Revista Española de Cardiología (English Edition) Revista Española de Cardiología (English Edition)
Rev Esp Cardiol. 2013;66:539-44 - Vol. 66 Num.07 DOI: 10.1016/j.rec.2013.03.009

Survival in Mediterranean Ambulatory Patients With Chronic Heart Failure. A Population-based Study

Eva Frigola-Capell a,b,c,d,, Josep Comin-Colet e,f,g, Josep Davins-Miralles h,i, Ignasi J. Gich-Saladich j, Michel Wensing a, Jose M. Verdú-Rotellar k

a Radboud University Nijmegen, Medical Centre, Scientific Institute for Quality of Healthcare, Nijmegen, The Netherlands
b Instituto Universitario Avedis Donabedian, Universitat Autònoma de Barcelona, Barcelona, Spain
c Red de Investigación en Servicios de Salud en Enfermedades Crónicas (REDISSEC), Spain
d Translab Research Group, Department de Ciències Mèdiques, Universitat de Girona, Girona, Spain
e Departamento de Cardiología, Hospital del Mar, Barcelona, Spain
f Facultad de Medicina, Universitat Autònoma de Barcelona, Barcelona, Spain
g Institut Municipal d’ Investigació Mèdica, Barcelona, Spain
h Subdirecció General de Serveis Sanitaris, Departament de Salut, Generalitat de Catalunya, Barcelona, Spain
i Escuela Andaluza de Salud Pública, Granada, Spain
j Servicio de Epidemiología Clínica y Salud Pública, IIB Sant Pau, CIBERESP, Universitat Autònoma de Barcelona, Barcelona, Spain
k Institut Català de la Salut, Barcelona, Spain

Refers to

Should Heart Failure Patients Move to Catalonia?
Wayne C. Levy
Rev Esp Cardiol. 2013;66:526-8
Full text - PDF

Keywords

Heart failure. Prognosis. Survival. Epidemiology. Population.

Abstract

Introduction and objectives

Scarce research has been performed in ambulatory patients with chronic heart failure in the Mediterranean area. Our aim was to describe survival trends in our target population and the impact of prognostic factors.

Methods

We carried out a population-based retrospective cohort study in Catalonia (north-east Spain) of 5659 ambulatory patients (60% women; mean age 77 [10] years) with incident chronic heart failure. Eligible patients were selected from the electronic patient records of primary care practices from 2005 and were followed-up until 2007.

Results

During the follow-up period deaths occurred in 950 patients (16.8%). Survival after the onset of chronic heart failure at 1, 2, and 3 years was 90%, 80%, 69%, respectively. No significant differences in survival were found between men and women (P=.13). Cox proportional hazard modelling confirmed an increased risk of death with older age (hazard ratio=1.06; 95% confidence interval, 1.06-1.07), diabetes mellitus (hazard ratio=1.53; 95% confidence interval, 1.33-1.76), chronic kidney disease (hazard ratio=1.73; 95% confidence interval, 1.45-2.05), and ischemic heart disease (hazard ratio=1.18; 95% confidence interval, 1.02-1.36). Hypertension (hazard ratio=0.73; 95% confidence interval, 0.64-0.84) had a protective effect.

Conclusions

Service planning and prevention programs should take into consideration the relatively high survival rates found in our area and the effect of prognostic factors that can help to identify high risk patients.

Article

INTRODUCTION

The prevalence of chronic heart failure (CHF) in developed countries is between 1% and 3% and increases with age.1, 2, 3, 4 The management of CHF represents an important economic burden for healthcare systems and accounts for 1% to 2% of the total health care expenditure.5, 6

CHF is also lethal, particularly after onset,7, 8 and worsens after hospital admission.9, 10, 11 Other factors such as age, sex and comorbidities have been described to be determinants of prognosis.12 The interaction among these factors is complex13 and complicates management particularly in primary care where diagnosis is often initiated14 and patients are followed-up. However, little research has been performed in ambulatory patients managed in the community.15 Most studies performed to date have been clinical trials or studies that included patients after hospital discharge.7 Moreover, little is known on specific trends and prognostic factors for community CHF cohorts in the Mediterranean area; a lower incidence16 and lower risk of fatal coronary heart disease have been described17, 18 even though cardiovascular risk factors are similar to those found in the rest of Europe.19 In Spain, trends in survival are mainly reported by the national data registry from hospital-based heart failure clinics20 and large community population-based studies representing the Mediterranean lifestyle are lacking.

