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. 2021 Jun 1;21:536. doi: 10.1186/s12913-021-06566-2

Costs and healthcare utilisation of patients with chronic kidney disease in Spain

Carlos Escobar 1,✉, Beatriz Palacios 2, Unai Aranda 2, Margarita Capel 2, Antoni Sicras 3, Aram Sicras 3, Antonio Hormigo 4, Roberto Alcázar 5, Nicolás Manito 6, Manuel Botana 7
PMCID: PMC8167969  PMID: 34074269

Abstract

Background

Data about the impact of chronic kidney disease (CKD) on health care costs in Spain are scarce This study was aimed to evaluate cumulative costs and healthcare utilisation in CKD in Spain.

Methods

Observational, retrospective, population-based study, which included adults who received care for CKD between 2015 and 2019. Healthcare and medication costs were summarized on a yearly basis starting from the index date (1st January 2015), and then cumulatively until 2019.

Results

We identified 44,214 patients with CKD (year 2015: age 76.4 ± 14.3 years, 49.0% women, albumin-to-creatinine ratio 362.9 ± 176.8 mg/g, estimated glomerular filtration rate 48.7 ± 13.2 mL/min/1.73 m2). During the 2015–2019 period, cumulative CKD associated costs reached 14,728.4 Euros, being cardiovascular disease hospitalizations, particularly due to heart failure and CKD, responsible for 77.1% of costs. Total medication cost accounted for 6.6% of the total cost. There was a progressive decrease in cardiovascular disease hospital costs per year (from 2741.1 Euros in 2015 to 1.971.7 Euros in 2019). This also occurred with cardiovascular and diabetic medication costs, as well as with the proportion of hospitalizations and mortality. Costs and healthcare resources use were higher in the DAPA-CKD like population, but also decreased over time.

Conclusions

Between 2015 and 2019, costs of patients with CKD in Spain were high, with cardiovascular hospitalizations as the key determinant. Medication costs were responsible for only a small proportion of total CKD costs. Improving CKD management, particularly with the use of cardiovascular and renal protective medications may be helpful to reduce CKD burden.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12913-021-06566-2.

Keywords: Chronic kidney disease, Cost, DAPA-CKD, Healthcare, Hospitalization, Medication

Introduction

Chronic kidney disease (CKD) is a common condition that affected nearly 700 million persons worldwide in 2017. However, these numbers are expected to rise due to the ageing of the population, and the increasing prevalence of hypertension and diabetes [1–3]. CKD markedly increases the risk of developing cardiovascular disease, particularly ischemic heart disease and heart failure [HF], as well as cardiovascular and all-cause death. In addition, CKD promotes the development of end-stage renal disease [2, 4]. Remarkably, the risk of adverse outcomes increases as renal function decreases or albuminuria develops [5].

The apropriate treatment of CKD patients has been associated with a reduction in the risk of developing cardiovascular and renal complications [6]. This is particularly true with the use of renin angiotensin system inhibitors, including angiotensin-converting enzyme inhibitors (ACEi) and angiotensin-receptor blockers (ARBs) [7, 8], and more recently, with the use of some sodium-glucose cotransporter-2 (SGLT-2) inhibitors [9, 10], even in the absence of type 2 diabetes (T2D) [9].

Of note, CKD represents a major and growing economic problem [3, 11, 12]. Increasing the knowledge about CKD-related costs is mandatory to ascertain how CKD management can be improved, leading to a significant decrease in CKD burden [5, 11–18]. Unfortunately, data about the impact of CKD on health care costs in Spain are scarce, and most importantly, not focused on a comprehensive approach [19, 20].

The aim of this study was to evaluate the cumulative costs and healthcare utilisation in CKD patients in Spain over the last 5 years, along with the epidemiological characterization of the population at index date (1st January 2015). This was also analyzed in a population who met the main inclusion criteria of the DAPA-CKD trial [9] (DAPA-CKD like population).

Methods

This was an observational cohort study, comprising cross-sectional and longitudinal retrospective analyses using secondary data captured in electronic health records from seven Spanish regions, from the BIG-PAC® database. BIG-PAC® database included information from non-selected 1.7 million persons of primary health centers and referral hospitals within the Spanish national health system. Before export to BIG-PAC®, data were rigorously anonymized and dissociated, making not possible individual identification. As a result, it automatically collects information from routine practice, without requiring manual inputting. Previous studies have demonstrated its representativeness of the Spanish population [21, 22].

This database has been validated as an information source for studies of epidemiology, therapeutic adaptation and health/non-healthcare resource use and associated costs. It is representative of the Spanish population [21]. The study was approved by the Investigation Ethics Committee of Consorci Sanitari from Terrassa. Informed consent was waived by the same ethics committee that approved the study, as this was a secondary data study and data were fully anonymized and dissociated from patients. All methods were performed in accordance with the relevant guidelines and current regulations [21, 23].

The study population included all adult patients with at least one diagnostic code of CKD or having laboratory results meeting the definition of CKD prior to the index date (1st January 2015). CKD stages 1–5 were defined according to the estimated glomerular filtration rate (eGFR; estimated by the CKD-Epidemiology Collaboration equation) and the urine albumin-to-creatinine ratio (UACR), as follows: stage 1: eGFR ≥90 mL/min/1.73m2 and UACR ≥30 mg/g; stage 2: eGFR 60–89 mL/min/1.73m2 and UACR ≥30 mg/g; stage 3a: eGFR 45–59 mL/min/1.73m2; stage 3b: eGFR 30–44 mL/min/1.73m2; stage 4: eGFR 15–29 mL/min/1.73m2; stage 5: eGFR < 15 mL/min/1.73m2 [23, 24]. T2D was defined as all adult patients filling a prescription of any antidiabetic medication, having a T2D diagnostic code or HbA1c > 7% prior to index date, excluding type 1 diabetes. The DAPA-CKD like population included those adult patients, with or without T2D, but not type 1 diabetes, who had an eGFR of 25 to 75 mL/min/1.73 m2 and a UACR of 200 to 5000 mg/g, on stable treatment with ACEi or ARBs for at least 1 month [9].

Baseline characteristics for the overall CKD and DAPA-CKD like populations, including demographics, comorbidities and medications, were calculated at index date (1st January 2015) for the full group and by T2D status and CKD stage. The main comorbidities included myocardial infarction (MI), HF, atrial fibrillation (AF), stroke, peripheral artery disease (PAD), hyperkalemia and diabetes. A minimum of 1-year of data before index date was required. ICD-9 and ICD-10 codes (https://eciemaps.mscbs.gob.es) were considered for the diagnosis of comorbidities (supplementary Table 1). Treatments were recorded from the registries for dispensing medicines, according to the Anatomical Therapeutic Chemical Classification System (supplementary Table 1) [25]. Treatment for hypertension/HF (ACEi, ARBs, direct renin inhibitors, aldosterone antagonists, angiotensin receptor and neprilysin inhibition, beta blockers, diuretics, calcium channel blockers), antidiabetics (SGLT-2 inhibitors, metformin, sulfonylureas, dipeptidyl peptidase 4 [DPP-4] inhibitors, glucagon-like peptide-1 [GLP-1] receptor agonists, meglitinides, glitazones, acarbose, miglitol, insulin), antithrombotic therapy (warfarin, aspirin, P2Y12 receptor antagonists) and statins were recorded. Prescriptions were performed according to physicians´ criteria in routine practice [23].

