Skip to main content
Springer Nature - PMC COVID-19 Collection logoLink to Springer Nature - PMC COVID-19 Collection
. 2021 Jul 7;16(7):1737–1742. doi: 10.1007/s11739-021-02799-5

Does CHA2DS2-VASc score predict mortality in chronic kidney disease?

Christos Goudis 1,, Stylianos Daios 1, Panagiotis Korantzopoulos 2, Tong Liu 3
PMCID: PMC8261034  PMID: 34232486

Abstract

Chronic kidney disease (CKD) is a leading cause of morbidity and mortality worldwide. Assessment of cardiovascular (CV) and all-cause mortality in CKD patients is of particular importance. CHA2DS2-VASc (congestive heart failure, hypertension, age ≥ 75 years, diabetes, prior stroke, vascular disease, age 65–74 years, and sex) score was originally formulated to predict the annual thromboembolic risk in patients with nonvalvular atrial fibrillation (AF). The calculation of R2CHADS2 and R2CHA2DS2VASc scores awarded an additional 2 points for CrCl < 60 mL/min and GFR < 60 mL/min/1.73 m2. Recent studies have investigated whether CHA2DS2-VASc and R2CHADS ± VASC scores could be used to predict CV or all-cause mortality in patients with CKD. CHA2DS2-VASc score was proven to be a significant predictor of CV and all-cause mortality in CKD patients, and a higher CHA2DS2-VASc score was associated with increased mortality. These findings are quite promising, and they may help physicians to identify high-risk groups in this population.

Keywords: CHA2DS2-VASc, Chronic kidney disease, Mortality

Introduction

Chronic kidney disease: epidemiology and definition

Chronic kidney disease (CKD) has been recognized as a leading cause of morbidity and mortality worldwide [1]. The global estimated prevalence of CKD is 13.4% [2] and has been almost doubled over the last three decades due to the decrease in mortality from cardiovascular (CV) and infectious diseases, as well as the population’s progressive aging [1]. According to international guidelines, CKD is diagnosed if one or both of the following two criteria are met for at least 3 months: (a) glomerular filtration rate (GFR) < 60 mL/min per 1.73 m2 (b) markers of kidney damage (1 or more): (i) albuminuria [albumin to creatinine ratio (ACR) ≥ 30 mg/g] (ii) urinary sediment abnormality (iii) electrolyte or other abnormality due to tubular disorder (iv) abnormalities on histology (v) structural abnormalities detected by imaging vi) history of kidney transplantation [3].

Chronic kidney disease and mortality

In 2016, CKD caused 1.19 million deaths globally, which has increased by 28.8% from 2006 [4]. According to World Health Organization estimates, CKD will rank 13th in 2030 among the most common causes of death [5]. The kidney early evaluation program (KEEP) enrolled patients at high risk for developing CKD and contributed to our understanding on CV risk stratification, prognosis and treatment in this setting [6]. CKD was proved to be a significant predictor (OR: 1.44; 95% CI 1.27–1.63) of premature CV death, defined as the occurrence of myocardial infarction or stroke before 55 years in males and 65 years in females [6]. In a longitudinal analysis restricted to a subgroup of KEEP cohort with CKD, lower eGFR, increased albuminuria and diabetes mellitus (DM) have been found as significant predictors of mortality [7]. Both proteinuria and reduction in GFR can predict the development of fatal and non-fatal CV events, regardless of traditional CV risk factors, namely hypertension, smoking, hyperlipidemia, age and gender [8]. Therefore, assessing the risk of adverse outcomes and especially mortality associated with CKD is of particular importance for both physicians and patients [9]. At the moment, only one risk model has been developed to predict the occurrence of non-fatal CV events, renal failure requiring transplantation and death, in patients with eGFR < 30 ml/min/1.73 m2 based on parameters that are readily available in routine clinical practice (age, sex, race, eGFR, systolic blood pressure, history of CV disease, DM, urine ACR and smoking) [10].

