Strategies to Modify the Risk of Heart Failure Readmission: A Systematic Review and Meta-Analysis

Effect of Chronic Disease Home Telehealth Monitoring in the Veterans Health Administration on Healthcare Utilization and Mortality

BACKGROUND

The high prevalence of chronic diseases, including congestive heart failure (CHF), chronic obstructive pulmonary disease (COPD), and diabetes mellitus (DM), accounts for a large burden of cost and poor health outcomes in US hospitals, and home telehealth (HT) monitoring has been proposed to improve outcomes.

OBJECTIVE

To measure the association between HT initiation and 12-month inpatient hospitalizations, emergency department (ED) visits, and mortality in veterans with CHF, COPD, or DM.

DESIGN

Comparative effectiveness matched cohort study.

PATIENTS

Veterans aged 65 years and older treated for CHF, COPD, or DM.

MAIN MEASURES

We matched veterans initiating HT with veterans with similar demographics who did not use HT (1:3). Our outcome measures included a 12-month risk of inpatient hospitalization, ED visits, and all-cause mortality.

KEY RESULTS

A total of 139,790 veterans with CHF, 65,966 with COPD, and 192,633 with DM were included in this study. In the year after HT initiation, the risk of hospitalization was not different in those with CHF (adjusted odds ratio [aOR] 1.01, 95% confidence interval [95%CI] 0.98-1.05) or DM (aOR 1.00, 95%CI 0.97-1.03), but it was higher in those with COPD (aOR 1.15, 95%CI 1.09-1.21). The risk of ED visits was higher among HT users with CHF (aOR 1.09, 95%CI 1.05-1.13), COPD (1.24, 95%CI 1.18-1.31), and DM (aOR 1.03, 95%CI 1.00-1.06). All-cause 12-month mortality was lower in those initiating HT monitoring with CHF (aOR 0.70, 95%CI 0.67-0.73) and DM (aOR 0.79, 95%CI 0.75-0.83), but higher in COPD (aOR 1.08, 95%CI 1.00-1.16).

CONCLUSIONS

The initiation of HT was associated with increased ED visits, no change in hospitalizations, and lower all-cause mortality in patients with CHF or DM, while those with COPD had both higher healthcare utilization and all-cause mortality.

Machine learning based readmission and mortality prediction in heart failure patients

This study intends to predict in-hospital and 6-month mortality, as well as 30-day and 90-day hospital readmission, using Machine Learning (ML) approach via conventional features. A total of 737 patients remained after applying the exclusion criteria to 1101 heart failure patients. Thirty-four conventional features were collected for each patient. First, the data were divided into train and test cohorts with a 70-30% ratio. Then train data were normalized using the Z-score method, and its mean and standard deviation were applied to the test data. Subsequently, Boruta, RFE, and MRMR feature selection methods were utilized to select more important features in the training set. In the next step, eight ML approaches were used for modeling. Next, hyperparameters were optimized using tenfold cross-validation and grid search in the train dataset. All model development steps (normalization, feature selection, and hyperparameter optimization) were performed on a train set without touching the hold-out test set. Then, bootstrapping was done 1000 times on the hold-out test data. Finally, the obtained results were evaluated using four metrics: area under the ROC curve (AUC), accuracy (ACC), specificity (SPE), and sensitivity (SEN). The RFE-LR (AUC: 0.91, ACC: 0.84, SPE: 0.84, SEN: 0.83) and Boruta-LR (AUC: 0.90, ACC: 0.85, SPE: 0.85, SEN: 0.83) models generated the best results in terms of in-hospital mortality. In terms of 30-day rehospitalization, Boruta-SVM (AUC: 0.73, ACC: 0.81, SPE: 0.85, SEN: 0.50) and MRMR-LR (AUC: 0.71, ACC: 0.68, SPE: 0.69, SEN: 0.63) models performed the best. The best model for 3-month rehospitalization was MRMR-KNN (AUC: 0.60, ACC: 0.63, SPE: 0.66, SEN: 0.53) and regarding 6-month mortality, the MRMR-LR (AUC: 0.61, ACC: 0.63, SPE: 0.44, SEN: 0.66) and MRMR-NB (AUC: 0.59, ACC: 0.61, SPE: 0.48, SEN: 0.63) models outperformed the others. Reliable models were developed in 30-day rehospitalization and in-hospital mortality using conventional features and ML techniques. Such models can effectively personalize treatment, decision-making, and wiser budget allocation. Obtained results in 3-month rehospitalization and 6-month mortality endpoints were not astonishing and further experiments with additional information are needed to fetch promising results in these endpoints.

