Abstract
Aims
Comorbidities are highly prevalent in patients with heart failure (HF) and affect clinical outcome. The CHA2DS2‐VASc score is a validated score to estimate assessment of thromboembolic risk in patients with atrial fibrillation.
Methods and results
We evaluated the predictive value of this score on clinical outcome in patients with HF. All patients with a diagnosis of chronic HF at a health maintenance organization were evaluated for the CHA2DS2‐VASc score. Patients were followed for cardiac related hospitalizations and death. The cohort included 7106 HF patients. Mean follow‐up was 744 days; the median CHA2DS2‐VASc score was 5.0 (range 4.0–6.0). The CHA2DS2‐VASc score was a significant predictor of survival and predictive of the combined end point of death and cardiovascular hospitalization. Survival rates were reduced with increasing quintiles of the CHA2DS2‐VASc score: 93.6 ± 0.7% vs. 83.0 ± 1.1% vs. 75.7 ± 1.0% vs. 73.0 ± 1.2% vs. 63.3 ± 1.2%, respectively P < 0.001. After adjustment for other significant predictors, increasing CHA2DS2‐VASc scores were independently predictive of survival and of the combined end point of death and cardiovascular hospitalization by Cox regression analysis. Analysing the CHA2DS2‐VASc score as a continuous parameter by cox regression analysis demonstrated a significant increase with each point increase in the CHA2DS2‐VASc score (hazard ratio 1.21, 95% confidence interval 1.17–1.26, P < 0.0001). Cox regression analysis using restricted cubic splines demonstrated an independent continuous increase in mortality with increasing CHA2DS2‐VASc score (P < 0.0001 adjusted linear model). The predictive value was present in HF with reduced as well as preserved ejection fraction.
Conclusions
The CHA2DS2‐VASc score has a significant impact on outcome in HF patients.
Keywords: CHA2DS2‐VASc score, Heart failure, Outcome
Introduction
Heart failure (HF) has emerged as a major epidemic and is a significant public health burden. It is associated with considerable morbidity and mortality. 1 Prognostic factors in this population include parameters specific for HF such as New York Heart Association (NYHA) class and left ventricle ejection fraction but also multiple comorbid conditions that complicate management and adversely affect clinical outcome. 2 The Candesartan in Heart Failure: Assessment of Reduction in Mortality and Morbidity programme identified 21 different independent predictors of mortality or morbidity in HF. The most powerful predictors for mortality in the Candesartan in Heart Failure: Assessment of Reduction in Mortality and Morbidity programme were older age, diabetes, and ejection fraction, and the first two were comorbid conditions. 3
The CHA2DS2‐VASc score [congestive HF; hypertension; age ≥ 75 years (doubled); type 2 diabetes; previous stroke or transient ischemic attack (doubled); vascular disease; age 65–74 years; and sex category] has been used for the assessment of thromboembolic risk and to guide antithrombotic treatment in patients with atrial fibrillation (AF). 4 This simple and well‐established score has been shown to predict risk for other conditions beyond its original field. Several studies have demonstrated an association between the CHA2DS2‐VASc score and adverse outcomes in several cardiovascular conditions such as chest pain, 5 acute coronary syndrome, 6 , 7 acute myocardial infarction, 8 and pulmonary emboli, 9 regardless of the presence of AF.
The CHA2DS2‐VASc is a simple score to calculate and could be expected to have an impact on outcome in HF. There are a limited number of studies that evaluated the prognostic significance of the CHA2DS2‐VASc in patients with HF 10 , 11 , 12 , 13 , 14 , 15 with emphasis on stroke and mortality. The majority assessed mortality in hospitalized patients, 12 , 13 , 14 , 15 and only one study evaluated its impact on hospitalizations. 10 Analysis of patients enrolled in the warfarin vs. aspirin in reduced cardiac ejection fraction (WARCEF) trial found that The CHA2DS2‐VASc score predicted adverse outcomes in patients with systolic HF in sinus rhythm. 13 In addition, Paoletti Perini et al. showed that in patients who are candidates for cardiac resynchronization therapy, CHA2DS2‐VASc score is an independent predictor of major clinical events including death and HF hospitalizations at long term follow‐up. 10 There is limited data regarding CHA2DS2‐VASc score in ambulatory stable patients with chronic HF and the predictive value on HF outcomes including mortality and cardiovascular hospitalizations.
The purpose of the present study was to evaluate the CHA2DS2‐VASc score as a predictor for cardiovascular outcomes in a large real‐world cohort of patients with chronic HF.
Methods
Clalit Health Services is the largest health maintenance organization (HMO) in Israel. It has a central computerized database in which all members have a complete digital record. The database includes demographic data, comprehensive clinical data, all diagnoses, and all laboratory data undertaken in a single centralized laboratory of the HMO. We identified and retrieved electronically from the computerized database all members with a diagnosis of HF as coded by the database in Jerusalem using the International Classification of Diseases, Ninth Revision (ICD‐9) code 428. Data were retrieved from January 2017. Patients were followed for clinical events including cardiovascular hospitalizations and death until to January 2019. Seven thousand one hundred six patients had a diagnosis of HF. Natriuretic peptides are not routinely performed in Israel and were not available for analysis. The type of HF (HF with reduced ejection fraction and HF with preserved ejection fraction (HFpEF) was based on a documented specific diagnosis and was available in 67% of the patients. The diagnosis of the remaining patients was ‘HF, unspecified’. All hospitalizations in cardiac and internal medicine departments including cardiac and internal intensive care units were retrieved and analysed. Data on mortality were retrieved from the National Census Bureau. The Institutional Committee for Human Studies of Clalit Health Services, approved the study protocol.