Prevalence and incidence of CKD were also calculated at index date (1st January 2015). Incidence was calculated as all newly diagnosed patients during 2015 divided by the number of patients without CKD in the population at the beginning of 2015 and expressed in cases per 1000 patient-years. Prevalence was calculated as all patients with a CKD diagnosis at the end of 2015, divided by all individuals in the total population covered by the database at that time. The denominator included all individuals that were attended by any reason in the Spanish health care system in the previous 3 years to the index date (2012–2014). Mortality data were updated every month in the BIG-PAC database.

Costs were not taken from BIG-PAC database. Data were calculated using sources from the Spanish National Healthcare System of 2019 (supplementary Table 2) and used for the overall study period [21]. The healthcare resource use and costs and medication and procedure costs were summarized on a yearly basis starting from index date (1st January 2015), and then cumulatively until the end of the last year of follow-up (31st December 2019). All hospital visits (total and cardiovascular events), medical and emergency room visits, medication costs, and procedure costs (total, dialysis, kidney transplant) were included for the analysis of the annual direct healthcare costs [21]. Costs per patient were calculated every year. Patients who died during the follow-up had a cost of 0 allocated to the remaining duration of the study, whereas a patient leaving the database prior to data cut off was not included in the denominator for the time after leaving the database. No double counting occurred, as for each cost (i.e CKD hospitalizations) only that category was counted.

Annual indirect non-health costs included the number of days of productivity lost due to disability [21]. Rates were obtained from hospital accounting, except for medication and indirect costs, which were calculated as follows, respectively: a) medical prescriptions: according to the retail price per package at the time of dispensing [26]; b) costs for days of productivity lost: according to the mean interprofessional wage. The estimation of days off of work were obtained by the temporary work incapacity reported in primary care setting [27]. Hospital admission costs of cardiovascular events during follow-up were obtained taking into consideration the daily hospital rate and the number of hospital days per stay.

Statistical analysis

Categorical variables were described by their absolute (n) and relative frequencies (%) and continuous variables by the mean and standard deviation. Categorical variables were compared with the Chi-square test and means by the t-student test. Analyses of health care cost were performed for the index date with 5 year of follow-up. The cumulative mean healthcare cost was estimated and presented on a yearly basis from the index date until last year of follow-up. Health care costs were presented per patient (mean cost). A level of statistical significance of 0.05 was applied in all the statistical tests. The data were analyzed using the statistical package SPSS v22.0 (SPSS Inc., Chicago, Illinois, USA) [21].

Results

Out of 1,7 million people included in the BIG-PAC® database in 2015, 1,3 were attended during the 2012–2014 period, of whom 964,862 were 18 years or older. At index date, 45.376 patients had CKD. As 1162 patients were excluded due to inconsistent data, 44,214 patients (97.4%) comprised the CKD study population (Fig. 1). Incidence at index date was 2.06 per 1000 patient-years and the prevalence was 4.90%.

Fig. 1.

Fig. 1

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Flowchart costs population (2015). CKD: chronic kidney disease

The baseline clinical characteristics of the CKD population according to the presence of T2D and CKD stage are presented in Table 1. Overall, mean age was 76.4 ± 14.3 years, 49.0% of patients were women, mean UACR was 362.9 ± 176.8 mg/g and mean eGFR 48.7 ± 13.2 mL/min/1.73 m2. Overall, 20.0% of patients had a history of HF, 15.3% MI, and 10.5% stroke. With regard to treatments, 71.0% were taking renin angiotensin system (RAAS) inhibitors, but only 4.4% of patients at maximal doses. A total of 19,985 (45.2%) patients had T2D. Patients with T2D were younger (75.8 ± 14.0 vs 76.4 ± 14.1 years; P = 0.001), but UACR (391.3 ± 189.4 vs 347.4 ± 172.3 mg/g, P < 0.001), and HbA1c (7.7 ± 2.0 vs 6.2 ± 1.2%; P < 0.001) were higher and eGFR lower (47.5 ± 12.4 vs 49.6 ± 11.3 mL/min/1.73 m2, P < 0.001) compared to those without T2D. Moreover, comorbidities were more common among patients with T2D. In addition, more T2D patients were taking RAAS inhibitors (82.2% vs 61.7%; P < 0.001). Overall, 71.0% of patients had stage ≥3 CKD. Age increased as renal function worsened (from 69.8 ± 14.7 years in patients with stage 1 CKD to 79.8 ± 14.6 years among stage 5 CKD patients; P < 0.001), as well as UACR (from 106.8 ± 49.8 mg/g to 1642.3 ± 769.2 mg/g; P < 0.001) and the proportion of patients treated with RAAS inhibitors (from 66.5 to 72.6%; P < 0.001). Similarly, comorbidities increased as renal function decreased (Table 1).

Table 1.

Baseline clinical characteristics of the CKD population at index date (1st January 2015) and according to the presence of type 2 diabetes and CKD stage

