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

Impact of mental disorders on unplanned readmissions for congestive heart failure patients: a population-level study

AIMS

Reducing preventable hospitalization for congestive heart failure (CHF) patients is a challenge for health systems worldwide. CHF patients who also have a recent or ongoing mental disorder may have worse health outcomes compared with CHF patients with no mental disorders. This study examined the impact of mental disorders on 28 day unplanned readmissions of CHF patients.

METHODS AND RESULTS

This retrospective cohort study used population-level linked public and private hospitalization and death data of adults aged ≥18 years who had a CHF admission in New South Wales, Australia, between 1 January 2014 and 31 December 2020. Individuals' mental disorder diagnosis and Charlson comorbidity and hospital frailty index scores were derived from admission records. Competing risk and cause-specific risk analyses were conducted to examine the impact of having a mental disorder diagnosis on all-cause hospital readmission. Of the 65 861 adults with index CHF admission discharged alive (mean age: 78.6 ± 12.1; 48% female), 19.2% (12 675) had at least one unplanned readmission within 28 days following discharge. Adults with CHF with a mental disorder diagnosis within 12 months had a higher risk of 28 day all-cause unplanned readmission [hazard ratio (HR): 1.21, 95% confidence interval (CI): 1.15-1.27, P-value < 0.001], particularly those with anxiety disorder (HR: 1.49, 95% CI: 1.35-1.65, P-value < 0.001). CHF patients aged ≥85 years (HR: 1.19, 95% CI: 1.11-1.28), having ≥3 other comorbidities (HR: 1.35, 95% CI: 1.25-1.46), and having an intermediate (HR: 1.34, 95% CI: 1.28-1.40) or high (HR: 1.37, 95% CI: 1.27-1.47) frailty score on admission had a higher risk of unplanned readmission. CHF patients with a mental disorder who have ≥3 other comorbidities and an intermediate frailty score had the highest probability of unplanned readmission (29.84%, 95% CI: 24.68-35.73%) after considering other patient-level factors and competing events.

CONCLUSIONS

CHF patients who had a mental disorder diagnosis in the past 12 months are more likely to be readmitted compared with those without a mental disorder diagnosis. CHF patients with frailty and a mental disorder have the highest probability of readmission. Addressing mental health care services in CHF patient's discharge plan could potentially assist reduce unplanned readmissions.

Association of living alone with clinical outcomes in patients with heart failure: A systematic review and meta-analysis

Living alone is an objective sign of social isolation. It is uncertain whether living alone worsens clinical outcomes in heart failure (HF) patients. We aimed to assess how living alone affected clinical outcomes in individuals with HF. We searched the electronic databases of PubMed, Embase, and Cochrane from 1990 to April 2022 for studies comparing living alone with HF. A random-effects model with inverse variance was used to pool adjusted hazard ratios (HRs) and 95% confidence intervals (CIs). Seven studies were deemed to meet the standards. In patients with HF, compared with living with others, living alone was associated with an elevated risk of any hospitalization at the 30-day (HR: 1.78, 95% CI: 1.09-2.89), 90-day (HR: 1.24, 95% CI: 1.02-1.51), or ≥1-year (HR: 1.14, 95% CI: 1.04-1.26) follow-up periods. HF patients living alone also had a greater risk of any hospitalization or death at the 30-day (HR: 1.56, 95% CI: 1.15-2.11), 90-day (HR: 1.26, 95% CI: 1.05-1.50), and ≥1-year (HR: 1.18, 95% CI: 1.09-1.28) follow-up periods. However, patients living alone had no increased risk of all-cause death at the 30-day (HR: 1.0, 95% CI: 0.19-5.36), 90-day (HR: 0.46, 95% CI: 0.03-7.42), or ≥ 1-year (HR: 1.10, 95% CI: 0.73-1.67) follow-up periods. In comparison to living with others, living alone was associated with an increased risk of any hospitalization but not all-cause death in HF patients.

Global Comparison of Readmission Rates for Patients With Heart Failure

BACKGROUND

Heart failure (HF) readmission rates are low in some jurisdictions. However, international comparisons are lacking and could serve as a foundation for identifying regional patient management strategies that could be shared to improve outcomes.

OBJECTIVES

This study sought to summarize 30-day and 1-year all-cause readmission and mortality rates of hospitalized HF patients across countries and to explore potential differences in rates globally.

METHODS

We performed a systematic review and meta-analysis using MEDLINE, Embase, and CENTRAL for observational reports on hospitalized adult HF patients at risk for readmission or mortality published between January 2010 and March 2021. We conducted a meta-analysis of proportions using a random-effects model, and sources of heterogeneity were evaluated with meta-regression.

RESULTS

In total, 24 papers reporting on 30-day and 23 papers on 1-year readmission were included. Of the 1.5 million individuals at risk, 13.2% (95% CI: 10.5%-16.1%) were readmitted within 30 days and 35.7% (95% CI: 27.1%-44.9%) within 1 year. A total of 33 papers reported on 30-day and 45 papers on 1-year mortality. Of the 1.5 million individuals hospitalized for HF, 7.6% (95% CI: 6.1%-9.3%) died within 30 days and 23.3% (95% CI: 20.8%-25.9%) died within 1 year. Substantial variation in risk across countries was unexplained by countries' gross domestic product, proportion of gross domestic product spent on health care, and Gini coefficient.

CONCLUSIONS

Globally, hospitalized HF patients exhibit high rates of readmission and mortality, and the variability in readmission rates was not explained by health care expenditure, risk of mortality, or comorbidities.

Clinical Perspectives on Cardiac Rehabilitation After Heart Failure in Elderly Patients with Frailty: A Narrative Review

The purpose of this narrative review is to examine rehabilitation modalities for patients with heart failure and Frailty who require comprehensive intervention. Ischemic heart disease is the leading cause of death worldwide, accounting for 16% of global mortality. Due to population growing and aging, the total number of heart failure patients continues to rise, a condition known as the heart failure pandemic. Furthermore, frailty has been associated with an increased risk for heart failure and increased morbidity and mortality. The 2021 update of the 2017 ACC expert consensus decision pathway for optimization of HF treatment has become more concerning, citing frailty as one of the 10 most important issues associated with heart failure with reduced ejection fraction (HFrEF). Frailty and heart failure share common pathological mechanisms and are associated with poor clinical outcomes. Most studies of frailty in patients with heart failure primarily focus on physical frailty, and associations between psycho-psychological and social factors such as cognitive dysfunction and social isolation have also been reported. These results suggest that a more comprehensive assessment of frailty is important to determine the risk in patients with heart failure. Therefore, mechanisms of the three domains, including not only physical frailty but also cognitive, psychological, spiritual, and social aspects, should be understood. In addition to interventions in these three domains, nutritional and pharmacological interventions are also important and require tailor-made interventions for the widely varied conditions associated with heart failure and frailty. Although several studies have shown a relationship between frailty and prognosis in patients with heart failure, interventions to improve the prognosis have not yet been established. Further information is needed on frailty intervention by a multidisciplinary team to improve the prognosis.

Application of a risk-guided strategy to secondary prevention of coronary heart disease: analysis from a state-wide data linkage in Queensland, Australia

OBJECTIVE

This study sought whether higher risk patients with coronary heart disease (CHD) benefit more from intensive disease management.

DESIGN

Longitudinal cohort study.

SETTING

State-wide public hospitals (Queensland, Australia).