Biochemical analyses were performed at the HMO single centralized core laboratory with routine standardized methodologies on fresh samples of blood obtained after an overnight fast. Glucose levels were measured in plasma, and all other biochemical analyses were performed on serum. Urinary albumin to creatinine ratio (μg/mg) was measured from a spot morning urine sample. The laboratory is authorized to perform tests according to the international quality standard ISO‐9001.
SPSS Version 17.0 for Windows (SPSS Inc., Chicago, Illinois, USA) and R Statistical Software Version 3.0.1 for Windows (R Development Core Team) were used for the analyses. Comparison of the clinical characteristics was performed using the Mann–Whitney U test for continuous variables and the Chi‐square test for categorical variables. Clinical predictors were transformed where appropriate. Log10 was used for logarithmic transformations with the exception of estimated glomerular filtration rate that a square root transformation was used. Follow‐up time was calculated using Kaplan–Meier estimate of potential follow‐up. Kaplan–Meier curves, with the log‐rank test, were used to compare survival according to CHA2DS2‐VASc score. Multivariate Cox proportional hazards regression analysis was used to evaluate independent variables that determined survival. Parameters included in the multivariate Cox regression analysis incorporated relevant parameters that were significant on univariable analysis with the addition of significant drug therapy in separate models. Restricted cubic spline multivariable cox regression analysis was performed to evaluate the relationship between CHA2DS2‐VASc score as a continuous parameter and mortality. Proportionality assumptions of the Cox regression models were evaluated by log–log survival curves and with the use of Schoenfeld residuals. An evaluation of the existence of confounding or interactive effects was made between variables and their possible collinearity. A P value of <0.05 was considered statistically significant.
Results
Clinical parameters
The study cohort included 7106 HF patients. Supporting Information Figure S1 demonstrates the distribution of the CHA2DS2‐VASc score in the HF cohort. The mean CHA2DS2‐VASc score was 5.1 ± 1.8, median 5.0 (interquartile range 4.0–6.0). We divided the cohort into five quintiles based on the CHA2DS2‐VASc score: ≤3, 4, 5, 6, and ≥7. The characteristics of the patients stratified according to the CHA2DS2‐VASc score quintiles are presented in Table 1. Patients with a higher CHA2DS2‐VASc score were more likely to be women, older, and with a higher number of comorbidities including AF. A higher CHA2DS2‐VASc score was associated with more advanced NYHA class and with HFpEF. Other predictors of a worse outcome in HF were also associated with a higher CHA2DS2‐VASc score including higher urea and lower estimated glomerular filtration rate, iron, haemoglobin, and albumin. CHA2DS2‐VASc score had a minor effect on pharmacological therapy with the exception that a higher score was associated with more furosemide therapy.
Table 1.
Demographics and clinical characteristics of patients with heart failure according to the CHA2DS2‐VASc score
| Variable | CHA2DS2‐VASc ≤3 (N = 1231) | CHA2DS2‐VASc 4 (N = 1182) | CHA2DS2‐VASc 5 (N = 1712) | CHA2DS2‐VASc 6 (N = 1456) | CHA2DS2‐VASc ≥7 (N = 1525) | Total (N = 7106) | P Value |
|---|---|---|---|---|---|---|---|
| Age (years) | 59 (50–65) | 70 (62–77) | 79 (71–86) | 82 (77–87) | 83 (78–88) | 77 (67–85) | <0.001 |
| Gender (male) | 957 (78) | 798 (68) | 872 (51) | 584 (40) | 521 (34) | 3732 (53) | <0.001 |
