Introduction: Chronic kidney disease (CKD) represents one of the most frequent comorbidities observed in heart failure (HF) patients and has been observed to increase this population’s risk of adverse outcomes. Nevertheless, evidence analyzing kidney dysfunction in HF is scarce in Latin American populations. We aimed to analyze the prevalence of kidney dysfunction and assess its association with mortality in patients diagnosed with HF enrolled in the Colombian Heart Failure Registry (RECOLFACA). Methods: RECOLFACA enrolled adult patients with HF diagnosis from 60 centers in Colombia during the period 2017–2019. The primary outcome was all-cause mortality. A Cox proportional-hazards regression model was used to assess the impact of the different categories of eGFR in mortality risk. A p value of <0.05 was considered significant. All statistical tests were two-tailed. Results: From the total 2,514 evaluated patients, 1,501 (59.7%) patients had moderate kidney dysfunction (eGFR <60 mL/min/1.73 m2), while 221 (8.8%) patients were classified as having a severe kidney dysfunction (eGFR <30 mL/min/1.73 m2). Patients with lower kidney function were most commonly males, had higher median age, and reported a higher prevalence of cardiovascular comorbidities. Moreover, different patterns of medications prescription were observed when comparing CKD versus non-CKD patients. Finally, eGFR <30 mL/min/1.73 m2 was significantly associated with a higher mortality risk compared to eGFR >90 mL/min/1.73 m2 status (HR: 1.87; 95% CI, 1.10–3.18), even after an extensive adjustment by relevant covariates. Conclusion: CKD represents a prevalent condition in the setting of HF. Patients with CKD and HF present with multiple sociodemographic, clinical, and laboratory differences compared with those only diagnosed with HF and present a significantly higher risk of mortality. A timely diagnosis and optimal treatment and follow-up of CKD in the setting of HF may improve the prognosis of these patients and prevent adverse outcomes.

Heart failure (HF) represents a highly prevalent non-transmissible chronic disease worldwide, and it is considered one of the most relevant public health issues nowadays [1]. Furthermore, chronic kidney disease (CKD) represents one of the most frequent comorbidities associated with HF and is associated with increased morbidity and mortality [2, 3]. Specifically, cardiorenal syndrome encompasses a wide spectrum of disorders that involve both the kidneys and the heart, originating from a dysfunction of one of the organs that leads to the malfunctioning of its counterpart [4]. The pathophysiology behind this syndrome is complex, as the heart and kidneys are closely linked together by neural, hormonal, and hemodynamic mechanisms [5]. However, the knowledge in this area has substantially increased in recent decades, especially in the setting of HF with reduced ejection fraction (HFrEF) [6].

Nevertheless, most of the current evidence in this area is derived from clinical trial populations, with few HF registry studies providing more “real-life” data to assess the impact of kidney function in the setting of HF [7, 8]. Furthermore, there is a lack of detailed descriptions of the prognostic value of kidney dysfunction by specific groups analyzing large sample sizes in the literature [9]. Finally, the evidence is exceptionally scarce in Latin American patients; therefore, forcing to extrapolate the results observed in other non-Latino cohorts, which has been proven to provide inaccurate estimates in related clinical conditions [10, 11]. In this study, we aimed to analyze the prevalence of kidney dysfunction and assess its association with mortality in patients diagnosed with HF enrolled in the Colombian Heart Failure Registry (RECOLFACA).

Study Design and Population

This prospective cohort study used data collected by the RECOLFACA project which was conducted at 60 medical centers, HF clinics, and cardiology outpatient centers in Colombia. Patient enrollment for RECOLFACA started in February 2017 and ended in 2019, including all individuals older than 18 years old with a clinical diagnosis of HF of any etiology based on the guideline recommendations at the time of inclusion which had at least one HF hospitalization in the 12 months prior to enrollment. Specific inclusion and exclusion criteria, along with additional methodologic characteristics of the registry, are described elsewhere [12, 13]. This study was approved 54 by the Ethics Committee of the [Blinded] under the act number 174-2017.

Data Collection

Information regarding sociodemographic, clinical, and laboratory variables was registered at baseline. HF severity was assessed using the New York Heart Association (NYHA) classification. Furthermore, an ischemic disease diagnosis was registered if the patient underwent a coronary revascularization procedure or if a previous myocardial infarction history was present. Patients with left ventricle ejection fraction (LVEF) ≥50% were classified as HF with preserved ejection fraction, whereas those with LVEF ≤40% were considered as having HFrEF [14]. Individuals with an EF between 41% and 49% were labeled as having HF with mid-range ejection fraction [14]. CKD was defined as having an estimated glomerular filtration rate of <60 mL/min/1.73 m2 according to the Modification of Diet in Renal Disease formula [15]. We classified the estimated GFR into 4 groups: >90, 60–89, 30–66 59, and <30 mL/min/1.73 m2. Individuals with eGFR 60–30 mL/min/1.73 m2 were classified as moderate kidney dysfunction and those with eGFR <30 mL/min/1.73 m2 with severe kidney dysfunction.