PARTICIPANTS

This longitudinal study included 20 426 patients hospitalised in 2010 with CHD as the principal diagnosis. Patients were followed-up for 5 years.

PRIMARY AND SECONDARY OUTCOMES AND MEASURES

The primary outcome was days alive and out of hospital (DAOH) within 5 years of hospital discharge. Secondary outcomes included all-cause readmission and all-cause mortality. A previously developed and validated risk score (PEGASUS-TIMI54) was used to estimate the risk of secondary events. Data on sociodemography, comorbidity, interventions and medications were also collected.

RESULTS

High-risk patients (n=6573, risk score ≥6) had fewer DAOH (∆=-142 days (95% CI: -152 to -131)), and were more likely to readmit or die (all p<0.001) than their low-risk counterparts (n=13 367, risk score <6). Compared with patients who were never prescribed a medication, those who consumed maximal dose of betablockers (∆=39 days (95% CI: 11 to 67)), angiotensin-converting enzyme inhibitors/angiotensin receptor blockers (∆=74 days (95% CI: 49 to 99)) or statins (∆=109 days (95% CI: 90 to 128)) had significantly greater DAOH. Patients who received percutaneous coronary intervention (∆=99 days (95% CI: 81 to 116)) or coronary artery bypass grafting (∆=120 days (95% CI: 92 to 148)) also had significantly greater DAOH than those who did not. The effect sizes of these therapies were significantly greater in high-risk patients, compared with low-risk patients (interaction p<0.001). Analysis of secondary outcomes also found significant interaction between both medical and interventional therapies with readmission and death, implicating greater benefits for high-risk patients.

CONCLUSIONS

CHD patients can be effectively risk-stratified, and use of this information for a risk-guided strategy to prioritise high-risk patients may maximise benefits from additional resources spent on intensive disease management.

Patient characteristics, short-term and long-term outcomes after incident heart failure admissions in a regional Australian setting

AIMS

This study aims to (1) define the characteristics of patients with a first admission for heart failure (HF), stratified by type (reduced (HFrEF) vs preserved (HFpEF) ejection fraction) in a regional Australian setting; (2) compare the outcomes in terms of mortality and rehospitalisation and (3) assess adherence to the treatment guidelines.

METHODS

We identified all index hospitalisations with HF to John Hunter Hospital and Tamworth Rural Referral Hospital in the Hunter New England Local Health District over a 12 months. We used the recent Australian HF guidelines to classify HFrEF and HFpEF and assess adherence to guideline-directed therapy. The primary outcome of the study was to compare short-term (1 year) and long-term all-cause mortality and the composite of all-cause hospitalisation or all-cause mortality of patients with HFrEF and HFpEF.

RESULTS

There were 664 patients who had an index HF admission to John Hunter and Tamworth hospitals in 2014. The median age was 80 years, 47% were female and 22 (3%) were Aboriginal. In terms of HF type, 29% had HFrEF, 37% had HFpEF, while the remainder (34%) did not have an echocardiogram within 1 year of admission and could not be classified. The median follow-up was 3.3 years. HFrEF patients were predominantly male (64%) and in 48% the aetiology was ischaemic heart disease. The 1-year all-cause mortality was 23% in HFpEF subgroup and 29% in HFrEF subgroup (p=0.15). Five-year mortality was 61% in HFpEF and HFrEF patients. Of the HFrEF patients, only 61% were on renin-angiotensin-aldosterone blockers, 74% were on β-blockers and 39% were on aldosterone antagonist.

CONCLUSION

HF patients are elderly and about evenly split between HFrEF and HFpEF. In this regional cohort, both HF types are associated with similar 1-year and 5-year mortality following incident HF hospitalisation. Echocardiography and guideline-directed therapies were underused.

Socio-economic status and 1 year mortality among patients hospitalized for heart failure in China

AIMS

This study explored the association between socio-economic status (SES) and mortality among patients hospitalized for heart failure (HF) in China.

METHODS AND RESULTS

We used data from the China Patient-centred Evaluative Assessment of Cardiac Events-Prospective Heart Failure Study (China PEACE 5p-HF Study), which enrolled patients hospitalized primarily for HF from 52 hospitals between 2016 and 2018. SES was measured using the income, employment status, educational attainment, and partner status. Individual socio-economic risk factor (SERF) scores were assigned based on the number of coexisting SERFs, including low income, unemployed status, low education, and unpartnered status. We assessed the effects of SES on 1 year all-cause mortality using Cox models. We used the Harrell c statistic to investigate whether SES added incremental prognostic information for mortality prediction. A total of 4725 patients were included in the analysis. The median (interquartile range) age was 67 (57-76) years; 37.6% were women. In risk-adjusted analyses, patients with low/middle income [low income: hazard ratio (HR) 1.61, 95% confidence interval (CI) 1.21-2.14; middle income: HR 1.32, 95% CI 1.00-1.74], unemployment status (HR 1.43, 95% CI 1.10-1.86), low education (HR 1.25, 95% CI 1.03-1.53), and unpartnered status (HR 1.22, 95% CI 1.03-1.46) had a higher risk of death than patients with high income, who were employed, who had a high education level, and who had a partner, respectively. Compared with the patients without SERFs, those with 1, 2, 3, and 4 SERFs had 1.52-, 2.01-, 2.45-, and 3.20-fold increased risk of death, respectively. The addition of SES to fully adjusted model improved the mortality prediction, with increments in c statistic of 0.01 (P < 0.01).

CONCLUSIONS

In a national Chinese cohort of patients hospitalized for HF, low income, unemployment status, low education, and unpartnered status were all associated with a higher risk of death 1 year following discharge. In addition, incorporating SES into a clinical-based model could better identify patients at risk for death. Tailored clinical interventions are needed to mitigate the excess risk experienced by those socio-economic deprived HF patients.

Impact of Social Frailty in Hospitalized Elderly Patients With Heart Failure: A FRAGILE-HF Registry Subanalysis

Background

Frailty is conceptualized as an accumulation of deficits in multiple areas and is strongly associated with the prognosis of heart failure (HF). However, the social domain of frailty is less well investigated. We prospectively evaluated the clinical characteristics and prognostic impact of social frailty (SF) in elderly patients with HF.

Methods and Results

FRAGILE‐HF (prevalence and prognostic value of physical and social frailty in geriatric patients hospitalized for heart failure) is a multicenter, prospective cohort study focusing on patients hospitalized for HF and aged ≥65 years. We defined SF by Makizako’s 5 items, which have been validated as associated with future disability. The primary end point was a composite of all‐cause death and rehospitalization because of HF. The impact of SF on all‐cause mortality alone was also evaluated. Among 1240 enrolled patients, 825 (66.5%) had SF. During the 1‐year observation period after discharge, the rates of the combined end point and all‐cause mortality were significantly higher in patients with SF than in those without SF (Log‐rank test: both P < 0.05). SF remained as significantly associated with both the combined end point (hazard ratio, 1.30; 95% CI, 1.02–1.66; P = 0.038) and all‐cause mortality (hazard ratio, 1.53; 95% CI, 1.01–2.30; P = 0.044), even after adjusting for key clinical risk factors. Furthermore, SF showed significant incremental prognostic value over known risk factors for both the combined end point (net‐reclassification improvement: 0.189, 95% CI, 0.063–0.316, P = 0.003) and all‐cause mortality (net‐reclassification improvement: 0.234, 95% CI, 0.073–0.395, P = 0.004).