| NYHA Class III/IV | 181 (21) | 274 (30) | 521 (41) | 489 (45) | 543 (48) | 2008 (38) | <0.001 |
| HF with reduced ejection fraction | 411 (54) | 358 (47) | 462 (38) | 335 (36) | 347 (34) | 1913 (41) | <0.001 |
| Diabetes mellitus | 284 (23) | 563 (48) | 824 (48) | 1033 (71) | 1091 (72) | 3795 (53) | <0.001 |
| Hypertension | 445 (36) | 937 (79) | 1608 (94) | 1407 (97) | 1507 (99) | 5904 (83) | <0.001 |
| Hyperlipidaemia | 892 (72) | 1040 (88) | 1533 (90) | 1383 (95) | 1467 (96) | 6315 (89) | <0.001 |
| Ischemic heart disease | 615 (50) | 742 (63) | 1062 (62) | 1048 (72) | 1198 (79) | 4665 (66) | <0.001 |
| Prior myocardial infarction | 330 (27) | 412 (35) | 632 (37) | 725 (50) | 915 (60) | 3014 (42) | <0.001 |
| Prior coronary bypass surgery | 31 (3) | 28 (2) | 28 (2) | 16 (1) | 19 (1) | 122 (2) | 0.01 |
| Atrial fibrillation | 246 (20) | 388 (33) | 692 (40) | 643 (44) | 740 (49) | 2709 (38) | <0.001 |
| Prior stroke/transient ischemic attack | 7 (0.6) | 47 (4) | 111 (6) | 289 (20) | 1225 (80) | 1679 (24) | <0.001 |
| Peripheral vascular disease | 28 (2) | 96 (8) | 206 (12) | 280 (19) | 446 (29) | 1056 (15) | <0.001 |
| Chronic obstructive lung disease | 204 (17) | 275 (23) | 405 (24) | 342 (23) | 325 (21) | 1551 (22) | <0.001 |
| Charlson score | 3.0 (2.0–4.0) | 5.0 (4.0–6.0) | 6.0 (5.0–7.0) | 7.0 (6.0–8.0) | 8.0 (7.0–9.0) | 6.0 (5.0–8.0) | <0.001 |
| Depression | 108 (9) | 139 (12) | 315 (18) | 309 (21) | 429 (28) | 1300 (18) | <0.001 |
| Dementia | 28 (2) | 82 (7) | 236 (14) | 256 (18) | 398 (26) | 1000 (14) | <0.001 |
| Dialysis | 43 (3) | 82 (7) | 95 (6) | 77 (5) | 97 (6) | 394 (6) | 0.003 |
| Malignancy | 143 (12) | 233 (20) | 422 (25) | 362 (25) | 404 (26) | 1564 (22) | <0.001 |
| Body mass index (kg/m2) | 28 (24–32) | 29 (26–33) | 29 (26–34) | 29 (26–33) | 29 (25–32) | 29 (25–33) | <0.001 |
| Pulse (beats per minute) | 76 (67–85) | 73 (64–80) | 72 (64–80) | 70 (64–79) | 71 (64–79) | 72 (64–80) | <0.001 |
| Systolic blood pressure (mmHg) | 122 (112–131) | 126 (117–135) | 129 (119–140) | 130 (120–140) | 130 (120–140) | 128 (118–139) | <0.001 |
| Diastolic blood pressure (mmHg) | 73 (66–80) | 72 (65–80) | 70 (64–79) | 70 (63–79) | 70 (63–79) | 71 (65–79) | <0.001 |
| Laboratory Data | |||||||
| Creatinine (mg/dL) | 0.9 (0.7–1.0) | 1.0 (0.8–1.3) | 1.0 (0.8–1.3) | 1.1 (0.8–1.4) | 1.1 (0.8–1.5) | 1.0 (0.8–1.3) | <0.001 |
| Estimated glomerular filtration rate (mL/min per 1.73m2) a | 94 (76–112) | 76 (55–95) | 67 (49–87) | 61 (42–80) | 58 (40–78) | 69 (48–92) | <0.001 |
| Urea (mg/dL) | 0.9 (0.7–1.0) | 1.0 (0.8–1.3) | 1.0 (0.8–1.3) | 1.1 (0.8–1.4) | 1.1 (0.8–1.5) | 1.0 (0.8–1.3) | <0.001 |
| Urine Albumin/Creatinine ratio | 15 (7.0–63) | 34 (11–171) | 38 (13–160) | 46 (17–173) | 60 (20–241) | 38 (13–161) | <0.001 |
| Sodium (mEq/L) | 140 (139–142) | 140 (138–142) | 140 (138–142) | 140 (138–142) | 140 (138–142) | 140 (138–142) | 0.006 |
| Potassium (mEql/L) | 4.5 (4.3–4.8) | 4.6 (4.3–4.9) | 4.6 (4.3–5.0) | 4.6 (4.3–5.0) | 4.6 (4.3–5.0) | 4.6 (4.3–4.9) | <0.001 |
| Haemoglobin (g/dL) | 14 (13–15) | 13 (12–14) | 13 (11–14) | 12 (11–13) | 12 (11–13) | 13 (11–14) | <0.001 |
| White blood count (x109/L) | 7.6 (6.2–9.2) | 7.4 (6.1–8.9) | 7.1 (5.9–8.6) | 7.3 (6.1–8.7) | 7.2 (5.9–8.9) | 7.3 (6.0–8.8) | <0.001 |
| Red cell distribution width (%) | 14 (14–15) | 15 (14–16) | 15 (14–16) | 15 (14–16) | 15 (14–17) | 15 (14–16) | <0.001 |
| Glucose (mg/dL) | 99 (91–113) | 107 (95–137) | 105 (94–131) | 112 (95–141) | 110 (95–143) | 106 (94–133) | <0.001 |
| Haemoglobin A1c (%) | 5.7 (5.3–6.2) | 6.1 (5.6–7.3) | 6.0 (5.6–7.0) | 6.3 (5.7–7.4) | 6.4 (5.7–7.4) | 6.1 (5.6–7.1) | <0.001 |
