Ten-Year Risk-Prediction Equations for Incident Heart Failure Hospitalizations in Chronic Kidney Disease: Findings from the Chronic Renal Insufficiency Cohort Study and the Multi-Ethnic Study of Atherosclerosis

The role of sacubitril/valsartan in abnormal renal function patients combined with heart failure: a meta-analysis and systematic analysis

AIMS

This study aimed to investigate the efficacy and safety of sacubitril/valsartan in abnormal renal function (eGFR < 60 ml/min/1.73m2) patients combined with heart failure based on randomized controlled trials (RCTs) and observational studies.

METHODS

The Embase, PubMed and the Cochrane Library were searched for relevant studies from inception to December 2023. Dichotomous variables were described as event counts with the odds ratio (OR) and 95% confidence interval (CI) values. Continuous variables were expressed as mean standard deviation (SD) with 95% CIs.

RESULTS

A total of 6 RCTs and 8 observational studies were included, involving 17335 eGFR below 60 ml/min/1.73m2 patients combined with heart failure. In terms of efficacy, we analyzed the incidence of cardiovascular events and found that sacubitril/valsartan significantly reduced the risk of cardiovascular death or heart failure hospitalization in chronic kidney disease (CKD) stages 3-5 patients with heart failure (OR: 0.65, 95%CI: 0.54-0.78). Moreover, sacubitril/valsartan prevented the serum creatinine elevation (OR: 0.81, 95%CI: 0.68-0.95), the eGFR decline (OR: 0.83, 95% CI: 0.73-0.95) and the development of end-stage renal disease in this population (OR:0.73, 95%CI:0.60-0.89). As for safety outcomes, we did not find that the rate of hyperkalemia (OR:1.31, 95%CI:0.79-2.17) and hypotension (OR:1.57, 95%CI:0.94-2.62) were increased in sacubitril/valsartan group among CKD stages 3-5 patients with heart failure.

CONCLUSIONS

Our meta-analysis proves that sacubitril/valsartan has a favorable effect on cardiac function without obvious risk of adverse events in abnormal renal function patients combined with heart failure, indicating that sacubitril/valsartan has the potential to become perspective treatment for these patients.

Integrating Phenotypic Information of Obstructive Sleep Apnea and Deep Representation of Sleep-Event Sequences for Cardiovascular Risk Prediction

Background

Advances in mobile, wearable and machine learning (ML) technologies for gathering and analyzing long-term health data have opened up new possibilities for predicting and preventing cardiovascular diseases (CVDs). Meanwhile, the association between obstructive sleep apnea (OSA) and CV risk has been well-recognized. This study seeks to explore effective strategies of incorporating OSA phenotypic information and overnight physiological information for precise CV risk prediction in the general population.

Methods

1,874 participants without a history of CVDs from the MESA dataset were included for the 5-year CV risk prediction. Four OSA phenotypes were first identified by the K-mean clustering based on static polysomnographic (PSG) features. Then several phenotype-agnostic and phenotype-specific ML models, along with deep learning (DL) models that integrate deep representations of overnight sleep-event feature sequences, were built for CV risk prediction. Finally, feature importance analysis was conducted by calculating SHapley Additive exPlanations (SHAP) values for all features across the four phenotypes to provide model interpretability.

Results

All ML models showed improved performance after incorporating the OSA phenotypic information. The DL model trained with the proposed phenotype-contrastive training strategy performed the best, achieving an area under the Receiver Operating Characteristic (ROC) curve of 0.877. Moreover, PSG and FOOD FREQUENCY features were recognized as significant CV risk factors across all phenotypes, with each phenotype emphasizing unique features.

Conclusion

Models that are aware of OSA phenotypes are preferred, and lifestyle factors should be a greater focus for precise CV prevention and risk management in the general population.

Optimizing Care Delivery in Patients with Chronic Kidney Disease in the United States: Proceedings of a Multidisciplinary Roundtable Discussion and Literature Review

BACKGROUND

Approximately 37 million individuals in the United States (US) have chronic kidney disease (CKD). Patients with CKD have a substantial morbidity and mortality, which contributes to a huge economic burden to the healthcare system. A limited number of clinical pathways or defined workflows exist for CKD care delivery in the US, primarily due to a lower prioritization of CKD care within health systems compared with other areas (e.g., cardiovascular disease [CVD], cancer screening). CKD is a public health crisis and by the year 2040, CKD will become the fifth leading cause of years of life lost. It is therefore critical to address these challenges to improve outcomes in patients with CKD.

METHODS

The CKD Leaders Network conducted a virtual, 3 h, multidisciplinary roundtable discussion with eight subject-matter experts to better understand key factors impacting CKD care delivery and barriers across the US. A premeeting survey identified topics for discussion covering the screening, diagnosis, risk stratification, and management of CKD across the care continuum. Findings from this roundtable are summarized and presented herein.

RESULTS

Universal challenges exist across health systems, including a lack of awareness amongst providers and patients, constrained care team bandwidth, inadequate financial incentives for early CKD identification, non-standardized diagnostic classification and triage processes, and non-centralized patient information. Proposed solutions include highlighting immediate and long-term financial implications linked with failure to identify and address at-risk individuals, identifying and managing early-stage CKD, enhancing efforts to support guideline-based education for providers and patients, and capitalizing on next-generation solutions.

CONCLUSIONS

Payers and other industry stakeholders have opportunities to contribute to optimal CKD care delivery. Beyond addressing the inadequacies that currently exist, actionable tactics can be implemented into clinical practice to improve clinical outcomes in patients at risk for or diagnosed with CKD in the US.

