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The health services burden of heart failure: an analysis using linked population health data-sets.
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PMID:  22533631     Owner:  NLM     Status:  MEDLINE    
Abstract/OtherAbstract:
BACKGROUND: The burden of patients with heart failure on health care systems is widely recognised, although there have been few attempts to quantify individual patterns of care and differences in health service utilisation related to age, socio-economic factors and the presence of co-morbidities. The aim of this study was to assess the typical profile, trajectory and resource use of a cohort of Australian patients with heart failure using linked population-based, patient-level data.
METHODS: Using hospital separations (Admitted Patient Data Collection) with death registrations (Registry of Births, Deaths and Marriages) for the period 2000-2007 we estimated age- and gender-specific rates of index admissions and readmissions, risk factors for hospital readmission, mean length of stay (LOS), median survival and bed-days occupied by patients with heart failure in New South Wales, Australia.
RESULTS: We identified 29,161 index admissions for heart failure. Admission rates increased with age, and were higher for males than females for all age groups. Age-standardised rates decreased over time (256.7 to 237.7/100,000 for males and 235.3 to 217.1/100,000 for females from 2002-3 to 2006-7; p = 0.0073 adjusted for gender). Readmission rates (any cause) were 27% and 73% at 28-days and one year respectively; readmission rates for heart failure were 11% and 32% respectively. All cause mortality was 10% and 28% at 28 days and one year. Increasing age was associated with more heart failure readmissions, longer LOS and shorter median survival. Increasing age, increasing Charlson comorbidity score and male gender were risk factors for hospital readmission. Cohort members occupied 954,888 hospital bed-days during the study period (any cause); 383,646 bed-days were attributed to heart failure admissions.
CONCLUSIONS: The rates of index admissions for heart failure decreased significantly in both males and females over the study period. However, the impact on acute care hospital beds was substantial, with heart failure patients occupying almost 200,000 bed-days per year in NSW over the five year study period. The strong age-related trends highlight the importance of stabilising elderly patients before discharge and community-based outreach programs to better manage heart failure and reduce readmissions.
Authors:
Jane Robertson; Patrick McElduff; Sallie-Anne Pearson; David A Henry; Kerry J Inder; John R Attia
Publication Detail:
Type:  Journal Article     Date:  2012-04-25
Journal Detail:
Title:  BMC health services research     Volume:  12     ISSN:  1472-6963     ISO Abbreviation:  BMC Health Serv Res     Publication Date:  2012  
Date Detail:
Created Date:  2012-08-08     Completed Date:  2013-01-03     Revised Date:  2013-07-19    
Medline Journal Info:
Nlm Unique ID:  101088677     Medline TA:  BMC Health Serv Res     Country:  England    
Other Details:
Languages:  eng     Pagination:  103     Citation Subset:  IM    
Affiliation:
School of Medicine and Public Health, The University of Newcastle, Newcastle, Australia. jane.robertson@newcastle.edu.au
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MeSH Terms
Descriptor/Qualifier:
Aged
Aged, 80 and over
Cohort Studies
Female
Health Services Needs and Demand / statistics & numerical data*
Health Services Research
Heart Failure / therapy*
Hospitalization / trends*
Humans
Male
Middle Aged
New South Wales
Patient Readmission / trends
Registries
Risk Factors
Sex Distribution
Comments/Corrections
Erratum In:
BMC Health Serv Res. 2013;13:179

From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine

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Journal ID (nlm-ta): BMC Health Serv Res
Journal ID (iso-abbrev): BMC Health Serv Res
ISSN: 1472-6963
Publisher: BioMed Central
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Copyright ©2012 Robertson et al.; licensee BioMed Central Ltd.
open-access:
Received Day: 13 Month: 6 Year: 2011
Accepted Day: 25 Month: 4 Year: 2012
collection publication date: Year: 2012
Electronic publication date: Day: 25 Month: 4 Year: 2012
Volume: 12First Page: 103 Last Page: 103
ID: 3413515
Publisher Id: 1472-6963-12-103
PubMed Id: 22533631
DOI: 10.1186/1472-6963-12-103

The health services burden of heart failure: an analysis using linked population health data-sets
Jane Robertson15 Email: jane.robertson@newcastle.edu.au
Patrick McElduff2 Email: Patrick.Mcelduff@newcastle.edu.au
Sallie-Anne Pearson3 Email: sallie.pearson@unsw.edu.au
David A Henry14 Email: David.Henry@ices.on.ca
Kerry J Inder1 Email: Kerry.Inder@newcastle.edu.au
John R Attia12 Email: John.Attia@newcastle.edu.au
1School of Medicine and Public Health, The University of Newcastle, Newcastle, Australia
2Hunter Medical Research Institute, The University of Newcastle, Newcastle, Australia
3UNSW Cancer Research Centre, University of New South Wales and Prince of Wales Clinical School, Sydney, Australia
4Institute for Clinical Evaluative Sciences and Department of Medicine, University of Toronto, Toronto, Canada
5Clinical Pharmacology, Calvary Mater Hospital, The University of Newcastle, Clinical Sciences Building, Waratah, NSW, 2298, Australia

Background

Heart failure is associated with considerable morbidity and poor survival. It is characterised by numerous hospital readmissions and extensive use of health care resources [1-3]. The resulting substantial burden on health care systems and the associated costs are a consequence of ageing populations and improvements in the medical management of heart failure with the use of therapies such as beta-blockers, ACE inhibitors, aldosterone inhibitors and device therapies that prolong survival after ischaemic heart damage or heart failure related to hypertension and valvular heart disease.