Conclusions

Among hospitalized geriatric patients with HF, two thirds have SF. Evaluating SF provides additive prognostic information in elderly patients with HF.

Registration

URL: https://upload.umin.ac.jp/. Unique identifier: UMIN000023929.

Factors associated with early 14-day unplanned hospital readmission: a matched case-control study

Background/Purpose

Early unplanned hospital readmissions are burdensome health care events and indicate low care quality. Identifying at-risk patients enables timely intervention. This study identified predictors for 14-day unplanned readmission.

Methods

We conducted a retrospective, matched, case–control study between September 1, 2018, and August 31, 2019, in an 1193-bed university hospital. Adult patients aged ≥ 20 years and readmitted for the same or related diagnosis within 14 days of discharge after initial admission (index admission) were included as cases. Cases were 1:1 matched for the disease-related group at index admission, age, and discharge date to controls. Variables were extracted from the hospital’s electronic health records.

Results

In total, 300 cases and 300 controls were analyzed. Six factors were independently associated with unplanned readmission within 14 days: previous admissions within 6 months (OR = 3.09; 95 % CI = 1.79–5.34, p < 0.001), number of diagnoses in the past year (OR = 1.07; 95 % CI = 1.01–1.13, p = 0.019), Malnutrition Universal Screening Tool score (OR = 1.46; 95 % CI = 1.04–2.05, p = 0.03), systolic blood pressure (OR = 0.98; 95 % CI = 0.97–0.99, p = 0.01) and ear temperature within 24 h before discharge (OR = 2.49; 95 % CI = 1.34–4.64, p = 0.004), and discharge with a nasogastric tube (OR = 0.13; 95 % CI = 0.03–0.60, p = 0.009).

Conclusions

Factors presented at admission (frequent prior hospitalizations, multimorbidity, and malnutrition) along with factors presented at discharge (clinical instability and the absence of a nasogastric tube) were associated with increased risk of early 14-day unplanned readmission.

Published models that predict hospital readmission: a critical appraisal

INTRODUCTION

The number of readmission risk prediction models available has increased rapidly, and these models are used extensively for health decision-making. Unfortunately, readmission models can be subject to flaws in their development and validation, as well as limitations in their clinical usefulness.

OBJECTIVE

To critically appraise readmission models in the published literature using Delphi-based recommendations for their development and validation.

METHODS

We used the modified Delphi process to create Critical Appraisal of Models that Predict Readmission (CAMPR), which lists expert recommendations focused on development and validation of readmission models. Guided by CAMPR, two researchers independently appraised published readmission models in two recent systematic reviews and concurrently extracted data to generate reference lists of eligibility criteria and risk factors.

RESULTS

We found that published models (n=81) followed 6.8 recommendations (45%) on average. Many models had weaknesses in their development, including failure to internally validate (12%), failure to account for readmission at other institutions (93%), failure to account for missing data (68%), failure to discuss data preprocessing (67%) and failure to state the model's eligibility criteria (33%).

CONCLUSIONS

The high prevalence of weaknesses in model development identified in the published literature is concerning, as these weaknesses are known to compromise predictive validity. CAMPR may support researchers, clinicians and administrators to identify and prevent future weaknesses in model development.

Living Alone and Clinical Outcomes in Patients With Heart Failure With Preserved Ejection Fraction

OBJECTIVE

In patients with heart failure with preserved ejection fraction (HFpEF), whether living alone could contribute to a poor prognosis remains unknown. We sought to investigate the association of living alone with clinical outcomes in patients with HFpEF.

METHODS

Symptomatic patients with HFpEF with a follow-up of 3.3 years (data collected from August 2006 to June 2013) in the Treatment of Preserved Cardiac Function Heart Failure with an Aldosterone Antagonist trial were classified as patients living alone and those living with others. The primary outcome was defined as a composite of cardiovascular death, aborted cardiac arrest, or HF hospitalization.

RESULTS

A total of 3103 patients with HFpEF were included; 25.2% of them were living alone and were older, predominantly female, and less likely to be White and have more comorbidities compared with the other patients. After multivariate adjustment for confounders, living alone was associated with increased risks of HF hospitalization (hazard ratio [HR] = 1.29, 95% confidence interval [CI] = 1.03-1.61) and any hospitalization (HR = 1.26, 95% CI = 1.12-1.42). A significantly increased risk of any hospitalization (HR = 1.16, 95% CI = 1.01-1.34) was also observed in the Americas-based sample. In addition, each year increase in age, female sex, non-White race, New York Heart Association functional classes III and IV, dyslipidemia, and chronic obstructive pulmonary disease were independently associated with living alone.

CONCLUSIONS

We assessed the effect of living arrangement status on clinical outcomes in patients with HFpEF and suggested that living alone was associated with an independent increase in any hospitalization.Clinical Trial Registration: ClinicalTrials.gov identifier: NCT00094302.

The influence of gender and race on heart failure self-care

OBJECTIVES

To explore gender and racial differences in heart failure (HF) self-care processes and examine whether gender and race predict HF self-care.

METHODS

A secondary analysis of baseline data (n = 107) from a longitudinal HF study (54.2% males; 56% non-Caucasians) was conducted. The self-care of heart failure index was used to measure self-care maintenance, management, and confidence. Descriptive statistics and univariate analyses examined gender and racial differences in HF self-care outcomes. Multiple linear regression examined whether gender and race predicted HF self-care maintenance, management, and confidence.

RESULTS

Univariate analyses indicated that Caucasians reported significantly better self-care maintenance (p = 0.042), while non-Caucasians reported significantly better self-care management (p = 0.003). Males had significantly higher self-care confidence scores versus women (p = 0.017). Multiple regression analysis indicated Caucasian race predicted significantly worse self-care management (β = -11.188; p = 0.006) versus non-Caucasian, while male gender predicted significantly higher self-care confidence scores (β = 7.592; p = 0.010) versus female gender. Gender nor race significantly predicted self-care maintenance.

DISCUSSION

Although gender and race may influence HF self-care, other factors may be more important. More research is needed to identify individual factors that contribute to HF self-care to improve education and intervention.

Prognostic role of albumin level in heart failure: A systematic review and meta-analysis

BACKGROUND

Hypoalbuminemia (HA) is common in HF, however, its pathophysiology and clinical implications are poorly understood. While multiple studies have been published in the past decade investigating the role of serum albumin in HF, there is still no consensus on the prognostic value of this widely available measure. The objective of this study is to assess the prognostic role of albumin in heart failure (HF) patient.

METHODS

Unrestricted searches of MEDLINE, EMBASE, Cochrane databases were performed. The results were screened for relevance and eligibility criteria. Relevant data were extracted and analyzed using Comprehensive Meta-Analysis software. The Begg and Mazumdar rank correlation test was utilized to evaluate for publication bias.

RESULTS

A total of 48 studies examining 44,048 patients with HF were analyzed. HA was found in 32% (95% confidence interval [CI] 28.4%-37.4%) HF patients with marked heterogeneity (I2 = 98%). In 10 studies evaluating acute HF, in-hospital mortality was almost 4 times more likely in HA with an odds ratios (OR) of 3.77 (95% CI 1.96-7.23). HA was also associated with a significant increase in long-term mortality (OR: 1.5; 95% CI: 1.36-1.64) especially at 1-year post-discharge (OR: 2.44; 95% CI: 2.05-2.91; I2 = 11%). Pooled area under the curve (AUC 0.73; 95% CI 0.67-0.78) was comparable to serum brain natriuretic peptide (BNP) in predicting mortality in HF patients.

