Readmissions after hospitalization for heart failure, acute myocardial infarction, or pneumonia among young and middle-aged adults: a retrospective observational cohort study

Patient-reported sleep and physical function during and after hospitalization

PURPOSE

Poor sleep is associated with morbidity and mortality in the community; however, the health impact of poor sleep during and after hospitalization is poorly characterized. Our purpose was to describe trends in patient-reported sleep and physical function during and after hospitalization and evaluate sleep as a predictor of function after discharge.

METHODS

This is a secondary analysis of trial data with 232 adults followed for 3months after hospital discharge. Main measures were patient-reported surveys on sleep (Pittsburgh Sleep Quality Index) and physical function (Katz Activities of Daily Living, Lawton Instrumental Activities of Daily Living, and Nagi Mobility Scale) were collected during hospitalization and at 1, 5, 9, and 13weeks postdischarge.

RESULTS

Patient-reported sleep declined significantly during hospitalization and remained worse for 3months postdischarge (median Pittsburgh Sleep Quality Index=8 vs. 6, p < .001). In parallel, mobility declined significantly from baseline and remained worse at each follow-up time (median Nagi score=2 vs. 0, p < .001). Instrumental activities of daily living similarly decreased during and after hospitalization, but basic activities of daily living were unaffected. In adjusted time-series logistic regression models, the odds of mobility impairment were 1.48 times higher for each 1-point increase in Pittsburgh Sleep Quality Index score over time (95% CI 1.27-1.71, p < .001).

CONCLUSIONS

Patient-reported sleep worsened during hospitalization, did not improve significantly for 3months after hospitalization, and poor sleep was a significant predictor of functional impairment over this time. Sleep dysfunction that begins with hospitalization may persist and prevent functional recovery after discharge.

TRIAL REGISTRATION

The primary study was registered at ClinicalTrials.gov NCT03321279.

Predictors of 30-Day Readmission in Patients Hospitalized With Heart Failure as a Primary Versus Secondary Diagnosis

Short-term rehospitalizations are common, costly, and detrimental to patients with heart failure (HF). Current research and policy have focused primarily on 30-day readmissions for patients with HF as a primary diagnosis at index hospitalization, whereas a much larger population of patients are admitted with HF as a secondary diagnosis. This study aims to compare patients initially hospitalized for HF as either a primary or a secondary diagnosis, and to identify the most important factors in predicting 30-day readmission. Patients admitted with HF between 2014 and 2016 in the Nationwide Readmissions Database were included and divided into 2 cohorts: those admitted with a primary and secondary diagnosis of HF. Multivariable logistic regression was performed to predict 30-day readmission. Statistically significant predictors in multivariable logistic regression were used for dominance analysis to rank these factors by relative importance. Co-morbidities were the major driver of increased risk of 30-day readmission in both groups. Individual Elixhauser co-morbidities and the Elixhauser co-morbidity indexes were significantly associated with an increase in 30-day readmission. The 5 most important predictors of 30-day readmission according to dominance analysis were age, Elixhauser co-morbidity indexes of co-morbidity complications and readmission, number of diagnoses, and renal failure. These 5 factors accounted for 68% of the 30-day readmission risk. Measures of patient co-morbidities were among the strongest predictors of readmission risk. This study highlights the importance of expanding predictive models to include a broader set of clinical measures to create better-performing models of readmission risk for HF patients.

Predictors of 30-Day Re-admission in Cardiac Patients at Heart Hospital, Qatar

Background

Cardiovascular disease patients are more likely to be readmitted within 30 days of being discharged alive. This causes an enormous burden on health-care systems in terms of poor care of patients and misutilization of resources.

Aims and Objective

This study aims to find out the risk factors associated with 30-day readmission in cardiac patients at Heart Hospital, Qatar.

Methods

A total of 10,550 cardiac patients who were discharged alive within 30 days at the heart hospital in Doha, Qatar, from January 2015 and December 2019 were analyzed. The bootstrap method, an internal validation statistical technique, was applied to present representative estimates for the population.

Results

Out of the 10,550 cardiac patients, there were 8418 (79.8%) index admissions and 2132 (20.2%) re-admitted at least once within 30 days after the index admission. The re-admissions group was older than the index admission group (65.6 ± 13.2 vs. 56.0 ± 13.5, P = 0.001). Multinomial regression analysis showed that females were 30% more likely to be re-admitted than males (adjusted odds ratio [aOR] 1.30, 95% confidence interval [CI]: 1.11-1.50, P = 0.001). Diabetes (aOR 1.36, 95% CI: 1.20-1.53, P = 0.001), chronic renal failure (aOR 1.93, 95% CI: 1.66-2.24, P = 0.001), previous MI (aOR 3.22, 95% CI: 2.85-3.64, P = 0.001), atrial fibrillation (aOR 2.17, 95% C.I. : 1.10-2.67, P = 0.01), cardiomyopathy (aOR 1.72, 95% CI 1.47-2.02, P = 0.001), and chronic heart failure (aOR 1.56, 95% C.I.: 1.33-1.82, P = 0.001) were also independent predictors for re-admission in the regression model. C-statistics showed these variables could predict 82% accurately hospital readmissions within 30 days after being discharged alive.

Conclusion

The model was more than 80% accurate in predicting 30-day readmission after being discharged alive. The presence of five or more risk factors was found to be crucial for readmissions within 30 days. The study may help design interventions that may result in better outcomes with fewer resources in the population.

Cumulative rehospitalizations and implications for subsequent mortality after first-ever ischemic stroke

INTRODUCTION

Clinical implications of readmission following initial hospitalization for acute ischemic stroke (AIS) are not known. We examined predictors of readmissions and impact of readmissions on subsequent mortality after first-ever AIS.

MATERIALS AND METHODS

Adults aged ≥18 years who survived to discharge after hospitalization for first-ever AIS from 2003 to 2019 were included in the study. For each patient, the overall burden of hospitalizations was measured as total number of hospitalizations and aggregate days spent hospitalized during follow-up. We used Poisson regression to estimate incident rate ratios (IRR) for predictors of re-hospitalization and time-dependent Cox regression to estimate hazard ratios (HR) for mortality.

RESULTS

Of 908 AIS survivors, 537 died, 669 had 2,645 readmissions over 4,535 person-years follow-up. Adjusted independent predictors of cumulative readmission inlcuded being white (IRR 1.21, 95% CI 1.03-1.42), dependency on discharge (IRR 1.27, 95% CI 1.17-1.38), cardio-embolism (IRR 1.35, 95% CI 1.18-1.45), smoking (IRR 1.21, 95% CI 1.08-1.35), anemia (IRR 1.40, 95% CI 1.24-1.57), arthritis (IRR 1.20, 95% CI 1.10-1.31), coronary artery disease (IRR 1.34, 95% CI 1.23-1.47), cancer (IRR 1.96, 95% CI 1.64-2.30), chronic kidney disease (IRR 1.36, 95% CI 1.21-1.57), COPD (IRR 1.18, 95% CI 1.04-1.34), depression (IRR 1.50, 95% CI 1.37-1.66), diabetes mellitus (IRR 1.48, 95% CI 1.36-1.48), and heart failure (IRR 1.17, 95% CI 1.03-1.34). Conversely, hyperlipidemia was associated with a lower risk of readmission (IRR 0.79, 95% CI 0.71-0.88). Mortality was significantly increased with each hospitalization and cumulative days spent in hospital.

CONCLUSIONS

Among survivors of AIS hospitalization, certain sociodemographic indicators, stroke-specific features, and several key comorbid conditions were associated with increased risk of readmissions, which in turn correlated with increased mortality. Therefore, lifestyle modification and optimal treatment of comorbidities are likely to improve the outcome after AIS.

Long-term survival and life expectancy following an acute heart failure hospitalization in Australia and New Zealand

AIMS

Contemporary long-term survival following a heart failure (HF) hospitalization is uncertain. We evaluated survival up to 10 years after a HF hospitalization using national data from Australia and New Zealand, identified predictors of survival, and estimated the attributable loss in life expectancy.

METHODS AND RESULTS

Patients hospitalized with a primary diagnosis of HF from 2008-2017 were identified and all-cause mortality assessed by linking with Death Registries. Flexible parametric survival models were used to estimate survival, predictors of survival and loss in life expectancy. A total of 283 048 patients with HF were included (mean age 78.2 ± 12.3 years, 50.8% male). Of these, 48.3% (48.1-48.5) were surviving by 3 years, 34.1% (33.9-34.3) by 5 years and 17.1% (16.8-17.4) by 10 years (median survival 2.8 years). Survival declined with age with 53.4% of patients aged 18-54 years and 6.2% aged ≥85 years alive by 10 years (adjusted hazard ratio [aHR] for mortality 4.84, 95% confidence interval [CI] 4.65-5.04 for ≥85 years vs. 18-54 years) and was worse in male patients (aHR 1.14, 95% CI 1.13-1.15). Prior HF (aHR 1.20, 95% CI 1.18-1.22), valvular and rheumatic heart disease (aHR 1.11, 95% CI 1.10-1.13) and vascular disease (aHR 1.07, 95% CI 1.04-1.09) were cardiovascular comorbidities most strongly associated with long-term death. Non-cardiovascular comorbidities and geriatric syndromes were common and associated with higher mortality. Compared with the general population, HF was associated with a loss of 7.3 years in life expectancy (or 56.6% of the expected life expectancy) and reached 20.5 years for those aged 18-54 years.