| Uric acid (mg/dL) | 5.9 (4.9–7.0) | 6.2 (5.2–7.5) | 6.3 (5.1–7.6) | 6.5 (5.3–7.9) | 6.5 (5.2–7.9) | 6.3 (5.1–7.6) | <0.001 |
| TSH (mIU/L) | 2.0 (1.4–3.1) | 2.2 (1.4–3.3) | 2.3 (1.5–3.5) | 2.3 (1.4–3.5) | 2.4 (1.5–3.5) | 2.2 (1.5–3.4) | <0.001 |
| Iron (μg/dL) | 68 (48–90) | 61 (44–81) | 58 (42–78) | 55 (41–71) | 55 (38–70) | 58 (42–77) | <0.001 |
| Transferrin (mg/dL) | 260 (232–297) | 248 (214–294) | 246 (211–289) | 244 (208–284) | 236 (201–275) | 245 (210–285) | <0.001 |
| Transferrin saturation (%) | 18 (13–26) | 17 (12–24) | 17 (12–23) | 16 (12–22) | 16 (11–22) | 17 (12–23) | <0.001 |
| Ferritin (ng/ml) | 88 (40–189) | 92 (40–180) | 82 (38–168) | 78 (39–176) | 86 (39–187) | 84 (39–179) | 0.23 |
| Calcium (mg/dL) | 9.3 (9.1–9.6) | 9.3 (8.9–9.6) | 9.2 (8.9–9.5) | 9.2 (8.9–9.5) | 9.2 (8.8–9.5) | 9.2 (8.9–9.6) | <0.001 |
| Phosphorus (mg/dL) | 3.4 (3.0–3.8) | 3.5 (3.1–3.9) | 3.5 (3.1–3.9) | 3.5 (3.2–3.9) | 3.6 (3.2–4.0) | 3.5 (3.1–3.9) | <0.001 |
| Magnesium (mg/dL) | 2.1 (2.0–2.3) | 2.1 (1.9–2.3) | 2.1 (2.0–2.3) | 2.1 (1.9–2.3) | 2.1 (1.9–2.3) | 2.1 (1.9–2.3) | 0.07 |
| Triglycerides (mg/dL) | 120 (87–170) | 123 (90–172) | 116 (87–160) | 120 (89–170) | 121 (89–168) | 120 (88–168) | 0.03 |
| Low‐density lipoprotein (mg/dL) | 92 (70–116) | 84 (66–106) | 82 (65–104) | 81 (64–103) | 79 (62–100) | 83 (65–106) | <0.001 |
| Albumin (g/dL) | 4.1 (3.9–4.3) | 4.0 (3.7–4.2) | 3.9 (3.6–4.1) | 3.8 (3.5–4.1) | 3.7 (3.4–4.0) | 3.9 (3.6–4.1) | <0.001 |
| C‐Reactive protein (mg/dL) | 0.5 (0.2–1.3) | 0.6 (0.2–1.6) | 0.6 (0.3–1.6) | 0.6 (0.2–1.7) | 0.7 (0.3–1.9) | 0.6 (0.3–1.6) | 0.002 |
| Alanine transaminase (IU) | 20 (15–27) | 17 (13–23) | 15 (11–20) | 14 (11–20) | 14 (10–19) | 16 (11–22) | <0.001 |
| Alkaline phosphatase (IU) | 85 (69–108) | 89 (72–112) | 90 (71–112) | 89 (71–114) | 92 (73–117) | 89 (71–113) | <0.001 |
| Total bilirubin (mg/dL) | 0.6 (0.5–0.8) | 0.6 (0.5–0.8) | 0.6 (0.4–0.8) | 0.6 (0.4–0.7) | 0.5 (0.4–0.7) | 0.6 (0.4–0.8) | <0.001 |
| Gamma‐glutamyltransferase (IU) | 26 (18–42) | 27 (18–47) | 26 (18–42) | 25 (17–43) | 25 (17–50) | 26 (18–44) | 0.05 |
| Medication | |||||||
| ACE‐I/ARB/ARNI | 838 (68) | 912 (77) | 1316 (77) | 1124 (77) | 1139 (75) | 5329 (75) | <0.001 |
| Beta blockers | 827 (67) | 883 (75) | 1238 (72) | 1098 (75) | 1107 (73) | 5153 (73) | <0.001 |
| Spironolactone | 387 (31) | 406 (34) | 603 (35) | 541 (37) | 516 (34) | 2453 (35) | 0.03 |
| Furosemide | 494 (40) | 718 (61) | 1183 (69) | 1084 (74) | 1136 (74) | 4615 (65) | <0.001 |
| Thiazide | 82 (7) | 173 (15) | 249 (15) | 208 (14) | 208 (14) | 920 (13) | <0.001 |
| Digoxin | 61 (5) | 63 (5) | 91 (5) | 95 (7) | 105 (7) | 415 (6) | 0.11 |
| Amiodarone | 142 (12) | 176 (15) | 291 (17) | 245 (17) | 237 (16) | 1,091 (15) | <0.001 |
| Aspirin | 634 (52) | 693 (59) | 937 (55) | 812 (56) | 745 (49) | 3,821 (54) | <0.001 |
| New oral anticoagulants b | 131 (11) | 253 (21) | 484 (28) | 454 (31) | 506 (33) | 1828 (26) | <0.001 |
| Vitamin K antagonists | 204 (17) | 182 (15) | 222 (13) | 187 (13) | 194 (13) | 989 (14) | 0.008 |
HF, heart failure; NYHA, New York Heart Association; TSH, thyroid stimulating hormone.
Data are presented as median (interquartile range) for continuous variables and counts (percentages) for categorical variables. P value by the Kruskal–Wallis test for continuous variables and the Chi‐square test for categorical variables. Diabetes mellitus defined as fasting plasma glucose ≥126 mg/dL or glucose lowering treatment, hypertension as blood pressure >140/90 mmHg measured on several occasions or anti‐hypertensive treatment and hyperlipidemia as low density lipoprotein >130 mg/dL, fasting serum triglycerides >200 mg/dL, or lipid lowering treatment.