There has been limited documentation of the health system impacts of heart failure in the Australian community. Morbidity estimates have typically been derived by applying international chronic heart failure incidence and prevalence data to Australian population estimates [4]. More direct estimates have been derived from the National Hospital Morbidity Data (NHMD) collection [5]. This information, however, is based on aggregated data collections, with no analysis at individual patient level.

Patient-level data are required to examine individual patterns of care, and differences in outcomes related to age, socio-demographic factors, and the presence of co-morbidities. In Australia, there is universal, publicly funded coverage for hospital and community-based medical services. Consequently, there are a number of administrative data sets that provide comprehensive coverage of patients with heart failure at the national or state level. Most experience with the analysis of linked health data-sets has been in Western Australia, which has validated the use of administrative data to identify patients with heart failure [6]. Although the rate of index hospital admission has fallen, the burden of disease has increased because of improved survival and the ageing of the community [7].

A capacity for linkage of publicly funded health administrative data sets at the level of the individual patient has recently become available in Australia’s most populous state, New South Wales (NSW). To inform planning for care of individuals in NSW suffering from heart failure we performed an analysis of the NSW linked data-sets. We measured the rates of index (first) admissions for heart failure, the readmission rates, mortality and median survival from first admission. To assess the burden of disease on the healthcare system, we examined the average length of stay (LOS) for the index admission, readmissions in the first year, and the impact of co-morbidities on readmission rates. Finally, we calculated the bed-days occupied by patients with heart failure during the study period.


Methods

Access to the relevant NSW data-sets was through the Centre for Health Record Linkage (CHeReL; http://www.cherel.org.au).

Data sets

Records were drawn from the NSW Admitted Patient Data Collection (APDC; representing all separations in public and private hospitals in NSW, including discharges, transfers and deaths) between 1 July 2000 and 30 June 2007; and death registrations (fact of death) in the NSW Registry of Births, Deaths and Marriages (RBDM) for the years 2000–2007.

Definitions
Heart failure cohort

The cohort comprised all NSW residents aged ≥45 years with a first (index) admission for heart failure i.e. a principal hospital discharge (separation) code for heart failure (ICD-10 code I50) or hypertensive heart disease (I11, I13) in the APDC between 1 July 2002 and 30 June 2007. Hypertensive heart disease was included in the definition based on evidence that some cases of heart failure are classified under this rubric [8].

Index admission for heart failure

An index admission was defined by the absence of a heart failure separation code (any diagnostic position) in the two years prior to the eligible admission.

Readmissions

Readmissions were counts of patients re-presenting at NSW hospitals for any cause within one month (28 days) and one year after the index admission for heart failure. Heart failure readmissions were any readmission where the principal separation code was heart failure (I50) or hypertensive heart disease (I11, I13).

Co-morbidities

We used the validated Charlson Index as a co-morbidity measure using an algorithm based on the work of Sandarajaran [9]. Given that heart failure is included in the Charlson Index and all patients had heart failure, no point was given for this. Co-morbidities were assessed in two ways: firstly, by examining hospital separation codes (all positions) at the index admission; and secondly by including separation codes from the index admission and all admissions in the two years prior to the index admission combined.

Burden of disease

We calculated actual bed-days occupied due to index admissions and readmissions.

Geography

Geography was defined using the Accessibility/Remoteness Index of Australia (ARIA) [10].

Socio-economic status

Socio-economic status was defined using the SEIFA (Socio-Economic Indexes for Areas) Index [11] based on 2006 Australian Census responses, and divided into quintiles.

Data linkage

We used extracts of linked hospital separation data from the APDC with RBDM death registrations with encryption to protect the identity of individual patients. Data linkage by the CHeReL uses probabilistic matching of patients’ names and other identifiers with ChoiceMaker software [12], supplemented with clerical review of doubtful matches.

Statistical methods

Age-specific rates of index (first) admissions for heart failure or hypertensive heart disease to public and private hospitals in NSW (1 July 2002–30 June 2007) were calculated by dividing the number of index events in 5-year age groups by the NSW population in that age group in that year. Age-standardised rates and 95% confidence intervals were calculated for each year using the indirect method with the 2006 NSW census population as the reference. Trends over time were examined with linear regression models.

The proportions of patients with a readmission (any cause or heart failure) in the first month (28 days) and first year post-index admission were calculated for the whole population and by 5-year age groups. The probabilities of readmission at one-month and one-year were estimated using Kaplan-Meier curves and the log rank test was used to test for statistically significant differences between age groups; individuals were censored at the time they died.

