Association of Long-term Change in N-Terminal Pro-B-Type Natriuretic Peptide With Incident Heart Failure and Death

A novel NT-proBNP assay for heart failure diagnosis: A prospective, multicenter clinical trial

OBJECTIVES

NT-proBNP is widely measured for the diagnosis of acute heart failure (HF). This study assessed the diagnostic performance of a novel N-terminal pro-B type natriuretic peptide (NT-proBNP) assay in evaluating the dyspneic patient in the acute care setting.

METHODS

This was a multicenter study of individuals presenting to the emergency department exhibiting clinical symptoms potentially due to acute HF. Blood was drawn for NT-proBNP assessment using the Beckman Coulter Access NT-proBNP assay with results compared to adjudicated diagnoses. Endpoints included negative predictive value and sensitivity of an age-independent cut point of < 300 ng/L to exclude acute HF, and the positive predictive value of the age-dependent cut points of >450, >900, and >1800 ng/L for ages < 50 years, 50-75 years, and >75 years, respectively, for the diagnosis of acute HF.

RESULTS

490 study participants were included, of which 41 % were adjudicated as having acute heart failure. The assay had an area under the receiver-operator characteristic curve (AUC) for the diagnosis of acute HF of 0.87 (P < 0.001), comparable AUC to other commercially available NT-proBNP assays. A rule-out cut point of <300 ng/L had 96 % sensitivity and negative predictive value of 95 %. Age-dependent cut points had sensitivity of 84 %, 90 %, and 87 %, specificity of 81 %, 70 % and 61 %, and positive predictive value of 72 %, 62 %, and 74 %, respectively.

CONCLUSIONS

This novel NT-proBNP assay demonstrated high clinical performance in the diagnosis and exclusion of acute HF in the undifferentiated dyspneic patient and performed similarly well to validated assays used in clinical practice.

History of Cancer and Atrial Cardiopathy: A Secondary Analysis of the ARCADIA Clinical Trial

BACKGROUND

Approximately 50% of strokes in patients with cancer are classified as cryptogenic after standard evaluation. Atrial cardiopathy could explain some cancer-related cryptogenic strokes. However, the relationship between cancer and atrial cardiopathy is uncertain.

METHODS AND RESULTS

We conducted a post hoc cross-sectional analysis of baseline data collected from participants enrolled in ARCADIA (Atrial Cardiopathy and Antithrombotic Drugs in Prevention After Cryptogenic Stroke), a clinical trial conducted from 2018 to 2023 at 185 sites. The analytical cohort presented herein included patients age ≥45 years with cryptogenic ischemic stroke within the past 180 days, of whom a subset had atrial cardiopathy and were randomized into the trial. Atrial fibrillation before enrollment was exclusionary. Linear regression models examined the associations between history of cancer and the atrial cardiopathy biomarkers analyzed in ARCADIA: serum NT-proBNP (N-terminal pro-B-type natriuretic peptide), P-wave terminal force in ECG lead V1, and left atrial diameter index on echocardiogram. Among 3745 patients with cryptogenic stroke, 506 (13.5%) had history of cancer. History of cancer was associated with higher median values of NT-proBNP (126 versus 103 pg/mL, P<0.001) and left atrial diameter index (1.9 versus 1.8 cm/m2, P<0.001) but similar median values of P-wave terminal force in ECG lead V1 (3000 versus 3025, P=0.08). After adjusting for demographics, tobacco use, and body mass index, no significant association remained between history of cancer and log-transformed NT-proBNP (standardized β $$ \beta $$ , -0.06 [95% CI, -0.15 to 0.02]), P-wave terminal force in ECG lead V1 (standardized β $$ \beta $$ , -0.02 [95% CI, -0.11 to 0.08]), or left atrial diameter index (standardized β $$ \beta $$ , 0.06 [95% CI, -0.05 to 0.16]).

CONCLUSIONS

In a multicenter, prospective, cryptogenic stroke cohort, history of cancer was not associated with selected biomarkers for atrial cardiopathy.

REGISTRATION

URL: https://www.ClinicalTrials.gov; Unique Identifier: NCT03192215.