CHADS2, CHA2DS2-VASc, R2CHADS2 and R2CHA2DS2VASc scores

CHADS2 is a simple score and was initially used for stroke risk stratification in AF. It was derived by the combination of stroke risk factors established in AFI and SPAF studies [11]. CHADS2 was formed by assigning 1 point each for the presence of congestive heart failure, hypertension, age ≥ 75 years, DM and 2 points for history of stroke or transient ischemic attack [11]. CHADS2 manages well in identifying high-risk patients but provides ambiguous results in those at low or moderate stroke risk [12]. As a result, it was subjected to criticism for: (i) low discrimination ability for patients at low risk of stroke (ii) absence of important independent stroke and thromboembolic risk factors (iii) discrepancy between the original validation and further applications in guidelines and real-world cohorts [13, 14].

CHA2DS2-VASc [congestive heart failure, hypertension, age ≥ 75 years, diabetes, prior stroke, vascular disease, age 65–74 years and sex (female) category] score was originally formulated to predict the annual thromboembolic risk in patients with nonvalvular AF [15, 16]. CHA2DS2-VASc is a simple, effective and easy-to-use tool for truly low-risk and truly high-risk patients, but it has difficulties in tailoring anticoagulant treatment in AF patients at intermediate risk of stroke [17]. In individuals with AF and a CHA2DS2-VASc score of 1, therapeutic decisions should be based on the individual balance between thromboembolic and bleeding risk [18].

R2CHADS2 score incorporates the components of CHADS2 score and awards 2 points for renal dysfunction (CrCl < 60 mL/min and GFR < 60 mL/min/1.73 m2) [19]. It was derived from study subjects enrolled in ROCKET-AF trial. The post hoc analysis of outcomes in ROCKET AF found that impaired renal function is independently associated with the occurrence of stroke or systemic embolism during follow-up of patients with a relatively high risk of stroke (mean CHADS2 score 3.5) receiving warfarin or rivaroxaban. R2CHADS2 score enhanced stroke risk assessment based on the net reclassification index by 8.2% compared with CHADS2 score and by 6.2% compared with CHA2DS2-VASc score [19]. Piccini et al. applied this model to the ATRIA cohort of > 13 000 patients with nonvalvular AF to validate R2CHADS2 score in an independent population of patients with AF across a broader range of inherent risk [19]. R2CHADS2 risk score exhibited similar power to CHADS2 score regarding stroke occurrence (C statistics 0.672 and 0.673, respectively), however, net stroke risk reclassification was improved 17.4% (95% CI 12.1–22.5%) with R2CHADS2 score. The findings were similar when applied to patients not receiving warfarin (C statistics 0.696 versus 0.704 for R2CHADS2 and CHADS2 scores, respectively). Net reclassification index improved with R2CHADS2 by 22.6% (95% CI 14.5–30.7%) [19].

In the same way, R2CHA2DS2VASc score awards an additional 2 points for CrCl < 60 mL/min and GFR < 60 mL/min/1.73 m2 and has already been used to predict long-term outcomes for patients with coronary artery disease (CAD)20 and acute coronary syndromes (ACS) [2022].

CHA2DS2-VASc score predicts mortality in several diseases

The validity of CHA2DS2-VASc score in predicting ischemic stroke and thromboembolism has been extended beyond the originally proposed AF field. High CHA2DS2-VASc scores have been associated with increased mortality in patients with several diseases, irrespective of the presence or absence of AF. CHADS2, CHA2DS2-VASc and R2CHADS2 scores have been associated with higher incidence of mortality in patients with stable CAD and ACS [23]. Compared to CHADS2, CHA2DS2-VASc and R2CHADS2 scores provide better discrimination and reclassification for mortality. In addition, CHA2DS2-VASc and R2CHADS2 scores have comparable predictive ability of mortality to GRACE score [23]. CHA2DS2-VASc score was proven to be useful in predicting mortality in HF patients, irrespective of the presence or absence of AF, ischemic or non-ischemic etiology, and reduced or preserved EF [24]. In the same line, Chen et al. also reported that CHADS2, CHA2DS2-VASc and R2CHADS2 scores are moderately accurate predictors of all-cause mortality in systolic HF patients with or without AF. However, only CKD and R2CHADS2 scores seem to be independent predictors of 1 year all-cause mortality in this setting. In terms of predicting all-cause mortality in systolic HF patients, R2CHADS2 seems to be the best of the three scoring systems, especially in systolic HF patients without AF [25]. Moreover, CHA2DS2-VASC score highly predicts all-cause mortality in patients’ acute ischemic stroke [26], ICD [27], and acute pulmonary embolism [28]. CHADS2, CHA2DS2-VASc and CHA2DS2-VASc-M (modified version of the CHA2DS2-VASc scoring system in which 1 point was assigned to male instead of female sex) have also been significantly associated with all-cause mortality in COVID-19 patients [29]. The purpose of this paper is to present the latest knowledge and to describe recent studies investigating whether CHA2DS2-VASc score could be used to predict CV or all-cause mortality in CKD patients, with or without AF.