The LOS for the index admission and re-admissions was summarised as the mean number of days (with standard deviation), and presented for the whole population and by 5-year age groups. Linear regression was used to examine differences in LOS between age groups, after adjusting for potential confounding variables (age, gender, Charlson Index based on hospital separation codes for the index admission, geography using the ARIA index, and socioeconomic status using SEIFA quintiles). LOS data are not normally distributed (highly skewed to the right) and a robust variance estimator was used to correct for this. Cox proportional hazards modelling were used to assess the impact of potential risk factors for readmission to hospital. Where there was a missing value for any outcome or co-variate, the record was excluded from the analysis; this affected fewer than 3% of observations in the dataset. We tested the assumption of proportional hazards by examining the graph of the log(−log(survival)) versus log survival time graph.

Mortality rates and median survival time were calculated from first admission for heart failure or hypertensive heart disease. Survival from index admission to death was estimated using Kaplan-Meier curves, with curves fitted by 5-year age groups. The log rank test was used to test for statistically significant differences between age groups.

All analyses were done using SAS version V9.1 (SAS Institute Inc., Cary, NC, USA) and analyses performed at the 5% significance level.

The study was approved by the NSW Population and Health Service and University of Newcastle Research Ethics Committees.


Results

There were 14,972,359 hospital separations in the APDC between 1 July 2000 and 30 June 2007, 67,018 of which had a principal diagnosis ICD-10 code of I50, I11 or I13, representing 41,904 persons. Of these, 29,735 persons had not been hospitalised with these separation codes (any position) in the two years prior to this event. Removing the 574 people aged <45 years at the index admission left 29,161 persons (with 645,245 separations) to form the heart failure study cohort. Patients with hypertensive heart disease comprised <2% of the study population.

Demographic characteristics

Most patients (67.9%) were ≥75 years of age and 28.7% were aged ≥85 years (Table 1). There were approximately equal numbers of males and females in the cohort (14,604 males, 14,557 females). Females represented around one-third of those aged 45–64 years, but just under two-thirds (63.5%) of those aged ≥85 years (data not shown).

Comorbidity burden

Patients had a median of 2.0 comorbidities recorded at baseline admission, although the range was wide (0–13, not including heart failure), with some evidence of an increase in comorbidity burden over time (Table 2). Re-calculation of the Charlson Index from hospital separation codes at the index admission and all admissions in the previous two years combined did not change the estimates substantially. Across the cohort this had the effect of increasing the mean number of comorbidities per patient by 0.5, with the median number of recorded comorbidities increasing from 2.0 to 3.0.

The most commonly recorded co-morbidities at the index admission were diabetes with chronic complications (27% of patients), pulmonary disease (15.6%), renal disease (14.2%), dementia (5.9%), acute myocardial infarction (5.3%), peripheral vascular disease (4.2%), and cerebral vascular disease (3.4%).

Age-specific and gender-specific rates of index admissions for heart failure

The rates of an index admission for heart failure increased consistently with increasing age; and were higher for males than females for all age groups (Table 3). Index admission rates decreased over time; the age-standardised rates for males decreased from 256.7 to 237.7 per 100,000 from 2002–3 to 2006–7 and for women from 235.3 to 217.1/100,000. These trends over time were statistically significant (p =0.0073, adjusted for gender).

Hospital readmissions and deaths

Of the 29,161 patients, 7,415 had a readmission (for any cause) within 28 days; 18,493 had one or more readmissions within one year of the index admission, giving readmission rates of 27% and 73% at 28 days and one year respectively (Table 4). Readmissions attributed to heart failure were 11% and 32% at 28 days and one year respectively (Figure 1). The proportions of readmissions attributed to an exacerbation of heart failure increased with age (Table 5). Heart failure readmission rates at one year increased by age from approximately 25% in the 45–49 year age group to 37% in people aged ≥85 years (data not shown).

All-cause mortality rates were 10% at 28 days and 28% at one year (Table 4). As expected, there were age-related trends, with 28 day and one year mortality increasing from 4.1% and 13.1% in those aged 45–49, up to 14.9% and 41.8% in those aged ≥85 years. Median survival after a heart failure index admission ranged from about 1.5 years in those aged over 85, three years for those aged 80–85, 4 years for those aged 75–79 to even longer for those younger than 75 (Figure 2).

Length of stay

There were strong and statistically significant age-related trends for LOS for the index admission, readmissions for any cause and heart failure readmissions (Table 6). Readmissions for heart failure were longer than readmissions for any cause for the entire cohort (8.3 versus 4.8 days) and for all age groups. In the case of those aged ≥85 years, the mean duration of a heart failure readmission was comparable to that of the index admission (9 versus 9.6 days).

Burden on hospital health services

There were a total of 954,888 hospital bed-days for any cause over the 5 years of this study (Table 7). Of these, 383,646 hospital bed-days were specifically for heart failure, with 321,281 (83.7%) of these occurring in the first year post index admission. Of those in the first year, 106,679 (33.2%) of the bed-days related to subjects aged ≥85 years.