Clinical comorbidities were described as follows: arterial hypertension (systolic blood pressure ≥140 mm Hg or diastolic blood pressure ≥90 mm Hg), atrial fibrillation (diagnosed based on a 12-lead ECG or the documented history of this condition), anemia (defined as the presence of a hemoglobin value <13 g/dL for men and <12 g/dL for women), and dyslipidemia (defined as an elevated total cholesterol [≥200 mg/dL] or low-density lipoprotein cholesterol [≥100 mg/dL] levels, or triglycerides ≥150 mg/dL, or currently receiving lipid-lowering medications). Clinical conditions such as valvular heart disease, chronic obstructive pulmonary disease, type 1 diabetes mellitus, cancer, liver failure, dementia, thyroid disease, and Chagas disease were used as reported in the RECOLFACA database. In selected patients, additional echocardiographic variables, such as the systolic diameter of the left ventricle, among others, were available.

Outcomes

The primary outcome was all-cause mortality. We collected these data using a questionnaire applied by each HF clinic and center two times per year. The current results represent data from the first follow-up after enrollment into the registry. Each center also reviewed each patient’s clinical records to assess specific data about the outcomes.

Statistical Analysis

Continuous variables were described as medians and quartiles and categorical variables as absolute counts and proportions. Differences between groups of eGFR were assessed using Pearson’s χ2 and Fisher’s exact tests for categorical variables and Mann-Whitney U tests for continuous ones. The cumulative incidence of the mortality events was calculated with their respective 95% confidence intervals. Survival analyses were performed using the Kaplan-Meier method, life table, and Cox proportional hazard models. A univariate and multivariate analysis using Cox’s proportional regression models was performed to evaluate the association between CKD and mortality. On the other hand, a multivariate logistic regression model was fitted to evaluate the clinical conditions associated with CKD diagnosis. For this purpose, we fitted a stepwise forward logistic regression model. A p value of <0.05 (two-tailed test) was considered statistically significant. All analyses were performed using Statistical Package STATA version 15 (Station College, TX, USA).

RECOLFACA included a total of 2,528 chronic HF ambulatory patients between February 2017 and October 2019. From these, 2,514 had complete information regarding sociodemographic, clinical, and laboratory variables.

Population Characteristics

The population’s median age was 69 years (Q1:59; Q3:78), mainly males (57.6%). 1,501 (59.7%) patients had moderate kidney dysfunction, while 221 (8.8%) patients were had severe kidney dysfunction. Regarding ethnicity, 73.7% were Hispanic, 18.2% Mestizo, 4.6% White, 3.0% African American, 0.4% Indigenous, and only one participant was Asian (0.0%). Table 1 summarizes the baseline characteristics of the patients enrolled in RECOLFACA according to the eGFR classification. Patients with lower kidney function were most commonly males and had a higher median age. Furthermore, a higher prevalence of hypertension, alcohol consumption, type 2 diabetes mellitus (T2DM) – diagnosed with a result of 6.5% or higher on two separate glycated hemoglobin (A1C) tests – coronary heart disease, chronic obstructive pulmonary disease, atrial fibrillation, coronary artery bypass graft history, dyslipidemia, and smoking was observed among patients with lowest eGFR category. Patients with lower kidney function also had a higher 111 prevalence of advanced (III–IV) NYHA classification.

Table 1.

Baseline characteristics of the patients enrolled in the RECOLFACA according to the eGFR categories