Artificial Intelligence-Enabled Prediction of Heart Failure Risk From Single-Lead Electrocardiograms

Importance

Despite the availability of disease-modifying therapies, scalable strategies for heart failure (HF) risk stratification remain elusive. Portable devices capable of recording single-lead electrocardiograms (ECGs) may enable large-scale community-based risk assessment.

Objective

To evaluate whether an artificial intelligence (AI) algorithm can predict HF risk from noisy single-lead ECGs.

Design, Setting, and Participants

A retrospective cohort study of individuals without HF at baseline was conducted among individuals with conventionally obtained outpatient ECGs in the integrated Yale New Haven Health System (YNHHS) and prospective population-based cohorts of the UK Biobank (UKB) and the Brazilian Longitudinal Study of Adult Health (ELSA-Brasil). Data analysis was performed from September 2023 to February 2025.

Exposure

AI-ECG-defined risk of left ventricular systolic dysfunction (LVSD).

Main Outcomes and Measures

Among individuals with ECGs, lead I ECGs were isolated and a noise-adapted AI-ECG model (to simulate ECG signals from wearable devices) trained to identify LVSD was deployed. The association of the model probability with new-onset HF, defined as the first HF hospitalization, was evaluated. The discrimination of AI-ECG was compared against 2 risk scores for new-onset HF (Pooled Cohort Equations to Prevent Heart Failure [PCP-HF] and Predicting Risk of Cardiovascular Disease Events [PREVENT] equations) using the Harrel C statistic, integrated discrimination improvement, and net reclassification improvement.

Results

There were 192 667 YNHHS patients (median [IQR] age, 56 [41-69] years; 111 181 women [57.7%]), 42 141 UKB participants (median [IQR] age, 65 [59-71] years; 21 795 women [51.7%]), and 13 454 ELSA-Brasil participants (median [IQR] age, 51 [45-58] years; 7348 women [54.6%]) with baseline ECGs. A total of 3697 (1.9%) developed HF in YNHHS over a median (IQR) of 4.6 (2.8-6.6) years, 46 (0.1%) in UKB over a median (IQR) of 3.1 (2.1-4.5) years, and 31 (0.2%) in ELSA-Brasil over a median (IQR) of 4.2 (3.7-4.5) years. A positive AI-ECG screening result for LVSD was associated with a 3- to 7-fold higher risk for HF, and each 0.1 increment in the model probability was associated with a 27% to 65% higher hazard across cohorts, independent of age, sex, comorbidities, and competing risk of death. AI-ECG's discrimination for new-onset HF was 0.723 (95% CI, 0.694-0.752) in YNHHS, 0.736 (95% CI, 0.606-0.867) in UKB, and 0.828 (95% CI, 0.692-0.964) in ELSA-Brasil. Across cohorts, incorporating AI-ECG predictions alongside PCP-HF and PREVENT equations was associated with a higher Harrel C statistic (difference in addition to PCP-HF, 0.080-0.107; difference in addition to PREVENT, 0.069-0.094). AI-ECG had an integrated discrimination improvement of 0.091 to 0.205 vs PCP-HF and 0.068 to 0.192 vs PREVENT; it had a net reclassification improvement of 18.2% to 47.2% vs PCP-HF and 11.8% to 47.5% vs PREVENT.

Conclusions and Relevance

Across multinational cohorts, a noise-adapted AI-ECG model estimated HF risk using lead I ECGs, suggesting a potential HF risk-stratification strategy requiring prospective study using wearable and portable ECG devices.

Development and validation of an integrated prognostic model for all-cause mortality in heart failure: a comprehensive analysis combining clinical, electrocardiographic, and echocardiographic parameters

BACKGROUND

Accurate risk prediction in heart failure remains challenging due to its complex pathophysiology. We aimed to develop and validate a comprehensive prognostic model integrating demographic, electrocardiographic, echocardiographic, and biochemical parameters.

METHODS

We conducted a retrospective cohort study of 445 heart failure patients. The cohort was randomly divided into training (n = 312) and validation (n = 133) sets. Feature selection was performed using LASSO regression followed by backward stepwise Cox regression. A nomogram was constructed based on independent predictors. Model performance was assessed through discrimination, calibration, and decision curve analyses. Random survival forest analysis was conducted to validate variable importance.