Diabetes status CKD stage Total (n = 44,214; 100%)
Non T2D (n = 24,229; 54.8%) T2D (n = 19,985; 45.2%) p Stage 1 (n = 2159; 4.9%) Stage 2 (n = 7386;16.7%) P2 vs 1 Stage 3a (n = 14,065; 31.8%) P3a vs 1 Stage 3b (n = 12,203; 27.6%) P3b vs 1 Stage 4 (n = 3537; 8.0%) P4 vs 1 Stage 5 (n = 1592; 3.6%) P5 vs 1 Unspecified (n = 3272; 7.4%) PUnsp. vs 1
Biodemographic data
 Age, years 76.4 ± 14.1 75.8 ± 14.0 0.001 69.8 ± 14.7 74.5 ± 13.9 < 0.001 76.8 ± 14.2 < 0.001 78.1 ± 14.3 < 0.001 79.2 ± 14.5 < 0.001 79.8 ± 14.6 < 0.001 65.1 ± 11.4 < 0.001 76.4 ± 14.3
 Sex, Female, n (%) 11,892 (49.1) 9773 (48.9) < 0.001 1105 (51.2) 3545 (48.0) < 0.001 6779 (48.2) < 0.001 5992 (49.1) 0.072 1680 (47.5) < 0.001 876 (55.0) < 0.001 1688 (51.6) 0.773 21,665 (49.0)
Physical examination and laboratory tests
 BMI, Kg/m2 28.4 ± 5.2 29.3 ± 5.0 < 0.001 29.3 ± 5.0 29.2 ± 5.0 0.307 28.6 ± 5.2 < 0.001 28.5 ± 5.1 < 0.001 28.1 ± 5.0 < 0.001 27.8 ± 4.9 < 0.001 29.6 ± 5.1 0.007 28.7 ± 5.1
 SBP, mmHg 136.2 ± 20.2 138.3 ± 20.3 < 0.001 135.9 ± 20.1 138.2 ± 20.6 < 0.001 138.3 ± 20.6 0.194 138.6 ± 20.4 0.148 137.5 ± 20.7 0.392 135.2 ± 20.5 0.791 137.8 ± 20.0 0.392 137.1 ± 20.2
 UACR, mg/g 347.4 ± 172.3 391.3 ± 189.4 < 0.001 106.8 ± 49.8 126.9 ± 53.5 < 0.001 250.3 ± 120.1 < 0.001 252.1 ± 120.9 < 0.001 1602.4 ± 782.6 < 0.001 1642.3 ± 769.2 < 0.001 132.7 ± 60.2 < 0.001 362.9 ± 176.8
 UACR A1, n (%) 137 (0.56) 80 (0.4) < 0.001 0 0 – 0 – 0 – 0 – 0 – 217 (6.6) – 217 (0.5)
 UACR A2, n (%) 16,493 (68.1) 9772 (48.9) < 0.001 2159 (100) 7386 (100) – 8650 (61.5) < 0.001 7480 (61.3) < 0.001 432 (12.2) < 0.001 53 (3.3) < 0.001 105 (3.2) < 0.001 26,265 (64.5)
 UACR A3, n (%) 7599 (31.4) 10,133 (50.7) < 0.001 0 0 – 5415 (38.5) < 0.001 4723 (38.7) < 0.001 3105 (87.8) < 0.001 1539 (96.7) < 0.001 2950 (90.2) < 0.001 17,729 (35.0)
 eGFRa 49.6 ± 11.3 47.5 ± 12.4 < 0.001 93.8 ± 4.7 75.1 ± 5.0 < 0.001 52.2 ± 4.9 < 0.001 37.2 ± 5.0 < 0.001 21.9 ± 4.5 < 0.001 8.7 ± 4.3 < 0.001 – – 48.7 ± 13.2
 eGFR ≥90a,n (%) 1324 (5.5) 835 (4.2) < 0.001 2159 (100) 0 – 0 – 0 – 0 – 0 – 0 – 2159 (4.9)
 eGFR 60–89a, n (%) 4216 (17.4) 3170 (15.9) < 0.001 0 7386 (100) – 0 – 0 – 0 – 0 – 0 – 7386 (16.7)
 eGFR 45–59a, n (%) 7931 (32.7) 6134 (30.7) < 0.001 0 0 – 14,065 (100) – 0 – 0 – 0 – 0 – 14,065 (31.8)
 eGFR 30–44a, n (%) 6572 (27.1) 5631 (28.2) < 0.001 0 0 – 0 – 12,203 (100) – 0 – 0 – 0 – 12,203 (27.6)
 eGFR 15–29a, n (%) 1730 (7.1) 1807 (9.0) < 0.001 0 0 – 0 – 0 – 3537 (100) – 0 – 0 – 3537 (8.0)
 eGFR < 15a, n (%) 644 (2.7) 948 (4.7) < 0.001 0 0 – 0 – 0 – 0 1592 (100) – 0 – 1592 (3.6)
 HbA1c. % 6.2 ± 1.2 7.7 ± 2.0 < 0.001 6.4 ± 1.3 6.6 ± 1.4 0.001 6.9 ± 1.6 0.001 6.8 ± 1.5 < 0.001 6.9 ± 1.6 0.001 7.0 ± 1.5 0.001 7.0 ± 1.6 0.389 6.9 ± 1.6
 Creatinine. g/dL 1.3 ± 0.4 1.3 ± 0.5 0.759 0.6 ± 0.3 0.9 ± 0.4 < 0.001 1.1 ± 0.5 < 0.001 1.5 ± 0.8 < 0.001 2.2 ± 0.8 < 0.001 0.6 ± 0.2 0.999 1.0 ± 0.3 < 0.001 1.3 ± 0.6
 Uric acid. g/dL 6.0 ± 2.0 7.1 ± 1.2 < 0.001 6.6 ± 1.5 6.8 ± 1.6 0.041 6.7 ± 1.6 0.001 6.6 ± 1.5 0.999 6.7 ± 1.6 0.284 6.6 ± 1.6 0.999 6.7 ± 1.7 0.229 6.6 ± 1.6
Comorbidities, n (%)
 CKD stage 1 1324 (5.5) 835 (4.2) < 0.001 2159 (100) 0 < 0.001 0 – 0 – 0 – 0 – 0 – 2159 (4.9)
 CKD stage 2 4216 (17.4) 3170 (15.9) < 0.001 0 7386 (100) < 0.001 0 – 0 – 0 – 0 – 0 – 7386 (16.7)
 CKD stage 3a 7931 (32.7) 6134 (30.7) < 0.001 0 0 – 14,065 (100) – 0 – 0 – 0 – 0 – 14,065 (31.8)
 CKD stage 3b 6572 (27.1) 5631 (28.2) < 0.001 0 0 – 0 – 12,203 (100) – 0 – 0 – 0 – 12,203 (27.6)
 CKD stage 4 1730 (7.1) 1807 (9.0) < 0.001 0 0 – 0 – 0 – 3537 (100) – 0 – 0 – 3537 (8.0)
 CKD stage 5 644 (2.7) 948 (4.7) < 0.001 0 0 – 0 – 0 – 0 – 1592 (100) – 0 – 1592 (3.6)
 CKD not staged 1812 (7.5) 1460 (7.0) < 0.001 0 0 – 0 – 0 – 0 – 0 – 3272 (100) – 3272 (7.4)