eGFR ≥90 (N = 792)eGFR 60–89 (N = 619)eGFR 30–59 (N = 882)eGFR <30 (N = 221)Total (N = 2,514)p value
Males, n (%) 294 (37.1) 286 (46.2) 693 (78.6) 174 (78.7) 1,447 (57.6) <0.001 
Age 66 (56.750, 76) 68 (57.500, 77) 71 (63, 79) 73 (66, 80) 69 (59, 78) <0.001 
Hypertension, n (%) 536 (67.7) 438 (70.8) 653 (74.0) 184 (83.3) 1,811 (72.0) <0.001 
Alcohol consumption, n (%) 16 (2.0) 17 (2.7) 38 (4.3) 15 (6.8) 86 (3.4) 0.002 
T2DM, n (%) 158 (19.9) 137 (22.1) 226 (25.6) 99 (44.8) 620 (24.7) <0.001 
Liver disease, n (%) 4 (0.5) 2 (0.3) 2 (0.2) 3 (1.4) 11 (0.4) 0.141 
Coronary heart disease, n (%) 176 (22.2) 164 (26.5) 290 (32.9) 76 (34.4) 706 (28.1) <0.001 
COPD, n (%) 128 (16.2) 93 (15.0) 178 (20.2) 42 (19.0) 441 (17.5) 0.040 
AF, n (%) 148 (18.7) 146 (23.6) 216 (24.5) 50 (22.6) 560 (22.3) 0.029 
Thyroid disease, n (%) 115 (14.5) 99 (16.0) 137 (15.5) 37 (16.7) 388 (15.4) 0.813 
Valvular heart disease, n (%) 126 (15.9) 111 (17.9) 158 (17.9) 34 (15.4) 429 (17.1) 0.579 
CABG, n (%) 37 (4.7) 44 (7.1) 67 (7.6) 22 (10.0) 170 (6.8) 0.017 
Dyslipidemia, n (%) 177 (22.3) 158 (25.5) 253 (28.7) 59 (26.7) 647 (25.7) 0.031 
Smoking, n (%) 106 (13.4) 113 (18.3) 194 (22) 39 (17.7) 452 (17.9) <0.001 
Chagas disease, n (%) 30 (3.8) 21 (3.4) 31 (3.5) 6 (2.7) 88 (3.5) 0.892 
NYHA, n (%) <0.001 
 I 90 (11.4) 92 (14.9) 93 (10.5) 23 (10.4) 298 (11.9) 
 II 483 (61.0) 322 (52.0) 453 (51.4) 92 (41.6) 1,350 (53.7) 
 III 196 (24.7) 173 (27.9) 289 (32.8) 89 (40.3) 747 (29.7) 
 IV 23 (2.9) 32 (5.2) 47 (5.3) 17 (7.7) 119 (4.7) 
ACEI/ARB, n (%) 609 (76.9) 458 (73.9) 667 (75.6) 146 (66.1) 1,880 (74.8) 0.010 
Beta-blockers, n (%) 673 (85.0) 532 (85.9) 786 (89.1) 198 (89.6) 2,189 (87.1) 0.040 
ARNIs, n (%) 58 (7.3) 70 (11.3) 95 (10.8) 22 (10.0) 245 (9.7) 0.045 
MRAs, n (%) 456 (57.6) 347 (56.1) 516 (58.5) 80 (36.2) 1,399 (55.6) <0.001 
Diuretics, n (%) 493 (62.2) 401 (64.8) 628 (71.2) 171 (77.4) 1,693 (67.3) <0.001 
Ivabradine, n (%) 52 (6.6) 47 (7.6) 40 (4.5) 11 (5.0) 150 (6.0) 0.071 
Nitrates, n (%) 22 (2.8) 22 (3.6) 34 (3.9) 13 (5.9) 91 (3.6) 0.172 
Antiplatelets, n (%) 352 (44.4) 273 (44.1) 430 (48.8) 105 (47.5) 1,160 (46.1) 0.209 
Statins, n (%) 391 (49.4) 355 (57.4) 520 (59.0) 125 (56.6) 1,391 (55.3) <0.001 
Anticoagulants, n (%) 165 (20.8) 176 (28.4) 251 (28.5) 51 (23.1) 643 (25.6) <0.001 
SBP 120 (108, 135) 120 (105, 133) 120 (104.250, 130) 122 (110, 140) 120 (106, 134) 0.004 
HR 73.500 (66, 82) 72 (64, 82) 70 (65, 80) 72 (64, 80) 72 (65, 81) 0.006 
Quality-of-life score 90 (70, 100) 80 (60, 100) 80 (60, 100) 80 (60, 90) 80 (65, 100) <0.001 
ICD, n (%) 740 (93.4) 563 (90.9) 764 (86.6) 204 (92.3) 2,271 (90.3) <0.001 
End-diastolic diameter of left ventricle 56 (47, 64) 56 (47, 64) 58 (49, 66) 55 (48, 65) 57 (48, 65) 0.038 
LVEF 35 (26, 43) 32 (24.750, 46.250) 30 (25, 40) 32 (23.750, 40) 33 (25, 42) <0.001 
Hemoglobin 12.9 (11, 14) 13 (11.800, 14.400) 13.200 (11.800, 14.800) 11.800 (10.100, 13.300) 13 (11.600, 14.300) <0.001 
NTproBNP 1,685 (571, 3,929) 2,273.500 (976.500, 4,331.750) 2,069 (973, 5,896) 7,744 (3,410.500, 22,363.250) 2,255.500 (954, 5,593.750) <0.001 
eGFR ≥90 (N = 792)eGFR 60–89 (N = 619)eGFR 30–59 (N = 882)eGFR <30 (N = 221)Total (N = 2,514)p value
Males, n (%) 294 (37.1) 286 (46.2) 693 (78.6) 174 (78.7) 1,447 (57.6) <0.001 
Age 66 (56.750, 76) 68 (57.500, 77) 71 (63, 79) 73 (66, 80) 69 (59, 78) <0.001 
Hypertension, n (%) 536 (67.7) 438 (70.8) 653 (74.0) 184 (83.3) 1,811 (72.0) <0.001 
Alcohol consumption, n (%) 16 (2.0) 17 (2.7) 38 (4.3) 15 (6.8) 86 (3.4) 0.002 
T2DM, n (%) 158 (19.9) 137 (22.1) 226 (25.6) 99 (44.8) 620 (24.7) <0.001 
Liver disease, n (%) 4 (0.5) 2 (0.3) 2 (0.2) 3 (1.4) 11 (0.4) 0.141 
Coronary heart disease, n (%) 176 (22.2) 164 (26.5) 290 (32.9) 76 (34.4) 706 (28.1) <0.001 
COPD, n (%) 128 (16.2) 93 (15.0) 178 (20.2) 42 (19.0) 441 (17.5) 0.040 
AF, n (%) 148 (18.7) 146 (23.6) 216 (24.5) 50 (22.6) 560 (22.3) 0.029 
Thyroid disease, n (%) 115 (14.5) 99 (16.0) 137 (15.5) 37 (16.7) 388 (15.4) 0.813 
Valvular heart disease, n (%) 126 (15.9) 111 (17.9) 158 (17.9) 34 (15.4) 429 (17.1) 0.579 
CABG, n (%) 37 (4.7) 44 (7.1) 67 (7.6) 22 (10.0) 170 (6.8) 0.017 
Dyslipidemia, n (%) 177 (22.3) 158 (25.5) 253 (28.7) 59 (26.7) 647 (25.7) 0.031 
Smoking, n (%) 106 (13.4) 113 (18.3) 194 (22) 39 (17.7) 452 (17.9) <0.001 
Chagas disease, n (%) 30 (3.8) 21 (3.4) 31 (3.5) 6 (2.7) 88 (3.5) 0.892 
NYHA, n (%) <0.001 
 I 90 (11.4) 92 (14.9) 93 (10.5) 23 (10.4) 298 (11.9) 
 II 483 (61.0) 322 (52.0) 453 (51.4) 92 (41.6) 1,350 (53.7) 
 III 196 (24.7) 173 (27.9) 289 (32.8) 89 (40.3) 747 (29.7) 
 IV 23 (2.9) 32 (5.2) 47 (5.3) 17 (7.7) 119 (4.7) 
ACEI/ARB, n (%) 609 (76.9) 458 (73.9) 667 (75.6) 146 (66.1) 1,880 (74.8) 0.010 
Beta-blockers, n (%) 673 (85.0) 532 (85.9) 786 (89.1) 198 (89.6) 2,189 (87.1) 0.040 
ARNIs, n (%) 58 (7.3) 70 (11.3) 95 (10.8) 22 (10.0) 245 (9.7) 0.045 
MRAs, n (%) 456 (57.6) 347 (56.1) 516 (58.5) 80 (36.2) 1,399 (55.6) <0.001 
Diuretics, n (%) 493 (62.2) 401 (64.8) 628 (71.2) 171 (77.4) 1,693 (67.3) <0.001 
Ivabradine, n (%) 52 (6.6) 47 (7.6) 40 (4.5) 11 (5.0) 150 (6.0) 0.071 
Nitrates, n (%) 22 (2.8) 22 (3.6) 34 (3.9) 13 (5.9) 91 (3.6) 0.172 
Antiplatelets, n (%) 352 (44.4) 273 (44.1) 430 (48.8) 105 (47.5) 1,160 (46.1) 0.209 
Statins, n (%) 391 (49.4) 355 (57.4) 520 (59.0) 125 (56.6) 1,391 (55.3) <0.001 
Anticoagulants, n (%) 165 (20.8) 176 (28.4) 251 (28.5) 51 (23.1) 643 (25.6) <0.001 
SBP 120 (108, 135) 120 (105, 133) 120 (104.250, 130) 122 (110, 140) 120 (106, 134) 0.004 
HR 73.500 (66, 82) 72 (64, 82) 70 (65, 80) 72 (64, 80) 72 (65, 81) 0.006 
Quality-of-life score 90 (70, 100) 80 (60, 100) 80 (60, 100) 80 (60, 90) 80 (65, 100) <0.001 
ICD, n (%) 740 (93.4) 563 (90.9) 764 (86.6) 204 (92.3) 2,271 (90.3) <0.001 
End-diastolic diameter of left ventricle 56 (47, 64) 56 (47, 64) 58 (49, 66) 55 (48, 65) 57 (48, 65) 0.038 
LVEF 35 (26, 43) 32 (24.750, 46.250) 30 (25, 40) 32 (23.750, 40) 33 (25, 42) <0.001 
Hemoglobin 12.9 (11, 14) 13 (11.800, 14.400) 13.200 (11.800, 14.800) 11.800 (10.100, 13.300) 13 (11.600, 14.300) <0.001 
NTproBNP 1,685 (571, 3,929) 2,273.500 (976.500, 4,331.750) 2,069 (973, 5,896) 7,744 (3,410.500, 22,363.250) 2,255.500 (954, 5,593.750) <0.001 

