Predictors of Frequent Readmissions in Patients With Heart Failure

Heart failure drug classes and 30-day unplanned hospital readmission among patients with heart failure in Ethiopia

BACKGROUND

Drug therapy is a crucial aspect of heart failure management and has been shown to reduce morbidity and mortality in heart failure patients. However, the comparative effects of these drug classes on readmission rates have not been well studied. Therefore, the aim of this study was to examine the association between different classes of heart failure drugs and 30-day readmission rates in patients with heart failure.

METHOD

A multicenter, hospital-based retrospective cohort design was employed and 572 randomly selected patients with heart failure were included. Data were entered in Epi-data version 4.6 and analyzed with STATA version 17. Kaplan-Meier and log-rank tests were used to estimate and compare survival time. A Cox proportional hazard model was utilized, employing both bi-variable and multi-variable analyses, to examine the effect of predictors on the timing of unplanned hospital readmissions. The strength of the association was assessed using an adjusted hazard ratio (aHR), and statistical significance was declared for p-values < 0.05 and a 95% confidence interval (CI).

RESULTS

In this study, a total of 151 (26.40%) heart failure patients were readmitted within 30 days of discharge. In the multivariate cox proportional hazards analysis being an age (> 65 year) (AHR: 2.34, 95%CI: 1.63, 3.37), rural in residency (AHR: 1.85, 95%CI: 1.07, 3.20), hospital stays > 7 Days (AHR: 3.68, 95%CI: 2.51,5.39), discharge with Diuretics (AHR: 2.37, 95%CI: 1.45, 3.86), and discharge with Beta-Blocker (AHR: 0.48, 95%CI: 0 0.34, 0.69) were identified as independent predictors of unplanned hospital readmission.

CONCLUSION

Elderly patients, being in rural areas, longer hospital stays, and discharges of patients on diuretics and not on beta-blockers were independent predictors of unplanned hospital readmission. Therefore, working on these factors will help to reduce the hazard of unplanned hospital readmissions, improve patient outcomes, and increase the efficiency of heart failure management.

Prediction of Early and Long-Term Hospital Readmission in Patients with Severe Obesity: A Retrospective Cohort Study

Adults with obesity have a higher risk of hospitalization and high hospitalization-related healthcare costs. However, a predictive model for the risk of readmission in patients with severe obesity is lacking. We conducted a retrospective cohort study enrolling all patients admitted for severe obesity (BMI ≥ 40 kg/m2) between 2009 and 2018 to the Istituto Auxologico Italiano in Piancavallo. For each patient, all subsequent hospitalizations were identified from the regional database by a deterministic record-linkage procedure. A total of 1136 patients were enrolled and followed up for a median of 5.7 years (IQR: 3.1-8.2). The predictive factors associated with hospital readmission were age (HR = 1.02, 95%CI: 1.01-1.03, p < 0.001), BMI (HR = 1.02, 95%CI: 1.01-1.03, p = 0.001), smoking habit (HR = 1.17, 95%CI: 0.99-1.38, p = 0.060), serum creatinine (HR = 1.22, 95%CI: 1.04-1.44, p = 0.016), diabetes (HR = 1.17, 95%CI: 1.00-1.36, p = 0.045), and number of admissions in the previous two years (HR = 1.15, 95%CI: 1.07-1.23, p < 0.001). BMI lost its predictive role when restricting the analysis to readmissions within 90 days. BMI and diabetes lost their predictive roles when further restricting the analysis to readmissions within 30 days. In conclusion, in this study, we identified predictive variables associated with early and long-term hospital readmission in patients with severe obesity. Whether addressing modifiable risk factors could improve the outcome remains to be established.

Development of a model predicting cardiac events in heart failure patients with decreased renal function: a retrospective study

BACKGROUND

Inappropriate and multiple medications affect the prognosis of patients with acute decompensated heart failure (ADHF). However, in ADHF patients with decreased renal function, there have been no reports on prognostic factors, including medication data, or models for predicting cardiac events.