 CKD unspecified 6844 (28.2) 799 (4.0) < 0.001 319 (14.8) 1079 (14.6) 0.629 2411 (17.1) < 0.001 2173 (17.8) 0.001 508 (14.4) 0.678 237 (14.9) 0.932 916 (28.0) < 0.001 7643 (17.3)
 Dialysis 226 (0.9) 430 (3.2) < 0.001 0 0 0 – 0 0 – 656 (41.2) – 0 – 656 (1.5)
 CVD 3616 (14.9) 4579 (22.9) < 0.001 296 (13.7) 1230 (16.7) < 0.001 2643 (18.8) < 0.001 2295 (18.8) < 0.001 597 (16.9) < 0.001 386 (24.3) < 0.001 748 (22.9) < 0.001 8195 (18.5)
 Myocardial infarction 3081 (12.7) 3671 (18.4) < 0.001 253 (11.7) 916 (12.4) 0.383 2255 (16.0) < 0.001 1920 (15.7) < 0.001 575 (16.3) < 0.001 274 (17.2) < 0.001 559 (17.1) < 0.001 6752 (15.3)
 Heart failure 4078 (16.8) 4782 (23.9) < 0.001 253 (11.7) 998 (13.5) < 0.001 2768 (19.7) < 0.001 2601 (21.3) < 0.001 831 (23.5) < 0.001 453 (28.4) < 0.001 956 (29.2) < 0.001 8860 (20.0)
 Stroke 2136 (8.8) 2492 (12.5) < 0.001 133 (6.2) 716 (9.7) < 0.001 1218 (8.7) < 0.001 1536 (12.6) < 0.001 398 (11.3) < 0.001 216 (13.6) < 0.001 411 (12.6) < 0.001 4628 (10.5)
 Atrial Fibrillation 3508 (14.5) 3306 (16.5) < 0.001 253 (11.7) 971 (13.2) 0.067 2374 (16.9) < 0.001 2000 (16.4) < 0.001 609 (17.2) < 0.001 278 (17.5) < 0.001 329 (10.1) 0.062 6814 (15.4)
 Peripheral artery disease 1003 (4.1) 921 (4.6) < 0.001 94 (4.4) 295 (4.0) 0.409 481 (3.4) 0.019 653 (5.4) 0.055 171 (4.8) 0.487 81 (5.1) 0.317 149 (4.6) 0.728 1924 (4.4)
 Diabetes 775 (3.2) 19,985 (100) < 0.001 933 (43.2) 3483 (47.2) < 0.001 6604 (47.0) < 0.001 5890 (48.3) < 0.001 1643 (46.5) 0.015 712 (44.7) 0.360 1495 (45.7) < 0.001 20,760 (47.0)
Medications, n (%)
 Antihypertensives 17,518 (72.3) 17,346 (86.8) < 0.001 1625 (75.3) 5628 (76.2) 0.389 10,844 (77.1) 0.065 9737 (79.8) < 0.001 2975 (84.1) < 0.001 1345 (84.5) < 0.001 2849 (87.1) < 0.001 33,337 (75.4)
  RAAS inhibitors 14,944 (61.7) 16,427 (82.2) < 0.001 1436 (66.5) 5449 (73.8) < 0.001 10,267 (73.0) < 0.001 8490 (69.6) < 0.001 2530 (71.5) < 0.001 1156 (72.6) < 0.001 2043 (62.4) < 0.001 31,371 (71.0)
  ACEi 7198 (29.7) 6789 (34.0) < 0.001 711 (32.9) 2539 (34.4) 0.196 4286 (30.5) < 0.001 3467 (28.4) < 0.001 1265 (35.8) 0.010 592 (37.2) < 0.001 1127 (34.4) 0.253 13,987 (31.6)
  ACEi at maximal doses 339 (1.4) 401 (2.0) < 0.001 88 (4.1) 28 (0.4) < 0.001 181 (1.3) < 0.001 475 (3.9) 0.659 30 (0.8) < 0.001 27 (1.7) < 0.001 33 (1.0) < 0.001 740 (1.7)
  ARBs 8509 (35.1) 10,960 (54.8) < 0.001 874 (40.5) 2953 (40.0) 0.677 6446 (45.8) < 0.001 5690 (46.6) < 0.001 1675 (47.4) < 0.001 684 (43.0) < 0.001 1147 (35.1) < 0.001 19,469 (44.0)
  ARBs at maximal doses 465 (1.9) 736 (3.7) < 0.001 22 (1.0) 0 – 660 (4.7) < 0.001 393 (3.2) < 0.001 110 (3.1) < 0.001 16 (1.0) 0.999 0 – 1201 (2.7)
  Aldosterone antagonists 1471 (6.1) 1469 (7.4) < 0.001 155 (7.2) 325 (4.4) < 0.001 858 (6.1) 0.049 1031 (8.4) 0.061 288 (8.1) 0.218 151 (9.5) < 0.001 132 (4.0) < 0.001 2940 (6.6)
  Direct renin inhibitors 170 (0.7) 118 (0.6) < 0.001 8 (0.4) 26 (0.4) 0.999 80 (0.6) 0.252 84 (0.7) 0.111 30 (0.8) 0.068 12 (0.8) 0.108 48 (1.5) < 0.001 288 (0.7)
  ARNI 1124 (4.6) 2581 (12.9) < 0.001 202 (9.4) 631 (8.5) 0.192 1374 (9.8) 0.560 914 (7.5) 0.002 231 (6.5) 0.001 143 (9.0) 0.676 210 (6.4) < 0.001 3705 (8.4)
  Beta blockers 8315 (34.3) 7656 (38.3) < 0.001 598 (27.7) 2370 (32.1) < 0.001 5186 (36.9) < 0.001 4682 (38.4) < 0.001 1434 (40.5) < 0.001 685 (43.0) < 0.001 1016 (31.1) < 0.001 15,971 (36.1)
  Diuretics 8957 (37.0) 9260 (46.3) < 0.001 611 (28.3) 2755 (37.3) < 0.001 5802 (41.3) < 0.001 5607 (45.9) < 0.001 1592 (45.0) < 0.001 785 (49.3) < 0.001 1065 (32.5) < 0.001 18,217 (41.2)
  Thiazide diuretics 906 (3.7) 1347 (6.7) < 0.001 33 (1.5) 420 (5.7) < 0.001 715 (5.1) < 0.001 742 (6.1) < 0.001 174 (4.9) < 0.001 39 (2.4) 0.045 130 (4.0) < 0.001 2253 (5.1)
  Loop diuretics 7986 (33.0) 7884 (39.4) < 0.001 511 (23.7) 2228 (30.2) < 0.001 5063 (36.0) < 0.001 4852 (39.8) < 0.001 1552 (43.9) < 0.001 724 (45.5) < 0.001 940 (28.7) < 0.001 15,870 (35.9)