eGFR ≥90 (N = 792), eGFR 60–89 (N = 619), eGFR 30–59 (N = 882), eGFR <30 (N = 221).

T2DM, type 2 diabetes mellitus; COPD, chronic obstructive pulmonary disease; CABG, coronary artery bypass graft; SBP, systolic blood pressure; HR, heart rate; LVEF, left ventricle ejection fraction; ICD, implantable cardioverter-defibrillator; CRT-P, cardiac resynchronization therapy pacemaker; CRT-D, cardiac resynchronization therapy defibrillator.

Pharmacological Therapy

At first, the use of angiotensin-converting enzyme inhibitor, angiotensin receptor blocker, and mineralocorticoid receptor antagonist (MRA) was less frequent among patients with severe kidney dysfunction compared to the other categories. On the other hand, beta-blockers were more frequently used in the groups with lower eGFR values, which was similar to what was observed for diuretics. Finally, statins were more frequently prescribed in patients with lower kidney function, while anticoagulants had a more common prescription in patients with moderate kidney dysfunction. No significant differences in prescription rates of ivabradine, nitrates, and antiplatelets were observed.

Physical Examination, Laboratory Results, and Echocardiographic Findings

Patients with lower kidney function had a significantly higher systolic blood pressure than those with moderate and preserved kidney function. On the other hand, patients with lowest eGFR category had significantly lower heart rate values and the quality-of-life score scale. Those with altered kidney function had a significantly lower prevalence of ICD use than those with preserved kidney function.