The aim of this study was to document longitudinal survival trends in ambulatory patients with CHF in Catalonia (north-east Spain) and to assess the impact of comorbidity and hospitalization on survival.

METHODS Study Design and Setting

We designed a population-based retrospective cohort study using the resources of a project published in clinical trials database (NCT00792402), which evaluated the impact of a computerized clinical guideline on CHF in Catalonia (population of 7 210 50821). Briefly, in this project, a pragmatic, nonequivalent, controlled, before-and-after quasi-experimental study was performed using a population-based approach. Patients from 2 regions (urban and rural) were included. This project was established to evaluate a complex intervention in the urban setting where primary care practices (PCPs) were randomized. Half the PCPs followed usual care management plus a computerized clinical guideline on CHF and half underwent a disease management intervention.

For the purpose of this study, data from both regions were combined. Despite urbanization differences, both regions shared the same organisational features22 (Table 1). Ethical approval for this study was obtained from the Health Care Ethics Committee of the Institut d’Investigació en Atenció Primària Jordi Gol.

Table 1. Organizational Features of Participating Primary Care Practices

Features Urban region Rural region
Health care provider Catalan Health Service for 80% of the population in the autonomous community of Catalonia (population of 7 210 508). This health care provider belongs to the Spanish National Health System
Coverage Universal coverage for both primary and secondary care
Funding State funded through general taxes. Coexistence with the private sector
Access to care All citizens are registered with a family physician who acts as a gatekeeper to specialized care
Medical records Electronic patient record system
Provision of care Network of practices that behave as a geographical and administrative unit where physicians are part of the staff (from 4 to 36 physicians per practice depending on the population attended)
Single health care center Single health care centers and satellite offices
Diagnosis process/integrated care Cardiologists and other specialized services attending practices weekly since 1990 to support physicians on the diagnostic process, management and training
Participants

We included incident patients attending PCPs from 2005 to 2007. Patients aged more than 30 years old with a diagnostic code related to CHF, registered by their primary care physician (I11.0, I13.0, I13.2, I50, I50.0, I50.1, I50.9, P29.0, according to the International Classification of Diseases Tenth Revision used in primary care) were included. As a measure of diagnostic accuracy we only included patients prescribed diuretics. In the urban region, we included the randomized set of PCPs that followed usual care plus clinical practice guideline on CHF and excluded those PCPs that underwent the disease management intervention. We included all PCPs in the rural region.

Measures

We measured survival after the onset of CHF until death or the end of follow-up (December 31st, 2007). Onset of CHF referred to the recorded date of diagnosis of CHF, which we extracted from the primary care electronic patient records. Registration of the diagnosis was done by primary care physicians and in our context is usually done after cardiologist consultation or advice (Table 1) or after the patient has been admitted to hospital. As a measure of the accuracy of the diagnosis, we only included patients prescribed diuretics. We also measured survival in patients with hospital admission for cardiovascular causes during the follow-up, as a primary discharge diagnosis (398-398.99, 402-402.91, 428-428.9, 997.1, 403.90-403.91, 404-404.93, 411-411.89, 414-414.9 according to the International Classification of Diseases Ninth Revision used by hospitals). We included mortality from any cause.

Potential comorbidities associated with survival were defined on the basis of the International Classification of Diseases Tenth Revision codes registered in the primary care electronic patient records either before or at the onset of CHF and were chosen according to a review of the literature, clinical relevance, and availability. We included patient age and sex, hypercholesterolemia, hypertension, diabetes mellitus, ischemic heart disease, chronic obstructive pulmonary disease, and chronic kidney disease (Table 2). We obtained age by calculating the difference between the date of inclusion in our study (January 1st 2005) and date of birth and considered 2 categories: patients aged less than 65 years and patients aged 65 years or older. Setting was also considered as a covariate.

