© 2002 European Society of Cardiology
Factors influencing the length of hospital stay of patients with heart failure
a Department of Medicine, Faculty of Medical and Health Sciences, University of Auckland Private Bag 92019, Auckland, New Zealand
b Department of Medicine, Sahlgrenska University Hospital/Östra, Göteborg University Goteborg, Sweden
* Corresponding author. Tel.: +64-9-307-4949x7654; fax: +64-9-302-2101 E-mail address: sp.wright{at}auckland.ac.nz
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Abstract |
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 Top Abstract 1. Introduction2. Methods3. Results4. DiscussionReferences |
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Introduction: Heart failure (HF) is characterised by frequent hospital admissions and prolonged length of hospital stay. Admissions for HF have increased over the last decade while length of stay has decreased; the reasons for this change in length of stay are uncertain. This study investigates the effect of patient-related variables, in-hospital progress and complications on length of stay.
Methods: Patients admitted to Auckland Hospital general medical service and randomised into the Auckland Heart Failure Management Programme were included in this study.
Results: One hundred and ninety-seven patients were included in this study. Mean age 73 years, mean left ventricular ejection fraction 32%; 52% had one or more previous HF admissions and 75% were New York Heart Association class IV at admission. Median length of hospital stay was 6 days (IQR 4, 9) which is comparable to the national average from New Zealand admission databases. Longer than average length of stay, defined as >6 days, was associated with the presence of peripheral congestion, duration of treatment with intravenous diuretic, the development of renal impairment, other acute medical problems at admission, iatrogenic complications during hospital stay, and social problems requiring intervention. Factors independently associated with length of stay in the top quartile (>10 days) on logistic regression included the presence of oedema at admission (OR 10.5), change in weight during stay (OR 1.3), duration of treatment with iv diuretic (OR 7.5), the development of renal impairment (OR 9.8), concurrent respiratory problems requiring specific treatment (OR 3.8), and social problems requiring intervention (OR 6.8).
Conclusions: Peripheral congestion, concomitant acute medical problems requiring specific treatment, the development of renal impairment and the presence of social problems were related to a longer than average length of hospital stay. Multivariate models only partly explained variance in hospital stay, suggesting the importance of pre-admission and post-discharge factors, including the healthcare environment, the availability of primary and secondary care resources, and the threshold for hospital admission.
Key Words: Heart failure • Length of stay • Healthcare use • Hospitalisation • Inpatient management
Received November 6, 2001; Revised July 9, 2002; Accepted October 4, 2002
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1. Introduction |
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TopAbstract1. Introduction2. Methods3. Results4. DiscussionReferences |
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Heart failure (HF) is characterised by frequent hospital admissions [1] and prolonged length of stay [2,3]. These hospital admissions contribute to the significant and increasing resource utilisation associated with HF, costing approximately 1.5% of the annual healthcare budget of most Western countries [2–5]. While hospital admissions for HF are increasing [3,6–9], the average length of hospital stay has decreased in many countries over the last decade, including Scotland [3] and the Netherlands [9]. For example, the average length of stay in Scotland has decreased from up to 3 weeks in 1985 to approximately 1 week in 1995 [3]. The shortest hospital stay has been reported from Oregon, USA, where the average length of stay for HF decreased from 5 days in 1991 to 4 days in 1995 [9]. The local healthcare environment may have an important role in determining the threshold for admission and subsequent length of hospital stay.
Possible determinants of length of hospital stay for patients with HF include socio-demographic variables [9,10], medical comorbidity [11,12], disease severity [13], clinical presentation, in-patient treatment, in-hospital progress [14] and the development of iatrogenic complications [16]. Several studies exhibit the importance of concurrent medical diagnoses, including studies from the US [10] and Scotland [11]. The latter study illustrates the particular importance of concurrent stroke, renal failure, atrial fibrillation, chronic lung disease and ischaemic heart disease in prolonging length of hospital stay for HF [12].