  Potassium sparing diuretics 1112 (4.6) 2457 (12.3) < 0.001 110 (5.1) 538 (7.3) < 0.001 1387 (9.9) < 0.001 1025 (8.4) < 0.001 210 (5.9) 0.203 175 (11.0) < 0.001 124 (3.8) 0.021 3569 (8.1)
  CCB 6280 (25.9) 7913 (39.6) < 0.001 596 (27.6) 2457 (33.3) < 0.001 4647 (33.0) < 0.001 3761 (30.8) < 0.001 1165 (32.9) < 0.001 521 (32.7) < 0.001 1046 (32.0) < 0.001 14,193 (32.1)
  Dihydropyridines 6300 (26.0) 6803 (34.0) < 0.001 613 (28.4) 2225 (30.1) < 0.001 3910 (27.8) 0.563 3716 (30.5) 0.050 1135 (32.1) < 0.001 490 (30.8) < 0.001 1014 (31.0) < 0.001 13,103 (29.6)
  Non-dihydropyridines 1265 (5.2) 397 (2.0) < 0.001 55 (2.5) 246 (3.3) 0.060 768 (5.5) < 0.001 210 (1.7) 0.010 198 (5.6) < 0.001 39 (2.4) 0.845 146 (4.5) < 0.001 1662 (3.8)
 Antidiabetics 218 (0.9) 16,685 (83.5) < 0.001 814 (37.7) 2968 (40.2) < 0.001 4965 (35.3) < 0.001 4596 (37.7) 0.999 1552 (43.9) < 0.001 816 (51.3) < 0.001 1192 (36.4) 0.331 16,903 (38.2)
  Metformin 0 9645 (48.3) < 0.001 564 (26.1) 1854 (25.1) 0.347 2768 (19.7) < 0.001 2454 (20.1) < 0.001 1062 (30.0) < 0.001 562 (35.3) < 0.001 381 (11.6) < 0.001 9645 (21.8)
  Sulfonylurea 0 2260 (11.3) < 0.001 122 (5.7) 477 (6.5) 0.179 603 (4.3) 0.003 602 (4.9) 0.117 230 (6.5) 0.224 144 (9.0) < 0.001 82 (2.5) < 0.001 2260 (5.1)
  DPP4 inhibitors 0 7682 (38.4) < 0.001 323 (15.0) 1288 (17.4) < 0.001 2607 (18.5) < 0.001 2178 (17.8) < 0.001 556 (15.7) 0.478 298 (18.7) < 0.001 432 (13.2) < 0.001 7682 (17.4)
  SGLT-2 inhibitors 0 401 (2.0) < 0.001 23 (1.1) 44 (0.6) 0.015 82 (0.6) 0.008 57 (0.5) 0.001 135 (3.8) < 0.001 44 (2.8) < 0.001 16 (0.5) 0.012 401 (0.9)
  GLP-1 receptor agonists 0 695 (3.5) < 0.001 47 (2.2) 104 (1.4) 0.001 121 (0.9) 0.001 223 (1.8) 0.205 95 (2.7) 0.242 53 (3.3) 0.039 52 (1.6) 0.107 695 (1.6)
  Metiglinides 0 2992 (15.0) < 0.001 89 (4.1) 390 (5.3) < 0.001 1067 (7.6) < 0.001 955 (7.8) < 0.001 164 (4.6) 0.373 69 (4.3) 0.762 258 (7.9) < 0.001 2992 (6.8)
  Glitazones 0 259 (1.3) < 0.001 17 (0.8) 5 (0.1) 0.114 52 (0.4) 0.010 68 (0.6) 0.279 58 (1.6) 0.010 33 (2.1) 0.001 26 (0.8) 0.999 259 (0.6)
  Acarbose 0 314 (1.6) < 0.001 48 (2.2) 53 (0.7) < 0.001 36 (0.3) < 0.001 75 (0.6) 0.001 65 (1.8) 0.290 35 (2.2) 0.999 2 (0.1) < 0.001 314 (0.7)
  Insulin 218 (0.9) 4246 (21.2) < 0.001 155 (7.2) 719 (9.7) < 0.001 1411 (10.0) < 0.001 1256 (10.3) < 0.001 376 (10.6) < 0.001 213 (13.4) < 0.001 334 (10.2) < 0.001 4464 (10.1)
  Statins 11,160 (46.1) 12,798 (64.0) < 0.001 1140 (52.8) 4014 (54.3) 0.218 7734 (55.0) < 0.001 6685 (54.8) 0.006 1906 (53.9) 0.419 879 (55.2) < 0.001 1600 (48.9) < 0.001 23,958 (54.2)
  Warfarin 2849 (11.8) 3092 (15.5) < 0.001 189 (8.8) 921 (12.5) < 0.001 1972 (14.0) < 0.001 1728 (14.2) < 0.001 504 (14.2) < 0.001 205 (12.9) < 0.001 422 (12.9) < 0.001 5941 (13.4)
  Low dose aspirin 6045 (25.0) 5499 (27.5) < 0.001 446 (20.7) 1792 (24.3) < 0.001 3847 (27.4) < 0.001 3198 (26.2) < 0.001 1026 (29.0) < 0.001 445 (28.0) < 0.001 790 (24.1) < 0.001 11,544 (26.1)
  Receptor P2Y12 antagonists 872 (3.6) 1998 (10.0) < 0.001 110 (5.1) 469 (6.3) 0.039 774 (5.5) 0.446 919 (7.5) < 0.001 274 (7.7) < 0.001 171 (10.7) < 0.001 153 (4.7) 0.502 2870 (6.5)
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ACEi angiotensin-converting enzyme inhibitors, ARBs angiotensin receptor blockers, ARNI angiotensin receptor and neprilysin inhibition, BMI body mass index, CCB Calcium channel blockers; CVD: cardiovascular disease, CKD chronic kidney disease, DPP4 dipeptidyl peptidase 4, eGFR estimated glomerular filtration rate, a mL/min/1.73 m2, GLP-1 glucagon-like peptide-1, PAD peripheral artery disease, RAAS renin angiotensin system, SBP systolic blood pressure, SGLT-2 sodium-glucose Cotransporter-2, UACR Urine albumin-to-Creatinine Ratio