AIM

To develop a model including medication data to predict cardiac events in ADHF patients with decreased renal function.

METHOD

This retrospective cohort study included 443 first-time admitted ADHF patients with decreased renal function (estimated glomerular filtration rate < 60 mL/min/1.73 m² at discharge) in the Showa University Fujigaoka Hospital. The primary outcome was cardiac events within one year after discharge, defined as the composite of HF readmission, HF mortality, and cardiovascular mortality. The model for predicting cardiac events was developed using predictive factors extracted by multivariable analysis. The cardiac events curves were visualized using the Kaplan-Meier method and estimated using a log-rank test.

RESULTS

The incidence of cardiac events within one year after discharge was 20.1%. By multivariable analysis, we observed that atrial fibrillation, weight loss < 5%, brain natriuretic peptide ≥ 200 pg/mL, polypharmacy, and beta-blockers use below target dosage were significantly associated with an increased risk of cardiac events. The developed model, the cardiac events rate in the high-risk group was significantly higher than in the low-risk group (41.0 vs. 9.2%, p < 0.001).

CONCLUSION

The developed model for predicting cardiac events will be useful in decision-making to support appropriate early management of ADHF patients with decreased renal function.

Impact of Transitioning Patients to Oral Diuretics 24 Hours Before Discharge from Heart Failure Hospitalization on 30 Day Outcomes

BACKGROUND

Patients hospitalized for heart failure (HF) are at high risk for post-discharge events. Although transition from intravenous to oral diuretics for >24 hours is commonly practiced to reduce post-discharge risk, evidence supporting this strategy is limited. We investigated the impact of this practice on 30 day post-discharge outcomes following HF hospitalization at our institution.

METHODS

Retrospective chart review of patients hospitalized with a primary HF diagnosis, discharged on oral diuretic, and followed at our institution. Admission, in-hospital, and pre-discharge characteristics of patients discharged with >24-hour observation were compared to those of patients observed for <24-hours on oral diuretics. Differences between groups in composite 30 day all-cause mortality and rehospitalization, each component, and HF rehospitalization were assessed.

RESULTS

Of 285 patients meeting entry criteria, 178 received oral diuretics >24 hours prior to discharge and 107 were discharged <24 hours after transitioning to oral diuretics. Baseline characteristics were similar between groups. Patients with >24 hours observation on oral diuretics had longer in-hospital stays and greater weight and net volume loss than those observed <24 hours. Patients receiving oral diuretics for <24 hours were more likely to have had neurohormonal drugs and diuretic dose changed within 24-hours of discharge. Oral diuretic treatment for >24 hours failed to reduce any study endpoint.

CONCLUSIONS

Transitioning patients to oral diuretics for >24 hours prior to discharge following HF hospitalization failed to improve 30-day outcomes. These results question this strategy for all patients hospitalized for worsening HF.

Depression and Hospital Readmissions in Patients with Heart Failure

Depression increases the risk of mortality in patients with heart failure (HF). Less is known about whether depression predicts multiple readmissions or whether multiple hospitalizations worsen depression in patients with HF. This study aimed to test the hypotheses that depression predicts multiple readmissions in patients hospitalized with HF, and conversely that multiple readmissions predict persistent or worsening depression. All-cause readmissions were ascertained over a 2-year follow-up of a cohort of 400 patients hospitalized with HF. The Patient Health Questionnaire-9 was used to assess depression at index and 3-month intervals. At enrollment in the study, 21% of the patients were mildly depressed and 22% were severely depressed. Higher Patient Health Questionnaire-9 depression scores predicted a higher rate of readmissions (adjusted hazard ratio 1.02, 95% confidence interval 1.00 to 1.04, p = 0.03). The readmission rate was higher in those who were severely depressed than in those without depression (p = 0.0003), but it did not differ between patients who were mildly depressed and patients without depression. Multiple readmissions did not predict persistent or worsening depression, but younger patients in higher New York Heart Association classes were more depressed than other patients. Depression is an independent risk factor for multiple all-cause readmissions in patients hospitalized with HF. Severe depression is a treatable psychiatric co-morbidity that warrants ongoing clinical attention in patients with HF.