Effectiveness of mobile telemonitoring applications in heart failure patients: systematic review of literature and meta-analysis

Close and frequent follow-up of heart failure (HF) patients improves clinical outcomes. Mobile telemonitoring applications are advantageous alternatives due to their wide availability, portability, low cost, computing power, and interconnectivity. This study aims to evaluate the impact of telemonitoring apps on mortality, hospitalization, and quality of life (QoL) in HF patients. We conducted a registered (PROSPERO CRD42022299516) systematic review of randomized clinical trials (RCTs) evaluating mobile-based telemonitoring strategies in patients with HF, published between January 2000 and December 2021 in 4 databases (PubMed, EMBASE, BVSalud/LILACS, Cochrane Reviews). We assessed the risk of bias using the RoB2 tool. The outcome of interest was the effect on mortality, hospitalization risk, and/or QoL. We performed meta-analysis when appropriate; heterogeneity and risk of publication bias were evaluated. Otherwise, descriptive analyses are offered. We screened 900 references and 19 RCTs were included for review. The risk of bias for mortality and hospitalization was mostly low, whereas for QoL was high. We observed a reduced risk of hospitalization due to HF with the use of mobile-based telemonitoring strategies (RR 0.77 [0.67; 0.89]; I2 7%). Non-statistically significant reduction in mortality risk was observed. The impact on QoL was variable between studies, with different scores and reporting measures used, thus limiting data pooling. The use of mobile-based telemonitoring strategies in patients with HF reduces risk of hospitalization due to HF. As smartphones and wirelessly connected devices are increasingly available, further research on this topic is warranted, particularly in the foundational therapy.

Implementation of a Multimodal Heart Failure Management Protocol in a Skilled Nursing Facility

Hospitals and skilled nursing facilities (SNFs) are incentivized to reduce hospital readmissions among patients with heart failure (HF). We used the RE-AIM framework and mixed quantitative and qualitative data to evaluate the implementation of a multimodal HF management protocol (HFMP) administered in a SNF in 2021. Over 90% of eligible patients were enrolled in the HFMP (REACH). Of the 42 enrolled patients (61.9% female, aged 81.9 ± 8.9 years, 9.5% Medicaid), 2 (4.8%) were readmitted within 30 days of hospital discharge and 4 (9.5%) were readmitted within 30 days of SNF discharge compared with historical (2020) rates of 16.7% and 22.2%, respectively (a potential savings of $132,418-$176,573 in hospital costs) (EFFECTIVENESS). Although stakeholder feedback about ADOPTION and IMPLEMENTATION was largely positive, challenges associated with clinical data collection, documentation, and staff turnover were described. Findings will inform refinement of the HFMP to facilitate further testing and sustainability (MAINTENANCE).

Predictors of readmission in hospitalized heart failure patients

Introduction: Heart failure(HF) related hospitalization constitutes a significant proportion of healthcare cost. Unchanging rates of readmission during recent years, shows the importance of addressing this problem. Methods: Patients admitted with heart failure diagnosis in our institution during April 2018to August 2018 were selected. Clinical, para-clinical and imaging data were recorded. All included patients were followed up for 6 months. The primary endpoints of the study were prevalence of early readmission and the predictors of that. Secondary end points were in-hospital and 6-month post-discharge mortality rate and late readmission rate. Results: After excluding 94 patients due to missing data, 428 patients were selected. Mean age of patients was 58.5 years (±17.4) and 61% of patients were male. During follow-up, 99patients (24%) were readmitted. Early re-admission (30-day) occurred in 27 of the patients(6.6%). The predictors of readmission were older age ( P=0.006), lower LVEF (P <0.0001), higher body weight (P=0.01), ICD/CRT implantation ( P=0.001), Lower sodium ( P=0.01), higher Pro-BNP(P=0.01), Higher WBC count (P=0.01) and higher BUN level (P=0.02). Independent predictors of early readmission were history of device implantation (P=0.007), lower LVEF (P=0.016), QRS duration more than 120 ms (P=0.037), higher levels of BUN (P=0.008), higher levels of Pro-BNP(P=0.037) and higher levels of uric acid (P=0.035). Secondary end points including in-hospital and 6-month post-discharge mortality occurred in 11% and 14.4% of patients respectively. Conclusion: Lower age of our heart failure patients and high prevalence of ischemic cardiomyopathy, necessitate focusing on more preventable factors related to heart failure.

Care bundle to reduce readmission in patients with heart failure: a modified Delphi consensus panel in Argentina

OBJECTIVES

The aim of this study was to develop consensus among Argentine cardiologists on a care bundle to reduce readmissions of patients with heart failure (HF).