CONCLUSION

Less than one in five patients hospitalized for HF were surviving by 10 years with patients experiencing almost 60% loss in life expectancy compared with the general population, highlighting the considerable persisting societal burden of HF. Concerted multidisciplinary efforts are needed to improve post-hospitalization outcomes of HF.

Mortality from Heart Failure with Mid-Range Ejection Fraction

BACKGROUND

The prognostic importance of the classification 'heart failure (HF) with mid-range ejection fraction (EF)' remains uncertain.

OBJECTIVE

To analyze the clinical characteristics, comorbidities, complications, and in-hospital and late mortality of patients classified as having HF with mid-range EF (HFmrEF - EF: 40%-49%), and to compare them to those of patients with HF with preserved EF (HFpEF - EF > 50%) and with HF with reduced EF (HFrEF - EF < 40%) on admission for decompensated HF.

METHODS

Ambispective cohort of patients admitted to the cardiac intensive care unit due to decompensated HF. Clinical characteristics, comorbidities, complications, and in-hospital and late mortality were assessed. The software R was used, with a 5% significance, for the tests chi-square, analysis of variance, Cox multivariate, and Kaplan-Meier survival curve, in addition to machine-learning techniques (Elastic Net and survival tree).

RESULTS

519 individuals were included between September 2011 and June 2019 (mean age, 74.87 ± 13.56 years; 57.6% were men). The frequencies of HFpEF, HFmrEF and HFrEF were 25.4%, 27% and 47.6%, respectively. Previous infarction was more frequent in HFmrEF. The mean follow-up time was 2.94 ± 2.55 years, with no statistical difference in mortality between the groups (53.8%, 52.1%, 57.9%). In the survival curve, there was difference between neither the HFpEF and HFmrEF groups, nor the HFpEF and HFrEF groups, but between the HFmrEF and HFrEF groups. Age over 77 years, previous HF, history of readmission, dementia and need for vasopressors were associated with higher late mortality in the survival tree.

CONCLUSION

The EF was not selected as a variable associated with mortality in patients with decompensated HF.

Identify Early and Involve Everyone: Interdisciplinary Comprehensive Care Pathway Developed for Inpatient Management and Transitions of Care for Heart Failure Patients Reported Using SQUIRE 2.0 Guidelines

Introduction

Heart failure accounts for 1-2% of overall healthcare costs. While the link between re-hospitalization and mortality is unclear, care pathways that standardize inpatient management and establish outpatient follow-up improve patient outcomes and reduce morbidity.

Aim

To implement a comprehensive interdisciplinary care pathway for heart failure patients with the goal of optimizing inpatient management and improving transitions of care.

Methods

To address this clinical need, New York-Presbyterian Brooklyn Methodist Hospital (NYP-BMH) identified resources needed to optimize patient care, developed an inpatient admission order set (so-called “power plan”), and implemented a multidisciplinary clinical care pathway. The Plan-Do-Study-Act cycle addressed the implementation obstacles. Interdisciplinary rounds guided day-to-day management and addressed barriers. Our team developed a sustainable care pathway, and measured the utilization of pharmacy, nutrition, physical therapy, case management, and social work resources; outpatient appointments were made prior to discharge. We used the Standards for Quality Improvement Reporting Excellence (SQUIRE) 2.0 guidelines to guide our planning and evaluation of this quality improvement initiative.

Results

Our intervention markedly increased the number of heart failure hospitalizations that were identified on admission, and the use of pharmacy/nutrition services was greater after the intervention. The utilization of our “power plan” promoted adherence to a series of evidence-based best practices, but these measures had no significant impact on readmissions as a whole. The involvement of the case management support team increased outpatient appointments made for patients prior to discharge and aided in the transition of care from inpatient to outpatient management.

Conclusion

The management of heart failure patients starts in the hospital and continues in the community. Patients who are treated in a standardized dedicated care pathway have reduced morbidity and better outcomes. Identifying these patients early, involving a comprehensive team, and transitioning their care to the outpatient setting improves the quality of care in these patients.

Associations between Multimorbidity Patterns and Subsequent Labor Market Marginalization among Refugees and Swedish-Born Young Adults-A Nationwide Registered-Based Cohort Study

Background: Young refugees are at increased risk of labor market marginalization (LMM). We sought to examine whether the association of multimorbidity patterns and LMM differs in refugee youth compared to Swedish-born youth and identify the diagnostic groups driving this association. Methodology: We analyzed 249,245 individuals between 20–25 years, on 31 December 2011, from a combined Swedish registry. Refugees were matched 1:5 to Swedish-born youth. A multimorbidity score was computed from a network of disease co-occurrences in 2009–2011. LMM was defined as disability pension (DP) or >180 days of unemployment during 2012–2016. Relative risks (RR) of LMM were calculated for 114 diagnostic groups (2009–2011). The odds of LMM as a function of multimorbidity score were estimated using logistic regression. Results: 2841 (1.1%) individuals received DP and 16,323 (6.5%) experienced >180 annual days of unemployment during follow-up. Refugee youth had a marginally higher risk of DP (OR (95% CI): 1.59 (1.52, 1.67)) depending on their multimorbidity score compared to Swedish-born youth (OR (95% CI): 1.51 (1.48, 1.54)); no differences were found for unemployment (OR (95% CI): 1.15 (1.12, 1.17), 1.12 (1.10, 1.14), respectively). Diabetes mellitus and influenza/pneumonia elevated RR of DP in refugees (RRs (95% CI) 2.4 (1.02, 5.6) and 1.75 (0.88, 3.45), respectively); most diagnostic groups were associated with a higher risk for unemployment in refugees. Conclusion: Multimorbidity related similarly to LMM in refugees and Swedish-born youth, but different diagnoses drove these associations. Targeted prevention, screening, and early intervention strategies towards specific diagnoses may effectively reduce LMM in young adult refugees.

Man vs. Machine: Comparing Physician vs. Electronic Health Record-Based Model Predictions for 30-Day Hospital Readmissions

BACKGROUND

Electronic health record (EHR)-based readmission risk prediction models can be automated in real-time but have modest discrimination and may be missing important readmission risk factors. Clinician predictions of readmissions may incorporate information unavailable in the EHR, but the comparative usefulness is unknown. We sought to compare clinicians versus a validated EHR-based prediction model in predicting 30-day hospital readmissions.

METHODS

We conducted a prospective survey of internal medicine clinicians in an urban safety-net hospital. Clinicians prospectively predicted patients' 30-day readmission risk on 5-point Likert scales, subsequently dichotomized into low- vs. high-risk. We compared human with machine predictions using discrimination, net reclassification, and diagnostic test characteristics. Observed readmissions were ascertained from a regional hospitalization database. We also developed and assessed a "human-plus-machine" logistic regression model incorporating both human and machine predictions.

RESULTS

We included 1183 hospitalizations from 106 clinicians, with a readmission rate of 20.8%. Both clinicians and the EHR model had similar discrimination (C-statistic 0.66 vs. 0.66, p = 0.91). Clinicians had higher specificity (79.0% vs. 48.9%, p < 0.001) but lower sensitivity (43.9 vs. 75.2%, p < 0.001) than EHR model predictions. Compared with machine, human was better at reclassifying non-readmissions (non-event NRI + 30.1%) but worse at reclassifying readmissions (event NRI - 31.3%). A human-plus-machine approach best optimized discrimination (C-statistic 0.70, 95% CI 0.67-0.74), sensitivity (65.5%), and specificity (66.7%).

CONCLUSION

Clinicians had similar discrimination but higher specificity and lower sensitivity than EHR model predictions. Human-plus-machine was better than either alone. Readmission risk prediction strategies should incorporate clinician assessments to optimize the accuracy of readmission predictions.

Trends in 30-day readmissions following hospitalisation for heart failure by sex, socioeconomic status and ethnicity

BACKGROUND

Reducing the high patient and economic burden of early readmissions after hospitalisation for heart failure (HF) has become a health policy priority of recent years.

METHODS

An observational study linking Hospital Episode Statistics to socioeconomic and death data in England (2002-2018). All first hospitalisations with a primary discharge code for HF were identified. Quasi-poisson models were used to investigate trends in 30-day readmissions by age, sex, socioeconomic status and ethnicity.

FINDINGS

There were 698,983 HF admissions, median age 81 years [IQR 14].In-hospital deaths reduced by 0.7% per annum (pa), whilst additional deaths at 30-days remained stable at 5%. Age adjusted 30-day readmissions (21% overall), increased by 1.4% pa (95% CI 1.3-1.5). Readmissions for HF (6%) and 'other cardiovascular disease (CVD)' (3%) remained stable, but readmissions for non-CVD causes (12%) increased at a rate of 2.6% (2.4-2.7) pa. Proportions were similar by sex but trends diverged by ethnicity. Black groups experienced an increase in readmissions for HF (1.8% pa, interaction-p 0.03) and South Asian groups had more rapidly increasing readmission rates for non-CVD causes (interaction-p 0.04). Non-CVD readmissions were also more prominent in the least (15%; 15-15) compared to the most affluent group (12%; 12-12). Strongest predictors for HF readmission were Black ethnicity and chronic kidney disease, whilst cardiac procedures were protective. For non-CVD readmissions, strongest predictors were non-CVD comorbidities, whilst cardiologist care was protective.

INTERPRETATION

In HF, despite readmission reduction policies, 30-day readmissions have increased, impacting the least affluent and ethnic minority groups the most.

FUNDING

NIHR.

The use of clinical decision support in reducing readmissions for patients with heart failure: a quasi-experimental study

BACKGROUND

Heart failure is a chronic, progressive condition which affects over six million Americans and 26 million people worldwide. Evidence-based guidelines, protocols, and decision-support tools are available to enhance the quality of care delivery but are not implemented consistently.