Increasing home-time after a first diagnosis of heart failure in Sweden, 20 years trends

AIMS

This study was performed to compare trends in home-time for patients with heart failure (HF) between those of working age and those of retirement age in Sweden from 1992 to 2012.

METHODS AND RESULTS

The National Inpatient Register (IPR) was used to identify all patients aged 18 to 84 years with a first hospitalization for HF in Sweden from 1992 to 2012. Information on date of death, comorbidities, and sociodemographic factors were collected from the Swedish National Register on Cause of Death, the IPR, and the longitudinal integration database for health insurance and labour market studies, respectively. The patients were divided into two groups according to their age: working age (<65 years) and retirement age (≥65 years). Follow-up was 4 years. In total, following exclusions, 388 775 patients aged 18 to 84 years who were alive 1 day after discharge from a first hospitalization for HF were included in the study. The working age group comprised 62 428 (16%) patients with a median age of 58 (interquartile range, 53-62) years and 31.2% women, and the retirement age group comprised 326 347 (84%) patients with a median age of 77 (interquartile range, 73-81) years and 47.4% women. Patients of working age had more home-time than patients of retirement age (83.8% vs. 68.2%, respectively), mainly because of their lower 4 year mortality rate (14.2% vs. 29.7%, respectively). Home-time increased over the study period for both age groups, but the increase levelled off for older women after 2007, most likely because of less reduction in mortality in older women than in the other groups.

CONCLUSIONS

This nationwide study showed increasing home-time over the study period except for women of retirement age and older for whom the increase stalled after 2007, mainly because of a lower mortality reduction in this group. Efforts to improve patient-related outcome measures specifically targeted to this group may be warranted.

Effect of Diabetes Mellitus on 30 and 90-Day Readmissions of Patients With Heart Failure

The prevalence of diabetes mellitus (DM) in hospitalized heart failure (HF) patients is increasing over time. However, the effect of DM on short-term readmissions for HF is not well established. We investigated the effects of DM on readmissions of HF patients. All adult hospitalizations with a primary diagnosis of HF were identified in the National Readmission Database (NRD) for 2018 and were categorized into those with and without a secondary diagnosis of DM. The primary outcome was to assess risk difference in 30 and 90-day all-cause readmissions. Multivariate Cox survival analysis and multivariate Cox regression were performed to estimate the readmission risk difference in HF patients with and without DM. Of 925,637 HF hospitalizations that met the inclusion criteria, 441,295 (47.6%) had concomitant DM. Diabetics hospitalized for HF had higher prevalence of obesity (37.3% vs 19.5%), kidney disease (58.4% vs 29.2%) and coronary disease (61.1% vs 51.0%), compared to HF hospitalizations without DM. In adjusted analyses, DM was associated with higher hazards for all-cause [hazards ratio (HR), 30 days: 1.04 (1.02-1.06); 90 days: 1.07 (1.05-1.09)], HF [HR, 30 days: 1.05 (1.02-1.07); 90 days: 1.08 (1.05-1.10)] and myocardial infarction (MI) [HR, 30 days: 1.26 (1.12-1.41); 90 days: 1.38 (1.25-1.52)] readmissions. In conclusion, in patients with HF-related hospitalizations, the presence of DM was associated with a higher risk of 30 and 90-day all-cause, HF and MI readmissions.

Prediction model of in-hospital mortality in intensive care unit patients with heart failure: machine learning-based, retrospective analysis of the MIMIC-III database

OBJECTIVE

The predictors of in-hospital mortality for intensive care units (ICUs)-admitted heart failure (HF) patients remain poorly characterised. We aimed to develop and validate a prediction model for all-cause in-hospital mortality among ICU-admitted HF patients.

DESIGN

A retrospective cohort study.

SETTING AND PARTICIPANTS

Data were extracted from the Medical Information Mart for Intensive Care (MIMIC-III) database. Data on 1177 heart failure patients were analysed.