Albuminuria: An Underappreciated Risk Factor for Cardiovascular Disease

Albuminuria, an established biomarker of the progression of chronic kidney disease, is also recognized as a biomarker for the risk of cardiovascular disease. Elevated urinary albumin excretion indicates kidney damage and systemic vascular disease, including myocardial capillary disease and arterial stiffness. Albuminuria is associated with an increased risk of coronary artery disease, stroke, heart failure, arrhythmias, and microvascular disease. There are now several therapeutic agents that can lead to albuminuria lowering and a reduction in cardiovascular risk. However, screening for albuminuria is still low. Considering the importance of multidisciplinary management of patients with cardiovascular disease, it is crucial that health care professionals managing such patients are aware of the benefits of albuminuria surveillance and management.

Albuminuria and Heart Failure: JACC State-of-the-Art Review

Although chronic kidney disease is characterized by low glomerular filtration rate (GFR) or albuminuria, estimated GFR (eGFR) is more widely utilized as a marker of risk profile in cardiovascular diseases, including heart failure (HF). The presence and magnitude of albuminuria confers a strong prognostic association in forecasting risk of incident HF as well as its progression, irrespective of eGFR. Despite the high prevalence of albuminuria in HF, whether it adds incremental prognostic information in clinical practice and serves as an independent risk marker, and whether there are any therapeutic implications of assessing albuminuria in patients with HF is less well-established. In this narrative review, we assess the potential role of albuminuria in risk profiling for development and progression of HF, strengths and limitations of utilizing albuminuria as a risk marker, its ability to serve in HF risk prediction models, and the implications of adopting albuminuria as an effective parameter in cardiovascular trials and practice.

Multi-variable biomarker approach in identifying incident heart failure in chronic kidney disease: results from the Chronic Renal Insufficiency Cohort study

AIMS

Heart failure (HF) is one of the leading causes of cardiovascular morbidity and mortality in the ever-growing population of patients with chronic kidney disease (CKD). There is a need to enhance early prediction to initiate treatment in CKD. We sought to study the feasibility of a multi-variable biomarker approach to predict incident HF risk in CKD.

METHODS AND RESULTS

We examined 3182 adults enrolled in the Chronic Renal Insufficiency Cohort (CRIC) without prevalent HF who underwent serum/plasma assays for 11 blood biomarkers at baseline visit (B-type natriuretic peptide [BNP], CXC motif chemokine ligand 12, fibrinogen, fractalkine, high-sensitivity C-reactive protein, myeloperoxidase, high-sensitivity troponin T (hsTnT), fibroblast growth factor 23 [FGF23], neutrophil gelatinase-associated lipocalin, fetuin A, aldosterone). The population was randomly divided into derivation (n = 1629) and validation (n = 1553) cohorts. Biomarkers that were associated with HF after adjustment for established HF risk factors were combined into an overall biomarker score (number of biomarkers above the Youden's index cut-off value). Cox regression was used to explore the predictive role of a biomarker panel to predict incident HF. A total of 411 patients developed incident HF at a median follow-up of 7 years. In the derivation cohort, four biomarkers were associated with HF (BNP, FGF23, fibrinogen, hsTnT). In a model combining all four biomarkers, BNP (hazard ratio [HR] 2.96 [95% confidence interval 2.14-4.09]), FGF23 (HR 1.74 [1.30-2.32]), fibrinogen (HR 2.40 [1.74-3.30]), and hsTnT (HR 2.89 [2.06-4.04]) were associated with incident HF. The incidence of HF increased with the biomarker score, to a similar degree in both derivation and validation cohorts: from 2.0% in score of 0% to 46.6% in score of 4 in the derivation cohort to 2.4% in score of 0% to 43.5% in score of 4 in the validation cohort. A model incorporating biomarkers in addition to clinical factors reclassified risk in 601 (19%) participants (352 [11%] participants to higher risk and 249 [8%] to lower risk) compared with clinical risk model alone (net reclassification improvement of 0.16).

CONCLUSION

A basic panel of four blood biomarkers (BNP, FGF23, fibrinogen, and hsTnT) can be used as a standalone score to predict incident HF in patients with CKD allowing early identification of patients at high-risk for HF. Addition of biomarker score to clinical risk model modestly reclassifies HF risk and slightly improves discrimination.

New strategies and therapies for the prevention of heart failure in high-risk patients

Despite declines in total cardiovascular mortality rates in the United States, heart failure (HF) mortality rates as well as hospitalizations and readmissions have increased in the past decade. Increases have been relatively higher among young and middle-aged adults (<65 years). Therefore, identification of individuals HF at-risk (Stage A) or with pre-HF (Stage B) before the onset of overt clinical signs and symptoms (Stage C) is urgently needed. Multivariate risk models (e.g., Pooled Cohort Equations to Prevent Heart Failure [PCP-HF]) have been externally validated in diverse populations and endorsed by the 2022 HF Guidelines to apply a risk-based framework for the prevention of HF. However, traditional risk factors included in the PCP-HF model only account for half of an individual's lifetime risk of HF; novel risk factors (e.g., adverse pregnancy outcomes, impaired lung health, COVID-19) are emerging as important risk-enhancing factors that need to be accounted for in personalized approaches to prevention. In addition to determining the role of novel risk-enhancing factors, integration of social determinants of health (SDoH) in identifying and addressing HF risk is needed to transform the current clinical paradigm for the prevention of HF. Comprehensive strategies to prevent the progression of HF must incorporate pharmacotherapies (e.g., sodium glucose co-transporter-2 inhibitors that have also been termed the "statins" of HF prevention), intensive blood pressure lowering, and heart-healthy behaviors. Future directions include investigation of novel prediction models leveraging machine learning, integration of risk-enhancing factors and SDoH, and equitable approaches to interventions for risk-based prevention of HF.