CHA2DS2VASC and R2CHADS ± VASC score in atrial fibrillation patients with chronic kidney disease

In AF patients with end-stage renal disease (ESRD) on dialysis, Wang et al. reported that CHA2DS2-VASc score predicts mortality during follow-up [30]. Shih et al. also reported mortality in patients with new onset AF undergoing hemodialysis. An increase in CHA2DS2-VASc score was associated with increased mortality in this setting. The annual risk of all-cause death for patients with a CHA2DS2-VASc score of 0 was 10.03% and for those with a CHA2DS2-VASc score of 9 was 63.10%, respectively [31]. Fu et al. refined the CHADS2 and CHA2DS2VASc scores by combining creatinine clearance (CrCl) and GFR, and evaluated the performance of CrCl-based and GFR-based schemes in death risk stratification of Chinese patients > 60 years old with AF. Renal function was evaluated with CrCl formula and different GFR formulas, as well. Five different kinds of R2CHADS2 and R2CHA2DS2VASc schemes were generated by combining CrCl and GFR with CHADS2 and CHA2DS2VASc scores [32]. Results provided evidence for the significantly better performance of GFR-based schemes—R2(GFR)CHADS2 and R2(GFR)CHA2DS2VASc—and the moderately better performance of CrCl-based schemes—R2(CrCl)CHADS2 and R2(CrCl)CHA2DS2VASc—in death risk stratification compared with CHADS2 and CHA2DS2VASc scores [32]. These findings, however, were not confirmed in a recent study by Premuzic et al. The investigators sought to determine the association of eGFR with CV mortality in AF patients after 24 months of follow-up [33]. They concluded that CV mortality was independently associated with eGFR (b = 0.169, P = 0.04), male gender (b = 0.156, P = 0.03), CHA2DS2VASc (b = 0.467, P = 0.02) and R2CHA2DS2VASc scores (b = 0.391, P = 0.04) but not with R2CHADS2 score. In addition, R2CHA2DS2VASc was not associated with CV mortality more than CHA2DS2VASc [33].

CHA2DS2VASC score in non-atrial fibrillation patients with chronic kidney disease

Recent studies have also shown that CHA2DS2-VASc score can predict mortality in CKD patients. Pravda et al. investigated the association of CHA2DS2-VASc with mortality and major adverse CV outcomes in patients with ESRD on chronic hemodialysis [34]. A higher CHA2DS2-VASc score was associated with an increased risk for the composite endpoint of all-cause mortality, myocardial infarction, and stroke within the first year of hemodialysis in the low (CHA2DS2-VASc 0–3), intermediate (CHA2DS2-VASc 4–5) and high (CHA2DS2-VASc ≥ 6) CHA2DS2-VASc groups, respectively (P < 0.01). A multivariate analysis using the CHA2DS2-VASc score group of 0–3 as the reference group yielded adjusted ORs for the composite endpoint at 1 year that increased as the CHA2DS2-VASc score was higher (OR = 2.6 95% CI 1.6–4.2 and OR = 4.2, 95% CI 3.3–7.5 for patients with the CHA2DS2-VASc score of 4–5 and ≥ 6, respectively, P < 0.01) [34]. CHA2DS2-VASc score was also used as a continuous variable to assess the risk, and was shown that CHA2DS2-VASc score is associated with a 38% increased risk for the composite endpoint [34]. Hsu et al. evaluated the usefulness of CHADS2 and CHA2DS2-VASc scores for the prediction of CV and all-cause mortality in CKD [35]. Age, male gender, hypertension, heart failure and CHA2DS2-VASc score (HR 1.600; 95% CI 1.254–2.040; P < 0.001) were proven to be significant predictors of CV mortality. Similarly, age, male gender, heart failure, CHA2DS2-VASc score (HR 1.503; 95% CI 1.300–1.739; P < 0.001) and angiotensin II receptor blockers use were significant predictors of all-cause mortality. Higher stage of CKD was also associated with increased all-cause mortality in CKD [35]. Recently, Vodošek Hojs et al. performed a prospective study in non-dialysis CKD patients and assessed the role of CHA2DS2-VASc score in predicting CV and all-cause mortality in CKD. Kaplan–Meier survival analysis showed that CV (P = 0.001) and all-cause (P = 0.001) mortality were higher in patients with CHA2DS2-VASc score > 2. CHA2DS2-VASc score was an independent predictor of CV (HR: 2.04, CI 1.20–3.45, P = 0.008) and all-cause mortality (HR: 2.06, CI 1.43–2.97, P = 0.001) [36]. The summarized studies investigating the ability of CHA2DS2-VASc score to predict CV and all-cause mortality in CKD are listed in Table 1.