Patient hospital mean costs per year are presented in Table 2. From 2015 to 2019 there was a progressive decrease in cardiovascular disease hospital cost per patient year (from 2741.1 to 1971.7 Euros) and patient cumulative cardiovascular disease hospital mean cost reached 11,349.2 Euros in 2019 (supplementary Table 3 and Fig. 2a). The great burden of hospital cost was due to cardiovascular hospitalizations, particularly HF and CKD. Regarding medications, from 2015 to 2019, diabetes drugs mean cost decreased from 102.71 to 89.99 Euros per patient and year, but HF medication mean cost slightly increased from 50.68 to 53.04 Euros, respectively (Table 2). The cumulative mean cost of diabetes and HF medications reached 503.9 and 220.6 Euros, respectively, in 2019 (supplementary Table 3 and Fig. 2b). Dialysis cost decreased from 2328.8 to 1624.2 Euros, respectively (cumulative cost of 9602.9 Euros) and kidney transplant from 655.7 to 465.2 Euros, respectively (cumulative cost of 2701.8 Euros) (supplementary Table 3 and Fig. 2c).

Table 2.

Patients hospital mean cost per yeara

2015 2016 2017 2018 2019 Cumulative cost in 2019
mean SD mean SD mean SD mean SD mean SD
Total hospital cost
 CVD 2741.1 5097.5 2452.6 5049.5 2308.3 5001.4 1875.5 4953.3 1971.7 4953.3 11,349.2
 Cardiorenal 2500.3 4924.3 2250.7 4654.3 2105.9 4180.7 1685.4 3412.9 1766.7 3935.4 10,309.0
 HF 1514.3 3602.4 1341.9 3160.5 1283.9 3023.9 1115.0 2520.9 1012.6 2426.7 6267.7
 CKD 986.0 4.044.9 908.7 4026.2 822.0 3860.2 570.4 2866.6 754.2 3410.9 4041.3
 MI 74.3 732.7 61.6 589.2 55.0 516.1 55.3 560.2 65.4 596.4 311.5
 Stroke 111.7 784.8 99.4 754.1 105.4 792.1 94.6 732.0 99.8 749.0 510.9
 PAD 54.9 658.9 41.0 506.8 42.0 544.6 40.2 521.5 39.7 551.9 217.8
Medication cost
 Total medication 181.80 384.70 178.83 382.2 180.97 352.0 142.66 249.7 167.76 323.0 852.0
 Diabetes medication 102.71 341.10 99.81 348.0 128.51 443.6 82.84 291.7 89.99 291.5 503.9
 HF medication 50.68 76.76 47.34 72.4 31.88 53.2 37.71 60.4 53.04 93.4 220.6
 CVD medication 28.41 62.40 31.68 71.7 20.58 46.1 22.11 45.6 24.73 53.0 127.5
Procedure costs
 Total procedures 2984.5 23,547.4 2657.5 21,616.1 2516.5 20,106.6 2056.8 15,954.3 2089.4 16,415.2 12,304.7
 Dialysis 2328.8 22,021.0 2081.3 19,672.1 1965.2 18,523.3 1603.4 15,265.7 1624.2 15,465.8 9602.9
 Kidney transplant 655.7 4720.9 576.2 4026.3 551.3 3898.3 453.4 3.267.1 465.2 3402.9 2701.8
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CVD cardiovascular disease, HF heart failure, CKD chronic kidney disease, MI myocardial infarction, PAD peripheral artery disease

aIn Euros. Cardiorenal disease includes HF and CKD

Fig. 2.

Fig. 2

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Patient cumulative hospital (A), medication (B) and procedures (C) mean costs*. *In Euros. CVD: cardiovascular disease; HF: heart failure; CKD: chronic kidney disease; MI: myocardial infarction; PAD: peripheral artery disease. Cardiorenal costs include HF and CKD costs

The health resources use for each year is shown in Table 3. The proportion of hospitalized patients decreased from 27.6% in 2015 to 21.2% in 2019; P < 0.001, the days for hospitalized patients from 16.4 to 11.2 days; P < 0.001, and the proportion of patients that died from 8.7 to 4.3%; P < 0.001, respectively. Total health-related cost decreased from 3561 Euros in 2015 to 2493 Euros in 2019. Including indirect costs, total cumulative patient mean costs reached 14,728.4 Euros in 2019; 651,203,550.3 Euros per total CKD population (Table 4).

Table 3.

Health resources use for each year per patient

2015 2016 P2016–2015 2017 P2017–2015 2018 P2018–2015 2019 P2019–2015 Total
mean SD mean SD mean SD mean SD mean SD mean SD
Primary care visits 12.4 14.8 10.6 12.3 < 0.001 9.2 10.4 < 0.001 8.4 9.6 < 0.001 7.5 8.3 < 0.001 48.1 55.8
Specialized care visits 1.9 4.1 1.1 4.3 < 0.001 1.0 4.2 < 0.001 0.9 4.1 < 0.001 0.7 3.9 < 0.001 5.6 7.2
Emergency room visits 0.7 2.3 0.6 2.6 < 0.001 0.5 2.1 < 0.001 0.5 2.1 < 0.001 0.4 1.9 < 0.001 2.7 4.5
Laboratory requests 0.8 1.2 0.7 1.5 < 0.001 0.7 1.5 < 0.001 0.6 1.4 < 0.001 0.6 1.3 < 0.001 3.4 5.1
Radiology and other tests 0.6 1.3 0.6 1.3 – 0.6 1.4 – 0.7 1.5 < 0.001 0.7 1.5 < 0.001 3.2 4.2
Hospitalization
 - Days (all patients) 5.7 10.6 5.1 10.5 < 0.001 4.8 10.4 < 0.001 3.9 10.3 < 0.001 4.1 10.3 < 0.001 23.6 37.6
 - Hospitalized patients, n (%) 12,203 (27.6) 10,832 (24.5) < 0.001 10,081 (22.8) < 0.001 9727 (22.0) < 0.001 9373 (21.2) < 0.001 19,108 (43.2)
 - Days (for patients hospitalized) 16.4 10.6 16.5 10.6 0.161 16.7 10.70 < 0.001 16.8 11.0 < 0.001 17.0 11.2 < 0.001 4.2 23.6
 - Frequency of hospitalization, n (%)
  0 32,011 (72.4) 32,497 (73.5) < 0.001 33,515 (75.8) < 0.001 34,089 (77.1) < 0.001 34,398 (77.8) < 0.001 25,106 (57.0)
  1 10,302 (23.3) 9904 (22.4) 0.001 8754 (19.8) < 0.001 8091 (18.3) < 0.001 7783 (17.6) < 0.001 6986 (15.8)
  2 1636 (3.7) 1680 (3.8) 0.434 1724 (3.9) 0.12 1769 (4.0) 0.02 1724 (3.9) 0.12 5261 (11.9)
  3+ 265 (0.6) 133 (0.3) < 0.001 221 (0.5) 0.044 265 (0.6) 0.999 309 (0.7) 0.064 6862 (15.5)
Disability
 Days of disability 0.3 4.2 0.3 4.5 – 0.4 5.6 0.003 0.4 5.9 0.004 0.4 5.9 0.004 1.9 17.5
 Average days of sick leave (disability only) 41.3 42.3 42.4 43.6 < 0.001 45.3 46.5 < 0.001 44.2 47.1 < 0.001 46.1 47.6 < 0.001 60.3 63.4
 Patients with disability, n (%) 354 (0.8) 354 (0.8) – 398 (0.9) 0.105 442 (1.0) 0.002 398 (0.9) 0.105 1326 (3.0)
Mortality, n (%) 3847 (8.7) 3007 (6.8) < 0.001 2432 (5.5) < 0.001 2166 (4.9) < 0.001 1901 (4.3) < 0.001 13,353 (30.2)
Patients alive at the end of the year, n 40,367 37,360 – 34,928 – 32,762 – 30,861 – –
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Table 4.