Risk factors for hospital readmission

A Cox regression model indicated that the major risk factors for any readmission were increasing age, increasing Charlson score, and male gender (Table 8).


Discussion

This study used linked administrative data to provide a picture of the typical trajectory of a heart failure patient from the time of initial admission, and to demonstrate the health system impacts of heart failure in New South Wales. The most important observations were the high rates of hospital admission to hospital, the age dependence of incidence rates and the 7-8% reduction in the rates of index admissions noted over the study period. There is substantial morbidity after a diagnosis of heart failure, with consequent health system impacts due to high readmission rates for heart failure, and long durations of hospital stay, particularly in the older age groups. Mortality was high at one year, although many deaths were not attributed to heart failure.

Consistent with other studies [2,5,13], we demonstrated a more than 10 fold increase in index admission rates for those aged ≥65 years compared to those aged 45–64 years. Incidence rates were higher for males than females at all ages, though the longer female life expectancy meant there were more females than males in the cohort in the oldest age groups (≥ 80 years). Age-standardised rates decreased over time in both males and females, a finding consistent with other studies and settings [7,14-16].

Direct comparisons of our estimates with other studies are challenging because of differences in the data sources used and the standardisation applied. Based on 4812 index admissions for heart failure between 2002 and 2005 in Western Australia, Teng et al [7] concluded age-standardised admission rates for heart failure of 249/100,000 and 176/100,000 for men and women respectively, with 41% of admissions in those aged <75 years. We found only 34% of index heart failure admissions were in those aged <75 years, and our age standardised rates were higher at 257 and 235 per 100,000 for males and females respectively in 2002–3, the first year of our analysis. Our standardisation was only for those aged ≥45 years as heart failure before the age of 45 is likely to be due to different aetiology, e.g. congenital heart disease; Teng et al used the population aged ≥20 years for the denominator and a look back period of 10 years to ensure first admission for heart failure, the limitations of our dataset meant we could only apply a two year look back period.

Najafi and colleagues [5] used Australian national data for 2003–4 on all episodes of care for heart failure (both index admissions and readmissions) and age adjustment based on a ‘European’ population standard to derive age-standardised separation rates for heart failure as principal diagnosis of 210 per 100,000 for males and 150 per 100,000 person-years for females. Using similar US hospital discharge data standardised to the 2000 US population, Fang et al [2] reported age-adjusted hospitalisation rates of 390 per 100,000, more than double the Australian estimate. These discrepancies likely reflect the methodological differences in coding procedures, admission policies, and differences in treatment thresholds and practices.

Co-morbidities

The median number of co-morbidities recorded for our cohort was low, regardless of whether the Charlson Index was based on records for the index admission only, or including admissions in the previous two years [17]. Despite likely under-reporting of co-morbidities in the APDC [18], our data showed similar co-morbidity profiles to those reported elsewhere [2,19]. The three most commonly recorded co-morbidities in our data set were diabetes (27%), pulmonary disease (15.4%) and renal failure (14.2%). These co-morbidities are predictors of higher patient treatment costs, particularly in the last six months of life [20,21]. In our study, increasing age, increasing Charlson comorbidity score and male gender were risk factors for hospital readmission.

Hospital readmission and deaths

Overall readmission rates were high. We found readmission rates of 27% at 28 days for any cause (73% at one year) and 11% for admissions due to heart failure (32% at one year) with strong age-related trends. Our estimate of 32% heart failure readmissions at one year is likely to be an underestimate as we only counted readmissions where heart failure was the principal separation code.

Overall mortality rates were 10% at 28 days and 28% at one year, similar to those reported by Teng et al (30-day and one year mortality rates 9.5% and 26.7% respectively) in Western Australia [7] and by Bueno et al in a US Medicare population (30-day mortality rate 10.7% in 2006) [22]. Mortality rates increased consistently with age, and the poor heart failure prognosis was most marked in the older age groups.

Length of stay (LOS)

The mean LOS for the index admission for the entire cohort was 7.8 days, and increased with increasing age, both for the index admission and for readmissions (all cause and for heart failure). Our estimates are higher than some US estimates (mean LOS 4–5 days [1], and 6.4 days in a Medicare population [22]), although comparable to LOS reported in the UK (median LOS 7 days for those aged <75 years and 8 days for those aged ≥75 years [23]). The mean duration of readmissions for heart failure was longer than for any cause and, for most age-groups comparable to the duration of the index admission. This highlights the need to stabilise patients with heart failure before discharge, particularly the oldest patients.

Bed-day usage

Use of acute hospital resources was substantial, with the patients occupying almost 200,000 bed-days per year in NSW over the five year study period. Of these, around 77,000 bed-days per year were attributed to admissions (index or readmissions) where heart failure was the principal separation diagnosis. Had we included readmissions where heart failure was an additional diagnosis and contributory factor, our estimates would have been even higher. Most notable is the impact of the disease on the elderly. The bed-days analysis included 8,370 patients aged ≥85 years. In the first year (index and readmissions), these patients accounted for 106,679 acute hospital bed-days and around 60% of these were for elderly women (66,920/106,679), reflecting the higher proportion of women in the older age groups and the longer LOS for these patients.