Table 2. International Classification of Diseases Tenth Revision Codes Related to CoMorbidity Included in This Study

Hypercholesterolemia (E78)
Hypertension (O10-O10.4; O11, O13, O14, O16; I10, I11, I11.9, I12, I12.0, I12.9; I13, I13.1, I13.9; I15-I15.2, I15.8, I15.9)
Diabetes mellitus (E10-E10.9; E11-E11.9; E12-E12.9; E13-E13.9;E14-E14.9;P70.2;N08.3; O24-O24.4, O24.9)
Ischemic heart disease (I20-I20.1; I20.8, I20.9; I21-I21.4, I21.9, I21.11; I22-I22.1, I22.8; I23-I23.6, I23.8; I24.1, I24.8, I24.9; I25-I25.6, I25.8; I40-I40.1, I40.8, I40.9; I41-I41.2, I41.8; I42-I42.9; I43-I43.2, I43.8; I51-I51.9; I52-I52.1, I52.8)
Chronic kidney disease (N13.2; N15.8; N16, N16.0, N16.2-N16.4; N17-N17.2, N17.8, N17.9; N18, N18.0, N18.8, N18.9;N19; N20.1; N02.3-N02.5; N07-N07.9; O90.4; Q27.1, Q27.2; Q61.4; P96.0; N25, N25.0; N14.1-N14.4; N15-N15.1, N15.9;I70.1; I72.2; M10.3; A98.5; Y84.1; R39.2; I82.3; Z99.2; K76.7)
Chronic obstructive pulmonary disease (J43-J43.2, J43.8, J43.9; J44-J44.1, J44.8, J44.9; J47)
Data Sources

From January 2005 to December 2007, we extracted data from 3 national database sources, which we were able to match because every Catalan citizen has a unique and anonymous identification number for health care use. The central database of the Catalan Health Service facilitated data abstraction from the electronic patient records of PCPs. Data from hospital admissions was gathered from the Division of Demand and Activity Registries (Minimum Basic Data Set for Acute-care Hospitals) of the Catalan Health Service, where all Catalan hospitals are required to send in their data for reimbursement. Information on mortality was gathered from the Mortality Register of Catalonia, where fatalities had to be notified. We recorded as fatality cases patients codified as such in the primary care electronic patient records register and those also identified in the Mortality Register of Catalonia.

None of the officers responsible for data abstraction were involved in the subsequent data analysis.

Statistical Methods

Descriptive data from our sample on age, sex and comorbidities are presented. We calculated the entire cohort follow-up period and reported the median, minimum and maximum values. Candidate variables significantly associated with mortality in the bivariate analysis (P<.05) were included as potential covariates in the multivariable model. In the bivariate model, the chi square text and Fisher's exact test were used for categorical variables and Student's t test for continuous variables. Kaplan Meier analysis was used to determine long-term survival. The Cox proportional hazards model was used to examine the independent effect of candidate variables. The forward and backward step technique with the likelihood ratio test was used. Discrimination of the model was calculated with Harrell's C-index, and calibration of the model was calculated with the Gronnesby and Borgan test. Constant hazard was tested with the log (−log S[x]) vs Log(time) graphic. All tests were 2-tailed and significance was set at the 5% level (α=0.05). Missing values were calculated (0.2% of our final sample) and were found not relevant in our analysis.

The statistical analysis was performed with SPSS v18 software and StataCorp12.

RESULTS

Initially we identified 13 008 potentially eligible patients with a diagnosis of CHF from 43 PCPs, covering a population of 1 039 342 listed citizens.