Table 1.

Studies investigating the ability of CHA2DS2-VASc score to predict CV and all-cause mortality in chronic kidney disease

Study (Reference) AF cases /number of Pts Age (years) Follow-up, years Database/type of study Key findings
Wang et al. [30] 141/774 61.2 ± 11.3 years 3.4 ± 2.5 years Retrospective observational CHA2DS2-VASc detects mortality during follow-up (c = 0.638)
Shih et al. [31] 6.772/77.397 68.8 ± 11.3 years 3.2 years Retrospective cohort The annual risk of all-cause mortality for patients with a CHA2DS2-VASc score of 0 was 10.03% and for those with a CHA2DS2-VASc score of 9 was 63.10%, respectively
Premužić et al. [33] 301 AF patients 70.6 years 2 years Retrospective observational-longitudinal CV mortality was independently associated with CHA2DS2VASc (b = 0.467, P = 0.02) and R2CHA2DS2VASc scores (b = 0.391, P = 0.04) but not with R2CHADS2 score
Pravda et al. [34] 105/457

66 ± 13

years

955 ± 765 days Retrospective observational CHA2DS2-VASc score predicted the primary composite endpoint (all-cause mortality, myocardial infarction, and stroke) with an AUC of 0.72 (95% CI 0.69–0.75)
Hsu et al. [35] 437 CKD patients 68 ± 12 years 91 months (25th–75th percentile: 59–101) Retrospective longitudinal CHADS2 and CHA2DS2-VASc scores (both P value < 0.001) were significant predictors of CVr and all-cause mortality in the multivariate analysis
Fu et al. [32] 219 AF patients 86 years 1.11 years Retrospective observational C-statistics of GFR-based schemes—R2(GFR)CHADS2 and R2(GFR)CHA2 DS2 VASc—significantly exceeded that of CHADS2 and CHA2DS2VASc scores (P < 0.05 for all)
Vodosek Hojs et al. [36] 87 CKD patients 60.3 ± 12.8 years 1696.5 ± 564.6 days Prospective CHA2DS2-VASc score was an independent predictor of all-cause (HR: 2.06, CI 1.43–2.97, P = 0.001) and CV mortality(HR: 2.04, CI 1.20–3.45, P = 0.008)

AF atrial fibrillation, CKD chronic kidney disease

Conclusion

CHA2DS2-VASc score has been widely used to evaluate the risk of stroke in AF patients [37], but has been shown to predict CV and all-cause mortality in CKD patients as well. Indeed, higher CHA2DS2-VASc scores have been associated with increased mortality. These findings are of particular importance because CKD is very common in clinical practice and utilization of CHA2DS2-VASc score may help physicians to identify high-risk groups in this setting. Intensive care along with modification of risk factors and treatment of coexisting diseases may improve prognosis in these patients. More studies are needed to further confirm these promising findings so that CHA2DS2-VASc score can be widely used in clinical setting.