Estimated glomerular filtration rate was calculated using the modified modification of diet in renal disease (MDRD) equation (175 * serum creatinine–1.154 * age–0.203. For female patients, a correction factor is used multiplying by 0.742.).
Dabigatran, Rivaroxaban, or Apixaban.
CHA2DS2‐VASc score and clinical outcomes
The overall 2 year‐mortality rate was 23.2%. Survival rate by Kaplan–Meier analysis suggested that the CHA2DS2‐VASc score was directly and incrementally associated with reduced survival. Survival rates were reduced with increasing quintiles of the CHA2DS2‐VASc score: 93.6 ± 0.7% vs. 83.0 ± 1.1% vs. 75.7 ± 1.0% vs. 73.0 ± 1.2% vs. 63.3 ± 1.2%, respectively P < 0.001; Figure 1A). Similarly, the CHA2DS2‐VASc score was also directly associated with decreased event‐free survival from death or cardiovascular‐hospitalizations (52.8 ± 1.4% vs. 37.8 ± 1.4% vs. 30.6 ± 1.1% vs. 25.1 ± 1.1% vs. 19.5 ± 1.0%, P < 0.001; Figure 1B). Multivariable Cox regression analysis after adjustment for significant predictors demonstrated that CHA2DS2‐VASc score was a significant predictor of mortality (Table 2). After adjustment for other significant predictors (Table 2 for predictors included), CHA2DS2‐VASc score was associated with an incremental increase in mortality. Inclusion of HF medications demonstrated a similar result (Supporting Information, Table S1). The CHA2DS2‐VASc score was also a significant independent predictor of the combined endpoint of death or cardiovascular‐hospitalizations with a graded increased risk with increasing CHA2DS2‐VASc score (Table S1). Analysing the CHA2DS2‐VASc score as a continuous parameter by Cox regression analysis demonstrated a significant increase in mortality with each point increase in the CHA2DS2‐VASc score after adjustment for clinical parameters and drug therapy (hazard ratio 1.21, 95% confidence interval 1.17–1.26, P < 0.0001).
Figure 1.
Survival and event‐free survival from death and cardiovascular hospitalization by Kaplan–Meier survival analysis stratified by quintiles of CHA2DS2‐VASc score. (A) Increasing CHA2DS2‐VASc scores were directly associated with reduced survival; log rank P < 0.001. (B) Increasing CHA2DS2‐VASc scores were directly associated with reduced event‐free survival from death or cardiovascular‐hospitalizations; log rank P < 0.001.
Table 2.
Predictors of mortality by Cox regression analysis
| Predictor | Univariable | Multivariable | ||
|---|---|---|---|---|
| Hazard ratio (95% CI) | P value | Hazard ratio (95% CI) | P Value | |
| Age (years) | 1.06 (1.05–1.06) | <0.001 | / | / |
| Gender (male) | 0.82 (0.74–0.90) | <0.001 | / | / |
| NYHA III/IV | 1.74 (1.56–1.94) | <0.001 | 1.29 (1.14–1.46) | <0.001 |
| Diabetes mellitus | 1.16 (1.05–1.28) | 0.003 | / | / |
| Hypertension | 2.36 (1.99–2.80) | <0.001 | / | / |
| Ischemic heart disease | 1.05 (0.95–1.17) | 0.33 | / | / |
| Atrial fibrillation | 1.61 (1.46–1.77) | <0.001 | 1.35 (1.20–1.51) | <0.001 |
| Body mass index a (kg/m2) | 0.12 (0.07–0.22) | <0.001 | 0.08 (0.04–0.15) | <0.001 |
| Urea (mg/dL) a | 12.19 (9.89–15.02) | <0.001 | 6.53 (4.41–9.68) | <0.001 |
| eGFR b (mL/min per 1.73m2) | 0.82 (0.80–0.84) | <0.001 | 0.98 (0.96–1.01) | 0.20 |
| Sodium (mEq/L) | 0.94 (0.92–0.95) | <0.001 | 0.95 (0.94–0.97) | <0.001 |
| Haemoglobin (g/dL) | 0.76 (0.74–0.78) | <0.001 | 0.86 (0.84–0.89) | <0.001 |
| CHA2DS2VASc | <0.001 | <0.001 | ||
| CHA2DS2VASc ≤ 3 | 1.0 (Reference ) | 1.0 (Reference ) | ||
| CHA2DS2VASc 4 | 2.81 (2.16–3.64) | <0.001 | 1.92 (1.42–2.60) | <0.001 |
| CHA2DS2VASc 5 | 4.17 (3.28–5.31) | <0.001 | 2.65 (2.00–3.51) | <0.001 |
| CHA2DS2VASc 6 | 4.71 (3.70–6.00) | <0.001 | 2.41 (1.81–3.21) | <0.001 |
| CHA2DS2VASc ≥ 7 | 6.93 (5.48–8.78) | <0.001 | 3.52 (2.66–4.67) | <0.001 |
CI, confidence interval; eGFR, estimated glomerular filtration rate; NYHA, New York Heart Association.
Data are presented as hazard ratio (95% confidence interval), P value.
Log‐transformed.
Square root‐transformed.