We excluded 2221 patients without registration of the date of diagnosis and 5128 patients with a prevalent diagnosis of CHF or without a prescription for diuretics. Our final sample consisted of 5659 patients from 43 PCPs: 3641 (64.3%) from urban areas (covering a population of 558 515 inhabitants) and 2018 (35.7%) from rural settings (covering a population of 480 827 inhabitants). The median follow-up period of the entire cohort was 3 years with a minimum of 0.08 years and a maximum of 3 years.

Patient characteristic in the sample did not differ substantially from those of the initial population (Table 3). The mean (standard deviation) age of our sample was 77 (10) years; 88.3% of the patients were aged 65 years or more and 60% were women. During the study period, 585 patients (10.3%) were hospitalized for cardiovascular causes. The median interval after the onset of CHF was 1.6 years. Regarding comorbidities, the bivariate analysis showed that in those patients who died during the follow-up, hypertension was more prevalent in women (P=.022) and in patients aged 65 years or more (P=.029), while chronic obstructive pulmonary disease (P<.001), ischemic heart disease (P<.001), and chronic kidney disease (P=.009) were more common in men. Diabetes mellitus was more frequent in patients aged less than 65 years (P=.041), while hypercholesterolemia was also more frequent, although this difference was not statistically significant (P=.771).

Table 3. Patient Characteristics

  Study sample
(n=5659)
Population
(n=13 008)
Age, years 77±10 (available for 5654) 76±11 (available for 12 963)
Age<65 656 (11.6) 1793 (13.8)
Age≥65 4998 (88.3) 11 170 (85.9)
Women 3402 (60) 7671 (59)
Hospitalization during follow up 585 (10.3) 1233 (9.5)
Time after the onset of CHF, years 1.6 [1.0, 2.2] 2.21 [1.23, 3.57] (available for 10 787 patients)
Patients on diuretics 5659 (100) 9391 (72.2)
Patients on ACE inhibitors/ARB 4537 (80.2) 9262 (71.2)
Patients on beta-blockers 2086 (36.9) 4137 (31.8)
Hypercholesterolemia 444 (7.8) 1206 (9.3)
Hypertension 3996 (70.6) 9134 (70.2)
Diabetes mellitus 1686 (29.8) 4105 (31.6)
Ischemic heart disease 1458 (25.8) 3540 (27.2)
CKD 624 (11.0) 2044 (15.7)
COPD 827 (14.6) 2060 (15.8)

ACE, angiotensin-converting enzyme; ARB, angiotensin receptor blockers; CHF, chronic heart failure; CKD, chronic kidney disease; COPD, chronic obstructive pulmonary disease.
Data are expressed as no. (%), mean±standard deviation or median [interquartile range].

During the follow-up period, 950 patients died (16.8%).

Survival after the first, second and third year of follow-up was 90%, 80%, 69%, respectively Figure; no significant differences (P=.13) in survival were found between men and women.

Overall survival in incident patients during the follow-up period. No statistically significant differences.

Figure. Overall survival in incident patients during the follow-up period. No statistically significant differences.

Cox proportional hazard modelling (Table 4) confirmed an increased risk of death for older age (hazard ratio [HR]=1.06; 95% confidence interval [95%CI], 1.06-1.07), diabetes mellitus (HR=1.53; 95%CI, 1.33-1.76), chronic kidney disease (HR=1.73; 95%CI, 1.45-2.05), and ischemic heart disease (HR=1.18; 95%CI, 1.02-1.36). Hypertension (HR=0.73; 95%CI, 0.64-0.84) had a protective effect. We did not find a significant effect according to the setting.

Table 4. Multivariable Model Reporting the Effect of Prognostic Factors on Survival (N=5647). Cox Proportional Hazards Model

  HR (95%CI)
Age, years 1.06 (1.06-1.07)
Diagnosis of chronic kidney disease 1.73 (1.45-2.05)
Diagnosis of diabetes mellitus 1.53 (1.33-1.76)
Diagnosis of ischemic heart disease 1.18 (1.02-1.36)
Diagnosis of hypertension 0.73 (0.64-0.84)

95%CI, 95% confidence interval; HR, hazard ratio.