RESULTS

During a median follow-up of 4.14 years, 142 deaths (31.91%) occurred. Our model development followed a systematic approach: initial feature selection using LASSO regression identified 15 potential predictors, which were further refined to nine independent predictors through backward stepwise Cox regression. The final predictors included age, NYHA class, left ventricular systolic dysfunction, atrial septal defect, aortic valve annulus calcification, tricuspid regurgitation severity, QRS duration, T wave offset, and NT-proBNP. The integrated model demonstrated good discrimination for 2-, 3-, and 5-year mortality prediction in both training (AUCs: 0.726, 0.755, 0.809) and validation cohorts (AUCs: 0.686, 0.678, 0.706). Calibration plots and decision curve analyses confirmed the model's reliability and clinical utility across different time horizons. A nomogram was constructed for individualized risk prediction. Kaplan-Meier analyses of individual predictors revealed significant stratification of survival outcomes, while restricted cubic spline analyses demonstrated non-linear relationships between continuous variables and mortality risk. Random survival forest analysis identified the top five predictors (age, NT-proBNP, QRS duration, tricuspid regurgitation severity, NYHA), which were compared with our nine-variable model, confirming the superior performance of the integrated model across all time points.

CONCLUSIONS

Our integrated prognostic model showed robust performance in predicting all-cause mortality in heart failure patients. The model's ability to provide individualized risk estimates through a nomogram may facilitate clinical decision-making and patient stratification.

CLINICAL TRIAL NUMBER

Not applicable.

Stress hyperglycemia ratio and 30-day mortality among critically ill patients with acute heart failure: analysis of the MIMIC-IV database

BACKGROUND

The association between the stress hyperglycemia ratio (SHR) and short-term prognosis of acute heart failure (AHF), particularly among those admitted to the intensive care unit (ICU), has not been elucidated. This study aimed to investigate the association between the SHR and adverse outcomes among critically ill patients with AHF and provide a reference for glycemic management range in these patients.

METHODS

We extracted the clinical data of patients from the MIMIC-IV (v3.0) database. The association between the SHR and short-term prognosis was analyzed using the Kaplan‒Meier survival curve, Cox regression, and subgroup analysis. Important features were identified utilizing machine learning methods. Furthermore, the association between the dynamic SHR level and mortality was explored using restricted cubic splines and Cox regression.

RESULTS

A total of 994 patients were included. Patients with the highest SHR (Quartile 4) had a higher risk of 30-day mortality (HR = 2.14; 95% CI = 1.32-3.45; P = 0.002) and in-hospital mortality (HR = 2.22; 95% CI = 1.27-3.88; P = 0.005) than those in Quartile 2 (as reference). The results of machine learning methods revealed the SHR was an important predictor for 30-day mortality of patients with critical AHF. Restricted cubic splines indicated a J-shaped association between the dynamic SHR level and mortality, and the cut-off values were 0.84 and 1.07.

CONCLUSION

The SHR was significantly associated with 30-day mortality and in-hospital mortality among patients with critical AHF. The SHR may be a useful indicator for the glycemic management of patients with AHF in the ICU.

Heart failure risk stratification using artificial intelligence applied to electrocardiogram images: a multinational study

BACKGROUND AND AIMS

Current heart failure (HF) risk stratification strategies require comprehensive clinical evaluation. In this study, artificial intelligence (AI) applied to electrocardiogram (ECG) images was examined as a strategy to predict HF risk.

METHODS

Across multinational cohorts in the Yale New Haven Health System (YNHHS), UK Biobank (UKB), and Brazilian Longitudinal Study of Adult Health (ELSA-Brasil), individuals without baseline HF were followed for the first HF hospitalization. An AI-ECG model that defines cross-sectional left ventricular systolic dysfunction from 12-lead ECG images was used, and its association with incident HF was evaluated. Discrimination was assessed using Harrell's C-statistic. Pooled cohort equations to prevent HF (PCP-HF) were used as a comparator.