Typical patient and typical trajectory

This unique dataset allows us to formulate a “typical” heart failure patient and “typical” trajectory from first hospital admission; these also suggest policy directions for health decision makers.

The typical patient is aged over 75 years, more likely to be female at this age, and with two co-morbidities, although the incidence has been decreasing over the last 5 years. Given that the burden of disease is in the elderly, prevention is the key to controlling this condition. The reduction in CHF may be partly due to the fall in AMI [14], better control of hypertension [24], and adherence to evidence-based guidelines for the management of chronic heart failure [25].

There is a high all cause re-admission rate, with 27% of patients being readmitted within the first 28 days, and 73% readmitted in the first year; on average 40% of these readmissions are due to CHF, and those readmissions are longer (8.3 days) than the initial admission (7.8 days). These figures suggest that chronic disease management plans are important to educate patients about controlling CHF. Community based outreach programs may be nurse-led interventions or involve multidisciplinary teams, with evidence that these heart failure specific management programs can reduce mortality and improve quality of life [26]. However, these multifaceted interventions may not always reduce readmission rates [27], and a large US trial of telemonitoring of heart failure patients failed to show differences in readmissions for any reason or death from any cause with this enhanced patient surveillance [28].

Patients with CHF use 200,000 bed-days per year in a state with a population of around 6 million people; over 80% of these bed days in the first year after the index admission. Failure to adequately stabilise patients before hospital discharge risks early re-admission and, particularly in the elderly, the duration of re-admissions can be long. With hospitals under cost and bed-occupancy pressures, these impacts can be substantial. Intensive specialist clinics and outpatient programs within the first few weeks of first hospital admission may be able to reduce this burden on the hospital system.

Median survival from time of initial diagnosis for CHF is 1.5 years for those aged ≫85 and about 3 years for those aged 80–85. This prognosis is similar to many cancers [29], and highlights the need to address palliative care issues in the management of CHF. Recent studies indicate that medical admissions tend to be concentrated in the last 6 months of life, at least in US settings [20,21], and better palliative care programs and more hospice places may avert these acute admissions.

Limitations and strengths of the study

This study shares the limitations of other data linkage studies, with reliability of study conclusions dependent on the accuracy of the record linkage, the study definitions used and the validity of the coding in the hospital records. A technical assessment of the record linkage quality for this project determined a false positive rate (invalid links) of 0.3% and a false negative rate (missed links) of <0.1%. The administrative data sets used in this study contained no information on the medical management of heart failure, in particular use of drugs such as ACE inhibitors, angiotensin receptor blockers and beta-blockers that have been shown to improve long-term survival.

Examining heart failure presents a particular challenge as it is not a clearly defined disease entity but rather a complex clinical syndrome often following a history of cardiovascular disease or as a complication of diabetes, resulting in ambiguity in its diagnosis and reporting in medical records and hospital separation data. We chose to present readmissions for any cause and those due to heart failure because of the greater reliability of the coding for disease in the primary diagnosis position [30]. We did not refer to the original medical records to confirm a diagnosis of heart failure in the study subjects. However a recent validation study conducted in Western Australia using similar datasets concluded a positive predictive value of 99.5% with a coding of heart failure in the principal diagnostic position [6].

The major strength of this study is that patient-level analyses allowed us to calculate readmission rates, median survival and importantly, quantify the strong age-related trends in incidence of disease, LOS and mortality. Record linkage studies based on the data sets of the CHeReL have no selection biases; analyses are based on records of hospital separations for all residents of NSW in both public and private hospitals.


Conclusions

We found that rates of index admissions for heart failure decreased significantly in both males and females over the study period. Rates of hospital readmission were high and were related to age and the presence of co-morbidities. LOS was longest in the oldest patients and duration of stay similar for both index admissions and readmissions for heart failure. Use of acute hospital resources was substantial, with heart failure patients occupying almost 200,000 bed-days per year in NSW over the five year study period. The strong age-related trends observed have implications for policy planners and decision makers and highlight the importance of stabilising elderly patients before discharge and community-based outreach programs to better manage heart failure and reduce readmissions.


Abbreviations

APDC = Admitted Patient Data Collection; ARIA = Accessibility/Remoteness Index of Australia; CHeReL = Centre for Health Record Linkage; ICD = International Classification of Diseases; LOS = Length of stay; NHMD = National Hospital Morbidity Data; NSW = New South Wales; RBDM = Registry of Births Deaths and Marriages; SEIFA = Socio-Economic Indexes for Areas.


Competing interest

The authors declare that they have no competing interests.


Author contributions

JR, S-AP, JA conceived the study; all authors participated in the study design and interpretation of results; PMcE was responsible for the statistical analyses; JR, DH, JA drafted the manuscript; S-AP, PMcE, KI provided critical review of the manuscript. All the authors read and approved the final manuscript.