SETTING

Hospitals and cardiology clinics in Argentina that provide in-hospital care for patients with HF.

PARTICIPANTS

Twenty-four cardiology experts participated in the two online rounds and 18 (75%) of them participated in the third-round meeting.

METHODS

This study used a mixed-method design; it was conducted between August 2019 and January 2020. The development of a care bundle (a set of evidence-based interventions applied to improve clinical outcomes) involved three phases: (1) a literature review to define the list of interventions to be evaluated; (2) a modified Delphi panel to select interventions for the bundle and (3) definition of the HF care bundle. Also, the process included three rounds of scoring.

RESULTS

Twenty-six interventions were evaluated. The interventions in the final bundle covered four categories: medication, continuum of care, lifestyle habits, predischarge tests. These were: medication: beta-blockers, angiotensin receptor neprilysin inhibitors or ACE-inhibitors, furosemide and antimineralocorticoids; continuum of care: follow-up appointment, daily weight monitoring; lifestyle habits: smoking cessation counselling and low-sodium diet; predischarge tests: renal function, ionogram, blood pressure control, echocardiogram and determination of decompensating cause.

CONCLUSION

Following a systematic mixed-method approach, we have developed a care bundle of interventions that could decrease readmission of patients with HF. The application of this bundle could contribute to scale evidence-based interventions.

Reducing All-cause 30-day Hospital Readmissions for Patients Presenting with Acute Heart Failure Exacerbations: A Quality Improvement Initiative

Background

Congestive heart failure (CHF) is the most common cause of hospitalization in the US for people older than 65 years of age. It has the highest 30-day re-hospitalization rate among medical and surgical conditions, accounting for up to 26.9% of the total readmission rates. We conducted a quality improvement project at our hospital with the objective to reduce the 30-day all-cause readmissions of patients with CHF by improving the transition of care and setting up scheduled follow-up appointments within two weeks of patient discharge.

Method

Retrospective data were collected to understand the pattern of admission for CHF during November 2017. Data on 30-day readmission post-discharge was also collected to understand readmission rates. Similarly, all patients who were admitted with acute CHF exacerbation to our hospital during the month of November 2018 were included in our intervention cohort. The 30-day readmission rates of these patients post-intervention were calculated and compared to the initial cohort.

Results

As part of our study, we ensured that 58% of the enrolled patients had a follow-up appointment scheduled within two weeks of discharge compared to only 30% in 2017. Also, 56% of the enrolled patients kept their follow-up appointments compared to 37% in 2017. The 30-day readmission rate of CHF patients was reduced in half after the implementation of our project, with a 14% readmission rate for our study patients compared to 28% in 2017.

Conclusion

Patient education and measures to augment post-discharge follow-up appointments can lead to substantial reductions in the readmission rates of heart failure (HF) patients.

Relative Effects of the Hospital Readmissions Reduction Program on Hospitals That Serve Poorer Patients

IMPORTANCE

Hospitals that serve poorer populations have higher readmission rates. It is unknown whether these hospitals effectively lowered readmission rates in response to the Hospital Readmissions Reduction Program (HRRP).

OBJECTIVE

To compare pre-post differences in readmission rates among hospitals with different proportion of dual-eligible patients both generally and among the most highly penalized (ie, low performing) hospitals.

DESIGN

Retrospective cohort study using piecewise linear model with estimated hospital-level risk-standardized readmission rates (RSRRs) as the dependent variable and a change point at HRRP passage (2010). Economic burden was assessed by proportion of dual-eligibles served.

SETTING

Acute care hospitals within the United States.

PARTICIPANTS

Medicare fee-for-service beneficiaries aged 65 years or older discharged alive from January 1, 2003 to November 30, 2014 with a principal discharge diagnosis of acute myocardial infarction (AMI), congestive heart failure (CHF), and pneumonia.

MAIN OUTCOME AND MEASURE

Decrease in hospital-level RSRRs in the post-law period, after controlling for the pre-law trend.

RESULTS

For AMI, the pre-post difference between hospitals that service high and low proportion of dual-eligibles was not significant (-65 vs. -64 risk-standardized readmissions per 10000 discharges per year, P=0.0678). For CHF, RSRRs declined more at high than low dual-eligible hospitals (-79 vs. -75 risk-standardized readmissions per 10000 discharges per year, P=0.0006). For pneumonia, RSRRs declined less at high than low dual-eligible hospitals (-44 vs. -47 risk-standardized readmissions per 10000 discharges per year, P=0.0003). Among the 742 highest penalized hospitals and all conditions, the pre-post decline in rate of change of RSRRs was less for high dual-eligible hospitals than low dual-eligible hospitals (-68 vs. -74 risk-standardized readmissions per 10000 discharges per year for AMI, -88 vs. -97 for CHF, and -47 vs. -56 for pneumonia, P<0.0001 for all).