The assumption of constant hazard was met. The discrimination of the model was 0.67.

Calibration of the model (P=.03) was significant for the first decile only and was not significant for the remaining deciles, indicating that the model failed in patients with lower risk.

DISCUSSION

In our study 90% of ambulatory patients with CHF survived 1 year after onset. Independent predictors of a worse outcome were older age, diabetes mellitus, chronic kidney disease, and ischemic heart disease. In contrast with previous studies carried out in hospital-based cohorts or selected patients from clinical trials, we evaluated mortality trends and predictors of mortality for ambulatory patients in a Mediterranean community-based cohort.

Comparisons with other studies are hampered by differences in patient selection, patient characteristics, and follow-up periods. Nevertheless, our survival rates were higher than those reported in the literature, such as the Framingham8 (57% for men and 64% for women) and Rochester23 (77%) studies in the United States, and the Hillingdon7 (62%) study in the United Kingdom. All of these studies reported incident CHF. In our study, survival was also higher than that in a study in the Netherlands24 (74% survival at 1 year), which used a selection process similar to ours. In prevalent CHF, studies in central Europe reported a 1-year survival rate of 87.4%25 and 89%.26

On the other hand, our survival rates for incident CHF were in line with those reported by the Spanish national data registry from hospital-based heart failure clinics20 (survival rates at 1 and 2 years of 90% and 80%, respectively). Higher mortality (about 30% mortality at 1 year) was found in the south of Spain,27 although this phenomenon has been described as a paradox,28 as this mortality is the highest in Spain and is associated with ischemic heart disease mortality, illiteracy, and unemployment.29 In contrast, we found a low rate of hypercholesterolemia and ischemic heart disease in our cohort, which could partly explain our positive results on survival. None of the above studies analyzed the effect of comorbidities on outcomes.

Our results show that Mediterranean countries may also have better outcomes for CHF in addition to the favorable trends reported in coronary heart disease.17, 18 It has been hypothesized that regional factors,30 as well as the implementation of evidence-based therapies and risk factor management,19 may confer protection. At the organizational level, there may be differences in the role played by primary care in health care systems on risk factor control and in fast tracking to specialist care. To allow comparisons, these issues should be further investigated in epidemiological studies in other Mediterranean countries.

Despite differences in patient selection and patient characteristics, the predictors of worse prognosis in our study were similar to those found in previous community-based studies. There is common agreement on the effect of older age both in the United States and Europe, although disparities exist regarding sex and other prognostic factors. Although we found no effect of sex on survival, several studies have reported better survival in women.8, 24, 25 Other factors that increase the risk of death are diabetes mellitus,8, 26, 31 hospitalization25 and renal failure.7, 23, 25, 26.

Hypertension has been previously reported23 as a positive predictor for survival. In a multivariable analysis, we found better survival among patients with a diagnosis of hypertension registered in their electronic medical records. The higher survival was probably due to the negative effect of low systolic blood pressure on prognosis described elsewhere7, 25, 26 rather than to the diagnosis of hypertension itself, which is one of the causes of diastolic heart failure and is more prevalent in women and older patients, who usually have a longstanding history of hypertension.24, 32, 33

Limitations

Our study has some potential design limitations concerning the inclusion of participants and measures. Although we did not have access to the set of PCPs excluded in the urban region, we found no significant differences among the regions included in this study. We expected that any other ethnic or socioeconomic effect on outcomes would have been minimized by our selection process, which developed from a previous randomization performed for a concomitant disease management intervention.

We identified CHF patients through their primary care physician's electronic patient records but did not know how many of them had undergone echocardiography and therefore lacked information on the etiology of CHF, which would have aided interpretation of our results. As a measure of diagnostic accuracy, we only selected patients who had been prescribed diuretics to perform our data analysis and possibly missed patients with CHF not receiving diuretics.