Funding

None.

Declarations

Conflict of interest

The author(s) declare that they have no conflict of interest.

Human and animal rights statement

There is no research involving human participants and/or animals.

Informed consent

There is no informed consent because there weren't any human participants.

Footnotes

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

References

  • 1.Provenzano M, Coppolino G, Faga T, et al. Epidemiology of cardiovascular risk in chronic kidney disease patients: the real silent killer. Rev Cardiovasc Med. 2019;20:209–220. doi: 10.31083/j.rcm.2019.04.548. [DOI] [PubMed] [Google Scholar]
  • 2.Lv JC, Zhang LX. Prevalence and disease burden of chronic kidney disease. Adv Exp Med Biol. 2019;1165:3–15. doi: 10.1007/978-981-13-8871-2_1. [DOI] [PubMed] [Google Scholar]
  • 3.Webster A, Nagler E, Morton R, et al. Chronic kidney disease. Lancet. 2017;389:1238–1252. doi: 10.1016/S0140-6736(16)32064-5. [DOI] [PubMed] [Google Scholar]
  • 4.Ng JK, Li PK. Chronic kidney disease epidemic: how do we deal with it? Nephrology (Carlton) 2018;23(Suppl 4):116–120. doi: 10.1111/nep.13464. [DOI] [PubMed] [Google Scholar]
  • 5.Mathers CD, Loncar D. Projections of global mortality and burden of disease from 2002 to 2030. PLoS Med. 2006;3(1):e442. doi: 10.1371/journal.pmed.0030442. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 6.Whaley-Connell AT, Kurella Tamura M, et al. Advances in CKD detection and determination of prognosis: executive summary of the National Kidney Foundation-kidney early evaluation program (KEEP) 2012 annual data report. Am J Kidney Dis. 2013;61(4 Suppl 2):S1–3. doi: 10.1053/j.ajkd.2013.01.006. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 7.McCullough PA, WhaleyConnell A, Brown WW, et al. Kidney early evaluation program (KEEP) investigators. Cardiovascular risk modification in participants with coronary disease screened by the kidney early evaluation program. Intern Med J. 2010;40:833–841. doi: 10.1111/j.1445-5994.2009.02158.x. [DOI] [PubMed] [Google Scholar]
  • 8.Matsushita K, van der Velde M, Astor BC, et al. Association of estimated glomerular filtration rate and albuminuria with all-cause and cardiovascular mortality in general population cohorts: a collaborative meta-analysis. Lancet. 2010;375:2073–2081. doi: 10.1016/S0140-6736(10)60674-5. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 9.Schneider MP, Eckardt KU. Risk scores in patients with chronic kidney disease. Dtsch Med Wochenschr. 2019;144:739–742. doi: 10.1055/a-0641-9625. [DOI] [PubMed] [Google Scholar]
  • 10.Grams ME, Sang Y, Ballew SH, et al. Predicting timing of clinical outcomes in patients with chronic kidney disease and severely decreased glomerular filtration rate. Kidney Int. 2018;93:1442–1451. doi: 10.1016/j.kint.2018.01.009. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 11.Gage BF, Waterman AD, Shannon W, et al. Validation of clinical classification schemes for predicting stroke: results from the National Registry of Atrial Fibrillation. JAMA. 2001;285:2864–2870. doi: 10.1001/jama.285.22.2864. [DOI] [PubMed] [Google Scholar]
  • 12.Nieuwlaat R, Capucci A, Lip GY, et al. Euro heart survey investigators antithrombotic treatment in real-life atrial fibrillation patients: a report from the Euro Heart Survey on atrial fibrillation. Eur Heart J. 2006;27:3018–3026. doi: 10.1093/eurheartj/ehl015. [DOI] [PubMed] [Google Scholar]
  • 13.Karthikeyan G, Eikelboom JW. The CHADS2 score for stroke risk stratification in atrial fibrillation—friend or foe? Thromb Haemost. 2010;104:45–48. doi: 10.1160/TH09-11-0757. [DOI] [PubMed] [Google Scholar]