A sensitivity analysis evaluating CHA2DS2‐VASc score as a continuous parameter using restricted cubic splines was performed. Knots were allocated at CHA2DS2‐VASc score of 2, 5, 6, and 8. Cox regression analysis demonstrated a direct relationship between the CHA2DS2‐VASc score and mortality. This analysis demonstrated that there was a continuous increase in the risk with increasing CHA2DS2‐VASc score (Figure 2A), P < 0.0001 for the linear model. After adjustment for significant parameters included in Table 2, there was a direct linear relationship between CHA2DS2‐VASc score and mortality, and this was independently significant (Figure 2B), P < 0.0001 for the adjusted linear model.
Figure 2.
Mortality as a function of CHA2DS2‐VASc score as a continuous variable using restricted cubic splines. (A) Cox regression analysis with hazard ratio for mortality [with 95% confidence interval]. There was a continuous increase in the risk with increasing CHA2DS2‐VASc score, P < 0.0001 for the linear model. (B) Cox regression analysis for mortality after adjustment, P < 0.0001 for the adjusted model. Variables included in the model included New York Heart Association class, atrial fibrillation, log transformed body mass index, log‐transformed serum urea levels, square root‐transformed estimated glomerular filtration rate, serum sodium, and haemoglobin.
The predictive value of the CHA2DS2‐VASc score was present over the entire cohort of HF patients including patients with HF with reduced ejection fraction and in HFpEF (Supporting Information, Table S1). In all groups, the CHA2DS2‐VASc score was a significant predictor of mortality and was associated with an incremental increase in mortality by itself and after adjustment for significant predictors. A similar result was demonstrated regarding event‐free survival from death or cardiovascular hospitalization. Adjustment for other significant predictors demonstrated that the increase in CHA2DS2‐VASc score was a significant predictor in both groups. In addition, the CHA2DS2‐VASc score was a predictor of outcome in patient regardless of AF. In patients with AF as well as in patients without AF, the CHA2DS2‐VASc score was predictive of death as well as death and cardiovascular hospitalizations after adjustment for significant predictors (Supporting Information, Table S3).
We also looked at the predictive value of the CHA2DS2‐VASc score by itself compared with the standard multivariable adjusted model in our cohort using the receiver operator characteristics curve for mortality. Although the adjusted model incorporating numerous HF specific parameters including NYHA class was better than the CHA2DS2‐VASc score (area under the curve = 0.765, P < 0.0001), the CHA2DS2‐VASc had a good predictive value on its own (area under the curve = 0.653, P < 0.0001).
Discussion
In this cohort of real‐world HF patients, the CHA2DS2‐VASc score was a significant predictor of death as well as death and cardiovascular hospitalizations. There was a direct incremental relationship between CHA2DS2‐VASc score and clinical outcomes. For example, 2 year survival among patients with CHA2DS2‐VASc score ≤3 was over 93% compared with less than 64% in patients with a score ≥7. A similar pattern was observed for the combined end point of mortality and cardiovascular hospitalizations. Moreover, the present study suggests this simple well‐known score incorporating readily available clinical parameters conveys predictive value over the whole spectrum of HF patients including patients with preserved as well as reduced ejection fraction and could be useful in estimating outcome in chronic HF patients.
Although the CHA2DS2‐VASc score is recommended for the assessment of the risk of thromboembolic event in patients with AF, in recent years, it is used to predict outcomes in several other cardiovascular diseases. High CHA2DS2‐VASc score is associated with worse long term outcomes in patients with coronary disease, 16 acute coronary syndrome, 6 and even in patients who were evaluated for chest pain. 5 It is reasonable to assume that CHA2DS2‐VASc score is valuable in HF, because its components are prognostic factors in HF. 17
There are limited data available regarding the CHA2DS2‐VASc score in predicting HF outcomes in patients with HF. Most of the published studies evaluated embolic events and death in hospitalized HF patients 12 , 13 , 14 , 15 demonstrating the value of the CHA2DS2‐VASc score in predicting mortality. In the Danish prospective cohort study of HF populations with or without AF, the CHA2DS2‐VASc score was associated with risk of ischemic stroke, thromboembolism, and death. 11 Similar to our findings, data from the Korean Acute Heart Failure (KorAHF) registry, showed in each 1‐point increase in CHA2DS2‐VASc score among hospitalized HF patients, were associated with significantly increased long‐term mortality. 15 The present study demonstrates that the CHA2DS2‐VASc score is not only a predictor of mortality but also predicts the risk of cardiovascular hospitalizations. Similarly, the CHA2DS2‐VASc score predicted HF hospitalizations in HF patients undergoing cardiac resynchronization therapy. 10
The burden of comorbidities is significant in patients with HF as seen in the present study. This was particularly evident in patients with HFpEF that was associated with a higher CHA2DS2‐VASc score. This is to be anticipated, as the typical characteristics of patients with HFpEF are older patients with accompanying comorbidities such as diabetes mellitus and prior stroke. 18 Nevertheless, the predictive value of these comorbidities regarding mortality was not restricted only to patients with HFpEF, suggesting that the CHA2DS2‐VASc score can be used over the entire spectrum of HF patients.
The present study emphasizes the importance of comorbidities that are part of the CHA2DS2‐VASc score as central players in driving morbidity and mortality in HF. Comorbidities are highly prevalent in HF and have a major impact on outcome. The reasons for the above phenomena are multifactorial. 18 Several comorbidities such as diabetes mellitus and hypertension predispose or cause HF and, furthermore, are important in the destabilization and progression of HF. Comorbidities and HF have numerous bidirectional interactions and are interrelated by several mechanisms, including inflammation, activation of the sympathetic, and renin‐angiotensin‐aldosterone system. HF influences comorbidities, with mutual effects, contributing to the progression of HF.