Regarding the laboratory parameters, patients with eGFR <30 mL/min/1.73 m2 had a significantly lower value of hemoglobin while reporting higher serum NT-proBNP levels than those with preserved kidney function in this cohort. Echocardiographic measures also differed among the evaluated eGFR groups, highlighting the LVEF, which was significantly lower in patients with the lowest eGFR category than those with preserved function.

Factors Associated with CKD in HF

Regarding the factors independently associated with CKD diagnosis with eGFR <60 mL/min/1.73 m2, we observed that female patients had an almost 10-fold higher risk of having CKD than men, while the risk of CKD increased with age and NYHA classification. Furthermore, patients with T2DM diagnosis had a 45% higher risk of CKD compared to non-T2DM individuals. Diuretics use was associated with a higher CKD prevalence, while MRAs use was significantly more prevalent in patients without CKD. In addition, reduced ejection fraction, hemoglobin levels below 12 mg/dL, hyperkalemia, and the presence of those with implantable devices (pacemakers, ICDs or CRT) were significantly associated with CKD diagnosis (shown in Fig. 1).

Fig. 1.

Multivariate logistic regression analysis evaluating the independent variables associated with chronic kidney disease (CKD) status (eGFR <60 mL/min/1.73 m2).

Fig. 1.

Multivariate logistic regression analysis evaluating the independent variables associated with chronic kidney disease (CKD) status (eGFR <60 mL/min/1.73 m2).

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Outcomes

The median follow-up time was 215 days (Q1: 188; Q3: 254), in which 170 patients (6.76%) died accounting for a mortality rate of 0.30 per-1,000 person-years (95% CI, 26.2–35.4). Interestingly, almost 15% of the patients in the group with the lowest eGFR category died during the follow-up, while only 5% of the group with preserved renal function reached this outcome. Table 2 summarizes the mortality trends across the eGFR groups, highlighting a significantly higher mortality rate per-1,000 person-years (0.57; 95% CI, 0.39–0.83) in the group of patients with eGFR <30 mL/min/1.73 m2. Furthermore, we observed that patients in this group had a significantly higher risk of mortality than those with eGFR >90 mL/min/1.73 m2 even after adjusting for age, gender, smoking, T2DM, dyslipidemia, LVEF, NYHA class, and medications in the Cox proportional-hazards models (Table 2). Finally, this pattern was still observed when dividing the sample by sex, T2DM diagnosis, and ejection fraction classification (shown in Fig. 2).

Table 2.

Mortality incidence, rate, and risk related to eGFR groups in patients with heart failure diagnosis from the RECOLFACA

eGFR ≥90 (N = 792)eGFR 60–89 (N = 619)eGFR 30–59 (N = 882)eGFR <30 (N = 221)
Mortality incidence (%) 5.7 6.8 6.4 12.2 
Mortality rate (per 1,000 person-years) 0.25 (0.18–0.33) 0.31 (0.23–0.42) 0.28 (0.22–0.38) 0.57 (0.39–0.83) 
Unadjusted mortality risk (HR) 1.30 (0.85–1.99) 1.18 (0.80–1.77) 2.31 (1.42–3.73) 
Mortality risk model 1 (HR) 1.30 (0.85–1.99) 1.28 (0.84–1.94) 2.44 (1.47–4.06) 
Mortality risk model 2 (HR) 1.25 (0.81–1.91) 1.09 (0.71–1.67) 1.86 (1.11–3.13) 
Mortality risk model 3 (HR) 1.24 (0.81–1.91) 1.12 (0.73–1.72) 1.87 (1.10–3.18) 
eGFR ≥90 (N = 792)eGFR 60–89 (N = 619)eGFR 30–59 (N = 882)eGFR <30 (N = 221)
Mortality incidence (%) 5.7 6.8 6.4 12.2 
Mortality rate (per 1,000 person-years) 0.25 (0.18–0.33) 0.31 (0.23–0.42) 0.28 (0.22–0.38) 0.57 (0.39–0.83) 
Unadjusted mortality risk (HR) 1.30 (0.85–1.99) 1.18 (0.80–1.77) 2.31 (1.42–3.73) 
Mortality risk model 1 (HR) 1.30 (0.85–1.99) 1.28 (0.84–1.94) 2.44 (1.47–4.06) 
Mortality risk model 2 (HR) 1.25 (0.81–1.91) 1.09 (0.71–1.67) 1.86 (1.11–3.13) 
Mortality risk model 3 (HR) 1.24 (0.81–1.91) 1.12 (0.73–1.72) 1.87 (1.10–3.18) 

eGFR ≥90 (N = 792), eGFR 60–89 (N = 619), eGFR 30–59 (N = 882), eGFR <30 (N = 221).

Values are proportion and HR based on Cox regression analysis (95% CI).

Results significant at a p value lower than 0.05 are bold in the table.

Model 1: This model was adjusted for age and gender.

Model 2: This model was adjusted for age, gender, smoking, type 2 diabetes mellitus, dyslipidemia, and the New York Heart Association (NYHA) class.

Model 3: This model was adjusted for age, gender, smoking, type 2 diabetes mellitus, dyslipidemia, left ventricular ejection fraction (LVEF), New York Heart Association (NYHA) class, and the following medications: ACE inhibitors, ARB, β-blockers, aldosterone antagonists, and statins.

eGFR, estimated glomerular filtration rate.