Usually, in our setting, primary care physicians register the diagnosis of CHF after confirmation by a specialist. This specialist is also involved in the diagnostic process and management of these patients in the community and provides support to primary care physicians in their setting, as this has been part of our integrated care program since 199034 (Table 1). Although we could not exclude the possibility that diagnosis of CHF was made clinically by the primary care physician alone in some cases, our results on survival were consistent with those found in the Spanish national data registry.

Our predictive model had a modest predictive power (c=0.67) and also low goodness-of-fit (P=.03), particularly for the first decile, with higher goodness-of-fit for the remaining deciles, indicating that the model failed in patients at lower risk. These results could be explained by 2 reasons: a) comorbidities were identified through the primary care physicians’ registration in electronic patient records and we were unable to capture how long patients had been exposed to the conditions or to obtain information on severity by using biomarkers, and b) the sample size was large (over 500 cases in each decile), increasing the possibility that every small difference would be significant. Nonetheless, our model was clinically relevant and in line with reports in the literature.

Because of the low registration on New York Heart Association classification and left ventricular ejection fraction, we could not present survival data according to these factors. Such information would have provided more detailed information on the severity of CHF. Primary care physicians do not usually record this information, nor do they do so as open access text, which also hampered access.

Finally, we considered the date of registration of CHF in the primary care physicians’ electronic medical records, which did not necessary reflect the date when the diagnosis was made, although our survival rates matched those in national database registers.

CONCLUSIONS

Survival rates in our community cohort of patients with CHF were relatively high. Clinicians and managers need to consider this favorable prognosis in service planning, as well as the effect of prognostic factors, which could help to identify high-risk patients.

FUNDING

This study was supported by Instituto de Salud Carlos III, Spanish Ministry of Health (grant number PI07/91020).

CONFLICTS OF INTEREST

None declared.

ACKNOWLEDGEMENTS

We gratefully acknowledge the researchers of the CHF Project: Concepció Morera, Valeria Pacheco, Joan Cabratosa, Núria Fabrellas, Júlia Roura, Margarita Puigvert, Josep Paredes, Carola Orrego, Joaquim Bañeres, and Carles Benet. We also thank Miguel Angel Muñoz, Xavier Bonfill, and Rosa Suñol for comments on a previous manuscript, and the Institut d’Investigació en Atenció Primària Jordi Gol. and Boni Bolibar for administering funding and providing research advice. We thank the directors and informatics officers of the national databases for providing the relevant information and particularly Josep M. Picas, Delfí Faixedas, and Maria Luisa Bernard-Antoranz. We are also grateful to the Catalan Division of Demand and Activity Registries and the Mortality Register of Catalonia for reporting data on hospitalizations and mortality respectively, and to Núria Mora for her advice on the statistical analysis.

Received 17 April 2012
Accepted 13 March 2013

Corresponding author: Instituto Universitario Avedis Donabedian, Universitat Autònoma de Barcelona, Provença 293, 08037 Barcelona, Spain. fad@fadq.org