Limitations
Several potential limitations of this study merit consideration. The present study was an observational study. Data regarding clinical parameters and drug therapy were based on a digitized database. Although this database was validated and found to be highly accurate, not all data could be verified. While we tried to adjust for clinically relevant parameters, not all clinical parameters were available, and it is impossible to adjust for all variables that may affect outcome. Data on natriuretic peptide levels were not available. Data on the specific HF type were not available in all of the patients, and this may cause a bias. In addition, the cohort was a community‐based cohort, and the findings may not be applicable in more advanced or hospital‐based HF cohorts.
Conclusions
In conclusion, given the high variability in clinical and demographic characteristics among patients with HF, the CHA2DS2‐VASc score calculated at bedside could be incorporated into the routine risk stratification and could be useful in estimating outcome in HF patients.
Conflict of interest
None declared.
Supporting information
Figure S1. Histogram of the distribution of the CHA2DS2‐VASc score in the heart failure cohort.
Table S1. Hazard ratio for clinical outcome according to CHA2DS2‐VASc score by Cox regression analysis.
Table S2. Hazard ratio for clinical outcome according to CHA2DS2‐VASc score by Cox regression analysis in subsets of HF types.
Table S3. Hazard ratio for clinical outcome according to CHA2DS2‐VASc score by Cox regression analysis in Patients with and without Atrial Fibrillation.
Shuvy, M. , Zwas, D. R. , Keren, A. , and Gotsman, I. (2020) Value of the CHA2DS2‐VASc score for predicting outcome in patients with heart failure. ESC Heart Failure, 7: 2553–2560. 10.1002/ehf2.12831.
References
- 1. Meta‐analysis Global Group in Chronic Heart Failure . The survival of patients with heart failure with preserved or reduced left ventricular ejection fraction: an individual patient data meta‐analysis. Eur Heart J 2011; 33: 1750–1757. [DOI] [PubMed] [Google Scholar]
- 2. Chioncel O, Lainscak M, Seferovic PM, Anker SD, Crespo‐Leiro MG, Harjola VP, Parissis J, Laroche C, Piepoli MF, Fonseca C, Mebazaa A, Lund L, Ambrosio GA, Coats AJ, Ferrari R, Ruschitzka F, Maggioni AP, Filippatos G. Epidemiology and one‐year outcomes in patients with chronic heart failure and preserved, mid‐range and reduced ejection fraction: an analysis of the esc heart failure long‐term registry. Eur J Heart Fail 2017; 19: 1574–1585. [DOI] [PubMed] [Google Scholar]
- 3. Pocock SJ, Wang D, Pfeffer MA, Yusuf S, Jjv MM, Swedberg KB, Östergren J, Michelson EL, Pieper KS, Granger CB. Predictors of mortality and morbidity in patients with chronic heart failure. Eur Heart J 2005; 27: 65–75. [DOI] [PubMed] [Google Scholar]
- 4. Association DwtscotEHR, Surgery EbtEAfC‐T, Members ATF , Camm AJ, Kirchhof P, Lip GYH, Schotten U, Savelieva I, Ernst S, Van Gelder IC, Al‐Attar N, Hindricks G, Prendergast B, Heidbuchel H, Alfieri O, Angelini A, Atar D, Colonna P, De Caterina R, De Sutter J, Goette A, Gorenek B, Heldal M, Hohloser SH, Kolh P, Le Heuzey J‐Y, Ponikowski P, Rutten FH, ECfP G, Vahanian A, Auricchio A, Bax J, Ceconi C, Dean V, Filippatos G, Funck‐Brentano C, Hobbs R, Kearney P, McDonagh T, Popescu BA, Reiner Z, Sechtem U, Sirnes PA, Tendera M, Vardas PE, Widimsky P, Reviewers D, Agladze V, Aliot E, Balabanski T, Blomstrom‐Lundqvist C, Capucci A, Crijns H, Dahlöf B, Folliguet T, Glikson M, Goethals M, Gulba DC, Ho SY, Klautz RJM, Kose S, McMurray J, Perrone Filardi P, Raatikainen P, Salvador MJ, Schalij MJ, Shpektor A, Sousa J, Stepinska J, Uuetoa H, Zamorano JL, Zupan I, 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] [PubMed] [Google Scholar]
- 5. Topaz G, Haisraely O, Shacham Y, Beery G, Shilo L, Kassem N, Pereg D, Kitay‐Cohen Y. CHA2DS2‐VASc score and clinical outcomes of patients with chest pain discharged from internal medicine wards following acute coronary syndrome rule‐out. Clin Cardiol 2018; 41: 539–543. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 6. Rozenbaum Z, Elis A, Shuvy M, Vorobeichik D, Shlomo N, Shlezinger M, Goldenberg I, Pereg D. CHA2DS2‐VASc score and clinical outcomes of patients with acute coronary syndrome. Eur J Intern Med 2016; 36: 57–61. [DOI] [PubMed] [Google Scholar]