Fig. 2.

Mortality incidence in relation to (a) Sex, (b) type 2 diabetes mellitus (T2DM) diagnosis, and (c) ejection fraction categories. eGFR ≥90 (N = 792), eGFR 60–89 (N = 619), eGFR 30–59 (N = 882), eGFR <30 (N = 221). LVEF, left ventricular ejection fraction; eGFR, estimated glomerular filtration rate.

Fig. 2.

Mortality incidence in relation to (a) Sex, (b) type 2 diabetes mellitus (T2DM) diagnosis, and (c) ejection fraction categories. eGFR ≥90 (N = 792), eGFR 60–89 (N = 619), eGFR 30–59 (N = 882), eGFR <30 (N = 221). LVEF, left ventricular ejection fraction; eGFR, estimated glomerular filtration rate.

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Our findings show the different profile of patients with CKD compared to those with mildly altered or normal renal function, highlighting an older age and a higher prevalence of comorbidities in the group of patients with end-stage renal disease. Furthermore, several clinical variables were independently associated with CKD diagnosis, while having an eGFR <30 mL/min/1.73 m2 was significantly associated with a higher mortality risk when compared with patients with normal kidney function despite extensive adjustments by relevant covariates. This represents the largest study assessing in detail kidney dysfunction in HF performed in a Latin American population.

In our study, almost half of the patients (44%) exhibited a moderate or severe kidney dysfunction, a finding that is consistent with reports from other similar registry-based studies and clinical trials around the world [16‒21]. Furthermore, our finding of older age, higher prevalence of hypertension, T2DM, and other cardiovascular comorbidities, along with a worse functional NYHA class in patients in the lowest eGFR group, has also been reported, highlighting the potential similarities of Latin American patients compared to other populations in these features [16, 17, 20, 21]. The high observed prevalence of CKD in HF is related to the close interconnection existing between renal and cardiac function, which is mainly based on three large pathophysiological groups; first, the hemodynamic alterations elicited either by an altered venous return or a low cardiac output generate a direct impact in the correct functioning of both organs [22]. Moreover, the neurohormonal axis plays an important role in regulating both the heart and the kidneys, mainly due to the activation of the sympathetic system and the effect of the renin-angiotensin-aldosterone system [22]. Finally, mineral disorders, anemia, metabolic derangements, and chronic inflammation play a significant role in the progression of both HF and CKD, promoting the emergence of a loop of deleterious effects for both organs [22, 23].

Regarding guideline-recommended therapies, CKD patients in our study were less frequently treated with MRAs, while receiving more commonly beta-blockers and statins. The observation of a different pattern of medications prescription in patients with CKD diagnosis and HF has been previously reported, highlighting concerns regarding drug toxicity and the lack of evidence from clinical trials as the most relevant reasons behind these differences [16, 17, 24]. Interestingly, after adjusting for medications prescriptions in our mortality analysis, we did not observe a large change in the estimates for CKD categories, potentially suggesting a small effect of treatment in the association between the eGFR <30 mL/min/1.73 m2 category and mortality outcome in this context.

Furthermore, the observation of higher mortality risk in patients with altered kidney function has been described extensively in the literature [16, 17, 25]. Individuals with CKD diagnosis present almost double the risk of mortality than the general population, being cardiovascular disease the main cause of death in these patients [25, 26]. The mortality risk is even higher in patients with CKD and HF, with a similar risk in patients with HFrEF and those with HF with preserved ejection fraction [25, 27]. Interestingly, it has been proposed that eGFR may be a stronger predictor of mortality and adverse CV outcomes than a reduction in LVEF, highlighting the critical role of kidney dysfunction in the prognosis of the HF patient [20]. The association found between eGFR <30 mL/min/1.73 m2 and mortality could be explained by the effect of relevant and well-known risk factors such as age, T2DM, and hypertension. Also, CKD remained as a significant predictor of mortality, a finding that is consistent with previous studies that have indicated that eGFR is a strong independent predictor of adverse outcomes in HF [16, 21].

Moreover, while elevated BNP in CKD patients is partly due to reduced clearance, the prevailing evidence suggests that these levels are largely a true-positive finding and reflect underlying heart disease. There is a strong relationship between elevated BNP/NT-proBNP and cardiovascular mortality/events in CKD. Patients with GFR <30 have significant anemia compared to GFR >30 regardless of the presence or absence of CHF. Anemia is a common consequence of CKD and is consistently associated with greater mortality, hospitalization, major cardiovascular events, and CKD progression [28].

Study Limitations

We had several limitations. At first, the registry’s participation was voluntary among medical centers; thus, selection biases could be present in the included sample. Additionally, the assessment of changes in eGFR across time was not possible, limiting a potential analysis assessing a relationship between lowest eGFR category and mortality in this context. Notably, RECOLFACA did not include information regarding pharmacological and non-pharmacological therapy of the evaluated comorbidities, limiting the possibility of including these relevant factors in the analyses of risk factors. Moreover, information regarding the severity and duration of the evaluated comorbidities was also not available, which limited a more detailed assessment of the impact of these conditions. Another limitation is that data on albuminuria was not available, hence, full “Kidney Disease: Improving Global Outcomes” (KDIGO) CKD criteria were not considered. Also, the Modification of Diet in Renal Disease equation was applied; however, it is not used anymore. Finally, we must also acknowledge the possibility of confounding selection bias, and residual confounding despite the performed adjustments.

