N-Terminal Pro-B-Type Natriuretic Peptide in Risk Stratification of Heart Failure Patients With Implantable Cardioverter-Defibrillator

N-terminal pro-B-type natriuretic peptide, eGFR, and progression of kidney disease in chronic kidney disease patients without heart failure

Background

Cardiorenal syndrome highlights the bidirectional relationship between kidney and heart dysfunction. N-terminal pro-B-type natriuretic peptide (NT-proBNP), which is the gold standard biomarker in heart failure (HF), may be an important biomarker for chronic kidney disease (CKD) progression. However, NT-proBNP is negatively related with estimated glomerular filtration rate (eGFR). In this study, we investigated the association of NT-proBNP, eGFR, and progression of kidney disease in CKD patients without HF.

Methods

This multicentric retrospective cohort study recruited 23 860 CKD patients without HF, who had at least one NT-proBNP record from China Renal Data System database. Linear regression model evaluated the relationship between eGFR and NT-proBNP. Cox regression analysis assessed the association between NT-proBNP and CKD progression. Sensitivity analysis examined the robustness of the main findings.

Results

This study involved 23 860 CKD patients without HF, distributed across different CKD stages: 10 526 in stages G1-2, 4665 in G3a, 3702 in G3b, 2704 in G4, and 2263 in G5. NT-proBNP was negatively correlated with eGFR, particularly in stages 4-5 CKD. A 15-unit decrease in eGFR was associated with increases in log (NT-proBNP) levels by 1.04-fold, 1.27-fold, 1.29-fold, 1.80-fold, and 3.50-fold for stages 1-2, 3a, 3b, 4, and 5, respectively. After excluding patients who developed CKD progression within 1 year, the Cox regression analysis revealed that the relationship between NT-proBNP and CKD progression was not significant in stages 4 and 5. However, for stages 1-3, each standard deviation increase in log (NT-proBNP) was associated with a 26%, 36%, and 28% higher risk of CKD progression, with P interaction ≤.001. The hazard ratios were 1.26 (95% confidence intervals (CI), 1.18 to 1.35), 1.36 (95% CI, 1.22 to 1.51), and 1.28 (95% CI, 1.14 to 1.43) for stages 1-2, stage 3a, and stage 3b, respectively.

Conclusions

Despite its strong inverse association with eGFR, NT-proBNP was positively associated with the risk of progression of kidney disease in CKD patients with stages 1-3 without HF. Future studies should investigate the effectiveness of NT-proBNP as a predictive biomarker for the progression of kidney disease across diverse racial groups and healthcare settings.

Incremental Value of Right Ventricular Outflow Tract Diameter in Risk Assessment of Chronic Heart Failure Patients with Implantable Cardioverter Defibrillators: Development of RVOTD-ICD Benefit Score in Real-World Setting

Background

Left ventricular ejection fraction (LVEF) remains the basic reference for the prevention of sudden cardiac death (SCD) patients, while right ventricular (RV) abnormalities have now been associated with SCD risk. A modified benefit assessment tool incorporating RV function parameters in consideration of implantable cardioverter defibrillators (ICD) insertion should be taken into account.

Methods

We enrolled 954 chronic heart failure (CHF) patients (age 58.8 ± 13.1 years; 79.0% male) with quantitative measurements of right ventricular outflow tract diameter (RVOTD) before ICD implantation and then divided them according to the median level of RVOTD. The predictive value of RVOTD in life-threatening ventricular tachycardia (VT)/ventricular fibrillation (VF) vs. non-arrhythmic mortality (defined as death without prior sustained VT/VF), was evaluated respectively. Based on RVOTD and other identified risk factors, a simple risk assessment tool, RVOTD-ICD benefit score, was developed.

Results

A higher RVOTD level was significantly associated with an increased risk of VT/VF (per 1 standard deviation (SD) increase, hazard ratio [HR], 1.22; 95% confidence interval [CI], 1.11-1.33; p = 0.002) but not non-arrhythmic mortality (per 1 SD increase, hazard ratio, 0.93; 95% CI, 0.66-1.33; p = 0.709) after multivariable adjustment. Three benefit groups were created based on RVOTD-ICD benefit score, which was calculated from VT/VF score (younger age, higher RVOTD, diuretic use, prior non-sustainable VT, prior sustainable VT/VF) and non-arrhythmic mortality scores (older age, renin-angiotensin-aldosterone system inhibitors use, diabetes, higher left ventricular end-diastolic diameter, New York Heart Association III/IV, higher N-terminal pro-B-type natriuretic peptide levels). In the highest RVOTD-ICD benefit group, the 3-year risk of VT/VF was nearly 8-fold higher than the corresponding risk of non-arrhythmic mortality (39.2% vs. 4.8%, p < 0.001). On the contrary, the 3-year risk of VT/VF was similar to the risk of non-arrhythmic mortality (21.9% vs. 21.3%, p = 0.405) in the lowest benefit group. RVOTD-ICD benefit score system yielded improvement in discrimination for VT/VF, non-arrhythmic mortality, and all-cause mortality than Multicenter Automatic Defibrillator Implantation Trial (MADIT)-ICD benefit score in this cohort.