CONCLUSION

Our results suggest that HA is associated with significantly higher in-hospital mortality as well as long-term mortality with a predictive accuracy comparable to that reported for serum BNP. These findings suggest that serum albumin may be useful in determining high-risk patients.

Readmitted Patients With Heart Failure Sick, Tired, and Symptomatic: A Qualitative Descriptive Study From a Quaternary Academic Medical Center

BACKGROUND AND OBJECTIVE

Heart failure (HF) readmissions will continue to grow unless we have a better understanding of why patients with HF are readmitted. Our purpose was to gain an understanding, from the patients' perspective, of how patients with HF viewed their discharge instructions and how they felt when they got home and were then readmitted in less than 30 days.

METHODS AND RESULTS

We used a qualitative descriptive approach using semistructured interviews with 22 patients with HF. Most participants had multimorbidities, were classified as New York Heart Association class III (n = 13) with reduced ejection fraction (n = 20), and were on home inotrope therapy (n = 13). The overarching theme that emerged was that these participants were sick, tired, and symptomatic. Additional categories within this theme highlight discharge instructions as being clear and easily understood; rich descriptions of physical, emotional, and other symptoms leading up to readmission; and reports of daily activities including what "good" and "not good" days looked like. Moreover, when participants experienced an exacerbation of their HF symptoms, they were sick enough to be readmitted to the hospital.

CONCLUSION

Our findings confirm ongoing challenges with a complex group of sick patients with HF, with the majority on home inotropes with reduced ejection fraction, who developed an unavoidable progression of their illness and subsequent hospital readmission.

The Utility of Predicting Hospitalizations Among Patients With Heart Failure Using mHealth: Observational Study

Background

Heart failure decompensation is a major driver of hospitalizations and represents a significant burden to the health care system. Identifying those at greatest risk of admission can allow for targeted interventions to reduce this risk.

Objective

This paper aims to compare the predictive value of objective and subjective heart failure respiratory symptoms on imminent heart failure decompensation and subsequent hospitalization within a 30-day period.

Methods

A prospective observational pilot study was conducted. People living at home with heart failure were recruited from a single-center heart failure outpatient clinic. Objective (blood pressure, heart rate, weight, B-type natriuretic peptide) and subjective (4 heart failure respiratory symptoms scored for severity on a 5-point Likert scale) data were collected twice weekly for a 30-day period.

Results

A total of 29 participants (median age 79 years; 18/29, 62% men) completed the study. During the study period, 10 of the 29 participants (34%) were hospitalized as a result of heart failure. For objective data, only heart rate exhibited a between-group difference. However, it was nonsignificant for variability (P=.71). Subjective symptom scores provided better prediction. Specifically, the highest precision of heart failure hospitalization was observed when patients with heart failure experienced severe dyspnea, orthopnea, and bendopnea on any given day (area under the curve of 0.77; sensitivity of 83%; specificity of 73%).

Conclusions

The use of subjective respiratory symptom reporting on a 5-point Likert scale may facilitate a simple and low-cost method of predicting heart failure decompensation and imminent hospitalization. Serial collection of symptom data could be augmented using ecological momentary assessment of self-reported symptoms within a mobile health monitoring strategy for patients at high risk for heart failure decompensation.

Persistent Heart Failure Symptoms at Hospital Discharge Predicts 30-Day Clinical Events

Background

The relationship between heart failure (HF) symptoms at hospital discharge and 30-day clinical events is unknown. Variability in HF symptom assessment may affect ability to predict readmission risk.

Objective

The aim of this study was to describe HF symptom profiles and burden at hospital discharge. A secondary aim was to examine the relationship between symptom burden at discharge and 30-day clinical events.

Methods

An exploratory descriptive design was used. Patients with HF (n = 186) were enrolled 24 to 48 hours pre hospital discharge. The HF Somatic Perception Scale quantified 18 HF physical signs and symptoms. Scores were divided into tertiles (0-10, 11-19, and 20 and higher). The Patient Health Questionnaire-9 quantified depressive symptoms. Self-assessed health, comorbid illnesses, and 30-day clinical events were documented. Chi-square and logistic regression were used to examine clinical events.

Results

The sample (n = 186) was predominantly White (87.6%), male (59.1%), elderly (mean [SD], 74.2 [12.5]), and symptomatic (92.5%) at discharge. Heart Failure Somatic Perception Scale scores ranged from 0 to 53, with a mean (SD) of 13.7 (10.1). Symptoms reported most frequently were fatigue (67%), nocturia (62%), need to rest (53%), and inability to do usual activities due to shortness of breath (52%). Thirty-day event rate was 28%, with significant differences between Heart Failure Somatic Perception Scale tertiles (9.4% vs 37.7% in the second and third tertiles, respectively; [chi]22(N = 186) = 16.73, P < .001). Heart Failure Somatic Perception Scale tertile 2 or 3 (odds ratio [OR], 5.7; P = .003; and OR, 4.3; P = .021), self-assessed health (OR, 2.6; P = .029), and being in a relationship predicted clinical events.

Conclusions

Heart failure symptom burden at discharge predicted 30-day clinical events. Comprehensive symptom assessment is important when determining readmission risk.

Composite Outcomes of Mortality and Readmission in Patients with Heart Failure: Retrospective Review of Administrative Datasets

Background

Controlling the quality of care through readmissions and mortality for patients with heart failure (HF) is a national priority for healthcare regulators in developed countries. In this longitudinal cohort study, using administrative data such as hospital discharge forms (HDFs), emergency departments (EDs) accesses, and vital statistics, we test new covariates for predicting mortality and readmissions of patients hospitalized for HF and discuss the use of combined outcome as an alternative.

Methods

Logistic models, with a stepwise selection method, were estimated on 70% of the sample and validated on the remaining 30% to evaluate 30-day mortality, 30-day readmissions, and the combined outcome. We followed an extraction method for any-cause mortality and unplanned readmission within 30 days after incident HF hospitalization. Data on patient admission and previous history were extracted by HDFs and ED dataset.

Results

Our principal findings demonstrate that the model’s discriminant ability is consistent with literature both for mortality (AUC=0.738, CI (0.729–0.748)) and readmissions (AUC=0.578, CI (0.562–0.594)). Additionally, the discriminant ability of the composite outcome model is satisfactory (AUC=0.675, CI (0.666–0.684)).

Conclusion

Hospitalization characteristics and patient history introduced in the logistic models do not improve their discriminant ability. The composite outcome prediction is led more by mortality than readmission, without improvements for the comprehension of the readmission phenomenon.

Risk Prediction in Transition: MAGGIC Score Performance at Discharge and Incremental Utility of Natriuretic Peptides

BACKGROUND

Risk stratification for hospitalized patients with heart failure (HF) remains a critical need. The Meta-Analysis Global Group in Chronic Heart Failure (MAGGIC) score is a robust model derived from patients with ambulatory HF. Its validity at the time of discharge and the incremental value of natriuretic peptides (NPs) in this setting is unclear.

METHODS

This was a single-center study examining a total of 4138 patients with HF from 2 groups; hospital discharge patients from administrative data (n = 2503, 60.5%) and a prospective registry of patients with ambulatory HF (n = 1635, 39.5%). The ambulatory registry patients underwent N-terminal pro-B-type NP (BNP) measurement at enrollment, and in the hospitalize discharge cohort clinical BNP levels were abstracted. The primary endpoint was all-cause mortality within 1 year. MAGGIC score performance was compared between cohorts utilizing Cox regression and calibration plots. The incremental value of NPs was assessed using calculated area under the curve and net reclassification improvement (NRI).