  • 14.Rietbrock S, Heeley E, Plumb J, et al. Chronic atrial fibrillation: incidence, prevalence, and prediction of stroke using the congestive HF, hypertension, age ≥ 75, diabetes mellitus, and prior stroke or transient ischemic attack (CHADS2) risk stratification scheme. Am Heart J. 2008;156:57–64. doi: 10.1016/j.ahj.2008.03.010. [DOI] [PubMed] [Google Scholar]
  • 15.Lip GYH, Nieuwlaat R, Pisters R, et al. 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;137:263–272. doi: 10.1378/chest.09-1584. [DOI] [PubMed] [Google Scholar]
  • 16.Camm AJ, Kirchhof P, Lip GYH, et al. Guidelines for the management of atrial fibrillation: the Task Force for the Management of Atrial Fibrillation of the European Society of Cardiology (ESC) Eur Heart J. 2010;31:2369–2429. doi: 10.1093/eurheartj/ehq278. [DOI] [PubMed] [Google Scholar]
  • 17.van Doorn S, Debray TPA, Kaasenbrood F, et al. Predictive performance of the CHA2DS2-VASc rule in atrial fibrillation: a systematic review and meta-analysis. J Thromb Haemost. 2017;15:1065–1077. doi: 10.1111/jth.13690. [DOI] [PubMed] [Google Scholar]
  • 18.Sulzgruber P, Wassmann S, Semb AG, et al. Oral anticoagulation in patients with non-valvular atrial fibrillation and a CHA2DS2-VASc score of 1: a current opinion of the European Society of Cardiology Working Group on Cardiovascular Pharmacotherapy and European Society of Cardiology Council on Stroke. Eur Heart J Cardiovasc Pharmacother. 2019;5:171–180. doi: 10.1093/ehjcvp/pvz016. [DOI] [PubMed] [Google Scholar]
  • 19.Piccini JP, Stevens SR, Chang Y, et al. Renal dysfunction as a predictor of stroke and systemic embolism in patients with nonvalvular atrial fibrillation: validation of the R(2)CHADS(2) index in the ROCKET AF (Rivaroxaban Once-daily, oral, direct factor Xa inhibition compared with vitamin K antagonism for prevention of stroke and Embolism Trial in Atrial Fibrillation) and ATRIA (AnTicoagulation and Risk factors In Atrial fibrillation) study cohorts. Circulation. 2013;127:224–232. doi: 10.1161/CIRCULATIONAHA.112.107128. [DOI] [PubMed] [Google Scholar]
  • 20.Li Y, Wang J, Lv L, et al. Usefulness of the CHADS2 and R2CHADS2 scores for prognostic stratification in patients with coronary artery disease. Clin Interv Aging. 2018;13:565–571. doi: 10.2147/CIA.S156208. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 21.Kiliszek M, Szpakowicz A, Filipiak KJ, et al. CHA2DS2-VASc and R2CHA2DS2-VASc scores have predictive value in patients with acute coronary syndromes. Pol Arch Med Wewn. 2015;125:545–552. doi: 10.20452/pamw.2965. [DOI] [PubMed] [Google Scholar]
  • 22.Wegiel M, Rakowski T, Dziewierz A, et al. CHA2DS2-VASc and R2-CHA2DS2-VASc scores predict in-hospital and post-discharge outcome in patients with myocardial infarction. Adv Interv Cardiol. 2018;14:391–398. doi: 10.5114/aic.2018.79869. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 23.Huang FY, Huang BT, Pu XB, et al. CHADS2, CHA2DS2-VASc and R2CHADS 2 scores predict mortality in patients with coronary artery disease. Intern Emerg Med. 2017;12:479–486. doi: 10.1007/s11739-017-1608-x. [DOI] [PubMed] [Google Scholar]