Conclusions

Higher RVOTD was associated with significantly increased risk of sustained VT/VF in CHF patients. A simple risk assessment tool incorporating RVOTD (RVOTD-ICD benefit score) could be generalized to ICD populations, and optimize the decision-making process of ICD implantation.

Role of N-terminal pro-B type natriuretic peptide as a predictor of poor outcomes in patients with HFrEF receiving primary prevention implantable cardioverter-defibrillator therapy: a systematic review and dose-response meta-analysis

INTRODUCTION

Several studies have demonstrated that combining left ventricular ejection fraction and New York Heart Association functional class is insufficient for predicting risk of appropriate implantable cardioverter-defibrillator (ICD) shock in primary prevention candidates. Hence, our aim was to assess the relationship between N-terminal pro-B type natriuretic peptide (NT-pro BNP) along with appropriate ICD shock and all-cause mortality in order to improve the stratification process of patients with heart failure with reduced ejection fraction (HFrEF) being considered for primary preventive ICD therapy.

METHODS

A systematic literature search from several databases was conducted up until 9 June 2022. Studies were eligible if they investigated the relationship of NT-pro BNP with all-cause mortality and appropriate ICD shock.

RESULTS

This meta-analysis comprised nine studies with a total of 5117 participants. Our study revealed that high levels of NT-pro BNP were associated with all-cause mortality (HR=2.12 (95% CI=1.53 to 2.93); p<0.001, I2=78.1%, p<0.001 for heterogeneity) and appropriate ICD shock (HR=1.71 (95% CI=1.18 to 2.49); p<0.001, I2=43.4%, p=0.102 for heterogeneity). The adjusted HR for all-cause mortality and appropriate ICD shock increased by approximately 3% and 5%, respectively per 100 pg/mL increment pursuant to concentration-response model (Pnon-linearity <0.001). The curves became steeper after NT-pro BNP reached its inflection point (3000 pg/mL).

CONCLUSION

A positive concentration-dependent association between elevated NT-pro BNP levels along with the risk of all-cause mortality and appropriate ICD shock was found in patients with HFrEF with ICD.

PROSPERO REGISTRATION NUMBER

CRD42022339285.

Toward Better Risk Stratification for Implantable Cardioverter-Defibrillator Recipients: Implications of Explainable Machine Learning Models

Background: Current guideline-based implantable cardioverter-defibrillator (ICD) implants fail to meet the demands for precision medicine. Machine learning (ML) designed for survival analysis might facilitate personalized risk stratification. We aimed to develop explainable ML models predicting mortality and the first appropriate shock and compare these to standard Cox proportional hazards (CPH) regression in ICD recipients. Methods and Results: Forty-five routine clinical variables were collected. Four fine-tuned ML approaches (elastic net Cox regression, random survival forests, survival support vector machine, and XGBoost) were applied and compared with the CPH model on the test set using Harrell’s C-index. Of 887 adult patients enrolled, 199 patients died (5.0 per 100 person-years) and 265 first appropriate shocks occurred (12.4 per 100 person-years) during the follow-up. Patients were randomly split into training (75%) and test (25%) sets. Among ML models predicting death, XGBoost achieved the highest accuracy and outperformed the CPH model (C-index: 0.794 vs. 0.760, p < 0.001). For appropriate shock, survival support vector machine showed the highest accuracy, although not statistically different from the CPH model (0.621 vs. 0.611, p = 0.243). The feature contribution of ML models assessed by SHAP values at individual and overall levels was in accordance with established knowledge. Accordingly, a bi-dimensional risk matrix integrating death and shock risk was built. This risk stratification framework further classified patients with different likelihoods of benefiting from ICD implant. Conclusions: Explainable ML models offer a promising tool to identify different risk scenarios in ICD-eligible patients and aid clinical decision making. Further evaluation is needed.