Hospital Readmission Reduction Program for Heart Failure: The Spread of Intended and Unintended Consequences

Demographic trends and disparities in mortality related to coexisting heart failure and diabetes mellitus among older adults in the United States between 1999 and 2020: A retrospective population-based cohort study from the CDC WONDER database

Background

Heart Failure (HF) and Diabetes Mellitus (DM) often coexist, and each condition independently increases the likelihood of developing the other. While there has been concern regarding the increasing burden of disease for both conditions individually over the last decade, a comprehensive examination of mortality trends and demographic and regional disparities needs to be thoroughly explored in the United States (US).

Methods

This study analyzed death certificates from the CDC WONDER database, focusing on mortality caused by the co-occurrence of HF and DM in adults aged 75 and older from 1999 to 2020. Age-adjusted mortality rates (AAMRs) and annual percent changes (APCs) were computed and categorized by year, gender, race, census region, state, and metropolitan status.

Results

A total of 663,016 deaths were reported in patients with coexisting HF and DM. Overall, AAMR increased from 154.1 to 186.1 per 100,000 population between 1999 and 2020, with a notable significant increase from 2018 to 2020 (APC: 11.30). Older men had consistently higher AAMRs than older women (185 vs. 135.4). Furthermore, we found that AAMRs were highest among non-Hispanic (NH) American Indian or Alaskan natives and lowest in NH Asian or Pacific Islanders (214.4 vs. 104.1). Similarly, AAMRs were highest in the Midwestern region and among those dwelling in non-metropolitan areas.

Conclusions

Mortality from HF and DM has risen significantly in recent years, especially among older men, NH American Indian or Alaska Natives, and those in non-metropolitan areas. Urgent policies need to be developed to address these disparities and promote equitable healthcare access.

SHAP based predictive modeling for 1 year all-cause readmission risk in elderly heart failure patients: feature selection and model interpretation

Heart failure (HF) is a significant global public health concern with a high readmission rate, posing a serious threat to the health of the elderly population. While several studies have used machine learning (ML) to develop all-cause readmission risk prediction models for elderly patients with HF, few have integrated ML-selected features with those chosen by human experts to assess HF patients readmission. A retrospective analysis of 8396 elderly HF patients hospitalized at the Affiliated Hospital of North Sichuan Medical College from January 1, 2018 to December 31, 2021 was conducted. Variables selected by XGBoost, LASSO regression, and random forest constituted the machine group, while the human expert group comprised variables chosen by two experienced cardiovascular professors. The variables selected by both groups were combined to form a human-machine collaboration group. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC). The SHapley Additive exPlanations (SHAP) method was used to elucidate the importance of each predictive feature, explain the impact of individual features on the model, and provide visual representation. A total of 73 features were included for model development. The human-machine collaboration model, utilizing CatBoost, achieved an AUC of 0.83617, an F1-score of 0.73521, and a Brier score of 0.16536 on the validation set. This model demonstrated superior predictive performance compared to those created solely by human experts or machine. The SHAP plot was then used to visually display the feature analysis of the human-machine collaboration model, revealing HGB, NT-proBNP, smoking history, NYHA classification, and LVEF as the 5 most important features. This study indicate that the human-machine collaboration model outperforms those relying solely on human expert selection or machine algorithm at predicting all-cause readmission in elderly HF patients. The application of the SHAP method enhanced the interpretability of the model outcomes, aiding clinicians in accurately pinpointing risk factors associated with HF readmission. This advancement enables the formulation of tailored treatment strategies, offering a more personalized approach to patient care.

Policy and Payment Challenges in the Postpandemic Treatment of Heart Failure: Value-Based Care and Telehealth

Increasing patient and therapeutic complexity have created both challenges and opportunities for heart failure care. Within this background, the coronavirus disease-2019 pandemic has disrupted care as usual, accelerating the need for transition from volume-based to value-based care, and demanding a rapid expansion of telehealth and remote care for heart failure. Patients, clinicians, health systems, and payors have by necessity become more invested in these issues. Herein we review recent changes in health care policy related to the movement from volume to value-based payment and from in-person to remote care delivery.

Association of Dual Eligibility for Medicare and Medicaid With Heart Failure Quality and Outcomes Among Get With The Guidelines-Heart Failure Hospitals

Importance

The Centers for Medicare & Medicaid Services uses a new peer group-based payment system to compare hospital performance as part of its Hospital Readmissions Reduction Program, which classifies hospitals into quintiles based on their share of dual-eligible beneficiaries for Medicare and Medicaid. However, little is known about the association of a hospital's share of dual-eligible beneficiaries with the quality of care and outcomes for patients with heart failure (HF).