Bibliography

1. Rodriguez-Artalejo F, Guallar-Castillón P, Banegas Banegas JR, Del Rey-Calero J. Trends in hospitalization and mortality for congestive heart failure in Spain. Eur Heart J. 1997;18:1771-9.
Medline
2. Dickstein K, Cohen-Solal A, Filippatos G, McMurray JJV, Ponikowski P, Poole-Wilson PA, et al, ESC guidelines for the diagnosis and treatment of acute and chronic heart failure 2008: the Task Force for the Diagnosis and Treatment of Acute and Chronic Heart Failure 2008 of the European Society of Cardiology. Developed in collaboration with the Heart Failure Association of the ESC (HFA) and endorsed by the European Society of Intensive Care Medicine (ESICM). Eur Heart J. 2008;29:2388-442.
Medline
3. McMurray JJ, Stewart S. Epidemiology, aetiology, and prognosis of heart failure. Heart. 2000;83:596-602.
Medline
4. Roger VL, Go AS, Lloyd-Jones DM, Benjamin EJ, Berry JD, Borden WB, et al. Heart disease and stroke statistics-2012 update: a report from the American Heart Association. Circulation. 2012;125:e2-e220.
Medline
5. Berry C, Murdoch DR, McMurray JJV. Economics of chronic heart failure. Eur J Heart Fail. 2001;3:283-91.
Medline
6. Stewart S, Jenkins A, Buchan S, McGuire A, Capewell S, McMurray JJ. The current cost of heart failure to the National Health Service in the UK. Eur J Heart Fail. 2002;4:361-71.
Medline
7. Cowie MR, Wood DA, Coats AJS, Thompson SG, Suresh V, Poole-Wilson PA, et al. Survival of patients with a new diagnosis of heart failure: a population based study. Heart. 2000;83:505-10.
Medline
8. Ho KK, Anderson KM, Kannel WB, Grossman W, Levy D. Survival after the onset of congestive heart failure in Framingham Heart Study subjects. Circulation. 1993;88:107-15.
Medline
9. MacIntyre K, Capewell MD, Stewart S, Chalmers JWT, Boyd J, Finlayson A, et al. Evidence of improving prognosis in heart failure. Trends in case fatality in 66547 patients hospitalized between 1986 and 1995. Circulation. 2000;102:1126-31.
Medline
10. Blackledge HM, Tomlinson J, Squire IB. Prognosis for patients newly admitted to hospital with heart failure: survival trends in 12 220 index admissions in Leicestershire 1993-2001. Heart. 2003;89:615-20.
Medline
11. Goldberg RJ, Ciampa J, Lessard D, Meyer TE, Spencer FA. Long-term survival after heart failure: a contemporary population-based perspective. Arch Intern Med. 2007;167:490-6.
Medline
12. McMurray JJV, Pfeffer MA. Heart failure. Lancet. 2005;365:1877-89.
Medline
13. Jong P, Vowinckel E, Liu PP, Gong Y, Tu JV. Prognosis and determinants of survival in patients newly hospitalized for heart failure: a population-based study. Arch Intern Med. 2002;162:1689-94.
Medline
14. Khunti K, Baker R, Grimshaw G. Diagnosis of patients with chronic heart failure in primary care: usefulness of history, examination, and investigations. Br J Gen Pract. 2000;50:50-4.
Medline
15. Mosterd A, Hoes AW. Reducing hospitalizations for heart failure. Eur Heart J. 2002;23:842-5.
Medline
16. Baena-Díez JM, Vidal-Solsona M, Byram AO, González-Casafont I, Ledesma-Ulloa G, Martí-Sans N. Epidemiología de las enfermedades cardiovasculares en atención primaria. Estudio Cohorte Zona Franca de Barcelona. Rev Esp Cardiol. 2010;63:1261-9.
Medline
17. Verschuren WM, Jacobs DR, Bloemberg BP, Kromhout D, Menotti A, Aravanis C, et al. Serum total cholesterol and long-term coronary heart disease mortality in different cultures. Twenty-five-year follow-up of the seven countries study. JAMA. 1995;274:131-6.
Medline
18. Van den Hoogen PC, Feskens EJ, Nagelkerke NJ, Menotti A, Nissinen A, Kromhout D. The relation between blood pressure and mortality due to coronary heart disease among men in different parts of the world. Seven Countries Study Research Group. N Engl J Med. 2000;342:1-8.
Medline