AIMS

To examine the effect of clinical decision-making support during patient discharge on 30-day hospital readmission among patients admitted with heart failure and evaluate provider utilization and satisfaction of clinical decision support tool.

DESIGN

A quasi-experimental study.

METHODS

An intervention group of hospitalized patients (N = 55) with heart failure were provided the intervention over a 3-month period and compared to the pre-intervention comparison group (N = 109) of patients who did not receive the intervention. An evidence-based discharge checklist and a pocket guide was implemented by an advanced practice nurse to assist health providers with clinical decision making. Descriptive statistics among samples, 30-day readmission rates, and provider utilization and satisfaction were examined.

RESULTS

Readmission rates slightly decreased (N = 109, 9.2% vs. N = 55, 9.1%) in the post-intervention period, but no significant difference. Heterogeneity between the two groups were minimal related to use of specific medications, age, length-of-stay and comorbidities. Descriptively, there was a significant difference the use of diuretics among each group (p = .002).The discharge checklist was used regularly by 67% of (N = 15) providers, and 93% expressed satisfaction with use.

CONCLUSION

There was no significant reduction in 30-day readmission rates between both groups. However, a slight reduction was noted which indicates the need for further examination into how the use of checklists for clinical decision support can reduce readmissions. A well-designed evidence-based discharge plan remains a critical component of the patient discharge process. Advance practice nurses are uniquely qualified to implement evidence-based interventions that promote practice change among health care providers and improve health outcomes.

Readmissions and costs among younger and older adults for targeted conditions during the enactment of the hospital readmission reduction program

Background

The Hospital Readmissions Reduction Program (HRRP) was introduced to reduce readmission rates among Medicare beneficiaries, however little is known about readmissions and costs for HRRP-targeted conditions in younger populations. The primary objective of this study was to examine readmission trends and costs for targeted conditions during policy implementation among younger and older adults in the U.S.

Methods

We analyzed the Nationwide Readmission Database from January 2010 to September 2015 in younger (18–64 years) and older (≥65 years) patients with acute myocardial infarction (AMI), heart failure (HF), pneumonia, and acute exacerbations of chronic obstructive pulmonary disease (AECOPD). Pre- and post-HRRP periods were defined based on implementation of the policy for each condition. Readmission rates were evaluated using an interrupted time series with difference-in-difference analyses and hospital cost differences between early and late readmissions (≤30 vs. > 30 days) were evaluated using generalized linear models.

Results

Overall, this study included 16,884,612 hospitalizations with 3,337,266 readmissions among all age groups and 5,977,177 hospitalizations with 1,104,940 readmissions in those aged 18–64 years. Readmission rates decreased in all conditions. In the HRRP announcement period, readmissions declined significantly for those aged 40–64 years for AMI (p < 0.0001) and HF (p = 0.003). Readmissions decreased significantly in the post-HRRP period for those aged 40–64 years at a slower rate for AMI (p = 0.003) and HF (p = 0.05). Readmission rates among younger patients (18–64 years) varied within all four targeted conditions in HRRP announcement and post-HRRP periods. Adjusted models showed a significantly higher readmission cost in those readmitted within 30 days among younger and older populations for AMI (p < 0.0001), HF (p < 0.0001), pneumonia (p < 0.0001), and AECOPD (p < 0.0001).

Conclusion

Readmissions for targeted conditions decreased in the U.S. during the enactment of the HRRP policy and younger age groups (< 65 years) not targeted by the policy saw a mixed effect. Healthcare expenditures in younger and older populations were significantly higher for early readmissions with all targeted conditions. Further research is necessary evaluating total healthcare utilization including emergency department visits, observation units, and hospital readmissions in order to better understand the extent of the HRRP on U.S. healthcare.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12913-021-06399-z.

High Job Burnout Predicts Low Heart Rate Variability in the Working Population after a First Episode of Acute Coronary Syndrome

(1) Background: Job burnout may affect the prognosis of patients with acute coronary syndrome (ACS) through mechanisms involving heart rate variability (HRV). However, no study has yet examined those potential associations. Hence, we conducted the present study to investigate this issue. (2) Method: Participants included patients who presented with a first episode of ACS and who were employed. The Copenhagen Burnout Inventory (CBI) was used to assess job burnout. Twenty-four-hour ambulatory electrocardiography recorded HRV on four occasions, i.e., during the hospitalization and follow-ups at one, six, and 12 months, respectively. (3) Results: A total of 120 participants who at least completed three Holter examinations throughout the study were enrolled in the final analysis. Job burnout scores at baseline were inversely associated with LnSDNN, LnTP, LnHF, LnLF, LnULF, and LnVLF during the consequent one-year follow-up. Each 1 SD increase in job burnout scores predicted a decline ranging from 0.10 to 0.47 in the parameters described above (all p < 0.05), and all relationships were independent of numerous confounders, including anxiety and depression. (4) Conclusion: High job burnout predicted reduced HRV parameters during the one-year period post-ACS in the working population.

Longitudinal course of GDF15 levels before acute hospitalization and death in the general population

Growth differentiation 15 (GDF15) is a potential novel biomarker of biological aging. To separate the effects of chronological age and birth cohort from biological age, longitudinal studies investigating the associations of GDF15 levels with adverse health outcomes are needed. We investigated changes in GDF15 levels over 10 years in an age-stratified sample of the general population and their relation to the risk of acute hospitalization and death. Serum levels of GDF15 were measured three times in 5-year intervals in 2176 participants aged 30, 40, 50, or 60 years from the Danish population-based DAN-MONICA cohort. We assessed the association of single and repeated GDF15 measurements with the risk of non-traumatic acute hospitalizations. We tested whether changes in GDF15 levels over 10 years differed according to the frequency of hospitalizations within 2 years or survival within 20 years, after the last GDF15 measurement. The change in GDF15 levels over time was dependent on age and sex. Higher GDF15 levels and a greater increase in GDF15 levels were associated with an increased risk of acute hospitalization in adjusted Cox regression analyses. Participants with more frequent admissions within 2 years, and those who died within 20 years, after the last GDF15 measurement already had elevated GDF15 levels at baseline and experienced greater increases in GDF15 levels during the study. The change in GDF15 levels was associated with changes in C-reactive protein and biomarkers of kidney, liver, and cardiac function. Monitoring of GDF15 starting in middle-aged could be valuable for the prediction of adverse health outcomes.

Social determinants of health affect unplanned readmissions following acute myocardial infarction

Background: Low socioeconomic status predicts inferior clinical outcomes in many patient populations. The effects of patient insurance status and hospital safety-net status on readmission rates following acute myocardial infarction are unclear. Materials & methods: A retrospective review of State Inpatient Databases for New York, California, Florida and Maryland, 2007-2014. Results: A total of 1,055,162 patients were included. Medicaid status was associated with 37.7 and 44.0% increases in risk-adjusted readmission odds at 30 and 90 days (p < 0.0001). Uninsured status was associated with reduced odds of readmission at both time points. High-burden safety-net status was associated with 9.6 and 9.5% increased odds of readmission at 30 and 90 days (p < 0.0003). Conclusion: Insurance status and hospital safety-net burden affect readmission odds following acute myocardial infarction.

Development of a risk prediction model of potentially avoidable readmission for patients hospitalised with community-acquired pneumonia: study protocol and population

INTRODUCTION

30-day readmission rate is considered an adverse outcome reflecting suboptimal quality of care during index hospitalisation for community-acquired pneumonia (CAP). However, potentially avoidable readmission would be a more relevant metric than all-cause readmission for tracking quality of hospital care for CAP. The objectives of this study are (1) to estimate potentially avoidable 30-day readmission rate and (2) to develop a risk prediction model intended to identify potentially avoidable readmissions for CAP.

METHODS AND ANALYSIS

The study population consists of consecutive patients admitted in two hospitals from the community or nursing home setting with pneumonia. To qualify for inclusion, patients must have a primary or secondary discharge diagnosis code of pneumonia. Data sources include routinely collected administrative claims data as part of diagnosis-related group prospective payment system and structured chart reviews. The main outcome measure is potentially avoidable readmission within 30 days of discharge from index hospitalisation. The likelihood that a readmission is potentially avoidable will be quantified using latent class analysis based on independent structured reviews performed by four panellists. We will use a two-stage approach to develop a claims data-based model intended to identify potentially avoidable readmissions. The first stage implies deriving a clinical model based on data collected through retrospective chart review only. In the second stage, the predictors comprising the medical record model will be translated into International Classification of Diseases, 10th revision discharge diagnosis codes in order to obtain a claim data-based risk model.The study sample consists of 1150 hospital stays with a diagnosis of CAP. 30-day index hospital readmission rate is 17.5%.

ETHICS AND DISSEMINATION

The protocol was reviewed by the Comité de Protection des Personnes Sud Est V (IRB#6705). Efforts will be made to release the primary study results within 6 months of data collection completion.

TRIAL REGISTRATION NUMBER

ClinicalTrials.gov Registry (NCT02833259).

Frequency, trends and institutional variation in 30-day all-cause mortality and unplanned readmissions following hospitalisation for heart failure in Australia and New Zealand

AIMS

National 30-day mortality and readmission rates after heart failure (HF) hospitalisations are a focus of US policy intervention and yet have rarely been assessed in other comparable countries. We examined the frequency, trends and institutional variation in 30-day mortality and unplanned readmission rates after HF hospitalisations in Australia and New Zealand.