METHODS

Patients meeting the inclusion criteria were identified from the MIMIC-III database and randomly divided into derivation (n=825, 70%) and a validation (n=352, 30%) group. Independent risk factors for in-hospital mortality were screened using the extreme gradient boosting (XGBoost) and the least absolute shrinkage and selection operator (LASSO) regression models in the derivation sample. Multivariate logistic regression analysis was used to build prediction models in derivation group, and then validated in validation cohort. Discrimination, calibration and clinical usefulness of the predicting model were assessed using the C-index, calibration plot and decision curve analysis. After pairwise comparison, the best performing model was chosen to build a nomogram according to the regression coefficients.

RESULTS

Among the 1177 admissions, in-hospital mortality was 13.52%. In both groups, the XGBoost, LASSO regression and Get With the Guidelines-Heart Failure (GWTG-HF) risk score models showed acceptable discrimination. The XGBoost and LASSO regression models also showed good calibration. In pairwise comparison, the prediction effectiveness was higher with the XGBoost and LASSO regression models than with the GWTG-HF risk score model (p<0.05). The XGBoost model was chosen as our final model for its more concise and wider net benefit threshold probability range and was presented as the nomogram.

CONCLUSIONS

Our nomogram enabled good prediction of in-hospital mortality in ICU-admitted HF patients, which may help clinical decision-making for such patients.

Data Science Methods for Nursing-Relevant Patient Outcomes and Clinical Processes: The 2019 Literature Year in Review

Data science continues to be recognized and used within healthcare due to the increased availability of large data sets and advanced analytics. It can be challenging for nurse leaders to remain apprised of this rapidly changing landscape. In this article, we describe our findings from a scoping literature review of papers published in 2019 that use data science to explore, explain, and/or predict 15 phenomena of interest to nurses. Fourteen of the 15 phenomena were associated with at least one paper published in 2019. We identified the use of many contemporary data science methods (eg, natural language processing, neural networks) for many of the outcomes. We found many studies exploring Readmissions and Pressure Injuries. The topics of Artificial Intelligence/Machine Learning Acceptance, Burnout, Patient Safety, and Unit Culture were poorly represented. We hope that the studies described in this article help readers: (1) understand the breadth and depth of data science's ability to improve clinical processes and patient outcomes that are relevant to nurses and (2) identify gaps in the literature that are in need of exploration.

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.

Identification of risk factors of 30-day readmission and 180-day in-hospital mortality, and its corresponding relative importance in patients with Ischemic heart disease: a machine learning approach

Background: The primary objective of this study is to identify non-laboratory predictors for 30-day hospital readmission and 180-day in-hospital mortality rates among patients hospitalized with ischemic heart disease (IHD).Research design and methods: This is a retrospective cohort study of hospitalized patients (≥ 40 years) with a primary diagnosis of IHD. Data were extracted from the Florida Agency for Health Care Administration dataset from 2006 to 2016. A machine learning approach was used to identify predictors of 30-day hospital readmission and 180-day in-hospital mortality.Results: 346,390 patient records for incident IHD cases were identified. The top two predictors of 30-day readmission were the length of stay and the Elixhauser comorbidity index for readmission [ECI] (Area Under the Curve [AUC]=88%) using decision tree algorithms. For in-hospital mortality, the top two predictors were LOS and ECI (AUC=92%) using gradient boosting regressors. The cumulative 30-day readmission and the 180-day probability of mortality rates were 9.82% and 4.6% respectively.Conclusions: Risk factors of 30-day readmission and 180-day mortality in hospitalized IHD patients identified by machine learning and their relative importance (value) will help pharmacists and other health care providers to prioritize their disease management strategies as they improve the care provided to IHD patients.

Prevalence and seasonal variation of precipitants of heart failure hospitalization and risk of readmission

AIMS

To determine the prevalence and seasonal variation in precipitants of heart failure (HF) hospitalization and the risk of subsequent HF hospitalizations.