RESULTS

The hospitalized and ambulatory cohorts differed with respect to primary outcome (777 and 100 deaths, respectively), sex (52.1% vs 41.7% female) and race (35% vs 49.5% African American). The MAGGIC score showed poor discrimination of mortality risk in the hospital discharge (C statistic: 0.668, hazard ratio [HR]: 1.1 per point, 95% confidence interval [CI]: 0.652, 0.684) but fair discrimination in the ambulatory cohorts (C statistic: 0.784, HR: 1.16 per point, 95% CI: 0.74, 0.83), respectively, a difference that was statistically significant (P = .001 for C statistic, 0.002 for HR). Calibration assessment indicated that the slope and intercept (of MAGGIC-predicted to observed mortality) did not statistically differ from ideal in either cohort and did not differ between the cohorts (all P > .1). NP levels did not significantly improve prediction in the hospitalized cohort (P = .127) but did in the ambulatory cohort (C statistic: 0.784 [95% CI: 0.74, 0.83] vs 0.82 [95% CI: 0.78, 0.85]; P = .018) with a favorable NRI of 0.354 (95% CI: 0.202-0.469; P = .002).

CONCLUSION

The MAGGIC score showed poor discrimination when used in patients with HF at hospital discharge, which was inferior to its performance in patients with ambulatory HF. Discrimination within the hospital discharge group was not improved by including hospital NP levels.

Readmission Risk Trajectories for Patients With Heart Failure Using a Dynamic Prediction Approach: Retrospective Study

Background

Patients hospitalized with heart failure suffer the highest rates of 30-day readmission among other clinically defined patient populations in the United States. Investigation into the predictability of 30-day readmissions can lead to clinical decision support tools and targeted interventions that can help care providers to improve individual patient care and reduce readmission risk.

Objective

This study aimed to develop a dynamic readmission risk prediction model that yields daily predictions for patients hospitalized with heart failure toward identifying risk trajectories over time and identifying clinical predictors associated with different patterns in readmission risk trajectories.

Methods

A two-stage predictive modeling approach combining logistic and beta regression was applied to electronic health record data accumulated daily to predict 30-day readmission for 534 hospital encounters of patients with heart failure over 2750 patient days. Unsupervised clustering was performed on predictions to uncover time-dependent trends in readmission risk over the patient’s hospital stay. We used data collected between September 1, 2013, and August 31, 2015, from a community hospital in Maryland (United States) for patients with a primary diagnosis of heart failure. Patients who died during the hospital stay or were transferred to other acute care hospitals or hospice care were excluded.

Results

Readmission occurred in 107 (107/534, 20.0%) encounters. The out-of-sample area under curve for the 2-stage predictive model was 0.73 (SD 0.08). Dynamic clinical predictors capturing laboratory results and vital signs had the highest predictive value compared with demographic, administrative, medical, and procedural data included. Unsupervised clustering identified four risk trajectory groups: decreasing risk (131/534, 24.5% encounters), high risk (113/534, 21.2%), moderate risk (177/534, 33.1%), and low risk (113/534, 21.2%). The decreasing risk group demonstrated change in average probability of readmission from admission (0.69) to discharge (0.30), whereas the high risk (0.75), moderate risk (0.61), and low risk (0.39) groups maintained consistency over the hospital course. A higher level of hemoglobin, larger decrease in potassium and diastolic blood pressure from admission to discharge, and smaller number of past hospitalizations are associated with decreasing readmission risk (P<.001).

Conclusions

Dynamically predicting readmission and quantifying trends over patients’ hospital stay illuminated differing risk trajectory groups. Identifying risk trajectory patterns and distinguishing predictors may shed new light on indicators of readmission and the isolated effects of the index hospitalization.

The impact of perceived and objective social isolation on hospital readmission in patients with heart failure: A systematic review and meta-analysis of observational studies

OBJECTIVE

Several psychosocial risk factors have been identified that increase the rate of readmission in heart failure (HF) patients. However, the impact of social isolation (SI) on the rate of readmission is unclear. Therefore, the current review focused on the impact of SI on readmission rates of patients with HF.

METHODS

A Medline-based strategy was applied to search PubMed, SCOPUS, Cochrane library, ProQuest, and Embase from inception until November 15, 2018. We performed a meta-analysis and pooled results using random effects model. The primary outcome was the odds ratio of readmission in HF patients suffering from SI. We examined the impact of both perceived and objective SI on readmission rates. We also examined the differences in readmission rates between these concepts. The secondary outcomes were the incidence of readmission and the prevalence of SI.

RESULTS

From 3326 titles, 13 studies (n = 6468 participants) were eligible. The mean follow-up period was 13 months. The cumulative incidence for HF-related hospital readmission was 35.47% (95% CI: 34.29-36.67). The pooled prevalence ratio (PR, (95% CI)) was 37.31% (36.14-38.49), 31.51% (30.36-32.68), 32.82% (29.90-35.88), and 39.57% (37.73-41.45) for SI, living alone, lack of social support, and poor social network, respectively. SI was associated with a 55% greater risk of hospital readmission in patients with HF (OR = 1.55; 95% CI = 1.39-1.73; p < .001). Our analysis did not show a significant difference in the rate of hospital readmission between perceived and objective SI.

CONCLUSION

SI is prevalent in patients with HF and seems to be consistently linked to hospital readmission in HF patients, regardless of how it is measured. Therefore, it is necessary to develop interventions to reduce the burden of SI in patients with HF.

High-Sensitivity Troponin T and Incident Heart Failure in Older Men: British Regional Heart Study

OBJECTIVE

The aim of this work was to study the association of high-sensitivity troponin T (hsTnT) with incident heart failure (HF), and implications for its use in prediction models.

METHODS AND RESULTS

In the British Regional Heart Study, 3852 men aged 60-79years without baseline HF (3165 without baseline chronic heart disease) were followed for a median of 12.6years, during which 295 incident cases of HF occurred (7.7%). A 1-SD increase in log-transformed hsTnT was associated with a higher risk of incident HF after adjusting for classic risk factors (hazard ratio [HR] 1.58, 95% confidence interval [CI] 1.42-1.77) and after additional adjustment for N-terminal pro-B-type natriuretic peptide (NT-proBNP; HR 1.34, 95% CI 1.19-1.52). The strength of the association between hsTnT and incident HF did not differ by strata of other risk factors. An hsTnT concentration of <5ng/L had a sensitivity of 99.7% (95% CI 98.1%-99.9%) and a specificity of 3.4% (95% CI 2.8%-4.0%). A risk-prediction model including classic risk factors and NT-proBNP yielded a C-index of 0.791, but addition of hsTnT did not further improve prediction (P = .28).

CONCLUSIONS

Elevated hsTnT is consistently associated with risk of HF in older men. HF occurred rarely over 12years when baseline hsTnT was below the limit of detection. hsTnT measurement, however, does not improve HF prediction in a model already containing NT-proBNP.