19. Flores-Mateo G, Grau M, O’Flaherty M, Ramos R, Elosua R, Violan-Fors C, et al. Análisis de la disminución de la mortalidad por enfermedad coronaria en una población mediterránea: España 1988-2005. Rev Esp Cardiol. 2011;64:988-96.
Medline
20. Anguita-Sánchez M, investigadores del Registro BADAPIC. Características clínicas, tratamiento y morbimortalidad a corto plazo de pacientes con insuficiencia cardiaca controlados en consultas específicas de insuficiencia cardiaca. Resultados del Registro BADAPIC. Rev Esp Cardiol. 2004;57:1159-69.
Medline
21. Cifras oficiales de población resultantes de la revisión del Padrón municipal a 1 de enero de 2007 (Real Decreto 1683/2007, de 14 de diciembre), Boletín Oficial del Estado número 311, de 28 de diciembre de 2007 [accessed 2011 Nov 29]. Available at: http://www.ine.es/jaxi/menu.do?type=pcaxis&path=/t20/e260/a2007/&file=pcaxis.
22. Bolíbar B, Pareja C, Astier-Peña MP, Morán J, Rodríguez-Blanco T, Rosell-Murphy M, et al. Variability in the performance of preventive services and in the degree of control of identified health problems: a primary care study protocol. BMC Public Health. 2008;8:281.
Medline
23. Senni M, Tribouilloy CM, Rodeheffer RJ, Jacobsen SJ, Evans JM, Bailey KR, et al. Congestive heart failure in the community: trends in incidence and survival in a 10-year period. Arch Intern Med. 1999;159:29-34.
Medline
24. Van Jaarsveld CHM, Ranchor AV, Kempen GIJM, Coyne JC, Van Veldhuisen DJ, Sanderman R. Epidemiology of heart failure in a community-based study of subjects aged ≥57 years: incidence and long-term survival. Eur J Heart Fail. 2006;8:23-30.
Medline
25. Muntwyler J, Abetel G, Gruner C, Follath F. One-year mortality among unselected outpatients with heart failure. Eur Heart J. 2002;23:1861-6.
Medline
26. Mosterd A, Cost B, Hoes AW, De Bruijne MC, Deckers JW, Hofman A, et al. The prognosis of heart failure in the general population. The Rotterdam Study. Eur Heart J. 2001;122:1318-27.
27. Gomez-Soto FM, Andrey JL, Garcia-Egido AA, Escobar MA, Romero SP, Garcia-Arjona R, et al. Incidence and mortality of heart failure: a community-based study. Int J Cardiol. 2011;151:40-5.
Medline
28. Jiménez-Navarro M, Gómez-Doblas JJ, Molero E, De Teresa Galván E. Mortalidad por insuficiencia cardíaca en España: ¿existe una paradoja andaluza?. Rev Clin Esp. 2006;206:276-7.
Medline
29. Rodríguez-Artalejo F, Guallar-Castillón P, Banegas Banegas JR, Del Rey Calero J. Variación geográfica en las hospitalizaciones y en la mortalidad por insuficiencia cardíaca congestiva en España, 1980-1993. Rev Esp Cardiol. 2000;53:776-82.
Medline
30. Marrugat J, D’Agostino R, Sullivan L, Elosua R, Wilson P, Ordovas J, et al. An adaptation of the Framingham coronary heart disease risk function to European Mediterranean areas. J Epidemiol Community Health. 2003;57:634-8.
Medline
31. Pons F, Lupón J, Urrutia A, González B, Crespo E, Díez C, et al. Mortalidad y causas de muerte en pacientes con insuficiencia cardiaca: experiencia de una unidad especializada multidisciplinaria. Rev Esp Cardiol. 2010;63:303-14.
Medline
32. Rutten FH, Cramer MJM, Grobbee DE, Sachs APE, Kirkels JH, Lammers JWJ, et al. Unrecognized heart failure in elderly patients with stable chronic obstructive pulmonary disease. Eur Heart J. 2005;26:1887-94.
Medline
33. Banerjee P, Banerjee T, Khand A, Clark AL, Cleland JGF. Diastolic heart failure: neglected or misdiagnosed?. J Am Coll Cardiol. 2002;39:138-41.
Medline
34. Pla estratègic d’ordenació de l’atenció especialitzada ambulatòria a Catalunya. Barcelona: Generalitat de Catalunya;2010 [accessed 2011 Nov 29]. Available at: http://www.gencat.cat/salut/depsalut/html/ca/dir2251/peaeadocnov2010.pdf.

1885-5857/© 2013 Sociedad Española de Cardiología. Published by Elsevier España, S.L.U. All rights reserved

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