METHODS

We analysed the characteristics and outcomes of patients hospitalized with HF and enrolled in the Management of Cardiac Failure program in Sydney, Australia. Potential precipitants of HF hospitalization were identified, and Cox-regression analyses performed according to the precipitant.

RESULTS

Among 6918 patients hospitalized with HF, 5384 (78%) had identified one or more precipitating factors leading to the hospitalization and 3648 (53%) had a single identifiable precipitant. Most precipitants were due to one or more of five prespecified causes - infection (n = 2014), ischemia (n = 1781), arrhythmia (n = 1724), medication related (n = 925) and diet non-compliance (n = 408). All precipitants were more common during winter (p < 0.001), especially infection related precipitants, of which 36% occurred during winter. Among patients with a single identifiable precipitant, one-year risk for HF readmission was lower when the precipitant was arrhythmia (16%) or infection (17%) than when the precipitant was ischemia (21%), dietary non-compliance (23%) or medication related (25%). The precipitant for HF rehospitalizations were more likely to be the same precipitant for the initial admission: infection vs no infection (HR 1.51, 95% CI 1.08-2.13), ischemia vs no ischemia (HR 2.79, 95% CI 1.83-4.25), arrhythmia vs no arrhythmia (HR 3.31, 95% CI 1.87-5.88) and medication related vs not medication related (HR 2.28, 95% CI 1.39-3.74).

CONCLUSION

The precipitant of HF hospitalization influences the risk and precipitant of subsequent HF hospitalizations. Identifying and targeting interventions towards the precipitating factor may be an important strategy to prevent future HF hospitalizations.

Factors Associated With Predischarge Versus Postdischarge Scheduling for Early Follow-up Appointments

BACKGROUND

Per national guidelines, early follow-up appointments should be scheduled before discharge, but in previous research, appointments scheduled before discharge were not associated with appointment adherence.

OBJECTIVES

The purpose of this study was to determine whether patient, heart failure (HF), and hospital factors were associated with predischarge appointment scheduling.

METHODS

A secondary analysis of a medical record review included patients hospitalized for decompensated HF at 3 health system hospitals who had a scheduled office appointment post discharge at 14 days or less. Patient demographics, and social, HF, and hospital factors were studied for association with predischarge scheduling.

RESULTS

In multivariable modeling, the odds of having an appointment scheduled predischarge were based on 3 factors: nonwhite race, history of chronic renal insufficiency, and no admission within 14 days before HF hospitalization.

CONCLUSIONS

Appointment scheduling may be based on provider perceptions of readmission risk. Follow-up appointment scheduling practices should be based on systematic processes.

Rate of Rehospitalization in 60 Days of Discharge and It's Determinants in Patients with Heart Failure with Reduced Ejection Fraction in a Tertiary Care Centre in India

Background and Objectives

Identifying the patients with acute heart failure (HF) at high risk for rehospitalization after hospital discharge will enable proper optimization of treatment. This study is aimed to evaluate the rehospitalization rate at 60 days of discharge and their predictors in patients of chronic heart failure with reduced ejection fraction (HFrEF).

Methods

This prospective observational study enrolled patients with left ventricle ejection fraction (LVEF) <40%, who were admitted because of acute decompensation. Patients were followed for 60 days to analyze rehospitalization rate and its predictors.

Results

Of 103 HFrEF patients (74% male; mean age 55.8 years) enrolled, 7 patients died during index admission and 3 patients lost to follow up. The 60-day rehospitalization rate was 37% (34/93). We studied 23 clinical and 9 biochemical predictors of rehospitalization. Out of 34 events of rehospitalization, 79.41% (n=28) was due to cardiac cause followed by respiratory 5.8% (n=2), renal 5.8% (n=2) and others 5.8% (n=2). Among all the parameters, on logistic regression analysis having longer length of index hospital stay (>7 days) (52.8% vs. 28.8%; odds ratio [OR], 1.79; confidence interval [CI], 1.2–7.25; p=0.040) and chronic kidney disease (CKD) (26.5% vs. 8.5%; OR, 3.06; CI, 1.1–57.04; p=0.050) independently increased the risk of rehospitalization at 60 days of discharge. Further higher haemoglobin level (11.3 vs. 9.9 gm/dL; OR, 0.71; CI, 0.48–0.97; p=0.050) and higher LVEF at index admission (30.4% vs. 26.5%; OR, 0.87; CI, 0.75–0.99; p=0.049) were associated with decreased the risk of rehospitalization.

