Analysis Of Re-Hospitalizations For Patients With Heart Failure Caused By Coronary Heart Disease: Data Of First Event And Recurrent Event

Current approaches to preventing heart failure readmissions and decompensated disease

Heart failure is a resource-intensive condition to manage and typically involves a multi-disciplinary and multi-modality approach leading to an expensive treatment paradigm. It is worth noting that hospital admissions constitute over 80% of heart failure management costs. In the past two decades, healthcare systems have developed new ways of following patients remotely to prevent them from being readmitted to the hospital. However, despite these efforts, hospital admissions have still increased. Many successful readmission reduction programs prioritize education and self-care to increase patients' awareness of their disease and promote lasting lifestyle changes. While socioeconomic factors impact success, interventions tend to be effective when medication adherence and guideline-directed medical therapy are emphasized. Monitoring intracardiac pressure can improve resource allocation efficiency and has demonstrated significant reductions in readmissions with improved quality of life in outpatient and remote settings. Data from several studies focused on remote monitoring devices strongly suggest that understanding congestion using physiological biomarkers is an effective management strategy. Since most cases of heart failure are first presented in acute hospitalization settings, immediate access to intracardiac pressure for treatment and decision-making purposes could result in substantial management improvements. However, a notable technology gap needs to be addressed to enable this at a low cost with less reliability on scarce specialist care resources. Contemporary evidence is conclusive that direct hemodynamic are the vital signs in heart failure with the highest clinical utility. Therefore, future ability to obtain these insights reliably using non-invasive methods will be a paradigm-changing technology.

Network and Experimental Pharmacology on Mechanism of Yixintai Regulates the TMAO/PKC/NF-κB Signaling Pathway in Treating Heart Failure

Objective

This study aims to explore the mechanism of action of Yixintai in treating chronic ischemic heart failure by combining bioinformatics and experimental validation.

Materials and Methods

Five potential drugs for treating heart failure were obtained from Yixintai (YXT) through early mass spectrometry detection. The targets of YXT for treating heart failure were obtained by a search of online databases. Gene ontology (GO) functional enrichment analysis and Kyoto encyclopedia of genes and genomes (KEGG) pathway enrichment analyses were conducted on the common targets using the DAVID database. A rat heart failure model was established by ligating the anterior descending branch of the left coronary artery. A small animal color Doppler ultrasound imaging system detected cardiac function indicators. Hematoxylin-eosin (HE), Masson's, and electron microscopy were used to observe the pathological morphology of the myocardium in rats with heart failure. The network pharmacology analysis results were validated by ELISA, qPCR, and Western blotting.

Results

A total of 107 effective targets were obtained by combining compound targets and eliminating duplicate values. PPI analysis showed that inflammation-related proteins (TNF and IL1B) were key targets for treating heart failure, and KEGG enrichment suggested that NF-κB signaling pathway was a key pathway for YXT treatment of heart failure. Animal model validation results indicated the following: YXT can significantly reduce the content of intestinal microbiota metabolites such as trimethylamine oxide (TMAO) and improve heart failure by improving the EF and FS values of heart ultrasound in rats and reducing the levels of serum NT-proBNP, ANP, and BNP to improve heart failure. Together, YXT can inhibit cardiac muscle hypertrophy and fibrosis in rats and improve myocardial ultrastructure and serum IL-1β, IL-6, and TNF-α levels. These effects are achieved by inhibiting the expressions of NF-κB and PKC.

Conclusion

YXT regulates the TMAO/PKC/NF-κB signaling pathway in heart failure.

Identification of echocardiographic subgroups in patients with coronary heart disease combined with heart failure based on latent variable stratification

BACKGROUND

The prognosis of chronic heart failure is poor, and it remains a challenge to classify patients for better personalized intervention. This study aimed to explore potential subgroups in patients with coronary heart disease and chronic heart failure using comprehensive echocardiographic indices.

METHODS

5126 patients with coronary heart disease with chronic heart failure were included. Latent class analysis was applied to identify the grouping patterns of patients based on echocardiographic indices. Network maps and radar charts of echocardiographic indices were drawn to visualize the distribution of echocardiographic findings. The incidence of adverse outcomes was presented on the Kaplan-Meier curve and compared using the log-rank test. The Cox regression model was used to analyze the relationship between subgroups and mortality.

RESULTS

Three groups were identified: eccentric hypertrophy, concentric hypertrophy, and decreased diastolic function. Network plots showed a higher correlation between left atrial diameter, left ventricular mass index, and left ventricle ejection fraction in the eccentric hypertrophy group than in the other groups. The Kaplan-Meier curve showed a significant difference in mortality between the three subgroups (P < 0.001). Multivariate Cox analysis indicated that the eccentric hypertrophy group had the highest risk of death (HR = 1.586, 95% CI: 1.310-1.921, P < 0.001) compared with the other groups.