Pre-publication history

The pre-publication history for this paper can be accessed here:

http://www.biomedcentral.com/1472-6963/12/103/prepub


Acknowledgments

We thank Daniel Barker for his work in conducting the statistical analyses for the study. We thank the data custodians of the APDC and RBDM for providing the data needed for this study and the CHeReL for providing the data linkage services.

Funding

This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.


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Unroe KT,Greiner MA,Hernandez AF,Whellan DJ,Kaul P,Schulman KA,Peterson ED,Curtis LH,Resource use in the last 6 months of life among Medicare beneficiaries with heart failure, 2000–2007Arch Intern MedYear: 201117119620310.1001/archinternmed.2010.37120937916
Kaul P,McAlister FA,Ezekowitz JA,Bakal JA,Curtis LH,Quan H,Knudtson ML,Armstrong PW,Resource use in the last 6 months of life among patients with heart failure in CanadaArch Intern MedYear: 2011171321121710.1001/archinternmed.2010.36520937918
Bueno H,Ross JS,Wang Y,Chen J,Vidán MT,Normand S-LT,Curtis JP,Drye EE,Lichtman JH,Keenan PS,Kosiborod M,Krumholz HM,Trends in length of stay and short-term outcomes among Medicare patients hospitalised for heart failure, 1993–2006JAMAYear: 2010303212141214710.1001/jama.2010.74820516414
Nicol ED,Fittall B,Roughton M,Cleland JGF,Dargie H,Cowie MR,NHS heart failure survey: a survey of acute heart failure admissions in England, Wales and Northern IrelandHeartYear: 20089417217710.1136/hrt.2007.12410718003672
Sciarretta S,Palano F,Tocci G,Baldini R,Volpe M,Antihypertensive treatment and development of heart failure in hypertension. A bayesian network meta-analysis of studies in patients with hypertension and high cardiovascular riskArch Intern MedYear: 2010 Published online November 8, 2010.. doi:10.1001/archinternmed.2010.427.
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Velez M,Westerfeldt B,Rahko PS,Why it pays for hospitals to initiate a heart failure disease management programDisease Management and Health OutcomesYear: 20081615517310.2165/00115677-200816030-00003
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Stewart S,Prognosis of patients with heart failure compared with common types of cancerHeart Fail MonitYear: 20033879412634878
Grijalva CG,Chung CP,Stein M,Gideon PS,Dyer SM,Mitchel EF,Griffin MR,Computerized definitions showed high positive predictive values for identifying hospitalizations for congestive heart failure and selected infections in Medicaid enrollees with rheumatoid arthritisPharmacoepidemiol Drug SafYear: 20081789089510.1002/pds.162518543352

Figures

[Figure ID: F1]
Figure 1 

Kaplan-Meier curves for time to heart failure readmission and time to any readmission.



[Figure ID: F2]
Figure 2 

Kaplan-Meier curves for time to death for each 5 year age group for patients over 45 year of age.



Tables
[TableWrap ID: T1] Table 1 

Number and percent of index admissions by gender, age group, marital status, diagnosis and ARIA and SEIFA indices


 
2002 – 03*
2002 – 03*
2003-04
2004-05
2005-06
2005-06
2006-07
 
N= 5854
 
N = 5935
N = 5606
N = 5813
N = 5953
  n % n % n % n % n %
Sex
Male
2899
50
2924
49
2804
50
2983
51
2994
50
Female
2955
50
3011
51
2802
50
2830
49
2959
50
Age Group
45-49
68
1.2
61
1.0
54
1.0
88
1.5
81
1.4
50-54
114
1.9
136
2.3
105
1.9
127
2.2
122
2.0
55-59
206
3.5
201
3.4
191
3.4
199
3.4
212
3.6
60-64
302
5.2
349
5.9
302
5.4
317
5.5
324
5.4
65-69
472
8.1
463
7.8
478
8.5
423
7.3
408
6.9
70-74
800
14
760
13
683
12
643
11
662
11
75-79
1085
19
1082
18
1019
18
1054
18
1000
17
80-84
1256
21
1205
20
1209
22
1205
21
1316
22
85+
1551
26
1678
28
1565
28
1757
30
1828
31
Marital Status
Married (including defacto)
2686
46
2755
46
2705
48
2764
48
2797
47
Never married, widowed, divorced, separated
2951
50
2932
49
2692
48
2872
49
3017
51
Unknown
214
3.7
241
4.1
180
3.2
176
3.0
138
2.3
Diagnosis
Hypertensive heart disease (I11, I13)
106
1.8
84
1.4
86
1.5
84
1.4
83
1.4
Congestive heart failure (I50.0)
3918
67
4018
68
3848
69
4151
71
4365
73
Left ventricular failure (I50.1)
1614
28
1633
27
1496
27
1389
24
1283
22
Heart failure, unspecified (I50.9)
216
3.7
200
3.4
176
3.1
189
3.3
222
3.7
ARIA
Highly Accessible
4264
75
4393
76
4164
76
4315
76
4394
76
Accessible
1159
20
1131
20
1022
19
1081
19
1099
19
Moderately Accessible
206
3.6
194
3.4
217
4.0
186
3.3
178
3.1
Remote / Very Remote
72
1.3
65
1.1
52
1.0
72
1.3
76
1.3
SEIFA
Most Disadvantaged (1)
784
14
771
13
666
12
759
13
713
12
2
985
17
1005
17
942
17
965
17
1042
18
3
1308
22
1356
23
1292
23
1326
23
1313
22
4
1220
21
1243
21
1242
22
1264
22
1299
22
Most Advantaged (5) 1526 26 1528 26 1438 26 1452 25 1514 26

* Financial Year (1 July – 30 June).