RESULTS

Among 231 285 YNHHS patients, 4472 had primary HF hospitalizations over 4.5 years (inter-quartile range 2.5-6.6). In UKB and ELSA-Brasil, among 42 141 and 13 454 people, 46 and 31 developed HF over 3.1 (2.1-4.5) and 4.2 (3.7-4.5) years. A positive AI-ECG screen portended a 4- to 24-fold higher risk of new-onset HF [age-, sex-adjusted hazard ratio: YNHHS, 3.88 (95% confidence interval 3.63-4.14); UKB, 12.85 (6.87-24.02); ELSA-Brasil, 23.50 (11.09-49.81)]. The association was consistent after accounting for comorbidities and the competing risk of death. Higher probabilities were associated with progressively higher HF risk. Model discrimination was 0.718 in YNHHS, 0.769 in UKB, and 0.810 in ELSA-Brasil. In YNHHS and ELSA-Brasil, incorporating AI-ECG with PCP-HF yielded a significant improvement in discrimination over PCP-HF alone.

CONCLUSIONS

An AI model applied to a single ECG image defined the risk of future HF, representing a digital biomarker for stratifying HF risk.

Intensive Lifestyle Intervention, Cardiac Biomarkers, and Cardiovascular Outcomes in Diabetes: Look AHEAD Cardiac Biomarker Ancillary Study

BACKGROUND

N-terminal pro-B-type natriuretic peptide (NT-proBNP) and high-sensitivity cardiac troponin T (hs-cTnT) are associated with cardiovascular outcomes and are recommended for measurement in type 2 diabetes (T2D). However, the effects of an intensive lifestyle intervention (ILI) targeting weight loss on cardiac biomarkers and the prognostic association of changes in these biomarkers with risk of adverse cardiovascular outcomes in T2D are not well-established.

OBJECTIVES

This study sought to evaluate the effects of an ILI on cardiac biomarkers and the association of changes in cardiac biomarkers with risk of cardiovascular outcomes in T2D.

METHODS

Participants of the Look AHEAD (Action for Health in Diabetes) trial underwent NT-proBNP and hs-cTnT measurement at baseline (N = 3,984) and 1 and 4 years. The effects of the ILI (vs diabetes support and education [DSE]) on cardiac biomarkers were assessed using adjusted linear mixed-effect models and summarized as geometric mean ratios (GMRs). Associations of longitudinal changes in cardiac biomarkers with risk of cardiovascular outcomes were assessed using adjusted Cox models.

RESULTS

Average baseline NT-proBNP and hs-cTnT was 77 and 10.7 ng/L, respectively. The ILI (vs DSE) led to an increase in NT-proBNP at 1 year (GMR: 1.14; 95% CI: 1.08-1.20), but this difference was attenuated by 4 years (GMR: 1.01; 95% CI: 0.96-1.07). The ILI (vs DSE) led to lower hs-cTnT at 1 year (GMR: 0.94; 95% CI: 0.91-0.97) and 4 years (GMR: 0.93; 95% CI: 0.90-0.96). Participants with meaningful weight loss by 1 year (≥5% vs <5%) had a significant increase in NT-proBNP in the short term (year 1), which attenuated in the long-term follow-up (year 4). Meaningful 1-year weight loss was significantly associated with reduction in hs-cTnT in the long term. In adjusted Cox models, increase in NT-proBNP was significantly associated with higher risk of the composite atherosclerotic cardiovascular disease (ASCVD) outcome and incident heart failure independent of baseline measure of the cardiac biomarker and changes in risk factors. In contrast, longitudinal increase in hs-cTnT was significantly associated with higher risk of the composite ASCVD outcome but not incident heart failure in the most adjusted model.

CONCLUSIONS

Among adults with T2D, an ILI led to a significant reduction in hs-cTnT on follow-up but a transient increase in NT-proBNP levels at 1 year that attenuated over time. Longitudinal assessment of NT-proBNP and hs-cTnT provide prognostic information for ASCVD risk, whereas only changes in NT-proBNP predicted HF risk.

Artificial Intelligence Enabled Prediction of Heart Failure Risk from Single-lead Electrocardiograms

Importance

Despite the availability of disease-modifying therapies, scalable strategies for heart failure (HF) risk stratification remain elusive. Portable devices capable of recording single-lead electrocardiograms (ECGs) can enable large-scale community-based risk assessment.