CONCLUSION

Patients with coronary heart disease and chronic heart failure can be classified into three subgroups based on echocardiographic indices. This grouping has been shown to be an independent risk factor for mortality in these patients. Accurate subgrouping based on echocardiographic indices is important for identifying high-risk patients.

Cost Effectiveness of the First-in-Class ARNI (Sacubitril/Valsartan) for the Treatment of Essential Hypertension in a Chinese Setting

OBJECTIVE

The aim of this study was to model the potential long-term disease progression and pharmacoeconomic value of sacubitril/valsartan for the treatment of essential hypertension from a Chinese healthcare system perspective.

METHODS

A Markov cohort model with five health states was constructed to simulate the incidence of acute cardiovascular events and cost per quality-adjusted life-year (QALY) gained with sacubitril/valsartan compared with allisartan isoproxil and valsartan over a lifetime horizon with an annual cycle. Multivariable risk regression models derived from China-PAR data accompanied by hazard ratios were used to transform the dual mechanism of sacubitril/valsartan to lower blood pressure and left ventricular mass index into long-term fatal and non-fatal cardiovascular risks. Efficacy data were calculated using a network meta-analysis integrated by the results of clinical trials. Healthcare costs were determined from a real-world study and published literature, supplemented by expert opinion. Utilities were derived from literature. Both costs and health outcomes were discounted at 5.0% annually, and prices corresponded to 2021. Model validation, deterministic and probabilistic sensitivity analyses were conducted to test the robustness of results.

RESULTS

For simulated patients with hypertension, sacubitril/valsartan reduced the rates of myocardial infarction by 6.67% and 6.39%, stroke by 9.38% and 8.98%, and heart failure hospitalization by 9.92% and 9.62% relative to allisartan isoproxil and valsartan, respectively. It was also associated with gains in life expectancy among hypertensive individuals of 0.362-0.382 years. Eventually, lifetime costs per patient were CN¥59,272 (US$9187) for sacubitril/valsartan, CN¥54,783 (US$8492) for allisartan isoproxil, and CN¥56,714 (US$8791) for valsartan; total QALYs were 11.38, 11.24, and 11.25, respectively. The incremental cost-effectiveness ratio was CN¥31,805/QALY (US$4930/QALY) compared with allisartan isoproxil, and CN¥19,247/QALY (US$2983/QALY) compared with valsartan, both of which are below the one time per-capita GDP of CN¥80,976/QALY (US$12,551/QALY) in China. Similar results were obtained in various extensive sensitivity analysis scenarios.

CONCLUSIONS

This was the first study to evaluate the cost effectiveness of sacubitril/valsartan in the treatment of hypertension. Sacubitril/valsartan compares favorably with allisartan isoproxil and valsartan in the Chinese setting, which is mainly due to its higher efficacy resulting in fewer cardiovascular events and ultimately less related mortality over time. The results could inform deliberations regarding reimbursement and access to this treatment in China and may provide reference for facilitating more reasonable and efficient allocation of limited resources in such low- and middle-income countries.

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

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

Interpretable prediction of 3-year all-cause mortality in patients with heart failure caused by coronary heart disease based on machine learning and SHAP

BACKGROUND

This study sought to evaluate the performance of machine learning (ML) models and establish an explainable ML model with good prediction of 3-year all-cause mortality in patients with heart failure (HF) caused by coronary heart disease (CHD).

METHODS

We established six ML models using follow-up data to predict 3-year all-cause mortality. Through comprehensive evaluation, the best performing model was used to predict and stratify patients. The log-rank test was used to assess the difference between Kaplan-Meier curves. The association between ML risk and 3-year all-cause mortality was also assessed using multivariable Cox regression. Finally, an explainable approach based on ML and the SHapley Additive exPlanations (SHAP) method was deployed to calculate 3-year all-cause mortality risk and to generate individual explanations of the model's decisions.

RESULTS

The best performing extreme gradient boosting (XGBoost) model was selected to predict and stratify patients. Subjects with a higher ML score had a high hazard of suffering events (hazard ratio [HR]: 10.351; P < 0.001), and this relationship persisted with a multivariable analysis (adjusted HR: 5.343; P < 0.001). Age, N-terminal pro-B-type natriuretic peptide, occupation, New York Heart Association classification, and nitrate drug use were important factors for both genders.

CONCLUSIONS

The ML-based risk stratification tool was able to accurately assess and stratify the risk of 3-year all-cause mortality in patients with HF caused by CHD. ML combined with SHAP could provide an explicit explanation of individualized risk prediction and give physicians an intuitive understanding of the influence of key features in the model.