Other studies of length of hospital stay for HF focus on inter-institutional variation [14,16] and differences in patient insurance status [9] using large hospital discharge databases. Alternatively, some studies have assessed the effect of specific clinical parameters. Such studies show, for example, that low left ventricular ejection fraction (LVEF) [13], severe renal impairment [17], or specific HF aetiology [15] are associated with longer hospital stay. However, these factors are usually associated with more severe or end-stage HF, when hospital stay maybe prolonged for many reasons.
There are little prospective data on determinants of length of stay in patients with HF, and the reasons for the decrease in length of stay documented in many countries are not well understood. Previous studies performed when length of hospital stay was longer for HF may no longer be relevant. This study examines the determinants of length of hospital stay in a cohort of prospectively-identified HF patients in New Zealand.
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2. Methods |
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TopAbstract1. Introduction2. Methods3. Results4. DiscussionReferences |
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Socio-demographic and clinical characteristics, treatment-related factors and in-hospital progress were examined in relation to length of stay in patients with HF in a single institution in New Zealand.
2.1. Patient population
Patients admitted to the acute general medical wards of Auckland Hospital, New Zealand with either a first diagnosis or an exacerbation of pre-existing HF between 1996 and 1997 were prospectively identified from hospital admission registers. Acute medical care in New Zealand is provided predominantly by state-funded public hospitals. Primary care is provided by general practitioners who are funded by a fee-for-service from patients with a per-patient government-funded subsidy. Auckland Hospital has a capacity of 500 beds and serves a population of approximately 350 000. Measurement of hospital inpatient volumes and the relative resource consumption of different diagnostic groups in New Zealand is performed using the Weighted Inlier Equivalent Separations (WEIS) system. Length of hospital stay in different diagnostic groups are classified as prolonged based on the average length of stay [19].
Patients were identified for inclusion in a randomised, controlled single-centre study of an integrated HF out-patient management programme, the Auckland Heart Failure Management Study [18]. Patients were randomised either to attend a hospital-based HF management clinic or to receive usual care (mainly based in primary care). This intervention did not commence until after discharge from hospital, and thus did not influence length of stay of the index hospital admission.
Detailed data were recorded prospectively from the hospital records of each index hospital admission. Data included social and demographic characteristics; clinical signs and symptoms at admission and discharge; laboratory and cardiac imaging parameters; in-patient social worker consults; and complications (such as acute renal failure secondary to treatment) or concomitant acute medical problems (such as angina or pneumonia) that occurred during the hospital stay and required specific treatment or intervention.
2.2. Statistical analysis
In univariate analyses, Spearman's rank coefficient was used to determine correlation between variables. For continuous variables, non-parametric analyses (Wilcoxon rank sum tests) were utilised to examine differences in patients stratified above or below the median length of hospital stay. For categorical variables, Fisher's exact tests were utilised. All tests were 2-tailed and significant at the 5% level. Length of hospital stay was stratified above and below the median for two reasons. Firstly, length of hospital stay was not normally distributed; secondly, in-patient funding weights in New Zealand hospitals are calculated on whether length of hospital stay is above or below the average for that diagnostic group [19]. Analyses using a variety of iterative multivariate linear regression methods (stepwise, forward and backward selection) were performed with length of hospital stay as a continuous variable. Logistic regression was performed with length of hospital stay stratified above and below the upper quartile. Parsimony and biological plausibility were added to the standard goodness of fit statistics to choose between competing models.
Doses of loop diuretics and angiotensin-converting-enzyme inhibitor medications are expressed in frusemide and enalapril equivalents respectively. The need for social intervention during in-hospital stay was coded if documented referral to social work services was recorded in the hospital records.
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3. Results |
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TopAbstract1. Introduction2. Methods3. Results4. DiscussionReferences |
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3.1. Patient characteristics
One hundred and ninety-seven patients were included in this study (Table 1). The mean age was 73 years (S.D. 10.8) and 60% were male. Forty-five percent of patients had a documented history of prior myocardial infarction, 52% had prior hypertension, 29% diabetes, 19% obstructive airways disease, and 21% a prior stroke. Half the patients had a prior admission for HF and 17% had 3 or more previous HF admissions. Three quarters of the patients were classified as being New York Heart Association (NYHA) functional class IV on admission. The median duration of symptoms prior to admission was 7 days (IQR 2, 21). Patients were on a median of 5 medications at admission (IQR 3, 12) and 6 medications at discharge (IQR 5, 8).