Objective

To evaluate the association between a hospital's proportion of patients with dual eligibility for Medicare and Medicaid and HF quality of care and outcomes.

Design, Setting, and Participants

This retrospective cohort study evaluated 436 196 patients hospitalized for HF using the Get With The Guidelines-Heart Failure registry from January 1, 2010, to December 31, 2017. The analysis included patients 65 years or older with available data on dual-eligibility status. Hospitals were divided into quintiles based on their share of dual-eligible patients. Quality and outcomes were analyzed using unadjusted and adjusted multivariable logistic regression models. Data analysis was performed from April 1, 2020, to January 1, 2021.

Main Outcomes and Measures

The primary outcome was 30-day all-cause readmission. The secondary outcomes included in-hospital mortality, 30-day HF readmissions, 30-day all-cause mortality, and HF process of care measures.

Results

A total of 436 196 hospitalized HF patients 65 years or older from 535 hospital sites were identified, with 258 995 hospitalized patients (median age, 81 years; interquartile range, 74-87 years) at 455 sites meeting the study criteria and included in the primary analysis. A total of 258 995 HF hospitalizations from 455 sites were included in the primary analysis of the study. Hospitals in the highest dual-eligibility quintile (quintile 5) tended to care for patients who were younger, were more likely to be female, belonged to racial minority groups, or were located in rural areas compared with quintile 1 sites. After multivariable adjustment, hospitals with the highest quintile of dual eligibility were associated with lower rates of key process measures, including evidence-based β-blocker prescription, measure of left ventricular function, and anticoagulation for atrial fibrillation or atrial flutter. Differences in clinical outcomes were seen with higher 30-day all-cause (adjusted odds ratio, 1.24; 95% CI, 1.14-1.35) and HF (adjusted odds ratio, 1.14; 95% CI, 1.03-1.27) readmissions in higher dual-eligible quintile 5 sites compared with quintile 1 sites. Risk-adjusted in-hospital and 30-day mortality did not significantly differ in quintile 1 vs quintile 5 hospitals.

Conclusions and Relevance

In this cohort study, hospitals with a higher share of dual-eligible patients provided care with lower rates of some of the key HF quality of care process measures and with higher 30-day all-cause or HF readmissions compared with lower dual-eligibility quintile hospitals.

Relation of Neighborhood Disadvantage to Heart Failure Symptoms and Hospitalizations

Residence in socioeconomically deprived neighborhoods may influence patient's health-related behaviors and overall health. We evaluated the association of neighborhood disadvantage on heart failure (HF) symptom burden and hospitalization rates. We characterized neighborhood deprivation in 359 HF subjects (age 56 ± 13 years, 52% black) in metropolitan Atlanta using the Area Deprivation Index (ADI). ANOVA was used to compare HF symptoms measured using the Kansas City Cardiomyopathy Questionnaire, and HF Self-Care Index across ADI tertiles. Zero-inflated Poisson regression was used to compare rates of recurrent HF hospitalization (HFH) across ADI tertiles. Subjects living in more deprived neighborhoods were more likely to be black, have Medicare or Medicaid insurance, and have a lower ejection fraction than those living in less deprived neighborhoods (all p ≤ 0.005). Subjects in more deprived neighborhoods had more severe HF symptoms (p < 0.001), but there was no difference in HF Self-Care Index scores across ADI tertiles. Subjects living in more deprived neighborhoods had a higher odds of being hospitalized for HF than subjects in less deprived neighborhoods. Once subjects had experienced a HFH, however, the association between ADI and the risk of recurrent HFH varied by racial group. In whites, increasing ADI was associated with a marginally decreased risk of recurrent HFH, while there was no association between ADI and recurrent HFH in blacks. In conclusion, patients with HF living in more deprived neighborhoods have greater symptom burden and are more likely to experience a HFH than those living in less deprived neighborhoods.

Leadless and Wireless Cardiac Devices: The Next Frontier in Remote Patient Monitoring

In the last decade, advances in wireless and sensor technologies, and the implementation of telemedicine, have led to innovative digital health care for cardiac patients. Continuous monitoring of patients' biomedical signals, and acute changes in these signals, may result in timely, accurate diagnoses and implementation of early interventions. In this review, we discuss commonly used wireless and leadless cardiac devices including pulmonary artery pressure sensors, implantable loop recorders, leadless pacemakers and subcutaneous implantable cardioverter-defibrillators. We discuss the concept and function of each device, indications, methods of delivery, potential complications, consideration for implantation, and cost-effectiveness.