CONCLUSIONS AND RELEVANCE

For all hospitals, differences in pre-post trends in RSRRs varied with disease conditions. However, for the highest-penalized hospitals, the pre-post decline in RSRRs was greater for low than high dual-eligible hospitals for all penalized conditions. These results suggest that high penalty, high dual-eligible hospitals may be less able to improve performance on readmission metrics.

Characteristics of Future Models of Integrated Outpatient Care

Replacement of fee-for-service with capitation arrangements, forces physicians and institutions to minimize health care costs, while maintaining high-quality care. In this report we described how patients and their families (or caregivers) can work with members of the medical care team to achieve these twin goals of maintaining-and perhaps improving-high-quality care and minimizing costs. We described how increased self-management enables patients and their families/caregivers to provide electronic patient-reported outcomes (i.e., symptoms, events) (ePROs), as frequently as the patient or the medical care team consider appropriate. These capabilities also allow ongoing assessments of physiological measurements/phenomena (mHealth). Remote surveillance of these communications allows longer intervals between (fewer) patient visits to the medical-care team, when this is appropriate, or earlier interventions, when it is appropriate. Systems are now available that alert medical care providers to situations when interventions might be needed.

Health Literacy but Not Frailty Predict Self-Care Behaviors in Patients with Heart Failure

Heart failure (HF) is a chronic condition requiring continuous self-care. Health literacy is increasingly recognized as a key factor of self-care behaviors in patients with chronic diseases. Recently, frailty in chronic diseases has also been associated with self-care behaviors. However, relationships among health literacy, frailty, and self-care in the HF population are not well understood. Therefore, this cross-sectional study aimed to identify the impact of health literacy and frailty on self-care behaviors in patients with HF. Data were collected from 281 adults attending a cardiovascular outpatient clinic in Korea. Health literacy, frailty, and self-care behaviors were measured using Korean-validated instruments. The mean scores of health literacy and self-care behaviors were 8.89 (±3.44) and 31.49 (±5.38), respectively. The prevalence of frailty was around 26.3%. Health literacy was significantly associated with frailty and self-care behaviors. In a hierarchical linear regression analysis, health literacy was a significant determinant of self-care behaviors after adjusting for confounding variables, but frailty was not. Educational level was also a significant predictor of self-care behaviors. Our main findings showed that health literacy can facilitate improvements in HF self-care behaviors. Healthcare professionals should assess patients’ health literacy and educational backgrounds when designing self-management programs.

Determinants of hospitalization in Chinese patients with type 2 diabetes receiving a peer support intervention and JADE integrated care: the PEARL randomised controlled trial

Background

In a randomized controlled trial of 628 Chinese patients with type 2 diabetes receiving multidisciplinary care in the Joint Asia Diabetes Evaluation (JADE) Progam, 372 were randomized to receive additional telephone-based peer support (Peer Empowerment And Remote communication Linked by information technology, PEARL) intervention. After 12 months, all-cause hospitalization was reduced by half in the PEARL group especially in those with high Depression Anxiety and Stress Scale (DASS) scores.

Methods

We used stratified analyses, negative binomial regression, and structural equation modelling (SEM) to examine the inter-relationships between emotions, self-management, cardiometabolic risk factors, and hospitalization.

Results

Hospitalized patients were older, more likely to have heart or kidney disease, and negative emotions than those without hospitalization. Patients with high DASS score who did not receive peer support had the highest hospitalization rates. After adjustment for confounders, peer support reduced the frequency of hospitalizations by 48% with a relative risk of 0.52 (95% CI 0·35-0·79;p = 0·0018). Using SEM, improvement of negative emotions reduced treatment nonadherence (Est = 0.240, p = 0.034) and hospitalizations (Est=-0.218, p = 0.001). The latter was also reduced by an interactive term of peer support and chronic kidney disease (Est = 0.833, p = < 0.001) and that of peer support and heart disease (Est = 0.455, p = 0.001).

Conclusions

In type 2 diabetes, improvement of negative emotions and peer support reduced hospitalizations, especially in those with comorbidities, in part mediated through improving treatment nonadherence. Integrating peer support is feasible and adds value to multidisciplinary care, augmented by information technology, especially in patients with comorbidities.

Trial registration

NCT00950716 Registered July 31, 2009.