METHODS AND RESULTS

We included patients >18 years hospitalised with HF at all public and most private hospitals from 2010-15. The primary outcomes were the frequencies of 30-day mortality and unplanned readmissions, and the institutional risk-standardised mortality rate (RSMR) and readmission rate (RSRR) evaluated using separate cohorts. The mortality cohort included 153 592 patients (mean age 78.9 ± 11.8 years, 51.5% male) with 16 442 (10.7%) deaths within 30 days. The readmission cohort included 148 704 patients (mean age 78.6 ± 11.9 years, 51.7% male) with 33 158 (22.3%) unplanned readmission within 30 days. In 392 hospitals with at least 25 HF hospitalisations, the median RSMR was 10.7% (range 6.1-17.3%) with 59 hospitals significantly different from the national average. Similarly, in 391 hospitals with at least 25 HF hospitalisations, the median RSRR was 22.3% (range 17.7-27.1%) with 24 hospitals significantly different from the average. From 2010-15, the adjusted 30-day mortality [odds ratio (OR) 0.991/month, 95% confidence interval (CI) 0.990-0.992, P < 0.01] and unplanned readmission (OR 0.998/month, 95% CI 0.998-0.999, P < 0.01) rates declined.

CONCLUSION

Within 30 days of a HF hospitalisation, one in 10 patients died and almost a quarter of those surviving experienced an unplanned readmission. The risk of these outcomes varied widely among hospitals suggesting disparities in HF care quality. Nevertheless, a substantial decline in 30-day mortality and a modest decline in readmissions occurred over the study period.

Patient Readmission Rates For All Insurance Types After Implementation Of The Hospital Readmissions Reduction Program

Since the implementation of the Hospital Readmissions Reduction Program (HRRP), readmissions have declined for Medicare patients with conditions targeted by the policy (acute myocardial infarction, heart failure, and pneumonia). To understand whether HRRP implementation was associated with a readmission decline for patients across all insurance types (Medicare, Medicaid, and private), we conducted a difference-in-differences analysis using information from the Nationwide Readmissions Database. We compared how quarterly readmissions for target conditions changed before (2010-12) and after (2012-14) HRRP implementation, using nontarget conditions as the control. Our results demonstrate that readmissions declined at a significantly faster rate after HRRP implementation not just for Medicare patients but also for those with Medicaid, both in the aggregate and for individual target conditions. However, composite Medicaid readmission rates remained higher than those for Medicare. Throughout the study period privately insured patients had the lowest aggregate readmission rates, which declined at a similar rate compared to nontarget conditions. The HRRP was associated with nationwide readmission reductions beyond the Medicare patients originally targeted by the policy. Further research is needed to understand the specific mechanisms by which hospitals have achieved reductions in readmissions.

Thirty-Day Hospital Readmission After Acute Myocardial Infarction in China

Background Readmission after acute myocardial infarction in low- and middle-income countries like China is not well characterized. Methods and Results We approached consecutive patients with acute myocardial infarction hospitalized within 24 hours of symptom onset and discharged alive from 53 geographically diverse hospitals in China. We described rates of unplanned 30-day readmission, their timing and admitting diagnoses, and fit Cox proportional hazards models to identify factors associated with readmission. Among 3387 patients, median (interquartile range) age was 61 (52-69) years, and 76.9% were men. The index median length of stay was 11 (8-14) days. Unplanned 30-day readmission occurred in 6.3% of the cohort; most readmissions (77.7%) were for cardiovascular diagnoses. Nearly half (41.9% of all-cause readmissions; 44.3% of cardiovascular readmissions) occurred within 5 days of discharge. Mini-Global Registry of Acute Coronary Events scores at admission (hazard ratio [HR], 1.15 for every 10-point increase; 95% CI, 1.05-1.25), longer length of stay (HR, 1.03; 95% CI, 1.00-1.06 for each extra day), and in-hospital recurrent angina (HR, 1.40; 95% CI, 1.04-1.89) were associated with higher unplanned all-cause readmission. Revascularization during the index hospitalization (70.2% of the cohort) was associated with lower risks of all-cause readmission (HR, 0.27; 95% CI, 0.18-0.42). In addition, left ventricular ejection fraction <0.4 (HR, 1.79; 95% CI, 1.05-3.07) and in-hospital complication (HR, 1.20; 95% CI, 1.03-1.39) were associated with higher risk of unplanned cardiovascular readmission, and ST-segment-elevation myocardial infarction (HR, 0.60; 95% CI, 0.36-0.98) was associated with lower risk of unplanned cardiovascular readmission. Sex, family income, depression, stress level, lower social support, disease-specific health status, and medications were not associated with readmission. Conclusions In China, most readmissions are for cardiovascular events, and almost half occur within 5 days of discharge. Clinical factors identify patients at higher and lower unplanned readmissions. Clinical Trial Registration URL: https://www.clinicaltrials.gov . Unique identifier: NCT01624909.

Clinical Characteristics and Factors Associated with Heart Failure Readmission at a Tertiary Hospital in North-Eastern Tanzania

INTRODUCTION

Heart failure (HF) is characterized by frequent episodes of decompensation, leading to a high hospitalization burden. More than 50% of index hospitalizations for HF patients return within 6 months of discharge. Once the patient is readmitted, the risk of further disease progression and the mortality rate are increased. A lot of patients are readmitted due to factors such as poor medication adherence, infections, or worsening comorbidities. The aim of our study was to identify the inpatient burden of HF readmission and to identify the factors associated with early readmission.

METHODS

A hospital-based cross-sectional analytical study was conducted from November 2018 to April 2019 within the medical wards at Kilimanjaro Christian Medical Centre (KCMC), which is a teaching and referral hospital in north-eastern Tanzania. The study population included all patients with HF admitted within the medical ward. Data were collected using questionnaires and blood and radiological investigations, and analysis was done using Statistical Package for Social Science (SPSS) version 25. Chi-square test was used to compare proportions of categorical variables. Logistic regression was used to determine the likelihood for readmission, and p-value of <0.05 was considered to be statistically significant.

RESULTS

A total of 353 patients were identified with HF, of whom 136 (38.5%) had a previous admission. Of the 136 patients analysed, the mean age was 62.8 years (SD 17.1), and 86 (63.2%) were females. Within 30 days after discharge, 34 (25.0%) of the patients were readmissions. Factors for early readmission were unemployment (OR = 2.38, 95% CI = 1.02-5.54, p = 0.043), poor medication adherence (OR = 3.87, 95% CI = 1.67-8.97, p = 0.002), absence of angiotensin-converting enzyme inhibitor (ACEI) or angiotensin receptor blocker (ARB) (OR = 2.40, 95% CI = 1.09-5.31, p = 0.030), and pleural effusion (OR 3.25, 95% CI = 1.44-7.32, p = 0.004).

CONCLUSION

Heart failure is a burden due to a large number of admissions and readmissions. Factors such as poor medication adherence and absence of adequate HF therapy, especially the absence of regimes containing ACEI or ARB, need to be targeted to reduce the number of readmissions. This will help reduce the risk of further decompensations, disease progression, and mortality rate.

Insights into hospital readmission patterns of atrial fibrillation patients

INTRODUCTION

Patients admitted to the hospital with atrial fibrillation have associated morbidity and mortality and incur significant costs. Data characterizing atrial fibrillation patients at high risk for readmission are scarce. We sought to inform this area by characterizing and categorizing unplanned readmissions of atrial fibrillation patients.

METHODS

Retrospective data were abstracted from the charts of patients discharged from 2008 to 2012 after an index hospitalization for atrial fibrillation and referred to the nurse practitioner-led transitional care program, Bridging the Discharge Gap Effectively. Unplanned readmissions were dichotomized as early (⩽30 days) or late (31-180 days) and further classified as either "atrial fibrillation/atrial fibrillation-related" (AF/AF-related), "Cardiac; not AF/AF-related," or "Not cardiac-related." Case classifications were adjudicated by a senior cardiologist. Patient demographics and readmission characteristics were then compared.

RESULTS

Of 255 patients, 97 (38.0%) had unplanned readmissions within 180 days of discharge; 45 (46.4%) were early and 52 (53.6%) were late. Atrial fibrillation and cardiac causes accounted for 68.9% (n=31) of early readmissions and 65.4% (n=34) of late. Patients with late readmissions were more likely to have diabetes (32.7% vs. 17.7%, p=.022) and higher CHA₂DS₂VASc scores (3.63 vs. 2.98, p=0.026) than those not readmitted. No other differences in baseline characteristics were seen within or between groups. The 30-day all-cause readmission rate in this sample was 17.6% (n=45).

CONCLUSION

Readmissions following hospital discharge for atrial fibrillation are common; approximately 50% of these readmissions are for reasons unrelated to atrial fibrillation. In order to reduce atrial fibrillation-related readmissions, further research is needed to characterize predictors of readmission and to develop effective transitional care interventions.

Association between age and readmission after percutaneous coronary intervention for acute myocardial infarction

OBJECTIVE

This study aimed to investigate the association between age and the risk of 30-day unplanned readmission among adult patients with acute myocardial infarction (AMI) undergoing percutaneous coronary intervention (PCI).

METHODS

This retrospective analysis included patients from the Nationwide Readmissions Database with AMI who underwent PCI during 2013-2014. We used multivariable logistic regression model to calculate adjusted odds ratios (AORs) for risk of readmission. To examine potential non-linear association, we performed logistic regression with restricted cubic splines (RCS).