Total mean cost for year and cumulative cost in 2019a per patient

2015 2016 2017 2018 2019 Cumulative cost in 2019
mean SD mean SD mean SD mean SD mean SD
Primary care visits 300.1 358.2 256.5 297.7 222.6 251.7 203.3 232.3 181.5 200.9 1164.0
Laboratory requests 25.8 38.8 22.6 48.5 22.6 48.5 19.4 45.2 19.4 42.0 109.8
Radiology and other tests 17.1 37.1 17.1 37.1 17.1 39.9 20.0 42.8 20.0 42.8 91.2
Specialized visits 179.6 387.5 104.0 406.4 94.5 396.9 85.1 387.5 66.2 368.6 529.2
Emergency room visits 83.0 272.6 71.1 308.1 59.3 248.9 59.3 248.9 47.4 225.2 320.0
Hospitalization 2741.1 5097.5 2452.6 5049.5 2308.3 5001.4 1875.5 4.953.3 1971.7 4.953.3 11,349.2
Medication 214.1 421.4 198.7 415.6 185.3 413.5 184.3 421.4 186.8 443.6 969.2
Health-related cost 3561 5492 3123 5276 2910 5128 2447 4516 2493 4628 14.532.6
Indirect Cost/Sick Leave 33 423 34 455 41 564 45 593 42 599 195.8
Total Cost 3594 5915 3157 5731 2951 5691 2491 5109 2535 5227 14,728.4
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aIn Euros

A specific analysis was performed in the DAPA-CKD like population (n = 5925). In this group of patients, mean age was 76.5 ± 14.6 years, 48.5% were women, and mean UACR was 420.7 ± 198.8 mg/g. Overall, 20.8% of patients had a history of HF, 13.4% MI, and 10.8% prior stroke. With regard to treatments, all patients were taking RAAS, but only 13.4% of patients at maximal doses. A total of 2951 (49.8%) patients had T2D. Patients with T2D had higher UACR (426.3 ± 201.5 vs 350.2 ± 171.4 mg/g; P < 0.001), and HbA1c (7.5 ± 2.0 vs 5.8 ± 1.3%; P < 0.001), but without significant differences in eGFR (49.5 ± 12.0 vs 50.0 ± 11.8 mL/min/1.73 m2; P = 0.336). In addition, comorbidities were more common among patients with T2D compared to those without T2D. Overall, in the DAPA-CKD like population, 95.2% had stage 3 or 4 CKD. UACR increased as renal function worsened (from 127.9 ± 58.5 in patients with stage 2 CKD to 1689.3 ± 841.3 mg/g among stage 4 CKD patients; P < 0.001), as well as comorbidities. In addition, the proportion of patients at maximal doses of ACEi or ARBs also increased as stage CKD worsened (from 10.3% in patients with stage 2 CKD to 17.2% among stage 4 CKD patients; P < 0.001) (supplementary Table 4).

With regard to patients hospital mean cost per year in the DAPA-CKD like population, there was a progressive decrease in cardiovascular disease hospital cost per year (from 3025.9 Euros in 2015 to 2022.9 Euros in 2019). Overall, patient cumulative cardiovascular disease hospital mean cost reached 12,219.0 Euros in 2019. The great burden of this cost was due to cardiovascular hospitalizations, particularly HF and CKD. Regarding medications, from 2015 to 2019, diabetes drugs mean cost decreased from 103.7 to 99.0 Euros and HF medication mean cost from 57.8 to 53.0 Euros, respectively. The cumulative mean cost of diabetes and HF medications reached 560.2 and 242.8 Euros, respectively, in 2019. Dialysis cost decreased from 2282.2 to 1591.7 Euros, respectively (cumulative cost of 9501.3 Euros) and kidney transplant from 727.8 to 502.4 Euros, respectively (cumulative cost of 2973.4 Euros) (supplementary Table 5).

Discussion

Our data showed that in Spain, during the 2015–2019 period, CKD-associated costs were substantial, being cardiovascular hospitalizations the most important contributing factor (77.1%), mainly HF and CKD hospitalizations; however, medication cost contribution was marginal (6.6%). Of note, the annual cardiovascular hospitalization mean cost and mortality progressively decreased over time.

In our study, the prevalence of CKD was nearly 5% (mean age 76 years; 71% stage ≥3 CKD). Previously performed studies in Spain have shown a higher prevalence of CKD, possibly due to differences in the inclusion criteria, the methods for renal function determination and the study design. Since this is database study, patients with CKD risk factors (such as diabetes mellitus, hypertension or cardiovascular disease, who are not regularly screened and therefore identified) cannot be reflected, showing the high underdiagnosis rate of CKD still present nowadays. Additionally, this difference in CKD prevalence might be a consequence of a higher use of CKD protective treatments [23, 28–30]. Despite the fact that 71% of patients were taking RAAS inhibitors (82% among T2D patients) in our study, only 4.4% of patients reached maximal doses, suggesting that there is still a potential benefit on CKD outcomes with uptitration). It is likely that the risk of hyperkalemia or renal function impairment associated with these drugs, mainly in elderly patients or in advanced CKD could have had some impact on these results [31]. However, these numbers were not significantly higher in stage 1–2 CKD patients. As it has been reported that achieving maximal doses of RAAS inhibitors (vs lower doses) may be associated with better outcomes, it is highly recommended the use of cardiovascular and renal protective drugs at adequate doses to reduce outcomes [32, 33]. The reduction in the proportion of hospitalized patients, days of hospitalization and mortality during the 2015–2019 period could be related with a better comprehensive management of CKD population, including the use of guidelines recommended drugs [33–36].

More recently, the CREDENCE and DAPA-CKD trials have shown that among CKD patients with T2D, the use of SGLT-2 inhibitors translates into better cardiovascular and renal outcomes [9, 10]. In addition, the DAPA-CKD and the DECLARE-TIMI 58 trials have demonstrated that the beneficial effect of dapagliflozin on the development of cardiovascular and renal complications can be extended to the CKD population without T2D, and to T2D individuals with normal renal function at baseline, respectively [9, 34]. These data suggest that the addition of these drugs to the treatment of CKD patients could reduce even more morbidity and mortality, and consequently, overall CKD burden.

Our study showed that total cumulative cost of CKD patients was high. This has also been confirmed by previous studies [11–18]. The most important contributors for total health care cost in CKD patients were cardiovascular hospitalizations (admissions and hospital stay), particularly HF and CKD hospitalizations. This is not surprising, as CKD is associated with an increased risk of cardiovascular death, and progression to end-stage renal disease [37, 38]. There is a close relationship between CKD and HF. Thus, the presence of one condition promotes the development of the other, and vice versa [39]. In fact, HF can be an early complication of CKD. This has also been observed in the overall T2D population [39]. In the last years, a number of clinical trials have demonstrated the marked benefits of treatment with SGLT-2 inhibitors in the reduction of HF hospitalizations among T2D population [40]. Similarly, SGLT-2 inhibitors substantially decrease kidney composite outcomes in patients with T2D [41]. Unfortunately, in our study, only 2% of patients with T2D were taking SGLT-2 inhibitors at index date since this was 2015 and SGLT-2 inhibitors had been recently launched. It is very likely that the higher use of these drugs in T2D and non T2D populations will translate into a reduction of cardiovascular and renal complications and secondarily to a decrease of health care related costs [42, 43].