- 7. Chua S‐K, Lo H‐M, Chiu C‐Z, Shyu K‐G. Use of CHA2DS2 and CHA2DS2‐VASc scores to predict subsequent myocardial infarction, stroke, and death in patients with acute coronary syndrome: data from Taiwan acute coronary syndrome full spectrum registry. PLOS ONE 2014; 9: e111167. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8. Li C‐Y, Chang C‐J, Chung W‐J, Lin C‐J, Hsueh S‐K, Lee C‐H, Wu C‐J, Tsai T‐H, Cheng C‐I. Assessment of CHA2DS2‐VASc score for predicting cardiovascular and cerebrovascular outcomes in acute myocardial infarction patients. Medicine (Baltimore) 2018; 97: e11230–e11230. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9. Gök M, Kurtul A, Harman M, Kara M, Süleymanoglu M, Ornek E. Relationship between CHA2DS2‐VASc score and right ventricular dysfunction in patients with acute pulmonary thromboembolism. Clin Appl Thromb Hemost 2018; 24: 56S–62S. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10. Paoletti Perini A, Bartolini S, Pieragnoli P, Ricciardi G, Perrotta L, Valleggi A, Vergaro G, Michelotti F, Boggian G, Sassone B, Mascioli G, Emdin M, Padeletti L. CHA2DS2 and CHA2DS2‐VASc scores to predict morbidity and mortality in heart failure patients candidates to cardiac resynchronization therapy. EP Europace 2013; 16: 71–80. [DOI] [PubMed] [Google Scholar]
- 11. Melgaard L, Gorst‐Rasmussen A, Lane DA, Rasmussen LH, Larsen TB, Lip GYH. Assessment of the CHA2DS2‐VASc score in predicting ischemic stroke, thromboembolism, and death in patients with heart failure with and without atrial fibrillation. JAMA 2015; 314: 1030–1038. [DOI] [PubMed] [Google Scholar]
- 12. Yoshihisa A, Watanabe S, Kanno Y, Takiguchi M, Sato A, Yokokawa T, Miura S, Shimizu T, Abe S, Sato T, Suzuki S, Oikawa M, Sakamoto N, Yamaki T, Sugimoto K, Kunii H, Nakazato K, Suzuki H, Saitoh S‐I, Takeishi Y. The CHA2DS2‐VASc score as a predictor of high mortality in hospitalized heart failure patients. ESC Heart Fail 2016; 3: 261–269. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13. Ye S, Qian M, Zhao B, Buchsbaum R, Sacco RL, Levin B, Di Tullio MR, Mann DL, Pullicino PM, Freudenberger RS, Teerlink JR, Mohr JP, Graham S, Labovitz AJ, Estol CJ, Lok DJ, Ponikowski P, Anker SD, Lip GYH, Thompson JLP, Homma S, Investigators W. CHA2DS2‐VASc score and adverse outcomes in patients with heart failure with reduced ejection fraction and sinus rhythm. Eur J Heart Fail 2016; 18: 1261–1266. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14. Chen Y‐L, Cheng C‐L, Huang J‐L, Yang N‐I, Chang H‐C, Chang K‐C, Sung S‐H, Shyu K‐G, Wang C‐C, Yin W‐H, Lin J‐L, Chen S‐M. investigators TS‐HR, committee. Mortality prediction using CHADS2/CHA2DS2‐VAS/R2CHADS2 scores in systolic heart failure patients with or without atrial fibrillation. Medicine (Baltimore). 2017; 96: e8338–e8338. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 15. Son MK, Lim N‐K, Park H‐Y. Predicting stroke and death in patients with heart failure using CHA2DS2‐VASc score in Asia. BMC Cardiovasc Disord 2019; 19: 193–193. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16. Scudiero F, Zocchi C, De Vito E, Tarantini G, Marcucci R, Valenti R, Migliorini A, Antoniucci D, Marchionni N, Parodi G. Relationship between CHA2DS2‐VASc score, coronary artery disease severity, residual platelet reactivity and long‐term clinical outcomes in patients with acute coronary syndrome. Int J Cardiol 2018; 262: 9–13. [DOI] [PubMed] [Google Scholar]
- 17. Dei Cas A, Khan SS, Butler J, Mentz RJ, Bonow RO, Avogaro A, Tschoepe D, Doehner W, Greene SJ, Senni M, Gheorghiade M, Fonarow GC. Impact of diabetes on epidemiology, treatment, and outcomes of patients with heart failure. JACC: Heart Failure 2015; 3: 136–145. [DOI] [PubMed] [Google Scholar]
- 18. Mentz RJ, Kelly JP, von Lueder TG, Voors AA, Lam CS, Cowie MR, Kjeldsen K, Jankowska EA, Atar D, Butler J, Fiuzat M, Zannad F, Pitt B, O'Connor CM. Noncardiac comorbidities in heart failure with reduced versus preserved ejection fraction. J Am Coll Cardiol 2014; 64: 2281–2293. [DOI] [PMC free article] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Figure S1. Histogram of the distribution of the CHA2DS2‐VASc score in the heart failure cohort.
Table S1. Hazard ratio for clinical outcome according to CHA2DS2‐VASc score by Cox regression analysis.
Table S2. Hazard ratio for clinical outcome according to CHA2DS2‐VASc score by Cox regression analysis in subsets of HF types.
Table S3. Hazard ratio for clinical outcome according to CHA2DS2‐VASc score by Cox regression analysis in Patients with and without Atrial Fibrillation.