† N = number of persons with index admissions.

‡ Data not available for all cohort members.


[TableWrap ID: T2] Table 2 

Co-morbidity burden assessed by Charlson Index


Variable
Statistic
2002 – 03*
2003 - 04
2004 - 05
2005 -06
2006 - 07
    (N = 5854) (N = 5935) (N = 5606) (N = 5813) (N = 5953)
Charlson Score
mean (sd)
2.2 (1.5)
2.2 (1.5)
2.5 (1.6)
2.3 (1.5)
2.4 (1.6)
(based on index admission)
median
2.0
2.0
2.0
2.0
2.0
(q1, q3)
(q1, q3)
(1.0, 3.0)
(1.0, 3.0)
(1.0, 3.0)
(1.0, 3.0)
(1.0, 3.0)
Charlson Score
mean (sd)
2.7 (1.8)
2.8 (1.9)
3.0 (2.0)
2.8 (1.9)
2.9 (2.0)
(based on two years history)
median
2.0
2.0
3.0
3.0
3.0
(q1, q3) (q1, q3) (1.0, 4.0) (1.0, 4.0) (1.0, 4.0) (1.0, 4.0) (1.0, 4.0)

* Financial Year (1 July – 30 June).

† N = number of persons with index admissions.

sd = standard deviation; q1,q3 = quartile 1, quartile 3.


[TableWrap ID: T3] Table 3 

Age-specific and age-standardised rates of index admission by gender and financial year


 
Rate of admission (per 100000 persons aged ≥45 years)
       
5 Year Age group Gender 2002 – 03* 2003 – 04 2004 – 05 2005 – 06 2006 – 07
45-49
Male
19.4
16.2
14.7
27.8
20.9
n = 352
Female
9.9
9.7
7.9
8.6
12.1
50-54
Male
33.8
37.0
30.8
40.6
39.1
n = 604
Female
19.1
25.9
17.4
17.2
15.5
55-59
Male
70.8
65.4
64.5
63.1
67.2
n = 1009
Female
38.2
37.3
30.7
33.8
34.6
60-64
Male
132.8
155.7
131.5
137.3
130.8
n = 1594
Female
75.8
79.3
64.5
60.6
61.9
65-69
Male
236.0
226.2
237.9
211.5
190.1
n = 2244
Female
152.1
147.5
139.9
116.1
119.0
70-74
Male
413.8
432.9
391.6
357.5
345.8
n = 3548
Female
311.9
271.6
249.7
247.1
266.8
75-79
Male
691.5
678.1
620.0
686.9
652.6
n = 5240
Female
493.7
486.3
466.2
442.4
420.4
80-84
Male
1165.3
1092.9
1026.5
1058.1
1133.1
n = 6191
Female
886.5
799.5
792.6
729.2
774.5
85+
Male
1950.3
1864.6
1781.1
1871.3
1784.9
n = 8379
Female
1430.5
1602.6
1393.9
1493.9
1496.0
Age Standardised
Male
256.7
252.6
236.1
244.1
237.7
 
 
(247.4, 266.1)
(243.4, 261.7)
(227.4, 244.8)
(235.4, 252.8)
(229.2, 246.2)
Rate
Female
235.3
236.8
215.7
213.2
217.1
(95% CI)   (226.8, 243.8) (228.3, 245.2) (207.8, 223.7) (205.4, 221.1) (209.3, 224.9)

* Financial Year (1 July – 30 June).


[TableWrap ID: T4] Table 4 

Patients with specified events and risk for events within 28 days and 1 year of the index admission


Outcome
At 28 days
Within 1 year
Number of Persons Number of Persons Probability of Outcome Number of Persons Probability of outcome+
Re-admission for any cause
7415
0.27
18493
0.73
Readmission - heart failure*
3007
0.11
7848
0.32
All-cause mortality
2531
0.10
6890
0.28
Readmission or death
9471
0.35
21125
0.79
Readmission HF or death 5302 0.20 12556 0.49

* heart failure or hypertensive heart disease as principal separation code.

derived from Kaplan-Meier curves.