RESULTS

Of the 492 550 patients with AMI aged above 18 years undergoing PCI during the index hospitalisation, 48 630 (9.87%) were readmitted within 30 days. Although the crude readmission rate of younger patients (aged 18-54 years) was the lowest (7.27%), younger patients had higher risk of readmission compared with patients aged 55-64 years for all-causes (AOR 1.06 (1.01 to 1.11), p=0.0129) and specific causes, such as AMI and chest pain (both cardiac and non-specific) after adjusted for covariates. Patients aged 65-74 years were at lower risk of all-cause readmission. Older patients (age ≥75 years) had higher risk of readmission for heart failure (AOR 1.50 (1.29 to 1.74)) and infection (AOR 1.44 (1.16 to 1.79)), but lower risk for chest pain. RCS analyses showed a U-shaped relationship between age and readmission risk.

CONCLUSIONS

Our results suggest higher risk of readmission in younger patients for all-cause unplanned readmission after adjusted for covariates. The trends of readmission risk along with age were different for specific causes. Age-targeted initiatives are warranted to reduce preventable readmissions in patients with AMI undergoing PCI.

"Bridging the Gap" Everything that Could Have Been Avoided If We Had Applied Gender Medicine, Pharmacogenetics and Personalized Medicine in the Gender-Omics and Sex-Omics Era

Gender medicine is the first step of personalized medicine and patient-centred care, an essential development to achieve the standard goal of a holistic approach to patients and diseases. By addressing the interrelation and integration of biological markers (i.e., sex) with indicators of psychological/cultural behaviour (i.e., gender), gender medicine represents the crucial assumption for achieving the personalized health-care required in the third millennium. However, ‘sex’ and ‘gender’ are often misused as synonyms, leading to frequent misunderstandings in those who are not deeply involved in the field. Overall, we have to face the evidence that biological, genetic, epigenetic, psycho-social, cultural, and environmental factors mutually interact in defining sex/gender differences, and at the same time in establishing potential unwanted sex/gender disparities. Prioritizing the role of sex/gender in physiological and pathological processes is crucial in terms of efficient prevention, clinical signs’ identification, prognosis definition, and therapy optimization. In this regard, the omics-approach has become a powerful tool to identify sex/gender-specific disease markers, with potential benefits also in terms of socio-psychological wellbeing for each individual, and cost-effectiveness for National Healthcare systems. “Being a male or being a female” is indeed important from a health point of view and it is no longer possible to avoid “sex and gender lens” when approaching patients. Accordingly, personalized healthcare must be based on evidence from targeted research studies aimed at understanding how sex and gender influence health across the entire life span. The rapid development of genetic tools in the molecular medicine approaches and their impact in healthcare is an example of highly specialized applications that have moved from specialists to primary care providers (e.g., pharmacogenetic and pharmacogenomic applications in routine medical practice). Gender medicine needs to follow the same path and become an established medical approach. To face the genetic, molecular and pharmacological bases of the existing sex/gender gap by means of omics approaches will pave the way to the discovery and identification of novel drug-targets/therapeutic protocols, personalized laboratory tests and diagnostic procedures (sex/gender-omics). In this scenario, the aim of the present review is not to simply resume the state-of-the-art in the field, rather an opportunity to gain insights into gender medicine, spanning from molecular up to social and psychological stances. The description and critical discussion of some key selected multidisciplinary topics considered as paradigmatic of sex/gender differences and sex/gender inequalities will allow to draft and design strategies useful to fill the existing gap and move forward.

Long-Term Time-Varying Risk of Readmission After Acute Myocardial Infarction

Background

Readmission after myocardial infarction (MI) is a publicly reported quality metric with hospital reimbursement linked to readmission rates. We describe the timing and pattern of readmission by cause within the first year after MI in consecutive patients, regardless of revascularization strategy, payer status, or age.

Methods and Results

We identified patients discharged after an MI from April 2008 to June 2012. Readmission within 12 months was the primary end point. Readmissions were classified into 4 groups: MI related, other cardiovascular, noncardiovascular, and planned. A total of 3069 patients were discharged after an MI (average age, 65±13 years; and 1941 [63%] men). A total of 655 patients (21.3%) were readmitted at least once (897 total readmissions). A total of 147 patients (4.8%) were readmitted ≥2 times, accounting for 389 readmissions (43%). The instantaneous risk of all‐cause readmission was highest (15 readmissions/100 patients per month; 95% confidence interval, 12–19 readmissions/100 patients per month) immediately after discharge, decreased by almost half (8.1 readmissions/100 patients per month; 95% confidence interval, 7.2–9.0 readmissions/100 patients per month) within 15 days, and was substantially lower and relatively constant (1.4 readmissions/100 patients per month; 95% confidence interval, 1.2–1.6 readmissions/100 patients per month) out to 1 year. Cardiovascular causes of readmission were more common early after discharge.

Conclusions

Most patients with MI are never readmitted, whereas a small minority (≈5%) account for nearly half of 1‐year readmissions. The readmission pattern after MI is characterized by an early peak (first 15 days) of cardiovascular readmissions, followed by a middle period (months 1–4) of noncardiovascular readmissions, and ending with a low‐risk period (>4 months) during which the risk appears independent of cause.

See Editorial by Levy and Allen

Effectiveness of a nursing training intervention in complex chronic patients

OBJECTIVE

To evaluate whether a training intervention performed by nursing professionals in complex chronic patients, during hospitalisation in an internal medicine service, can modify the pattern of readmissions or reduce their number.

METHOD

Pragmatic clinical trial of a nursing training intervention vs. habitual performance. For the intervention group, a training plan in care was designed, personalised for each patient, according to the needs detected in a first interview. The intervention was extended during the time of admission and a contact phone was available after discharge to resolve doubts.

RESULTS

Among the 498 patients interviewed initially, 131 were excluded because they were not a complex chronic patient or because they found no deficiencies in their training or care. One patient (.20%) did not agree to participate and there were no dropouts. Of the 366 participants, 190 were included in the intervention group and 176 in the control group. In the first 8 days after discharge, 2 (1.05%) patients from the intervention group and 8 (4.54%) from the control group were re-admitted (p=.05). In the first 30 days after discharge, 26 patients (13.70%) and 33 patients (18.75%) respectively (p=.10) were readmitted.

CONCLUSIONS

This study shows how a nursing training intervention during hospitalisation in Internal Medicine in complex chronic patients reduces short-term readmissions.

Understanding Readmissions in Medicare Beneficiaries During the 90-Day Follow-Up Period of an Acute Myocardial Infarction Admission

Background

Medicare has a voluntary episodic payment model for Medicare beneficiaries that bundles payment for the index acute myocardial infarction (AMI) hospitalization and all post‐discharge services for a 90‐day follow‐up period. The purpose of this study is to report on the types and frequency of readmissions and identify demographic and clinical factors associated with readmission of Medicare beneficiaries that survived their AMI hospitalization.

Methods and Results

This retrospective study used the Inpatient Standard Analytical File for 2014. There were 143 286 Medicare beneficiaries with AMI who were discharged alive from 3619 hospitals. All readmissions occurring in any hospital within 90 days of the index AMI discharge date were identified. Of 143 286 Medicare beneficiaries discharged alive from their index AMI hospitalization, 28% (40 145) experienced at least 1 readmission within 90 days and 8% (11 477) had >1 readmission. Readmission rates were higher among Medicare beneficiaries who did not undergo a percutaneous coronary intervention in their index AMI admission (34%) compared with those that underwent a percutaneous coronary intervention (20.2%). Using all Medicare beneficiary's index AMI, 27 comorbid conditions were significantly associated with the likelihood of a Medicare beneficiary having a readmission during the follow‐up period. The strongest clinical characteristics associated with readmissions were dialysis dependence, type 1 diabetes mellitus, and heart failure.

Conclusions

This study provides benchmark information on the types of hospital readmissions Medicare beneficiaries experience during a 90‐day AMI bundle. This paper also suggests that interventions are needed to alleviate the need for readmissions in high‐risk populations, such as, those managed medically and those at risk of heart failure.

Readmissions After Acute Myocardial Infarction: How Often Do Patients Return to the Discharging Hospital?

Background

When patients require readmission after a recent myocardial infarction (MI), returning to the discharging (index) hospital may be associated with better outcomes as a result of greater continuity in care. However, little evidence exists to answer this frequent patient question.

Methods and Results

Among Medicare patients aged ≥65 years discharged home alive post‐MI from 491 US hospitals in the ACTION (Acute Coronary Treatment Intervention Outcomes Network) Registry, we compared reason for readmission, duration of rehospitalization, and 30‐day mortality between patients readmitted to the index versus nonindex hospital within 30 days of index MI discharge. Among 53 471 MI patients, 7715 (14%) were readmitted within 30 days, and most readmitted patients (73%) returned to the discharging hospital. Reason for readmission was not significantly associated with location of readmission. In multivariable modeling, the strongest factors associated with readmission to a nonindex hospital were distance from the discharging hospital, transfer‐in during the index MI hospitalization, and frequency of nonindex hospital admissions in the year preceding to the index MI. Duration of rehospitalization did not differ significantly between patients readmitted to the index versus nonindex hospital (median, 4 versus 3 days; P=0.17). Mortality risk was also not significantly different between patients readmitted to the index versus nonindex hospital overall (7.4 versus 7.7%; adjusted odds ratio, 0.89; 95% CI, 0.73–1.10) and when stratified by reason for readmission (P for interaction=0.61).

Conclusions

Post‐MI readmissions did not differ in reason for readmission, duration of rehospitalization, or associated mortality when compared between patients who returned to the discharging hospital and those who sought care elsewhere.