Conclusions

Our study reveals that patients with HFrEF have significantly higher rehospitalization rate (37%) and in-hospital mortality rates (6.78%) of any chronic cardiac disease conditions. Correction of low hemoglobin and special care in those who are having very low LVEF, CKD and longer length of stay, including tailored therapy and frequent visits may play an important role in preventing future rehospitalization in these patients.

Defining a 'frequent admitter' phenotype among patients with repeat heart failure admissions

AIMS

We aimed to identify a 'frequent admitter' phenotype among patients admitted for acute decompensated heart failure (HF).

METHODS AND RESULTS

We studied 10 363 patients in a population-based prospective HF registry (2008-2012), segregated into clusters based on their 3-year HF readmission frequency trajectories. Using receiver-operating characteristic analysis, we identified the index year readmission frequency threshold that most accurately predicts HF admission frequency clusters. Two clusters of HF patients were identified: a high frequency cluster (90.9%, mean 2.35 ± 3.68 admissions/year) and a low frequency cluster (9.1%, mean 0.50 ± 0.81 admission/year). An index year threshold of two admissions was optimal for distinguishing between clusters. Based on this threshold, 'frequent admitters', defined as patients with ≥ 2 HF admissions in the index year (n = 2587), were of younger age (68 ± 13 vs 69 ± 13 years), more often male (58% vs. 54%), smokers (38.4% vs. 34.4%) and had lower left ventricular ejection fraction (37 ± 17 vs. 41 ± 17%) compared to 'non-frequent admitters' (< 2 HF admissions in the index year; n = 7776) (all P < 0.001). Despite similar rates of advanced care utilization, frequent admitters had longer length of stay (median 4.3 vs. 4.0 days), higher annual inpatient costs (€ 7015 vs. € 2967) and higher all-cause mortality at 3 years compared to the non-frequent admitters (adjusted odds ratio 2.33, 95% confidence interval 2.11-2.58; P < 0.001).

CONCLUSION

'Frequent admitters' have distinct clinical characteristics and worse outcomes compared to non-frequent admitters. This study may provide a means of anticipating the HF readmission burden and thereby aid in healthcare resource distribution relative to the HF admission frequency phenotype.

Perspective Review: Type 2 Diabetes and Readmission for Heart Failure

Heart failure is a leading cause for hospitalisation and for readmission, especially in patients over the age of 65. Diabetes is an increasingly common companion to heart failure. The presence of diabetes and its associated comorbidity increases the risk of adverse outcomes and premature mortality in patients with heart failure. In particular, patients with diabetes are more likely to be readmitted to hospital soon after discharge. This may partly reflect the greater severity of heart disease in these patients. In addition, agents that reduce the chances of readmission such as β-blockers, renin-angiotensin-aldosterone system blockers, and mineralocorticoid receptor antagonists are underutilised because of the perceived increased risks of adverse drug reactions and other limitations. In some cases, readmission to hospital is precipitated by acute decompensation of heart failure (re-exacerbation) leading to pulmonary congestion and/or refractory oedema. However, it appears that for most of the patients admitted and then discharged with a primary diagnosis of heart failure, most readmissions are not due to heart failure, but rather due to comorbidity including arrhythmia, infection, adverse drug reactions, and renal impairment/reduced hydration. All of these are more common in patients who also have diabetes, and all may be partly preventable. The many different reasons for readmission underline the critical value of multidisciplinary comprehensive care in patients admitted with heart failure, especially those with diabetes. A number of new strategies are also being developed to address this area of need, including the use of SGLT2 inhibitors, novel nonsteroidal mineralocorticoid antagonists, and neprilysin inhibitors.