrial fibrillation taking non-vitamin K antagonist oral anticoagulants or warfarin: a nationwide cohort study. Diabetes Obes Metab. 2021;23(2):499–507.

    Article  CAS  Google Scholar 

  16. Cheung CL, Sing CW, Lau WCY, Li GHY, Lip GYH, Tan KCB, Cheung BMY, Chan EWY, Wong ICK. Treatment with direct oral anticoagulants or warfarin and the risk for incident diabetes among patients with atrial fibrillation: a population-based cohort study. Cardiovasc Diabetol. 2021;20(1):71.

    Article  CAS  Google Scholar 

  17. Huang HK, Liu PP, Lin SM, Hsu JY, Yeh JI, Lai EC, Peng CC, Munir KM, Loh CH, Tu YK. Diabetes-related complications and mortality in patients with atrial fibrillation receiving different oral anticoagulants : a nationwide analysis. Ann Intern Med. 2022;175(4):490–8.

    Article  Google Scholar 

  18. Esmon CT. Targeting factor Xa and thrombin: impact on coagulation and beyond. Thromb Haemost. 2014;111(4):625–33.

    Article  CAS  Google Scholar 

  19. Tsujino Y, Sakamoto T, Kinoshita K, Nakatani Y, Yamaguchi Y, Kataoka N, Nishida K, Kinugawa K. Edoxaban suppresses the progression of atrial fibrosis and atrial fibrillation in a canine congestive heart failure model. Heart Vessels. 2019;34(8):1381–8.

    Article  Google Scholar 

  20. Liu J, Nishida M, Inui H, Chang J, Zhu Y, Kanno K, Matsuda H, Sairyo M, Okada T, Nakaoka H, et al. Rivaroxaban suppresses the progression of ischemic cardiomyopathy in a murine model of diet-induced myocardial infarction. J Atheroscler Thromb. 2019;26(10):915–30.

    Article  CAS  Google Scholar 

  21. Hsieh CY, Su CC, Shao SC, Sung SF, Lin SJ, Kao Yang YH, Lai EC. Taiwan’s national health insurance research database: past and future. Clin Epidemiol. 2019;11:349–58.

    Article  Google Scholar 

  22. Hsing AW, Ioannidis JP. Nationwide population science: lessons from the Taiwan national health insurance research database. JAMA Intern Med. 2015;175(9):1527–9.

    Article  Google Scholar 

  23. Hernán MA. Methods of public health research—strengthening causal inference from observational data. N Engl J Med. 2021;385(15):1345–8.

    Article  Google Scholar 

  24. Kutcher SA, Brophy JM, Banack HR, Kaufman JS, Samuel M. Emulating a randomised controlled trial with observational data: an introduction to the target trial framework. Can J Cardiol. 2021;37(9):1365–77.

    Article  Google Scholar 

  25. Chen A, Stecker E, Warden BA. Direct oral anticoagulant use: a practical guide to common clinical challenges. J Am Heart Assoc. 2020;9(13):e017559.

    Article  CAS  Google Scholar 

  26. Charlson ME, Pompei P, Ales KL, MacKenzie CR. A new method of classifying prognostic comorbidity in longitudinal studies: development and validation. J Chronic Dis. 1987;40(5):373–83.

    Article  CAS  Google Scholar 

  27. Lip GY, Nieuwlaat R, Pisters R, Lane DA, Crijns HJ. Refining clinical risk stratification for predicting stroke and thromboembolism in atrial fibrillation using a novel risk factor-based approach: the euro heart survey on atrial fibrillation. Chest. 2010;137(2):263–72.

    Article  Google Scholar 

  28. Pamukcu B, Lip GY, Lane DA. Simplifying stroke risk stratification in atrial fibrillation patients: implications of the CHA2DS2-VASc risk stratification scores. Age Ageing. 2010;39(5):533–5.

    Article  Google Scholar 

  29. Desai RJ, Franklin JM. Alternative approaches for confounding adjustment in observational studies using weighting based on the propensity score: a primer for practitioners. BMJ. 2019;367:l5657.

    Article  Google Scholar 

  30. Heinze G, Jüni P. An overview of the objectives of and the approaches to propensity score analyses. Eur Heart J. 2011;32(14):1704–8.

