Biomarkers of Hemodynamic Congestion in Heart Failure.

PURPOSE OF REVIEW: The purpose of this review is to describe the evidence behind various blood and imaging-based biomarkers that can improve the identification of congestion when not clearly evident on routine examination. RECENT FINDINGS: The natriuretic peptides (NPs) BNP and NT-proBNP have been shown to closely correlate with intra-cardiac filling pressures, both at baseline and when trended following improvement in congestion. Additionally, NPs rise well before clinical congestion is apparent so can be used as a tool to help identify subclinical HF decompensation. Additional serum-based biomarkers including MR-proANP and CA-125 can be helpful in assisting with diagnostic certainty when BNP or NT-proBNP are in the "grey zone" or when factors are present which may confound NP levels. Additionally, the emerging use of ultrasound techniques may enhance our ability to fine-tune the assessment and treatment of congestion. Biomarkers, including the blood-based natriuretic peptides and markers on bedside point of care ultrasound, can be used as non-invasive indices of hemodynamic congestion. These biomarkers are particularly valuable to incorporate when the degree of a patient's congestion is not apparent on clinical exam, and they can provide important prognostic information and help guide clinical management.

Impact of Diabetes and Glycemia on Cardiac Improvement and Adverse Events Following Mechanical Circulatory Support.

BACKGROUND: Type 2 diabetes is prevalent in cardiovascular disease and contributes to excess morbidity and mortality. We sought to investigate the effect of glycemia on functional cardiac improvement, morbidity, and mortality in durable left ventricular assist device (LVAD) recipients. METHODS AND RESULTS: Consecutive patients with an LVAD were prospectively evaluated (n=531). After excluding patients missing pre-LVAD glycated hemoglobin (HbA1c) measurements or having inadequate post-LVAD follow-up, 375 patients were studied. To assess functional cardiac improvement, we used absolute left ventricular ejection fraction change (ΔLVEF: LVEF post-LVAD-LVEF pre-LVAD). We quantified the association of pre-LVAD HbA1c with ΔLVEF as the primary outcome, and all-cause mortality and LVAD-related adverse event rates (ischemic stroke/transient ischemic attack, intracerebral hemorrhage, gastrointestinal bleeding, LVAD-related infection, device thrombosis) as secondary outcomes. Last, we assessed HbA1c differences pre- and post-LVAD. Patients with type 2 diabetes were older, more likely men suffering ischemic cardiomyopathy, and had longer heart failure duration. Pre-LVAD HbA1c was inversely associated with ΔLVEF in patients with nonischemic cardiomyopathy but not in those with ischemic cardiomyopathy, after adjusting for age, sex, heart failure duration, and left ventricular end-diastolic diameter. Pre-LVAD HbA1c was not associated with all-cause mortality, but higher pre-LVAD HbA1c was shown to increase the risk of intracerebral hemorrhage, LVAD-related infection, and device thrombosis by 3 years on LVAD support (P<0.05 for all). HbA1c decreased from 6.68±1.52% pre-LVAD to 6.11±1.33% post-LVAD (P<0.001). CONCLUSIONS: Type 2 diabetes and pre-LVAD glycemia modify the potential for functional cardiac improvement and the risk for adverse events on LVAD support. The degree and duration of pre-LVAD glycemic control optimization to favorably affect these outcomes warrants further investigation.

Machine Learning Multicenter Risk Model to Predict Right Ventricular Failure After Mechanical Circulatory Support: The STOP-RVF Score.

Importance: The existing models predicting right ventricular failure (RVF) after durable left ventricular assist device (LVAD) support might be limited, partly due to lack of external validation, marginal predictive power, and absence of intraoperative characteristics. Objective: To derive and validate a risk model to predict RVF after LVAD implantation. Design, Setting, and Participants: This was a hybrid prospective-retrospective multicenter cohort study conducted from April 2008 to July 2019 of patients with advanced heart failure (HF) requiring continuous-flow LVAD. The derivation cohort included patients enrolled at 5 institutions. The external validation cohort included patients enrolled at a sixth institution within the same period. Study data were analyzed October 2022 to August 2023. Exposures: Study participants underwent chronic continuous-flow LVAD support. Main Outcome and Measures: The primary outcome was RVF incidence, defined as the need for RV assist device or intravenous inotropes for greater than 14 days. Bootstrap imputation and adaptive least absolute shrinkage and selection operator variable selection techniques were used to derive a predictive model. An RVF risk calculator (STOP-RVF) was then developed and subsequently externally validated, which can provide personalized quantification of the risk for LVAD candidates. Its predictive accuracy was compared with previously published RVF scores. Results: The derivation cohort included 798 patients (mean [SE] age, 56.1 [13.2] years; 668 male [83.7%]). The external validation cohort included 327 patients. RVF developed in 193 of 798 patients (24.2%) in the derivation cohort and 107 of 327 patients (32.7%) in the validation cohort. Preimplant variables associated with postoperative RVF included nonischemic cardiomyopathy, intra-aortic balloon pump, microaxial percutaneous left ventricular assist device/venoarterial extracorporeal membrane oxygenation, LVAD configuration, Interagency Registry for Mechanically Assisted Circulatory Support profiles 1 to 2, right atrial/pulmonary capillary wedge pressure ratio, use of angiotensin-converting enzyme inhibitors, platelet count, and serum sodium, albumin, and creatinine levels. Inclusion of intraoperative characteristics did not improve model performance. The calculator achieved a C statistic of 0.75 (95% CI, 0.71-0.79) in the derivation cohort and 0.73 (95% CI, 0.67-0.80) in the validation cohort. Cumulative survival was higher in patients composing the low-risk group (estimated <20% RVF risk) compared with those in the higher-risk groups. The STOP-RVF risk calculator exhibited a significantly better performance than commonly used risk scores proposed by Kormos et al (C statistic, 0.58; 95% CI, 0.53-0.63) and Drakos et al (C statistic, 0.62; 95% CI, 0.57-0.67). Conclusions and Relevance: Implementing routine clinical data, this multicenter cohort study derived and validated the STOP-RVF calculator as a personalized risk assessment tool for the prediction of RVF and RVF-associated all-cause mortality.