Readmissions following hospitalisations for cardiovascular disease: a scoping review of the Australian literature

Objective International studies suggest high rates of readmissions after cardiovascular hospitalisations, but the burden in Australia is uncertain. We summarised the characteristics, frequency, risk factors of readmissions and interventions to reduce readmissions following cardiovascular hospitalisation in Australia. Methods A scoping review of the published literature from 2000-2016 was performed using Medline, EMBASE and Cumulative Index to Nursing and Allied Health Literature (CINAHL) databases and relevant grey literature. Results We identified 35 studies (25 observational, 10 reporting outcomes of interventions). Observational studies were typically single-centre (11/25) and reported readmissions following hospitalisations for heart failure (HF; 10/25), acute coronary syndrome (7/25) and stroke (6/25), with other conditions infrequently reported. The definition of a readmission was heterogeneous and was assessed using diverse methods. Readmission rate, most commonly reported at 1 month (14/25), varied from 6.3% to 27%, with readmission rates of 10.1-27% for HF, 6.5-11% for stroke and 12.7-17% for acute myocardial infarction, with a high degree of heterogeneity among studies. Of the 10 studies of interventions to reduce readmissions, most (n=8) evaluated HF management programs and three reported a significant reduction in readmissions. We identified a lack of national studies of readmissions and those assessing the cost and resource impact of readmissions on the healthcare system as well as a paucity of successful interventions to lower readmissions. Conclusions High rates of readmissions are reported for cardiovascular conditions, although substantial methodological heterogeneity exists among studies. Nationally standardised definitions are required to accurately measure readmissions and further studies are needed to address knowledge gaps and test interventions to lower readmissions in Australia. What is known about the topic? International studies suggest readmissions are common following cardiovascular hospitalisations and are costly to the health system, yet little is known about the burden of readmission in the Australian setting or the effectiveness of intervention to reduce readmissions. What does this paper add? We found relatively high rates of readmissions following common cardiovascular conditions although studies differed in their methodology making it difficult to accurately gauge the readmission rate. We also found several knowledge gaps including lack of national studies, studies assessing the impact on the health system and few interventions proven to reduce readmissions in the Australian setting. What are the implications for practitioners? Practitioners should be cautious when interpreting studies of readmissions due the heterogeneity in definitions and methods used in Australian literature. Further studies are needed to test interventions to reduce readmissions in the Australians setting.

An argument for reporting data standardization procedures in multi-site predictive modeling: case study on the impact of LOINC standardization on model performance

Objectives

We aimed to gain a better understanding of how standardization of laboratory data can impact predictive model performance in multi-site datasets. We hypothesized that standardizing local laboratory codes to logical observation identifiers names and codes (LOINC) would produce predictive models that significantly outperform those learned utilizing local laboratory codes.

Materials and Methods

We predicted 30-day hospital readmission for a set of heart failure-specific visits to 13 hospitals from 2008 to 2012. Laboratory test results were extracted and then manually cleaned and mapped to LOINC. We extracted features to summarize laboratory data for each patient and used a training dataset (2008–2011) to learn models using a variety of feature selection techniques and classifiers. We evaluated our hypothesis by comparing model performance on an independent test dataset (2012).

Results

Models that utilized LOINC performed significantly better than models that utilized local laboratory test codes, regardless of the feature selection technique and classifier approach used.

Discussion and Conclusion

We quantitatively demonstrated the positive impact of standardizing multi-site laboratory data to LOINC prior to use in predictive models. We used our findings to argue for the need for detailed reporting of data standardization procedures in predictive modeling, especially in studies leveraging multi-site datasets extracted from electronic health records.

Readmission and mortality in patients ≥70 years with acute myocardial infarction or heart failure in the Netherlands: a retrospective cohort study of incidences and changes in risk factors over time

Objectives

To determine the risk of first unplanned all-cause readmission and mortality of patients ≥70 years with acute myocardial infarction (AMI) or heart failure (HF) and to explore which effects of baseline risk factors vary over time.

Methods

A retrospective cohort study was performed on hospital and mortality data (2008) from Statistics Netherlands including 5,175 (AMI) and 9,837 (HF) patients. We calculated cumulative weekly incidences for first unplanned all-cause readmission and mortality during 6 months post-discharge and explored patient characteristics associated with these events.

Results

At 6 months, 20.4% and 9.9% (AMI) and 24.6% and 22.4% (HF) of patients had been readmitted or had died, respectively. The highest incidences were found in week 1. An increased risk for 14-day mortality after AMI was observed in patients who lived alone (hazard ratio (HR) 1.57, 95% confidence interval (CI) 1.01–2.44) and within 30 and 42 days in patients with a Charlson Comorbidity Index ≥3. In HF patients, increased risks for readmissions within 7, 30 and 42 days were found for a Charlson Comorbidity Index ≥3 and within 42 days for patients with an admission in the previous 6 months (HR 1.42, 95% CI 1.12–1.80). Non-native Dutch HF patients had an increased risk of 14-day mortality (HR 1.74, 95% CI 1.09–2.78).

Conclusion

The risk of unplanned readmission and mortality in older AMI and HF patients was highest in the 1st week post-discharge, and the effect of some risk factors changed over time. Transitional care interventions need to be provided as soon as possible to prevent early readmission and mortality.

Electronic supplementary material

The online version of this article (10.1007/s12471-019-1227-4) contains supplementary material, which is available to authorized users.

Predictors of Hospitalization for Heart Failure Decompensation in 18-months Follow-up After Index Hospitalization for Acute Heart Failure

Introduction

Heart failure (HF) has very high rate of repeat hospitalizations due to HF decompensation (HHFD), sometimes very shortly after discharge for acute HF.

Aim

The aim of this paper is to investigate rate of HHFD and to identify their possible predictors.

Patients and Methods

Total amount of 222 patients hospitalized at Clinic for heart and vessel disease and rheumatism in acute HF were followed for next 18 months for occurrence of HHFD. During hospitalization were collected demographic data, risk factors, routine laboratory tests and admission BNP (brain natriuretic peptide), discharge BNP, percentage change of BNP during hospitalization, high sensitive troponin I, CA125 (cancer antigen125) and cystatin C.

Results

In next 18 months 129 patients (58.11%) reached end-point HHFD- mean time of its occurrence was 2.2 (95% CI=1.67-2.7) months. Patients with HHFD had more often arterial hypertension (HTA) (p=0.006), had higher BMI (p=0.035) and had higher values of bilirubin, admission BNP (p=0.031), discharge BNP (p <0.001), CA125 (p=0.023) and cystatin C (p=0.028). There was no difference in troponin values (p=0.095), while % reduction of BNP during hospitalization was lower (p<0.001) in group with HHFD. In univariate Cox hazard analysis HTA was positively and BMI negatively correlated with HHFD, while in multivariate Cox hazard analysis independent predictors were HTA (HR 1.6; 95% CI=1.1-2.2; p=0.018) and BMI<25 (HR 1.6; 95% CI=1.1-2.3; p=0.007). In univariate Cox hazard analysis admission BNP, discharge BNP, rise of BNP during hospitalization, CA125 and bilirubin were positively correlated, while sodium was negatively correlated with HHFD. In multivariate Cox hazard analysis there was only one independent predictor of HHFD - discharge BNP (HR 6.05; 95% CI=1.89-19.4; p=0.002). Conclusion: Arterial hypertension, BMI>25 and discharge BNP are independent predictors of HHFD. This could help us to identify high-risk patients for readmission who should be monitored more frequently and treated with sophisticated drug and device therapy.