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The average LVEF was 32% (S.D. 13). The mean serum sodium, potassium, albumin and haemoglobin values at admission were within the normal range for our laboratory (Table 1). However, renal function was impaired: mean serum creatinine was 0.13 mmol/l (normal range 0.05–0.12 mmol/l); the average creatinine clearance for the cohort was 48.9 ml/min (S.D. 24, normal range 90–140 ml/min).
3.2. Length of hospital stay
The median length of stay was 6 days (IQR 4, 9). Thirty-four patients (17%) had a hospital stay in the upper quartile (9 days or longer), and 10 patients above the 95th centile (21 days or longer). There was no difference in length of hospital stay between patients admitted with HF for the first time and those with one or more previous admissions. All patients were managed on general medical wards and none required transfer to the intensive care unit (ICU). Patients were treated with intravenous diuretic for a median of 1 day (IQR 0, 3), with the maximum at 33 days. During their hospital stay, patients’ weight, heart rate and blood pressures decreased significantly (Table 2). The mean decrease in weight was 2.6 kg (S.D. 3.8). The mean increase in serum creatinine was 0.013 mmol/l (S.D. 0.036). Doses of frusemide and ACE-inhibitor were significantly increased from admission to discharge.
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3.3. Concomitant acute medical conditions and in-hospital complications
Many patients had a complication or concomitant acute medical condition requiring specific treatment during their hospital stay (Table 3). Approximately 1 in 10 patients had concomitant renal problems, such as the development of acute renal failure or the exacerbation of pre-existing renal impairment. Cardiac conditions were common (38%), particularly angina, myocardial infarction and arrhythmias. Approximately 20% of the cohort had concomitant acute respiratory diagnoses, mostly pneumonia or exacerbation of chronic obstructive airways disease. Other concurrent medical problems included infections such as sepsis and cellulitis; gastrointestinal complaints including abdominal pain, nausea, vomiting, and acute peptic ulceration; neurological problems including delirium, headache and seizures; diabetes needing monitoring, intervention and education; musculoskeletal complaints including back pain and acute gout; anemia requiring transfusion; and metabolic abnormalities requiring further diagnostic tests. Eighteen patients (9.1%) had a longer than average hospital stay due to the need to wait for inpatient hospital consultations and investigations for various co-existing conditions.
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Twelve patients had iatrogenic complications prolonging their stay (Table 3): 5 had drug side effects and 2 had complications from procedures. Thirty-two patients (16%) had social problems requiring intervention, including issues of poor mobility and independence, the need for home help or other home-based social interventions, or problems with language needing medical translators.
3.4. Predictors of length of hospital stay: univariate analysis
3.4.1. Clinical factors
Length of stay greater than 6 days was associated with the presence at hospital admission of the symptoms of peripheral oedema, chest pain, or fatigue; the clinical findings of elevated jugular venous pressure or a third heart sound; and weight increase during hospital stay (Table 4). Longer than average length of hospital stay was also associated with the number of patients’ medications at admission and at discharge, diuretic dose at admission and at discharge, change in diuretic dose during hospital stay, number of days treated with intravenous diuretic, and the development of complications or other acute medical problems. Factors not associated with length of hospital stay greater than 6 days included age, gender, ethnicity, the number of previous admissions with HF, duration of symptoms prior to admission, NYHA functional class at admission, the presence at admission of rales, orthopnea or paroxysmal nocturnal dyspnoea.
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There was no difference in length of hospital stay between incident and prevalent cases; both had a median length of stay of 6 days (P=0.24). Thus incident and prevalent cases were combined for analysis.