The prevalence of 30-day readmission after acute myocardial infarction: A systematic review and meta-analysis

Objective

The 30‐day readmission is associated with increased medical costs, which has become an important quality metric in several medical institutions. This current study is aimed at clarifying the prevalence, the underlying risk factors, and reasons of the 30‐day readmission after acute myocardial infarction (AMI).

Methods

PubMed, Cochrane Library, and EMBASE were systematically searched to identify eligible studies. Random‐effect models were employed to perform pooled analyses. Means and 95% confidence intervals (CIs) were used to estimate prevalence and reasons for 30‐day readmission. We also used Odds ratios (ORs) to explore the potential significant predictors of risk factors of 30‐day readmission after AMI. Potential publication bias was assessed using funnel plot and Begg'test.

Results

A total of 14 relevant studies were included in this systematic review and meta‐analysis. The pooled 30‐day readmission rate of AMI was 12% (95% CI 0.11‐0.14). Acute coronary syndrome (ACS), angina and acute ischemic heart disease, and heart failure (HF) were the principal cardiovascular reasons of 30‐day readmission. Meanwhile, non‐specific chest pain was regarded as the significant cause among non‐cardiovascular reasons. The common co‐morbidities kidney disease, HF and diabetes mellitus were significant risk factors for 30‐day readmission. No significant publication bias was found by funnel plot and statistical tests.

Conclusions

The 30‐day readmission rate of post‐AMI ranged from 11% to 14% and can be mainly attributed to cardiovascular and non‐cardiovascular events. The common co‐morbidities, such as kidney disease, HF, and diabetes mellitus were significant risk factors for 30‐day readmission.

Etiological Role of Diet in 30-Day Readmissions for Heart Failure: Implications for Reducing Heart Failure-Associated Costs via Culinary Medicine

Background. Reducing the under-30-day readmission for heart failure (HF) patients is a modifiable quality-of-care measure, yet the role of diet in HF readmissions and cost-effective HF care remain ill-defined. Methods. Medical chart review was conducted to determine cause(s) for HF treatment failure. Randomized controlled trial-backed machine learning models were employed to assess the relationship of culinary medicine education with HF 30-day readmission rate and cost. Results. Of 1031 HF admissions, 130 occurred within 30 days of discharge (12.61%.) Nearly two-thirds of individuals were male (64.02%), while the mean age and median length of stay were 64.33 ± 14.02 and 2, respectively. Medication noncompliance (34.62%) was the most common etiology for 30-day readmissions, followed by dietary noncompliance (16.92%), comorbidity (16.92%), a combination of dietary and medication noncompliance (10%), HF exacerbation (10%), iatrogenic (10%), and drug abuse (1.54%). Medication noncompliance contributed to the highest gross charge by readmission, costing a total of $1 802 096. Compared with traditional care, culinary medicine education for HF patients would prevent 93 HF readmissions and save $3.9 million in an estimated 4-year period. Conclusion. Though pharmacological treatment remains a focal point of HF management, diet-based approaches may improve tertiary HF prevention and reduce HF-associated health care expenditures.

Clinical and Socioeconomic Predictors of Heart Failure Readmissions: A Review of Contemporary Literature

Heart failure represents a clinical syndrome that results from a constellation of disease processes affecting myocardial function. Although recent studies have suggested a declining or stable incidence of heart failure, patients with heart failure continue to have high hospitalization and readmission rates, resulting in a substantial economic and public health burden. We searched PubMed and Google Scholar to identify published literature from 1998 through 2018 using the following keywords: heart failure, readmissions, predictors, prediction models, and interventions. Cited references were also used to identify relevant literature. Developments in the diagnosis and management of patients with heart failure have improved hospitalization and readmission rates in the past few decades. However, heart failure remains the most common cause of hospitalization in persons older than 65 years. As a result, given the enormous clinical and financial burden associated with heart failure readmissions on health care, there has been growing interest in the investigation of mechanisms aimed at improving outcomes and curtailing associated costs of care. Herein, we review the current literature on clinical and socioeconomic predictors of heart failure readmissions, briefly discussing limitations of existing strategies and providing an overview of current technology aimed at reducing hospitalizations.

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.

Can hospitalization be hazardous to your health? A nosocomial based stress model for hospitalization

OBJECTIVE

Hospitalization places patients at elevated risk for the development of "nosocomial" or hospital acquired complications, ranging from multidrug resistant infections to delirium and physical deconditioning. Adverse nosocomial psychological effects of hospitalization may also exist. This paper introduces a nosocomial based stress model, conceptualizing hospitalization as a unique period of biopsychosocial vulnerability, due to physiologic effects of acute illness and psychosocial variables of the hospital experience.

METHOD

A research synthesis and narrative review was performed to evaluate evidence supporting this model, integrating existing knowledge of the psychological and physiological effects of acute life threatening events, with known sequelae associated with hospitalization.

RESULT

Psychosocial factors during hospitalization may act as independent predictors of recovery following hospitalization, moderating variables impacting ongoing physiologic changes due to acute illness, and/or dynamic bidirectional elements, influencing medical and psychological outcomes in the near and long-term setting.

CONCLUSION

The Nosocomial Stress model provides a novel framework to understanding the biopsychosocial interactions between the psychological and physiologic processes associated with illness and hospitalization. Based on this model, a research agenda is proposed to assess the contributions of acute illness, the hospital experience, and their interactions on the recovery of patients following hospitalization.

All-cause mortality, cardiovascular events, and health care costs after 12 months of dual platelet aggregation inhibition after acute myocardial infarction in real-world patients: findings from the Platelet-aggregation Inhibition: Persistence with treatment and cardiovascular Events in Real world (PIPER) study

Objectives

The aim of the study was to assess all-cause mortality and cardiovascular (CV) events in patients after a period of 12 months of treatment with dual antiplatelet therapy (DAPT) after hospitalization for acute myocardial infarction (AMI) in a real-world setting. Health care costs for the management of patients post-AMI was also assessed.

Methods

A retrospective analysis using data from the administrative databases of six local health units (LHUs) was performed. All beneficiaries of these LHUs hospitalized with AMI between January 01, 2010, and December 31, 2011, and exposed to a treatment period with DAPT up to 12 months after AMI discharge were included. All-cause mortality, CV hospitalizations, and health care costs occurring during the 36-month follow-up period from end of treatment with DAPT were considered. For the cost analysis, only patients still alive at the end of the follow-up period were included.

Results

A total of 2,721 patients were included (mean ± SD age 63.6±17.3 years, 67.8% males). About 17% and 18% of all patients had CV events and died during the follow-up period, respectively. The annual mean cost per patient was €3,523.27. During the follow-up period, 63 patients had a second AMI event; for whom, the mean health care cost per patient was €19,570.70.

Conclusion

In a real-world setting in Italy, considering a 36-month follow-up period, all-cause mortality, CV events, and related health care cost of patients hospitalized with an AMI undergoing a 12-month treatment period with DAPT remained relevant. This study suggests that increased efforts aimed at the prevention of recurrent AMI are warranted, as well as an accurate risk stratification in order to improve long-term outcome.

Gender Differences in the Rate of 30-Day Readmissions after Percutaneous Coronary Intervention for Acute Coronary Syndrome

BACKGROUND

It has been reported that women have higher 30-day readmission rates than men after acute coronary syndrome (ACS). However, readmission after percutaneous coronary intervention (PCI) for ACS is a distinct subset of patients in whom gender differences have not been adequately studied.

METHODS

Hawaii statewide hospitalization data from 2010 to 2015 were assessed to compare gender differences in 30-day readmission rates among patients hospitalized with ACS who underwent PCI during the index hospitalization. Readmission diagnoses were categorized using an aggregated version of the Centers for Medicare and Medicaid Services Condition Categories. Multivariable logistic regression was applied to evaluate the effect of gender on the 30-day readmission rate.

RESULTS

A total of 5,354 patients (29.4% women) who were hospitalized with a diagnosis of ACS and underwent PCI were studied. Overall, women were older, with more identified as Native Hawaiian, and had a higher prevalence of cardiovascular risk factors compared with men. The 30-day readmission rate was 13.9% in women and 9.6% in men (p < .0001). In the multivariable model, female gender (odds ratio [OR], 1.32; 95% confidence interval [CI], 1.09-1.60), Medicaid (OR, 1.48; 95% CI, 1.07-2.06), Medicare (1.72; 95% CI, 1.35-2.19), heart failure (1.88; 95% CI, 1.53-2.33), atrial fibrillation (OR, 1.54; 95% CI-1.21-1.95), substance use (OR, 1.88; 95% CI, 1.27-2.77), history of gastrointestinal bleeding (OR, 2.43; 95% CI, 1.29-4.58), and chronic kidney disease (OR, 1.78; 95% CI, 1.42-2.22) were independent predictors of 30-day readmissions. Readmission rates were highest during days 1 through 6 (peak, day 3) after discharge. The top three cardiac causes of readmissions were heart failure, recurrent angina, and recurrent ACS.

CONCLUSIONS

Female gender is an independent predictor of 30-day readmission after ACS that requires PCI. Our finding suggests women are at a higher risk of post-ACS cardiac events such as heart failure and recurrent ACS, and further gender-specific intervention is needed to reduce 30-day readmission rate in women after ACS.

Readmissions of adults within three age groups following hospitalization for pneumonia: Analysis from the Nationwide Readmissions Database

Background

While 30-day readmissions following hospitalization for pneumonia have been well-studied in the elderly, their burden in young adults remains poorly understood.