    Article  Google Scholar 

  31. Austin PC, Lee DS, Fine JP. Introduction to the analysis of survival data in the presence of competing risks. Circulation. 2016;133(6):601–9.

    Article  Google Scholar 

  32. Austin PC. A tutorial on multilevel survival analysis: methods models and applications. Int Stat Rev. 2017;85(2):185–203.

    Article  Google Scholar 

  33. Balan TA, Putter H. A tutorial on frailty models. Stat Methods Med Res. 2020;29(11):3424–54.

    Article  Google Scholar 

  34. Andrade SE, Kahler KH, Frech F, Chan KA. Methods for evaluation of medication adherence and persistence using automated databases. Pharmacoepidemiol Drug Saf. 2006;15(8):565–74.

    Article  Google Scholar 

  35. Austin PC. An introduction to propensity score methods for reducing the effects of confounding in observational studies. Multivariate Behav Res. 2011;46(3):399–424.

    Article  Google Scholar 

  36. Austin PC. Optimal caliper widths for propensity-score matching when estimating differences in means and differences in proportions in observational studies. Pharm Stat. 2011;10(2):150–61.

    Article  Google Scholar 

  37. Gradolí J, Vidal V, Brady AJ, Facila L. Anticoagulation in patients with ischaemic heart disease and peripheral arterial disease: clinical implications of COMPASS study. Eur Cardiol. 2018;13(2):115–8.

    Article  Google Scholar 

  38. Ferri LA, Bassanelli G, Savonitto S. Use of direct oral anticoagulant in ischaemic heart disease: the COMPASS study. Eur Heart J Suppl. 2019;21(Suppl B):B84-b87.

    Article  Google Scholar 

  39. van Melle JP, Bot M, de Jonge P, de Boer RA, van Veldhuisen DJ, Whooley MA. Diabetes, glycemic control, and new-onset heart failure in patients with stable coronary artery disease: data from the heart and soul study. Diabetes Care. 2010;33(9):2084–9.

    Article  Google Scholar 

  40. Iribarren C, Karter AJ, Go AS, Ferrara A, Liu JY, Sidney S, Selby JV. Glycemic control and heart failure among adult patients with diabetes. Circulation. 2001;103(22):2668–73.

    Article  CAS  Google Scholar 

  41. Li Y, Chen JP, Duan L, Li S. Effect of vitamin K2 on type 2 diabetes mellitus: a review. Diabetes Res Clin Pract. 2018;136:39–51.

    Article  CAS  Google Scholar 

  42. Manna P, Kalita J. Beneficial role of vitamin K supplementation on insulin sensitivity, glucose metabolism, and the reduced risk of type 2 diabetes: a review. Nutrition. 2016;32(7–8):732–9.

    Article  CAS  Google Scholar 

  43. Karamzad N, Maleki V, Carson-Chahhoud K, Azizi S, Sahebkar A, Gargari BP. A systematic review on the mechanisms of vitamin K effects on the complications of diabetes and pre-diabetes. BioFactors. 2020;46(1):21–37.

    Article  CAS  Google Scholar 

  44. Zhao L, Wang WYS, Yang X. Anticoagulation in atrial fibrillation with heart failure. Heart Fail Rev. 2018;23(4):563–71.

    Article  CAS  Google Scholar 

  45. Brown LAE, Boos CJ. Atrial fibrillation and heart failure: factors influencing the choice of oral anticoagulant. Int J Cardiol. 2017;227:863–8.

    Article  Google Scholar 

  46. Copeland KT, Checkoway H, McMichael AJ, Holbrook RH. Bias due to misclassification in the estimation of relative risk. Am J Epidemiol. 1977;105(5):488–95.

    Article  CAS  Google Scholar 

  47. Höfler M. The effect of misclassification on the estimation of association: a review. Int J Methods Psychiatr Res. 2005;14(2):92–101.

    Article  Google Scholar 

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Acknowledgements

The authors thank the Health and Welfare Data Science Center, Ministry of Health and Welfare, Taiwan, for approving our access to the database, and the Health and Welfare Data Science Center of Tzu Chi University for facilitating data extraction. The authors thank Editage for English language editing.

Funding

This work was supported by a grant from the Hualien Tzu Chi Hospital (TCRD108-21). The funder had no role in study design, data collection, data analysis, data interpretation, writing of the report, decision to submit for publication, or approval of the manuscript for publication.