Comorbidities, Sociodemographic Factors, and Hospitalizations in Outpatients With Heart Failure and Preserved Ejection Fraction

Patients with heart failure and preserved ejection fraction (HFpEF) tend to be older and have a high co-morbidity burden. The impact of co-morbid conditions and sociodemographic risk factors on outcomes in these patients has not been quantified. We evaluated 445 consecutive outpatients with HFpEF, defined as established diagnosis of heart failure (HF) with left ventricular ejection fraction at presentation >40% and no previous left ventricular ejection fraction ≤40%. Patients with specific cardiomyopathies, congenital heart disease, primary right-sided disease, valvular disease, or previous advanced HF therapies were excluded. After 2 years, there were 44 deaths and 609 all-cause hospitalizations; of these, 260 (42.7%) were cardiovascular hospitalizations, including HF, and 173 (28.4%) were specifically for HF. The highest attributable risk for hospitalizations was associated with marital status (single, divorced, and widowed had higher hospitalization rates compared with married patients), hypoalbuminemia, diabetes, atrial fibrillation, and renal dysfunction. The proportion of hospitalizations potentially attributable to these factors was 66.6% (95% confidence interval [CI] 56.4 to 74.4) for all-cause hospitalizations, 76.9% (95% CI 65.2 to 84.6) for cardiovascular hospitalizations, and 83.0% (95% CI 70.3 to 90.3) for HF hospitalizations. For composite end points, the proportion was 46.9% (95% CI 34.0% to 57.3%) for death or all-cause hospitalization, 45.7% (95% CI 29.3% to 58.2%) for death or cardiovascular hospitalization, and 43.7% (95% CI 24.2% to 58.2%) for death or HF-related hospitalization. In conclusion, among outpatients with HFpEF, most hospitalizations could be attributed to co-morbidities and sociodemographic factors. Effects of HF therapies on hospitalizations and related end points may be difficult to demonstrate in these patients. Multidisciplinary approaches are more likely to impact hospitalizations in HFpEF.

Association of ambient particulate matter with heart failure incidence and all-cause readmissions in Tasmania: an observational study

OBJECTIVES

We sought to investigate the relationship between air quality and heart failure (HF) incidence and rehospitalisation to elucidate whether there is a threshold in this relationship and whether this relationship differs for HF incidence and rehospitalisation.

METHODS

This retrospective observational study was performed in an Australian state-wide setting, where air pollution is mainly associated with wood-burning for winter heating. Data included all 1246 patients with a first-ever HF hospitalisation and their 3011 subsequent all-cause readmissions during 2009-2012. Daily particulate matter <2.5 µm (PM_2.5), temperature, relative humidity and influenza infection were recorded. Poisson regression was used, with adjustment for time trend, public and school holiday and day of week.

RESULTS

Tasmania has excellent air quality (median PM_2.5=2.9 µg/m³ (IQR: 1.8-6.0)). Greater HF incidences and readmissions occurred in winter than in other seasons (p<0.001). PM_2.5 was detrimentally associated with HF incidence (risk ratio (RR)=1.29 (1.15-1.42)) and weakly so with readmission (RR=1.07 (1.02-1.17)), with 1 day time lag. In multivariable analyses, PM_2.5 significantly predicted HF incidence (RR=1.12 (1.01-1.24)) but not readmission (RR=0.96 (0.89-1.04)). HF incidence was similarly low when PM <4 µg/m³ and only started to rise when PM_2.5≥4 µg/m³. Stratified analyses showed that PM_2.5 was associated with readmissions among patients not taking beta-blockers but not among those taking beta-blockers (p_interaction=0.011).

CONCLUSIONS

PM_2.5 predicted HF incidence, independent of other environmental factors. A possible threshold of PM_2.5=4 µg/m³ is far below the daily Australian national standard of 25 µg/m³. Our data suggest that beta-blockers might play a role in preventing adverse association between air pollution and patients with HF.

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

AIMS

The 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 RESULTS

We 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.

CONCLUSIONS

Heart 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.

Mortality and Readmission Following Hospitalisation for Heart Failure in Australia: A Systematic Review and Meta-Analysis

BACKGROUND

Heart failure (HF) is a common, costly condition with an increasing burden on Australian health care system resources. Knowledge of the burden of HF on patients and on the health system is important for resource allocation. This study is the first systematic review to estimate the mortality and readmission rates after hospitalisation for HF in the Australian population.

METHODS

We searched for studies of HF hospitalisation in Australia published between January 1990 and May 2016, using a systematic search of PubMed, Medline, Scopus, Web of Science, EMBASE and Cochrane Library databases. Studies reporting 30-day and/or 1-year outcomes for mortality or readmission following hospitalisation were eligible and included in this study.

RESULTS

Out of 2889 articles matching the initial search criteria, a total of 13 studies representing 67,255 patients were included in the final analysis. The pooled mean age of heart failure patients was 76.3 years and 51% were male (n=34,271). The pooled estimated 30-day and 1-year all-cause mortality were 8% and 25% respectively. The pooled estimated 30-day and 1-year all-cause readmission rates were 20% and 56% respectively. There is a high prevalence of comorbidities in heart failure patients. There were limited data on readmission and mortality in rural patients and Indigenous people.

CONCLUSIONS

Heart failure hospitalisations in Australia are followed by substantial readmission and mortality rates.

Risk Stratification Model for 30-Day Heart Failure Readmission in a Multiethnic South East Asian Community

There are limited accurate 30-day heart failure (HF) readmission risk scores using readily available clinical patient information on a well-defined HF cohort. We analyzed 1,475 admissions discharged from our hospital with a primary diagnosis of HF between 2010 and 2012. HF diagnostic criteria included satisfying clinical Framingham criteria, elevated serum N-terminal pro-natriuretic peptide, and evidence of cardiac dysfunction on transthoracic echocardiography. The patients were randomly divided into 2 groups; 60% were used as the derivation cohort and 40% as the validation cohort. Bivariate analysis and logistic regression were used to develop the model. Weighted risk scores were derived from the odds ratio of the logistic regression model. Total risk scores were computed by simple summation for each patient. The 7 significant independent predictors of 30-day HF readmission used to derive the risk scoring tool were the number of previous HF-related admission in the preceding 1 year, index admission length of stay, serum creatinine level, electrocardiograph QRS duration, serum N-terminal pro-natriuretic peptide level, number of Medical Social Service needs, and β blocker prescription on discharge. The area under the curve was 0.76. Sensitivity and specificity were 78.3% and 60.7%, respectively. The positive predictive value and negative predictive value were 18.9% and 96%, respectively. The actual observed and predicted 30-day heart failure readmission rates matched. In conclusion, we have developed the first 30-day HF readmission risk score, with good discriminatory ability, for an urban multiethnic Asian heart failure cohort with stringent diagnostic criteria. It consists of 7 easily obtained variables.

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

Background

Hospitalisation 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.

Methods

Prospective 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.

Results

There 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).

Conclusions

The 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 material

The online version of this article (doi:10.1186/s12913-017-2152-0) contains supplementary material, which is available to authorized users.

Integrating the Principles of Evidence Based Medicine and Evidence Based Public Health: Impact on the Quality of Patient Care and Hospital Readmission Rates in Jordan

INTRODUCTION

Hospital readmissions impose not only an extra burden on health care systems but impact patient health outcomes. Identifying modifiable behavioural risk factors that are possible causes of potentially avoidable readmissions can lower readmission rates and healthcare costs.