Objective

To study patterns of readmissions following hospitalization for pneumonia across age groups and insurance payers.

Methods

In the Nationwide Readmission Database for the years 2013 and 2014 we identified all adults (≥18 years) discharged alive after a hospitalization with the primary diagnosis of pneumonia, and examined rates of readmissions within 30-days of discharge. Using covariates included in the Center for Medicare & Medicaid Services risk-adjustment model for pneumonia readmissions in a multivariable regression model for survey data, we identified predictors of 30-day readmission.

Results

We identified 629,939 index pneumonia hospitalizations with a weighted estimate of 1,472,069 nationally. Overall, 16.2% of patients were readmitted within 30 days of their hospitalization for pneumonia, with 30-day readmission rates of 12.4% in the 18–44 year age-group, 16.1% in the 45–64 year age-group, and 16.7% in the ≥65-year age-group. In risk-adjusted analyses, compared with elderly, middle-aged adults were more likely to be readmitted (risk-adjusted OR 1.05, 95% CI 1.03–1.07). Mean cost per readmission was also highest for this age group at $15,976.

Conclusion

Middle-aged adults experience substantial rates of 30-day readmission that are comparable to those over 65 years of age, with a higher cost per readmission event. Future efforts are needed to identify potential interventions to alleviate the high burden of pneumonia readmissions in middle-aged adults.

An assessment of the UK inpatient care for heart failure patients with diabetes

BACKGROUND

Diabetes is a common co-morbidity for patients with heart failure. Diabetes as a co-morbidity means that inpatient care should focus on both conditions to maximize the treatment regimen. However, this pressing issue is not widely researched and so it is unclear whether the acute care management needs of these patients are being met.

AIMS

(1) To assess the differences in the number of hospital readmissions between patients with heart failure and patients with heart failure-diabetes; (2) to assess the use of integrated care approach for patients with heart failure-diabetes during the index heart failure-related admission; (3) to explore patient experiences of admissions.

METHODS

A mixed methods design was used: we identified heart failure-related admissions between 1 April 2011 and 31 March 2012 in two hospitals, then reviewed medical records and interviewed 14 patients.

RESULTS

Over a 12 month period patients with heart failure-diabetes ( n=172) had more heart failure-related Accident and Emergency attendance episodes (incident rate ratio 1.24, p<0.01) and hospital readmissions (incident rate ratio 1.23, p=0.01) than patients with heart failure ( n=370). We reviewed 72 medical records which met inclusion criteria (adults with heart failure-diabetes, ejection fraction <45%): during admission most of them were reviewed by heart failure specialists but less than one-third were reviewed by diabetes specialists. The interview respondents addressed the need for better integration and co-ordination of care.

CONCLUSIONS

This is one of the first UK studies to assess the integration of inpatient care for those with heart failure and multi-morbidities. The findings suggest that maximal care management during admission should be explored as a way of reducing the frequent readmissions and improving patient outcomes.

Cost-of-illness studies in heart failure: a systematic review 2004-2016

Background

Heart failure is a major and growing medical and economic problem worldwide as 1–2% of the healthcare budget are spent for heart failure. The prevalence of heart failure has increased over the past decades and it is expected that there will be further raise due to the higher proportion of elderly in the western societies. In this context cost-of-illness studies can significantly contribute to a better understanding of the drivers and problems which lead to the increasing costs in heart failure.

The aim of this study was to perform a systematic review of published cost-of-illness studies related to heart failure to highlight the increasing cost impact of heart failure.

Methods

A systematic review was conducted from 2004 to 2016 to identify cost-of-illness studies related to heart failure, searching PubMed (Medline), Cochrane, Science Direct (Embase), Scopus and CRD York Database.

Results

Of the total of 16 studies identified, 11 studies reported prevalence-based estimates, 2 studies focused on incidence-based data and 3 articles presented both types of cost data. A large variation concerning cost components and estimates can be noted. Only three studies estimated indirect costs. Most of the included studies have shown that the costs for hospital admission are the most expensive cost element. Estimates for annual prevalence-based costs for heart failure patients range from $868 for South Korea to $25,532 for Germany. The lifetime costs for heart failure patients have been estimated to $126.819 per patient.

Conclusions

Our review highlights the considerable and growing economic burden of heart failure on the health care systems. The cost-of-illness studies included in this review show large variations in methodology used and the cost results vary consequently. High quality data from cost-of-illness studies with a robust methodology applied can inform policy makers about the major cost drivers of heart failure and can be used as the basis of further economic evaluations.

Electronic supplementary material

The online version of this article (10.1186/s12872-018-0815-3) contains supplementary material, which is available to authorized users.

Risk Trajectories of Readmission and Death in the First Year after Hospitalization for Chronic Obstructive Pulmonary Disease

RATIONALE

Characterization of the dynamic nature of posthospital risk in chronic obstructive pulmonary disease (COPD) is needed to provide counseling and plan clinical services.

OBJECTIVES

To analyze risk of readmission and death after discharge for COPD among Medicare beneficiaries aged 65 years and older and to determine the association between ventilator support and risk trajectory.

METHODS

We computed daily absolute risks of hospital readmission and death for 1 year after discharge for COPD, stratified by ventilator support. We determined the time required for risks to decline by 50% from maximum daily values after discharge and for daily risks to plateau. We compared risks with those found in the general elderly population.

MEASUREMENTS AND MAIN RESULTS

Among 2,340,637 hospitalizations, the readmission rate at 1 year was 64.2%, including 63.5%, 66.0%, and 64.1% among those receiving invasive, noninvasive, and no ventilation, respectively. Among 1,283,069 hospitalizations, mortality at 1 year was 26.2%, including 45.7%, 41.8%, and 24.4% among those same respective groups. Daily risk of readmission declined by 50% within 28, 39, and 43 days and plateaued at 46, 54, and 61 days among those receiving invasive, noninvasive, and no ventilation, respectively. Risk of death declined by 50% by 3, 4, and 17 days and plateaued by 21, 18, and 24 days in the same respective groups. Risks of hospitalization and death were significantly higher after discharge for COPD than among the general Medicare population.

CONCLUSIONS

Discharge from the hospital is associated with prolonged risks of readmission and death that vary with need for ventilator support. Interventions limited to the first month after discharge may be insufficient to improve longitudinal outcomes.

Review of the Role of the Pharmacist in Reducing Hospital Readmissions

Hospital readmissions remain a public health concern despite progress in reducing and preventing its occurrence. Among strategies that have been implemented to reduce readmission most involves medication management. Our objective was to evaluate the effectiveness of interventions involving pharmacists to reduce hospital readmissions. PubMed and Google Scholar were searched for primary literature from January 1990 to July 2016 with search terms such as “hospital readmission,” and “Pharmacist,” or “Pharmacy,” or “medications.” Studies with an abstract in English which highlighted a pharmacist involvement based on the type of intervention, country of origin, type of study, and findings were summarized. The outcomes of these interventions to reduce hospital readmissions were mixed. Of the 29 studies, 16 (55%) showed a statistically significant reduction in readmissions ranging from 3.3% to 30%. Most of the interventions focused mainly on patient education postdischarge (8) or in addition to medication reconciliation predischarge (9). There were no studies from Africa or Asia but mainly from the United States (72%). Although multiple factors contribute to hospital readmission, this review highlights the important role pharmacists can play singularly and as part of interdisciplinary teams. Most effective interventions often involved medication review and patient education postdischarge.

Trends in Readmission Rates, Hospital Charges, and Mortality for Patients With Chronic Obstructive Pulmonary Disease (COPD) in Florida From 2009 to 2014

PURPOSE

Chronic obstructive pulmonary disease (COPD) is a leading and costly cause of readmissions to the hospital, with one of the highest rates reported in Florida. From 2009 to 2014, strategies such as readmission reduction programs, as well as updated guidelines for COPD management, were instituted to reduce readmission rates for patients with COPD. Thus, the question has been raised whether COPD-related 30-day hospital readmission rates in Florida have decreased and whether COPD-related readmission costs during this period have changed. In addition, we examined trends in length of stay, hospital charges, and in-hospital mortality associated with COPD, as well as identified patient-level risk factors associated with 30-day readmissions.

METHODS

A retrospective analysis of adult patients (≥18 years of age) with COPD was conducted by using the Healthcare Cost and Utilization Project Florida State Inpatient Database, 2009 to 2014. Weighted least squares regression was used to assess trends in the COPD readmission rate on a yearly basis, as well as other outcomes of interest. A multivariable logistic regression was used to identify patient characteristics that were associated with 30-day COPD readmissions.

FINDINGS

Overall, 268,084 adults were identified as having COPD. Between 2009 and 2014, more than half of patients aged 65-84 years, most were white, 55% were female, and 73% had Medicare. The unadjusted rate for COPD-related 30-day readmissions did not change (8.04% to 7.85%; P = 0.434). However, the mean total charge for 30-day COPD-related readmissions was significantly higher in 2014 ($40,611) compared with that in 2009 ($36,714) (P = 0.011). The overall unadjusted in-hospital mortality of COPD-related hospitalizations significantly decreased from 1.83% in 2009 to 1.34% in 2014 (P < 0.001). In a multivariable logistic regression model, patients with COPD were 2% less likely to be readmitted to the hospital for each additional year (odds ratio [OR], 0.98 [95% confidence interval (CI), 0.97-0.99]). Factors associated with significantly higher odds of COPD-related readmission were: older age (45 ≤ age ≤ 64 years; OR, 1.91 [95% CI, 1.70-2.14]), being male (OR, 1.14 [95% CI, 1.10-1.17]), and being a Medicaid beneficiary (OR, 1.28 [95% CI, 1.21-1.35]).