  • 24.Chen YL, Cheng CL, Huang JL, et al. TSOC-HFrEF Registry investigators and committee. Mortality prediction using CHADS2/CHA2DS2-VASc/R2CHADS2 scores in systolic heart failure patients with or without atrial fibrillation. Medicine (Baltimore) 2017;96:e8338. doi: 10.1097/MD.0000000000008338. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 25.Yoshihisa A, Watanabe S, Kanno Y, et al. The CHA(2)DS(2)-VASc score as a predictor of high mortality in hospitalized heart failure patients. ESC Heart Fail. 2016;3:261–269. doi: 10.1002/ehf2.12098. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 26.Ntaios G, Lip GY, Makaritsis K, et al. CHADS2, CHA2S2DS2-VASc, and long-term stroke outcome in patients without atrial fibrillation. Neurology. 2013;80:1009–1017. doi: 10.1212/WNL.0b013e318287281b. [DOI] [PubMed] [Google Scholar]
  • 27.Hong C, Alluri K, Shariff N, et al. Usefulness of the CHA 2 DS 2-VASc score to predict mortality in defibrillator recipients. Am J Cardiol. 2017;120:83–86. doi: 10.1016/j.amjcard.2017.03.257. [DOI] [PubMed] [Google Scholar]
  • 28.Onuk T, Karataş MB, İpek G, et al. Higher CHA2DS2-VASc score is associated with increased mortality in acute pulmonary embolism. Clin Appl Thromb Hemost. 2017;23:631–637. doi: 10.1177/1076029615627341. [DOI] [PubMed] [Google Scholar]
  • 29.Caro-Codón J, Lip GH, Rey JR, et al. Prediction of thromboembolic events and mortality by the CHADS2 and the CHA2DS2-VASc in COVID-19. Europace. 2021 doi: 10.1093/europace/euab015. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 30.Wang TK, Sathananthan J, Marshall M, et al. Relationships between anticoagulation, risk scores and adverse outcomes in dialysis patients with atrial fibrillation. Heart Lung Circ. 2016;25:243–249. doi: 10.1016/j.hlc.2015.08.012. [DOI] [PubMed] [Google Scholar]
  • 31.Shih CJ, Ou SM, Chao PW, et al. Risks of death and stroke in patients undergoing hemodialysis with new-onset atrial fibrillation: a competing-risk analysis of a nationwide cohort. Circulation. 2016;133:265–272. doi: 10.1161/CIRCULATIONAHA.115.018294. [DOI] [PubMed] [Google Scholar]
  • 32.Fu S, Zhou S, Luo L, et al. R2(GFR) CHADS 2 and R2(GFR) CHA2DS2VASc schemes improved the performance of CHADS2 and CHA2DS2VASc scores in death risk stratification of Chinese older patients with atrial fibrillation. Clin Interv Aging. 2017;2017(12):1233–1238. doi: 10.2147/CIA.S138405.eCollection. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 33.Premužić V, Stevanović R, Radić P, et al. Chronic kidney disease and cardiovascular mortality in patients with atrial fibrillation: European Society of Hypertension project–ESH A Fib. Medicine (Baltimore) 2021;100:e23975. doi: 10.1097/MD.0000000000023975. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 34.Pravda MS, Hagai KC, Topaz G, et al. Assessment of the CHA2DS2-VASc score in predicting mortality and adverse cardiovascular outcomes of patients on hemodialysis. Am J Nephrol. 2020;51:635–640. doi: 10.1159/000508836. [DOI] [PubMed] [Google Scholar]
  • 35.Hsu PC, Lee WH, Chen SC, et al. Using CHADS(2) and CHA(2)DS(2)-VASc scores for mortality prediction in patients with chronic kidney disease. Sci Rep. 2020;10:18942. doi: 10.1038/s41598-020-76098-y. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 36.Vodošek Hojs N, Ekart R, Bevc S, et al. CHA2DS2-VASc score as a predictor of cardiovascular and all-cause mortality in chronic kidney disease patients. Am J Nephrol. 2021 doi: 10.1159/000516121. [DOI] [PubMed] [Google Scholar]
  • 37.Hindricks G, Potpara T, Dagres N, et al. ESC Scientific Document Group. 2020 ESC Guidelines for the diagnosis and management of atrial fibrillation developed in collaboration with the European Association for Cardio-Thoracic Surgery (EACTS) Eur Heart J. 2021;42:373–498. doi: 10.1093/eurheartj/ehaa612. [DOI] [PubMed] [Google Scholar]

Articles from Internal and Emergency Medicine are provided here courtesy of Nature Publishing Group

RESOURCES