3.4.2. Laboratory parameters and indices of LV function
Serum albumin, serum sodium at admission and peak creatinine were also associated with length of stay greater than 6 days. Radiographic cardio-thoracic ratio, echocardiographic LVEF and left ventricular end-diastolic and end-systolic volumes were not associated with longer than average length of stay.
3.5. Predictors of length of stay: multivariate analysis
Variables relating to the clinical status at admission and in-hospital progress were modeled separately using logistic regression methods. Admission variables associated with longer than average hospital stay (
6 days) included the presence of peripheral oedema, living alone at home, oral diuretic dose at admission, and cardio-thoracic ratio (Table 5). This model explained 31% of the variance in length of hospital stay.
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In-hospital progress variables associated with longer than average length of stay (
6 days) included duration of treatment with intravenous diuretic, the development of renal or non-renal iatrogenic complications, change in weight, and comorbid respiratory problems requiring specific treatment. The number of days of treatment with intravenous diuretic was strongly associated with length of hospital stay, partial r2 0.32 (P=0.001). This model explained 55% of the variance in length of hospital stay. Mulivariate logistic regression was performed in order to investigate independent predictors of hospital stay longer than the top quartile of 10 days. Odds ratios are shown in Table 6. Admission variables associated with length of hospital stay of more than 10 days included the presence of oedema at admission. In-hospital progress variables associated with length of stay >10 days included duration of treatment with intravenous diuretic; change in weight during stay; the development of renal impairment; concurrent acute respiratory conditions requiring medical treatment (such as pneumonia or exacerbation of existing airways disease); and social problems requiring in-hospital assessment.
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3.6. Charlson comorbidity index
A simple model for predicting length of stay from admission and in-hospital progress data would be useful for administrative purposes, including casemix analysis and cost calculations. The Charlson comorbidity index [20] is one such score, using patient age, ICD coding at discharge and weighting for specific conditions including ventricular arrhythmias, shock, malignancies, and the need for care in the ICU. However, the patients in this study were managed on general medical wards and none required ICU care. In this cohort of elderly patients with exacerbations of chronic HF who survived to hospital discharge, shock did not occur, metastatic malignancy was an exclusion criterion for the study, and ventricular arrhythmias were uncommon (in contrast to atrial arrhythmias which were common). Hence, patient age was the main component of Charlson scores in this study. The mean Charlson score for this cohort was 2.4 (S.D. 1.32; range 0.5–9). The Charlson index was not predictive of length of stay in this cohort and correlated poorly to length of stay with a r value of 0.08 (P=0.26).
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4. Discussion |
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TopAbstract1. Introduction2. Methods3. Results4. DiscussionReferences |
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This study investigates the determinants of length of hospital stay in a cohort of patients representative of HF patients admitted to the acute general medical service in a general hospital setting. Commonly, these patients are elderly, with multiple comorbidities and are receiving multiple medications. Several clinical variables were associated with longer than average hospital stay (> median of 6 days). Hospital admission variables included the presence of co-existing medical problems and symptoms of peripheral congestion. Factors relating to in-hospital progress included the duration of treatment with intravenous diuretic; change in weight during hospital stay; the development of renal and non-renal iatrogenic complications; concomitant respiratory conditions requiring specific treatment; and social problems requiring in-patient assessment. Stratifying length of hospital stay at the top quartile reinforced the importance of these clinical variables, particularly concurrent social and medical comorbidity. Many studies of length of hospital stay predict duration of stay from laboratory parameters or other quantifiable variables [13,15,17]. In this study, laboratory and echocardiographic variables were not associated with length of hospital stay.
Duration of treatment with intravenous diuretic is a surrogate measure of disease severity and the degree of peripheral congestion, reflecting the need for prolonged hospitalisation and treatment. Patients with hospital stays less than 6 days lost a mean of 1.2 kg compared with a loss of 3.6 kg for those who stayed longer (P=0.001), and change of weight was independently associated with length of hospital stay. Peripheral congestion is often resistant to the initial in-patient treatment of HF. The effect of peripheral congestion as opposed to pulmonary congestion on the prolongation of length of hospital stay in HF has not previously been documented.