CKD represents a prevalent condition in the setting of HF. Patients with CKD and HF present with multiple sociodemographic, clinical, and laboratory differences compared to those diagnosed with HF only. Nevertheless, after extensive adjusting by these covariates, kidney dysfunction was significantly associated with mortality in HF patients from the RECOLFACA. Our findings highlight the relevance of a timely diagnosis and optimal treatment and follow-up of CKD as a pillar for preventing adverse outcomes.

This study protocol was reviewed and approved by the Biomedical Research Ethics Committee of the Fundación [Blinded], approval number 174-2017.

The authors have no conflicts of interest to declare.

No funding bodies had any role in the study design, data collection, analysis, decision to publish, or preparation of the manuscript.

Juan David López-Ponce de León, Alejandro Posada-Bastidas, Juan Camilo García, Alejandro David Ochoa-Morón, Balkis Rolong, Fernando Manzur-Jatin, Jose Ignacio Mosquera-Jiménez, Oscar Alfredo Pacheco-Jiménez, Álvaro Hernán Rodríguez-Cerón, Patricia Rodríguez-Gómez, and Fernando Rivera-Toquica: investigation; writing – review and editing. Juan Esteban Gómez-Mesa: conceptualization; data curation; formal analysis; funding acquisition; investigation; methodology; project administration; resources; software; supervision; validation; visualization; roles/writing – original draft; and writing – review and editing. Clara Saldarriaga and Luis Eduardo Echeverría: conceptualization; formal analysis; funding acquisition; investigation; methodology; project administration; resources; validation; visualization; roles/writing – original draft; and writing – review and editing. Alex Rivera-Toquica: investigation; visualization; roles/writing – original draft; and writing – review and editing.

The data that support the findings of this study are available from the corresponding author, upon reasonable request.

