Roles of Nonclinical and Clinical Data in Prediction of 30-Day Rehospitalization or Death Among Heart Failure Patients

Huynh, Quan; Saito, Makoto; Blizzard, Christopher Leigh; Eskandari, Mehdi; Johnson, Ben; Adabi, Golsa; Hawson, Joshua; Negishi, Kazuaki; Marwick, Thomas H.; Depasquale, C; Leung, Dominic Y.; Hare, James L.; Stanton, Tony

doi:10.1016/j.cardfail.2015.02.002

Cited by 55 publications

(53 citation statements)

References 31 publications

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“…They are comparable to those reported in a similar study in the UK (18%) [11]. The risks of mortality following heart failure admission and discharge are broadly similar to those reported previously in Australia [4, 5, 7, 17], and internationally [10–12], allowing for differences in methodology, underlying populations and/or changes over time in survival for heart failure. That both sociodemographic factors and clinical factors are independently associated with the outcomes is a consistent finding in previous studies in Australia [4, 5, 7, 17, 18], and elsewhere [10–12, 34, 35].…”

Section: Discussionsupporting

confidence: 87%

“…Although treatment for heart failure has improved and both mortality and hospital rates have been declining [2–5], the annual rate of hospitalisation for this condition remains relatively high; it was the primary reason for admission to hospital in over 47 thousand admissions in Australia in 2013–14 [6]. Of particular concern is the high hospital readmission rate, with around one in every four or five patients admitted to hospital with a heart failure diagnosis being readmitted within one-month of discharge—three-quarters within one year [5, 7]. Mortality rates following admission to hospital for heart failure are also high, although possibly declining over time [4], with around one in ten dying within one month of admission for heart failure and one quarter within a year [4, 5].…”

Section: Introductionmentioning

confidence: 99%

“…Moreover, to understand hospital variation in heart failure outcomes requires a quantitative understanding of the contributions of variation at both the hospital and patient level. A recent Australian report has investigated the distribution of these performance measures according to hospital, risk adjusting for patient age, sex and comorbidities, with an emphasis on those hospitals which are “outliers” [14]; and other Australian studies have examined patient-level risk factors derived from linked hospital records [5, 7, 11, 17, 18]. However, there remains a lack of large-scale quantitative data that quantify both patient and hospital-level variation in post-discharge heart failure outcomes, and associations with patient characteristics.…”

Section: Introductionmentioning

confidence: 99%

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Variation in readmission and mortality following hospitalisation with a diagnosis of heart failure: prospective cohort study using linked data

Korda

Day

et al. 2017

BMC Health Serv Res

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BackgroundHospitalisation for heart failure is common and post-discharge outcomes, including readmission and mortality, are often poor and are poorly understood. The purpose of this study was to examine patient- and hospital-level variation in the risk of 30-day unplanned readmission and mortality following discharge from hospital with a diagnosis of heart failure.MethodsProspective cohort study using data from the Sax Institute’s 45 and Up Study, linking baseline survey (Jan 2006-April 2009) to hospital and mortality data (to Dec 2011). Primary outcomes in those admitted to hospital with heart failure included unplanned readmission, mortality and combined unplanned readmission/mortality, within 30 days of discharge. Multilevel models quantified the variation in outcomes between hospitals and examined associations with patient- and hospital-level characteristics.ResultsThere were 5074 participants with a heart failure admission discharged from 251 hospitals; 1052 (21%) had unplanned readmissions, 186 (3.7%) died, and 1146 (23%) had either/both outcomes within 30 days of discharge. Crude outcomes varied across hospitals, but between-hospital variation explained little of the total variation in outcomes (intraclass correlation coefficients (ICC) after inclusion of patient factors: 30-day unplanned readmission ICC = 0.0125 (p = 0.24); death ICC = 0.0000 (p > 0.99); unplanned readmission/death ICC = 0.0266 (p = 0.07)). Patient characteristics associated with a higher risk of unplanned readmission included: being male (male vs female, adjusted odds ratio (aOR) = 1.18, 95% CI: 1.00–1.37); prior hospitalisation for cardiovascular disease (aOR = 1.44, 1.08–1.91) and for anemia (aOR = 1.36, 1.14–1.63); comorbidities at admission (severe vs none: aOR = 1.26, 1.03–1.54); lower body-mass-index (obese vs normal weight: aOR = 0.77, 0.63–0.94); and lower social interaction scores. Similarly, risk of 30-day mortality was associated with patient- rather than hospital-level factors, in particular age (≥85y vs 45–< 75y: aOR = 3.23, 1.93–5.41) and comorbidity (severe vs none: aOR = 2.68, 1.82–3.94).ConclusionsThe issue of high readmission and mortality rates in people with heart failure appear to be system-wide, with the variation in these outcomes essentially attributable to variation between patients rather than hospitals. The findings suggest that there are limitations in using these outcomes as hospital performance measures in this patient population and support the need for patient-centred strategies to optimise heart failure management and outcomes.Electronic supplementary materialThe online version of this article (doi:10.1186/s12913-017-2152-0) contains supplementary material, which is available to authorized users.