The occurrence of concomitant acute respiratory problems requiring treatment, and the development of renal failure or iatrogenic complications were also independently associated with longer than average length of hospital stay as shown in previous studies [8,12,15]. Administrative scores used to calculate casemix and in-hospital costs should recognise the importance of peripheral congestion at admission, social problems and concurrent medical problems requiring specific treatment.
Social ‘complications’ and living alone were also independently associated with longer than average hospital stay in this cohort of patients with HF. The burden of social problems on healthcare services is not easily documented as social problems may not be consistently recorded in hospital case records or captured using ICD codes and may have been under-estimated. The direct effect of patients’ social environment on length of hospital stay, while clinically self-evident and an obvious feature of chronic disease has not been previously reported. The importance of the social and medical comorbidities in this cohort of patients with HF may suggest that length of stay in HF may not be further modifiable in our institution by adjusting in-patient treatment strategies. It may be more appropriate for future interventions to target social problems affecting readiness for discharge such as targeted assessment of the home situation, social support, patient mobility, and independence issues.
The multivariate models in this study explained only 30–50% of the variance in hospital stay of patients with HF. Other factors more difficult to measure are likely to have important effects on hospital stay. Examples may include compliance, mobility, delay in awaiting rest home placement, the use of inappropriate medications and factors relating to the healthcare environment in a broader sense. It is also likely that the impact of social needs of patients with HF and the role of comorbid medical conditions on hospital stay were under-estimated in this study. Length of hospital stay in HF is affected by many factors, which in part reflect the clinical spectrum of HF presenting to hospital, the admission threshold, the importance of concomitant medical problems and the consequences of HF therapy (Fig. 1).
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The functioning of the greater healthcare environment is also likely to be important, including healthcare funding, access to hospital and primary care, and the socio-cultural environment of each patient. The severity of HF symptoms in patients admitted to hospital depends on an institution's admission ‘threshold’. In New Zealand, this is affected by the evaluating primary care practitioner who has a gate-keeping role, and by hospital bed availability. Lastly, the discharge environment will impact on length of stay. The assessment of patient readiness for discharge encompasses the presence of community supports and access to primary care as well as a patient's social situation and medical status (Fig. 1). The availability of community-based social support may offset any increased risk of early readmission if patients with medical and social comorbidity are discharged too early. In sum, variables influencing length of hospital stay in HF include a broad range of societal, economic and political factors outside the scope of this study, which is confined to the impact of clinical and patient factors.
The optimal duration of hospital stay for treatment of an exacerbation of chronic HF remains unknown. The median length of hospital stay in this study, although comparable to other countries [3], is still considerably longer than in Oregon, where the average length of stay in HF is 4 days [9]. It is possible that patients admitted in Oregon have milder HF or the threshold for hospital admission is lower leading to shorter hospital stay.
This study is confined to the examination of length of hospital stay of patients who survived until discharge and were recruited into an outpatient HF management programme. Patients who died during their hospital stay were not included in this study. Other studies have shown that patients who die in hospital with HF have a longer mean stay than those who survive to discharge [21]. The factors affecting the length of stay of patients with HF who die in hospital may differ from factors that have been shown to be important in this study. This study also excluded patients who refused informed consent (16% of patients approached) or who fulfilled exclusion criteria specified in the Auckland Heart Failure Management Study (43% of patients approached).
The substantial burden of HF in terms of healthcare resource consumption includes hospital readmissions and primary care consultations, two important components not investigated in this study. Future studies of the impact of HF on healthcare systems must include both primary and secondary care in order to assess the total burden of HF. Likewise, strategies for reducing hospital use in HF must be developed and assessed with the entire framework of healthcare services in mind, including the phenomenon of readmissions and the role of primary care.
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Acknowledgements |
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The Auckland Heart Failure Management Study was funded by a Project Grant from the National Heart Foundation of New Zealand and an unrestricted educational grant from Merck Sharp and Dohme (NZ) Ltd. RND was the recipient of the NZ Heart Foundation BNZ Senior Fellowship. We acknowledge the involvement of participating Auckland general practitioners [19].
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