Different studies have shown that health care costs increase as renal function worsens or albuminuria develops, particularly in patients that finally require kidney replacement therapy [5, 11–14, 16, 20, 44–46]. As a result, although renal replacement therapy has been the object of constant analysis in order to improve the efficiency and sustainability, the fact is that preventing the occurrence and progression of CKD is the best way to reduce health care resource consumption and health care costs. Therefore, interventions designed to minimize decline in progressive kidney function, particularly among patients with stage 3 or 4 CKD, may reduce the economic CKD burden [5, 11–14, 16, 20, 44–47]. It has been reported that the addition of RAAS inhibitors to prevent the advance of nephropathy is worthwhile not only from a clinical perspective, but also from an economic point of view, even in patients with end stage renal disease, mainly driven by a reduction of hospitalization costs [47, 48]. Both, the CREDENCE and the DAPA-CKD trials showed that among CKD patients, the use of SGLT-2 inhibitors could prevent or delay the development of kidney complications, including end-stage renal disease [9, 10]. Our data showed a progressive reduction of costs associated with dialysis and kidney transplant. Although this is hopeful, a higher use of renal protective drugs, including RAAS inhibitors and SGLT-2 inhibitors with proven renal benefit, could provide additional benefits, including health care costs reduction.

Other contributors to total CKD cost included primary care visits, specialized visits, and diagnostic tests. It has been reported that not only the costs of specialized care decrease with the length of hospital stay reduction [19], but also a nephrologist/nurse-based multifaceted intervention for stage 3 to 4 CKD patients may be a cost effective approach [49], suggesting that an integrated management of CKD patients in both specialist and primary care settings is warranted to reduce CKD burden.

In our study, cardiovascular outcomes were more common in the DAPA-CKD like subpopulation than in the general CKD population [23], translating into higher costs. This has been confirmed in a real-world population similar to that of DAPA-CKD [50]. Despite the beneficial effects shown in the DAPA-CKD trial with dapagliflozin on the prevention of cardiovascular and renal outcomes in CKD patients, regardless the presence of T2D [9], in our study, less than 4% of T2D patients from the DAPA-CKD like subpopulation were taking SGLT-2 inhibitors [50]. As a result, it would be desirable a higher use of these drugs in this population with the double aim of decreasing outcomes and health care costs.

Limitations

As this study had a retrospective design, only indirect causality may be suggested. In addition, some relevant data, such as albuminuria could not be documented in all patients, leading to an underdiagnosis of CKD. However, this is the best design to ascertain the therapeutic management of patients and health care costs in clinical practice, as no specific intervention was required to be included. Furthermore, the high number of patients included, as well as the robustness of the data allow achieving the objectives of the study. Unfortunately, medications were only recorded at baseline and no direct association can be determined between the decrease of events and costs and the use of cardiovascular medications. On the other hand, patients without a CKD diagnosis who met the definition of CKD stage 1 or higher were also considered as CKD patients and selected for the study. Although multiple readings of the eGFR are required to define CKD, due to the characteristics of the study, only one measurement was considered. However, the later represented only 6.9% of the total CKD study population and it was not expected that this had a significant impact on the results. Costs were taken from the Spanish National Healthcare System of 2019 and used for the overall study period. Although this could be a limitation, changes in costs during this time were marginal. In addition, improvements in efficiencies in hospital process may also reduce costs. Unfortunately, this could not be determined. Finally, although data came from seven Spanish regions, previous studies have shown that these data are representative of the entire Spanish population [21].

Conclusions

During the period 2015–2019, costs of patients with CKD in Spain were substantial, with cardiovascular hospitalizations being the key determinant, particularly in HF with CKD. Medication costs were responsible for only a small proportion of total CKD costs. Costs and healthcare resources use were even higher in the DAPA-CKD like population. Improving CKD management, particularly with the use of cardiovascular and renal protective medications may be helpful to reduce CKD burden.

Supplementary Information

12913_2021_6566_MOESM1_ESM.docx (52.6KB, docx)

Additional file 1: Supplementary table 1. Definition of variables. Supplementary table 2. Description of costs / units (year 2019). Supplementary table 3. Patient cumulative hospital mean cost*. Supplementary table 4. Baseline clinical characteristics of the DAPA-CKD population at index date (1st January 2015) and according to the presence of type 2 diabetes and CKD stage. Supplementary table 5. DAPA-CKD patients hospital mean cost for year and cumulative cost in 2019*.

Acknowledgements

None.

Abbreviations

ACEi

Angiotensin-converting enzyme inhibitors

ARBs

Angiotensin-receptor blockers

AF

Atrial fibrillation

CKD

Chronic kidney disease

eGFR

estimated glomerular filtration rate

HF

Heart failure

MI

Myocardial infarction

PAD

Peripheral artery disease

RAAS

Renin angiotensin aldosterone system

SGLT-2

Sodium-glucose cotransporter-2

T2D

Type 2 diabetes

UACR

Urine albumin-to-creatinine ratio

Authors’ contributions

CE, BP, UA, MC, AS, ArS, AH, RA, NM, and MB have all contributed to the study design, result review, manuscript preparation and final approval of the manuscript.

Funding

This study was fully funded by AstraZeneca.

Availability of data and materials

The data that support the findings of this study are available from BIG PAC® but restrictions apply to the availability of these data, which were used under license for the current study, and so are not publicly available. Data are however available from the authors upon reasonable request and with permission of BIG PAC®.

Declarations

Ethics approval and consent to participate

The study was approved by the Investigation Ethics Committee of Consorci Sanitari from Terrassa. Informed consent was waived by the same ethics committee that approved the study, as this was a secondary data study and data were fully anonymized and dissociated from patients. All methods were performed in accordance with the relevant guidelines and current regulations.

Consent for publication

Not applicable.

Competing interests

None.

Footnotes

Publisher’s Note

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

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

12913_2021_6566_MOESM1_ESM.docx (52.6KB, docx)

Additional file 1: Supplementary table 1. Definition of variables. Supplementary table 2. Description of costs / units (year 2019). Supplementary table 3. Patient cumulative hospital mean cost*. Supplementary table 4. Baseline clinical characteristics of the DAPA-CKD population at index date (1st January 2015) and according to the presence of type 2 diabetes and CKD stage. Supplementary table 5. DAPA-CKD patients hospital mean cost for year and cumulative cost in 2019*.

Data Availability Statement

The data that support the findings of this study are available from BIG PAC® but restrictions apply to the availability of these data, which were used under license for the current study, and so are not publicly available. Data are however available from the authors upon reasonable request and with permission of BIG PAC®.

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