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Section: Discussionsupporting

confidence: 87%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Variation in readmission and mortality following hospitalisation with a diagnosis of heart failure: prospective cohort study using linked data

Korda

Day

et al. 2017

BMC Health Serv Res

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“…, 32 We had an equal distribution among men and women, similar to other national and international figures 9. Among the predictors of outcomes, age and female gender always predicted outcomes.…”

Section: Discussionsupporting

confidence: 72%

Outcomes following heart failure hospitalization in a regional Australian setting between 2005 and 2014

et al. 2017

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AimsThe aim of the current study is to examine 10 year trends in mortality and readmission following heart failure (HF) hospitalization in metropolitan and regional Australian settings.Methods and resultsWe identified all index HF hospitalizations in the Hunter New England region from 2005 to 2014, using a 10 year ‘look back’ period. The primary endpoint was a composite of all‐cause mortality or all‐cause readmission at 1 year. Secondary endpoints included all‐cause mortality, all‐cause readmission, and HF readmission at 30 days and 1 year. We used logistic regression to explore the predictors of the composite outcome of either all‐cause death or readmission at 1 year. There were 12 114 patients admitted with a first episode of HF between 2005 and 2014, followed up until death or the end of 2015. The mean age was 78 ± 12 years and 49% (n = 5906) were male. A total of 4831 (40%) resided in regional areas and the remainder in metropolitan areas. One hundred sixty‐eight patients (1.4%) were Aboriginal. Approximately 69% of patients had either died or been readmitted for any cause within 12 months of their index event. The 30 day and 1 year all‐cause mortality rates were 13% and 32%, respectively, with no change in the trend over the study period. Age, socio‐economic disadvantage, ischaemic heart disease, renal failure, and chronic lower respiratory disease were predictors of the primary endpoint.ConclusionsHeart failure hospitalizations are followed by high rates of death or readmission. There was no change in this composite endpoint over the 10 year study period.

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“…28 As detailed above, an elevated cTn level has been shown to predict mortality and readmissions and this predictive value has been retained as a significant variable in multiple models predicting death and readmission. 29,30 Patients presenting with acute cardiogenic pulmonary oedema without MI who have a cTnT level >0.1 ng/ml are associated with a higher long-term mortality risk. 31 Overall, the evidence overwhelmingly demonstrates that an elevated cTn level in patients with AHF predicts increased mortality as well as increased risk of readmission and correlation with more severe cardiac dysfunction.…”

Section: Prognostic Value Of Troponin With Conventional Assaysmentioning

confidence: 99%

Role of Cardiac Troponin Levels in Acute Heart Failure

Wettersten

Maisel

2015

Cardiac Failure Review

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Cardiac troponin (cTn) is the primary biomarker for the diagnosis of myocardial necrosis in an acute coronary syndrome (ACS). cTn levels can also be elevated in many other conditions, including heart failure, with significant prognostic value. An elevated cTn level can be found in both acute and chronic heart failure and its presence is believed to be due to multiple different pathophysiological processes. In acute decompensated heart failure (AHF), an elevated cTn level has been repeatedly shown to correlate with increased short- and long-term mortality and, to a lesser extent, readmission rates. These associations have been demonstrated with both I and T isoforms of cTn, as well as when troponin is measured with conventional assays or new high-sense assays. In multimarker models, cTn has repeatedly been found to be an independent predictive variable enhancing prognostic ability of the model. cTn is therefore an important biomarker for prognosis in AHF.

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Roles of Nonclinical and Clinical Data in Prediction of 30-Day Rehospitalization or Death Among Heart Failure Patients

Cited by 55 publications

References 31 publications

Variation in readmission and mortality following hospitalisation with a diagnosis of heart failure: prospective cohort study using linked data

Variation in readmission and mortality following hospitalisation with a diagnosis of heart failure: prospective cohort study using linked data

Outcomes following heart failure hospitalization in a regional Australian setting between 2005 and 2014

Role of Cardiac Troponin Levels in Acute Heart Failure

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