[TableWrap ID: T5] Table 5 

Proportion of readmissions attributed to heart failure within 28 days and one year of the index admission


Age Group
Readmissions within 28 days
Readmissions within 1 year
  Any cause Heart Failure* % of readmissions Any cause Heart Failure* % of readmissions
Whole population
7415
3007
41
18493
7848
40
45 – 49
107
36
34
221
80
36
50 – 54
155
48
31
366
124
34
55 – 59
288
76
36
663
219
33
60 – 64
423
131
31
1012
378
37
65 – 69
598
210
35
1447
575
40
70 – 74
905
321
36
2274
911
40
75 – 79
1297
496
38
3453
1425
41
80 – 84
1565
656
42
3985
1717
43
85+ 2077 1033 50 5072 2419 48

* heart failure or hypertensive heart disease as principal separation code.


[TableWrap ID: T6] Table 6 

Mean length of stay in days for index admission and readmissions


Age group
Number of patients
Mean LOS in days
    Index admission Mean (SD) Readmission for any cause Mean (SD) Heart failure readmission* Mean (SD)
Whole population
29161
7.8 (18.1)
4.8 (12.1)
8.3 (12.1)
45 – 49 years
352
5.9 (6.5)
2.1 (6.0)
5.7 (7.6)
50 – 54
604
6.3 (7.9)
2.7 (5.5)
7.0 (7.2)
55 – 59
1009
5.9 (6.7)
2.4 (6.1)
8.0 (12.1)
60 – 64
1594
6.2 (6.0)
3.3 (7.5)
7.4 (8.3)
65 – 69
2244
6.6 (7.2)
3.6 (8.1)
7.9 (9.4)
70 – 74
3548
6.9 (7.8)
3.9 (9.0)
8.1 (9.8)
75 – 79
5240
7.2 (7.3)
4.7 (11.5)
8.2 (12.3)
80 - 84
6191
7.6 (9.5)
6.0 (12.8)
8.4 (9.5)
85+ 8379 9.6 (31.2) 8.5 (20.4) 9.0 (15.8)

* heart failure or hypertensive heart disease as principal separation code.


[TableWrap ID: T7] Table 7 

Hospital bed-days occupied during study period (2002–2007)


 
 
Number of hospital bed-days
Number of hospital bed-days
 
Age group Gender Number of subjects* N = 29161 Index admissions +  readmissions any cause Index admissions +  readmissions for heart failure
45-49
Male
236
7 799
2 121
Female
  116
3 803
1 117
50-54
Male
396
9 484
3 739
Female
  208
5 587
2 133
55-59
Male
664
24 613
7 489
Female
  345
12 247
3 852
60-64
Male
1 068
36 294
12 262
Female
  526
17 004
5 737
65-69
Male
1 374
48 279
17 634
Female
  870
30 220
10 182
70-74
Male
2 015
65 630
24 480
Female
  1 533
55 206
19 534
75-79
Male
2 830
96 495
36 486
Female
  2 410
84 129
31 738
80-84
Male
2 971
98 375
39 050
Female
  3 220
108 054
44 089
85+
Male
3 050
95 002
46 032
Female
  5 329
156 667
75 971
Total
Male
14 604
481 971
189 293
Female   14 557 472 917 194 353

[TableWrap ID: T8] Table 8 

Cox regression models with time to any readmission as the outcome


Crude
Crude
  Adjusted*
 
Hazard Ratio Hazard Ratio 95% CI Hazard Ratio 95% CI p†
Sex
 
 
 
 
 
 
 
 
 
 
Male
1
 
1
 
 
 
 
     
Female
0.95
0.92, 0.98
0.93
0.89, 0.96
<.0001
         
Age Group
 
 
 
 
 
 
 
 
 
 
< 55
1
1
 
 
 
 
 
     
55-59
1.10
0.99, 1.22
1.03
0.90, 1.18
0.6189
         
60-64
1.07
0.98, 1.18
1.00
0.89, 1.13
0.9971
         
65-69
1.12
1.03, 1.23
1.03
0.92, 1.16
0.5847
         
70-74
1.12
1.03, 1.22
1.05
0.94, 1.17
0.3873
         
75-79
1.21
1.12, 1.31
1.12
1.00, 1.24
0.0437
         
80-84
1.22
1.12, 1.32
1.15
1.03, 1.28
0.0101
         
85+
1.23
1.13, 1.33
1.14
1.02, 1.27
0.0167
         
Charlson Index
1.06
1.06, 1.07
1.07
1.06, 1.09
<0.0001
         
Index Length of stay
1.00
1.00, 1.00
1
1.00, 1.00
0.0006
         
Hypertension
         
No
1
 
1
 
 
 
 
     
Yes
1
0.97, 1.02
0.98
0.95, 1.02
0.4133
         
Depression
         
No
1
 
 
 
 
 
 
 
 
 
Yes 1.01 0.91, 1.12 0.99 0.85, 1.14 0.8582          

* models were adjusted for age, sex, marital status, ARIA category, SEIFA quintile, financial class, hospital type, Charlson Index, length of stay, hypertension, depression.

† P-values and confidence intervals calculated in SAS.



Article Categories:
  • Research Article

Keywords: Heart failure, Hospitalization, Health services research, Australia.

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