1.
Lupón
J
,
Bayés-Genís
A
.
Mortality and heart failure hospitalizations. The need for an exhaustive, official, and standardized registry
.
Rev Esp Cardiol
.
2019 Dec
72
12
988
90
.
2.
Ahmed
A
,
Campbell
RC
.
Epidemiology of chronic kidney disease in heart failure
.
Heart Fail Clin
.
2008 Oct
4
4
387
99
.
3.
Vallianou
NG
,
Mitesh
S
,
Gkogkou
A
,
Geladari
E
.
Chronic kidney disease and cardiovascular disease: is there any relationship
.
Curr Cardiol Rev
.
2019 Feb
15
1
55
63
.
4.
Rangaswami
J
,
Bhalla
V
,
Blair
JEA
,
Chang
TI
,
Costa
S
,
Lentine
KL
.
Cardiorenal syndrome: classification, pathophysiology, diagnosis, and treatment strategies: a scientific statement from the American heart association
.
Circulation
.
2019 Apr
139
16
e840
78
.
5.
Damman
K
,
Voors
AA
,
Navis
G
,
van Veldhuisen
DJ
,
Hillege
HL
.
The cardiorenal syndrome in heart failure
.
Prog Cardiovasc Dis
.
2011 Oct
54
2
144
53
.
6.
Metra
M
,
Cotter
G
,
Gheorghiade
M
,
Dei Cas
L
,
Voors
AA
.
The role of the kidney in heart failure
.
Eur Heart J
.
2012 Sep
33
17
2135
42
.
7.
Herzog
CA
,
Muster
HA
,
Li
S
,
Collins
AJ
.
Impact of congestive heart failure, chronic kidney disease, and anemia on survival in the Medicare population
.
J Card Fail
.
2004 Dec
10
6
467
72
.
8.
Kao
DP
,
Kreso
E
,
Fonarow
GC
,
Krantz
MJ
.
Characteristics and outcomes among heart failure patients with anemia and renal insufficiency with and without blood transfusions (public discharge data from California 2000–2006)
.
Am J Cardiol
.
2011 Jan
107
1
69
73
.
9.
Komajda
M
,
Follath
F
,
Swedberg
K
,
Cleland
J
,
Aguilar
JC
,
Cohen-Solal
A
.
The EuroHeart Failure Survey programme: a survey on the quality of care among patients with heart failure in Europe. Part 2–treatment
.
Eur Heart J
.
2003 Mar
24
5
464
74
.
10.
Conde Martel
A
.
Extrapolation of results from clinical trials to heart failure patients hospitalized in Internal Medicine
.
Med Clin
.
2014 May
142
10
463
7
.
11.
Becher
PM
,
Fluschnik
N
,
Blankenberg
S
,
Westermann
D
.
Challenging aspects of treatment strategies in heart failure with preserved ejection fraction: “Why did recent clinical trials fail?”
.
World J Cardiol
.
2015 Sep
7
9
544
54
.
12.
Gómez-Mesa
JE
,
Saldarriaga-Giraldo
CI
,
Echeverría
LE
,
Luna-Bonilla
P
Grupo Investigador RECOLFACA
.
Registro colombiano de falla cardiaca (RECOLFACA): resultados
.
Rev Colomb Cardiol
.
2021
;
28
(
4
):
334
42
.
13.
Gómez-Mesa
JE
,
Saldarriaga
CI
,
Echeverría
LE
,
Luna
P
RECOLFACA Research Group
.
Colombian heart failure registry (RECOLFACA): methodology and preliminary data
.
Rev Colomb Cardiol
.
2021
;
28
(
3
):
217
30
.
14.
Heidenreich
PA
,
Bozkurt
B
,
Aguilar
D
,
Allen
LA
,
Byun
JJ
,
Colvin
MM
.
2022 AHA/ACC/HFSA guideline for the management of heart failure: a report of the American College of cardiology/American heart association joint committee on clinical practice guidelines
.
Circulation
.
2022
;
145
(
18
):
e895
94
.
15.
Levin
A
,
Stevens
PE
,
Bilous
RW
,
Coresh
J
,
De Francisco
ALM
,
De Jong
PE
.
Kidney disease: Improving global outcomes (KDIGO) CKD work group. KDIGO 2012 clinical practice guideline for the evaluation and management of chronic kidney disease
.
Kidney Int Suppl
.
2013 Jan 1
3
1
1
150
.
16.
Löfman
I
,
Szummer
K
,
Hagerman
I
,
Dahlström
U
,
Lund
LH
,
Jernberg
T
.
Prevalence and prognostic impact of kidney disease on heart failure patients
.
Open Heart
.
2016 Jan
3
1
e000324
.
17.
Löfman
I
,
Szummer
K
,
Dahlström
U
,
Jernberg
T
,
Lund
LH
.
Associations with and prognostic impact of chronic kidney disease in heart failure with preserved, mid-range, and reduced ejection fraction
.
Eur J Heart Fail
.
2017 Dec
19
12
1606
14
.
18.
Blair
JEA
,
Pang
PS
,
Schrier
RW
,
Metra
M
,
Traver
B
,
Cook
T
.
Changes in renal function during hospitalization and soon after discharge in patients admitted for worsening heart failure in the placebo group of the EVEREST trial
.
Eur Heart J
.
2011 Oct
32
20
2563
72
.
19.
Deferrari
G
,
Cipriani
A
,
La Porta
E
.
Renal dysfunction in cardiovascular diseases and its consequences
.
J Nephrol
.
2021 Feb
34
1
137
53
.
20.
Hillege
HL
,
Nitsch
D
,
Pfeffer
MA
,
Swedberg
K
,
McMurray
JJV
,
Yusuf
S
.
Renal function as a predictor of outcome in a broad spectrum of patients with heart failure
.
Circulation
.
2006 Feb
113
5
671
8
.
21.
Go
AS
,
Chertow
GM
,
Fan
D
,
McCulloch
CE
,
Hsu
C
.
Chronic kidney disease and the risks of death, cardiovascular events, and hospitalization
.
N Engl J Med
.
2004 Sep
351
13
1296
305
.
22.
Schefold
JC
,
Filippatos
G
,
Hasenfuss
G
,
Anker
SD
,
von Haehling
S
.
Heart failure and kidney dysfunction: epidemiology, mechanisms and management
.
Nat Rev Nephrol
.
2016 Oct
12
10
610
23
.
23.
Tsuruya
K
,
Eriguchi
M
.
Cardiorenal syndrome in chronic kidney disease
.
Curr Opin Nephrol Hypertens
.
2015 Mar
24
2
154
62
.
24.
Heywood
JT
,
Fonarow
GC
,
Costanzo
MR
,
Mathur
VS
,
Wigneswaran
JR
,
Wynne
J
.
High prevalence of renal dysfunction and its impact on outcome in 118,465 patients hospitalized with acute decompensated heart failure: a report from the ADHERE database
.
J Card Fail
.
2007 Aug
13
6
422
30
.
25.
Damman
K
,
Valente
MAE
,
Voors
AA
,
O’Connor
CM
,
van Veldhuisen
DJ
,
Hillege
HL
.
Renal impairment, worsening renal function, and outcome in patients with heart failure: an updated meta-analysis
.
Eur Heart J
.
2014 Feb
35
7
455
69
.
26.
Chronic Kidney Disease Prognosis Consortium
Matsushita
K
,
van der Velde
M
,
Astor
BC
,
Woodward
M
,
Levey
AS
.
Association of estimated glomerular filtration rate and albuminuria with all-cause and cardiovascular mortality in general population cohorts: a collaborative meta-analysis
.
Lancet
.
2010 Jun
375
9731
2073
81
.
27.
Smith
DH
,
Thorp
ML
,
Gurwitz
JH
,
McManus
DD
,
Goldberg
RJ
,
Allen
LA
.
Chronic kidney disease and outcomes in heart failure with preserved versus reduced ejection fraction: the Cardiovascular Research Network PRESERVE Study
.
Circ Cardiovasc Qual Outcomes
.
2013 May
6
3
333
42
.
28.
Palaka
E
,
Grandy
S
,
van Haalen
H
,
McEwan
P
,
Darlington
O
.
The impact of CKD anaemia on patients: incidence, risk factors, and clinical outcomes–a systematic literature review
.
Int J Nephrol
.
2020
;
2020
:
7692376
.