IMPLICATIONS

Although the adjusted odds of COPD readmissions slightly decreased, as did the length of stay and all-cause in-patient mortality, the financial burden increased substantially. Future strategies to further reduce readmissions of patients with COPD and curb financial burden in Florida are needed.

Age trends in 30 day hospital readmissions: US national retrospective analysis

OBJECTIVE

To assess trends in and risk factors for readmission to hospital across the age continuum.

DESIGN

Retrospective analysis.

SETTING AND PARTICIPANTS

31 729 762 index hospital admissions for all conditions in 2013 from the US Agency for Healthcare Research and Quality Nationwide Readmissions Database.

MAIN OUTCOME MEASURE

30 day, all cause, unplanned hospital readmissions. Odds of readmission were compared by patients' age in one year epochs with logistic regression, accounting for sex, payer, length of stay, discharge disposition, number of chronic conditions, reason for and severity of admission, and data clustering by hospital. The middle (45 years) of the age range (0-90+ years) was selected as the age reference group.

RESULTS

The 30 day unplanned readmission rate following all US index admissions was 11.6% (n=3 678 018). Referenced by patients aged 45 years, the adjusted odds ratio for readmission increased between ages 16 and 20 years (from 0.70 (95% confidence interval 0.68 to 0.71) to 1.04 (1.02 to 1.06)), remained elevated between ages 21 and 44 years (range 1.02 (1.00 to 1.03) to 1.12 (1.10 to 1.14)), steadily decreased between ages 46 and 64 years (range 1.02 (1.00 to 1.04) to 0.91 (0.90 to 0.93)), and decreased abruptly at age 65 years (0.78 (0.77 to 0.79)), after which the odds remained relatively constant with advancing age. Across all ages, multiple chronic conditions were associated with the highest adjusted odds of readmission (for example, 3.67 (3.64 to 3.69) for six or more versus no chronic conditions). Among children, young adults, and middle aged adults, mental health was one of the most common reasons for index admissions that had high adjusted readmission rates (≥75th centile).

CONCLUSIONS

The likelihood of readmission was elevated for children transitioning to adulthood, children and younger adults with mental health disorders, and patients of all ages with multiple chronic conditions. Further attention to the measurement and causes of readmission and opportunities for its reduction in these groups is warranted.

Sex Differences in Trajectories of Risk After Rehospitalization for Heart Failure, Acute Myocardial Infarction, or Pneumonia

BACKGROUND

Women have an increased risk of rehospitalization in the immediate postdischarge period; however, few studies have determined how readmission risk dynamically changes on a day-to-day basis over the full year after hospitalization by sex and how these differences compare with the risk for mortality.

METHODS AND RESULTS

We identified >3 000 000 hospitalizations of patients with a principal discharge diagnosis of heart failure, acute myocardial infarction, or pneumonia and estimated sex differences in the daily risk of rehospitalization/death 1 year after discharge from a population of Medicare fee-for-service beneficiaries aged 65 years and older. We calculated the (1) time required for adjusted rehospitalization/mortality risks to decline 50% from maximum values after discharge, (2) time required for the adjusted readmission risk to approach plateau periods of minimal day-to-day change, and (3) extent to which adjusted risks are greater among recently hospitalized patients versus Medicare patients. We identified 1 392 289, 530 771, and 1 125 231 hospitalizations for heart failure, acute myocardial infarction, and pneumonia, respectively. The adjusted daily risk of rehospitalization varied by admitting condition (hazard rate ratio for women versus men, 1.10 for acute myocardial infarction; hazard rate ratio, 1.04 for heart failure; and hazard rate ratio, 0.98 for pneumonia). However, for all conditions, the adjusted daily risk of death was higher among men versus women (hazard rate ratio women versus with men, <1). For both sexes, there was a similar timing of peak daily risk, half daily risk, and reaching plateau.

CONCLUSIONS

Although the association of sex with daily risk of rehospitalization varies across conditions, women are at highest risk after discharge for acute myocardial infarction. Future studies should focus on understanding the determinants of sex differences in rehospitalization risk among conditions.

National Estimates of 30-Day Unplanned Readmissions of Patients on Maintenance Hemodialysis

BACKGROUND AND OBJECTIVES

Patients on hemodialysis have high 30-day unplanned readmission rates. Using a national all-payer administrative database, we describe the epidemiology of 30-day unplanned readmissions in patients on hemodialysis, determine concordance of reasons for initial admission and readmission, and identify predictors for readmission.

DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS

This is a retrospective cohort study using the Nationwide Readmission Database from the year 2013 to identify index admissions and readmission in patients with ESRD on hemodialysis. The Clinical Classification Software was used to categorize admission diagnosis into mutually exclusive clinically meaningful categories and determine concordance of reasons for admission on index hospitalizations and readmissions. Survey logistic regression was used to identify predictors of at least one readmission.

RESULTS

During 2013, there were 87,302 (22%) index admissions with at least one 30-day unplanned readmission. Although patient and hospital characteristics were statistically different between those with and without readmissions, there were small absolute differences. The highest readmission rate was for acute myocardial infarction (25%), whereas the lowest readmission rate was for hypertension (20%). The primary reasons for initial hospitalization and subsequent 30-day readmission were discordant in 80% of admissions. Comorbidities that were associated with readmissions included depression (odds ratio, 1.10; 95% confidence interval [95% CI], 1.05 to 1.15; P<0.001), drug abuse (odds ratio, 1.41; 95% CI, 1.31 to 1.51; P<0.001), and discharge against medical advice (odds ratio, 1.57; 95% CI, 1.45 to 1.70; P<0.001). A group of high utilizers, which constituted 2% of the population, was responsible for 20% of all readmissions.

CONCLUSIONS

In patients with ESRD on hemodialysis, nearly one quarter of admissions were followed by a 30-day unplanned readmission. Most readmissions were for primary diagnoses that were different from initial hospitalization. A small proportion of patients accounted for a disproportionate number of readmissions.

Patterns of Readmissions for Three Common Conditions Among Younger US Adults

BACKGROUND

Thirty-day readmissions among elderly Medicare patients are an important hospital quality measure. Although plans for using 30-day readmission measures are under consideration for younger patients, little is known about readmission in younger patients or the relationship between readmissions in younger and elderly patients at the same hospital.

METHODS

By using the 2014 Nationwide Readmissions Database, we examined readmission patterns in younger patients (18-64 years) using hierarchical models to evaluate associations between hospital 30-day, risk-standardized readmission rates in elderly Medicare patients and readmission risk in younger patients with acute myocardial infarction, heart failure, or pneumonia.

RESULTS

There were 87,818, 98,315, and 103,251 admissions in younger patients for acute myocardial infarction, heart failure, and pneumonia, respectively, with overall 30-day unplanned readmission rates of 8.5%, 21.4%, and 13.7%, respectively. Readmission risk in younger patients was significantly associated with hospital 30-day risk-standardized readmission rates for elderly Medicare patients for all 3 conditions. A decrease in an average hospital's 30-day, risk-standardized readmission rates from the 75th percentile to the 25th percentile was associated with reduction in younger patients' risk of readmission from 8.8% to 8.0% (difference: 0.7%; 95% confidence interval, 0.5-0.9) for acute myocardial infarction; 21.8% to 20.0% (difference: 1.8%; 95% confidence interval, 1.4-2.2) for heart failure; and 13.9% to 13.1% (difference: 0.8%; 95% confidence interval, 0.5-1.0) for pneumonia.

CONCLUSIONS

Among younger patients, readmission risk was moderately associated with hospital 30-day, risk-standardized readmission rates in elderly Medicare beneficiaries. Efforts to reduce readmissions among older patients may have important areas of overlap with younger patients, although further research may be necessary to identify specific mechanisms to tailor initiatives to younger patients.

Seasonality and Readmission after Heart Failure, Myocardial Infarction, and Pneumonia

OBJECTIVE

To investigate whether hospital readmission after admission for heart failure (HF), myocardial infarction (MI), and pneumonia varies by season.

DATA SOURCES

All patients in 2005-2009 Healthcare Cost and Utilization Project State Inpatient Databases for New York and California hospitalized for HF, MI, or pneumonia.

STUDY DESIGN

The relationship between discharge season and unplanned readmission within 30 days was evaluated using multivariate modified Poisson regression.

PRINCIPAL FINDINGS

Cohorts included 869,512 patients with HF, 448,945 patients with MI, and 813,593 patients with pneumonia. While admissions varied widely by season, readmission rates only ranged from 25.0 percent (spring) to 25.6 percent (winter) for HF (p > .05), 18.9 percent (summer) to 20.0 percent (winter) for MI (p < .001), and 19.4 percent (spring) to 20.3 percent (summer) for pneumonia (p < .001). In adjusted models, in New York, there was lower readmission in spring and fall (RR: 0.98, 95% CI: 0.96-0.99 for both) after admission for HF and higher readmission in spring (RR: 1.04, 95% CI: 1.01-1.07) after MI. In California, there was lower readmission in spring and winter (RR: 0.95, 95% CI: 0.93-0.96 and RR: 0.96, 95% CI: 0.94-0.98, respectively) after pneumonia.

CONCLUSIONS

Given marked seasonality in incidence and mortality of HF, MI, and pneumonia, the modest seasonality in readmissions suggests that readmissions may be more related to non-seasonally dependent factors than to the seasonal nature of these diseases.