METHODS

Using the core principles of evidence based medicine and public health, the purpose of this study was to develop a heuristic guide that could identify what behavioural risk factors influence hospital readmissions through adopting various methods of analysis including regression models, t-tests, data mining, and logistic regression. This study was a retrospective cohort review of internal medicine patients admitted between December 1, 2012 and December 31, 2013 at King Abdullah University Hospital, in Jordan.

RESULTS

29% of all hospitalized patients were readmitted during the study period. Among all readmissions, 44% were identified as potentially avoidable. Behavioural factors including smoking, unclear follow-up and discharge planning, and being non-compliant with treatment regimen as well as discharge against medical advice were all associated with increased risk of avoidable readmissions.

CONCLUSION

Implementing evidence based health programs that focus on modifiable behavioural risk factors for both patients and clinicians would yield a higher response in terms of reducing potentially avoidable readmissions, and could reduce direct medical costs.

Prognostic Utility of the Braden Scale and the Morse Fall Scale in Hospitalized Patients With Heart Failure

Geriatric syndromes are common in hospitalized elders with heart failure (HF), but association with clinical outcomes is not well characterized. The purpose of this study ( N = 289) was to assess presence of geriatric syndromes using Joint Commission-mandated measures, the Braden Scale (BS) and Morse Fall Scale (MFS), and to explore prognostic utility in hospitalized HF patients. Data extracted from the electronic medical record included sociodemographics, medications, clinical data, comorbid conditions, and the BS and MFS. The primary outcome of mortality was assessed using Social Security Death Master File. Statistical analysis included Cox proportional hazards models to assess association between BS and MFS scores and all-cause mortality with adjustment for known clinical prognostic factors. Higher risk BS and MFS scores were common in hospitalized HF patients, but were not independent predictors of survival. Further study of the clinical utility of these scores and other measures of geriatric syndromes in HF is warranted.

Utility of models to predict 28-day or 30-day unplanned hospital readmissions: an updated systematic review

OBJECTIVE

To update previous systematic review of predictive models for 28-day or 30-day unplanned hospital readmissions.

DESIGN

Systematic review.

SETTING/DATA SOURCE

CINAHL, Embase, MEDLINE from 2011 to 2015.

PARTICIPANTS

All studies of 28-day and 30-day readmission predictive model.

OUTCOME MEASURES

Characteristics of the included studies, performance of the identified predictive models and key predictive variables included in the models.

RESULTS

Of 7310 records, a total of 60 studies with 73 unique predictive models met the inclusion criteria. The utilisation outcome of the models included all-cause readmissions, cardiovascular disease including pneumonia, medical conditions, surgical conditions and mental health condition-related readmissions. Overall, a wide-range C-statistic was reported in 56/60 studies (0.21-0.88). 11 of 13 predictive models for medical condition-related readmissions were found to have consistent moderate discrimination ability (C-statistic ≥0.7). Only two models were designed for the potentially preventable/avoidable readmissions and had C-statistic >0.8. The variables 'comorbidities', 'length of stay' and 'previous admissions' were frequently cited across 73 models. The variables 'laboratory tests' and 'medication' had more weight in the models for cardiovascular disease and medical condition-related readmissions.

CONCLUSIONS

The predictive models which focused on general medical condition-related unplanned hospital readmissions reported moderate discriminative ability. Two models for potentially preventable/avoidable readmissions showed high discriminative ability. This updated systematic review, however, found inconsistent performance across the included unique 73 risk predictive models. It is critical to define clearly the utilisation outcomes and the type of accessible data source before the selection of the predictive model. Rigorous validation of the predictive models with moderate-to-high discriminative ability is essential, especially for the two models for the potentially preventable/avoidable readmissions. Given the limited available evidence, the development of a predictive model specifically for paediatric 28-day all-cause, unplanned hospital readmissions is a high priority.

Automated identification and predictive tools to help identify high-risk heart failure patients: pilot evaluation

OBJECTIVE

Develop and evaluate an automated identification and predictive risk report for hospitalized heart failure (HF) patients.

METHODS

Dictated free-text reports from the previous 24 h were analyzed each day with natural language processing (NLP), to help improve the early identification of hospitalized patients with HF. A second application that uses an Intermountain Healthcare-developed predictive score to determine each HF patient's risk for 30-day hospital readmission and 30-day mortality was also developed. That information was included in an identification and predictive risk report, which was evaluated at a 354-bed hospital that treats high-risk HF patients.

RESULTS

The addition of NLP-identified HF patients increased the identification score's sensitivity from 82.6% to 95.3% and its specificity from 82.7% to 97.5%, and the model's positive predictive value is 97.45%. Daily multidisciplinary discharge planning meetings are now based on the information provided by the HF identification and predictive report, and clinician's review of potential HF admissions takes less time compared to the previously used manual methodology (10 vs 40 min). An evaluation of the use of the HF predictive report identified a significant reduction in 30-day mortality and a significant increase in patient discharges to home care instead of to a specialized nursing facility.

CONCLUSIONS

Using clinical decision support to help identify HF patients and automatically calculating their 30-day all-cause readmission and 30-day mortality risks, coupled with a multidisciplinary care process pathway, was found to be an effective process to improve HF patient identification, significantly reduce 30-day mortality, and significantly increase patient discharges to home care.

Electronic medical record-based multicondition models to predict the risk of 30 day readmission or death among adult medicine patients: validation and comparison to existing models

Background

There is increasing interest in using prediction models to identify patients at risk of readmission or death after hospital discharge, but existing models have significant limitations. Electronic medical record (EMR) based models that can be used to predict risk on multiple disease conditions among a wide range of patient demographics early in the hospitalization are needed. The objective of this study was to evaluate the degree to which EMR-based risk models for 30-day readmission or mortality accurately identify high risk patients and to compare these models with published claims-based models.

Methods

Data were analyzed from all consecutive adult patients admitted to internal medicine services at 7 large hospitals belonging to 3 health systems in Dallas/Fort Worth between November 2009 and October 2010 and split randomly into derivation and validation cohorts. Performance of the model was evaluated against the Canadian LACE mortality or readmission model and the Centers for Medicare and Medicaid Services (CMS) Hospital Wide Readmission model.

Results

Among the 39,604 adults hospitalized for a broad range of medical reasons, 2.8 % of patients died, 12.7 % were readmitted, and 14.7 % were readmitted or died within 30 days after discharge. The electronic multicondition models for the composite outcome of 30-day mortality or readmission had good discrimination using data available within 24 h of admission (C statistic 0.69; 95 % CI, 0.68-0.70), or at discharge (0.71; 95 % CI, 0.70-0.72), and were significantly better than the LACE model (0.65; 95 % CI, 0.64-0.66; P =0.02) with significant NRI (0.16) and IDI (0.039, 95 % CI, 0.035-0.044). The electronic multicondition model for 30-day readmission alone had good discrimination using data available within 24 h of admission (C statistic 0.66; 95 % CI, 0.65-0.67) or at discharge (0.68; 95 % CI, 0.67-0.69), and performed significantly better than the CMS model (0.61; 95 % CI, 0.59-0.62; P < 0.01) with significant NRI (0.20) and IDI (0.037, 95 % CI, 0.033-0.041).

Conclusions

A new electronic multicondition model based on information derived from the EMR predicted mortality and readmission at 30 days, and was superior to previously published claims-based models.

Electronic supplementary material

The online version of this article (doi:10.1186/s12911-015-0162-6) contains supplementary material, which is available to authorized users.

Role of Cardiac Troponin Levels in Acute Heart Failure

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.