Author information

Author notes

  1. Huei-Kai Huang and Ching-Hui Loh have contributed equally to this work

Authors and Affiliations

  1. Department of Physical Medicine and Rehabilitation, Hualien Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, Hualien, Taiwan

    Shu-Man Lin

  2. School of Medicine, Tzu Chi University, Hualien, Taiwan

    Shu-Man Lin, Jih-I Yeh, Jin-Yi Hsu, Huei-Kai Huang & Ching-Hui Loh

  3. Center for Aging and Health, Hualien Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, No. 707, Sec. 3, Chung Yang Rd., Hualien, 97002, Taiwan

    Peter Pin-Sung Liu, Jin-Yi Hsu & Ching-Hui Loh

  4. Institute of Medical Sciences, Tzu Chi University, Hualien, Taiwan

    Peter Pin-Sung Liu

  5. Institute of Epidemiology and Preventive Medicine, College of Public Health, National Taiwan University, Taipei, Taiwan

    Yu-Kang Tu & Huei-Kai Huang

  6. Department of Dentistry, National Taiwan University Hospital and School of Dentistry, National Taiwan University, Taipei, Taiwan

    Yu-Kang Tu

  7. School of Pharmacy, Institute of Clinical Pharmacy and Pharmaceutical Sciences, College of Medicine, National Cheng Kung University, Tainan, Taiwan

    Edward Chia-Cheng Lai

  8. Department of Family Medicine, Hualien Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, No. 707, Sec. 3, Chung Yang Rd., Hualien, 97002, Taiwan

    Jih-I Yeh & Huei-Kai Huang

  9. Division of Endocrinology, Diabetes and Nutrition, University of Maryland School of Medicine, Baltimore, MD, USA

    Kashif M. Munir

  10. Section of Endocrinology, Diabetes, Nutrition & Weight Management, Boston University School of Medicine, Boston, MA, USA

    Carol Chiung-Hui Peng

Authors
  1. Shu-Man Lin
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  5. Jih-I Yeh
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  10. Ching-Hui Loh
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Contributions

Concept and design: SL, PL, HH and CL. Acquisition, analysis, or interpretation of data: all authors. Drafting of the manuscript: SL and HH. Critical revision of the manuscript for important intellectual content: all authors. Statistical analysis: PL, EL, and HH. Administrative, technical, or material support: YT, JY, and CL. Supervision: JY and CL. All authors read and approved the final manuscript.

Corresponding authors

Correspondence to Huei-Kai Huang or Ching-Hui Loh.

Ethics declarations

Ethics approval and consent to participate

Our study was approved by the Research Ethics Committee of Hualien Tzu Chi Hospital (REC No: IRB107-152-C); the requirement for informed consent was waived due to the retrospective use of anonymized data.

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Not applicable.

Competing interests

The authors declare that no competing interests exist.

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Supplementary Information

Additional file 1: Figure S1.

Flowchart of patient selection. Table S1. Specification and emulation of a target trial evaluating the effect of NOACs versus warfarin on the risk of incident heart failure using real-world data from Taiwan’s NHIRD. Table S2. Baseline characteristics of elderly patients with atrial fibrillation and diabetes receiving NOAC or warfarin in the original population, without weighting or matching. Table S3. Risk of heart failure in elderly patients with atrial fibrillation and diabetes receiving NOAC versus warfarin in the sensitivity analysis applying propensity score matching and that applying multivariable regression models without propensity score methods. Table S4. Baseline characteristics of elderly patients with atrial fibrillation and diabetes receiving NOAC or warfarin in the population after propensity score matching.

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Lin, SM., Liu, P.PS., Tu, YK. et al. Risk of heart failure in elderly patients with atrial fibrillation and diabetes taking different oral anticoagulants: a nationwide cohort study. Cardiovasc Diabetol 22, 1 (2023). https://doi.org/10.1186/s12933-022-01688-1

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  • DOI: https://doi.org/10.1186/s12933-022-01688-1

Keywords

  • Oral anticoagulant
  • Heart failure
  • Atrial fibrillation
  • Diabetes mellitus
  • Elderly

Cardiovascular Diabetology

ISSN: 1475-2840