Objective

To evaluate an artificial intelligence (AI) algorithm to predict HF risk from noisy single-lead ECGs.

Design

Multicohort study.

Setting

Retrospective cohort of individuals with outpatient ECGs in the integrated Yale New Haven Health System (YNHHS) and prospective population-based cohorts of UK Biobank (UKB) and Brazilian Longitudinal Study of Adult Health (ELSA-Brasil).

Participants

Individuals without HF at baseline.

Exposures

AI-ECG-defined risk of left ventricular systolic dysfunction (LVSD).

Main Outcomes and Measures

Among individuals with ECGs, we isolated lead I ECGs and deployed a noise-adapted AI-ECG model trained to identify LVSD. We evaluated the association of the model probability with new-onset HF, defined as the first HF hospitalization. We compared the discrimination of AI-ECG against two risk scores for new-onset HF (PCP-HF and PREVENT equations) using Harrel's C-statistic, integrated discrimination improvement (IDI), and net reclassification improvement (NRI).

Results

There were 192,667 YNHHS patients (age 56 years [IQR, 41-69], 112,082 women [58%]), 42,141 UKB participants (65 years [59-71], 21,795 women [52%]), and 13,454 ELSA-Brasil participants (56 years [41-69], 7,348 women [55%]) with baseline ECGs. A total of 3,697 developed HF in YNHHS over 4.6 years (2.8-6.6), 46 in UKB over 3.1 years (2.1-4.5), and 31 in ELSA-Brasil over 4.2 years (3.7-4.5). A positive AI-ECG screen was associated with a 3- to 7-fold higher risk for HF, and each 0.1 increment in the model probability portended a 27-65% higher hazard across cohorts, independent of age, sex, comorbidities, and competing risk of death. AI-ECG's discrimination for new-onset HF was 0.725 in YNHHS, 0.792 in UKB, and 0.833 in ELSA-Brasil. Across cohorts, incorporating AI-ECG predictions in addition to PCP-HF and PREVENT equations resulted in improved Harrel's C-statistic (ΔPCP-HF=0.112-0.114; ΔPREVENT=0.080-0.101). AI-ECG had IDI of 0.094-0.238 and 0.090-0.192, and NRI of 15.8%-48.8% and 12.8%-36.3%, vs. PCP-HF and PREVENT, respectively.

Conclusions and Relevance

Across multinational cohorts, a noise-adapted AI model defined HF risk using lead I ECGs, suggesting a potential portable and wearable device-based HF risk-stratification strategy.

Irisin Predicts Poor Clinical Outcomes in Patients with Heart Failure with Preserved Ejection Fraction and Low Levels of N-Terminal Pro-B-Type Natriuretic Peptide

BACKGROUND

Despite existing evidence of the high predictive value of natriuretic peptides (NPs) in patients with heart failure (HF), patients treated with guideline-directed therapy who have low or near-normal NP levels are unlikely to be correctly stratified for risk of clinical outcomes. The aim of this study is to detect plausible predictors for poor one-year clinical outcomes in patients with HFpEF and low NT-proBNP treated with in accordance with conventional guidelines.

METHODS

A total of 337 patients with HF with preserved ejection fraction (HFpEF) who had low levels of N-terminal natriuretic pro-peptide (NT-proBNP) at discharge due to optimal guideline-based therapy were enrolled in the study. The course of the observation was 3 years. Echocardiography and the assessment of conventional hematological and biochemical parameters, including NT-proBNP, tumor necrosis factor-alpha, high-sensitivity C-reactive protein (hs-CRP), adropin, irisin, visfatin, and fetuin-A, were performed at baseline and at the end of the study.

RESULTS

Three-year cumulative clinical endpoints (cardiovascular death, myocardial infarction or unstable angina or acute coronary syndrome, worsening HF, sudden cardiac death, or cardiac-related surgery or all-cause death) were detected in 104 patients, whereas 233 did not meet the endpoint. After adjusting for an age ≥ 64 years and a presence of atrial fibrillation, diabetes mellitus, chronic kidney disease (CKD) stages 1-3 and dilated cardiomyopathy, the multivariable Cox regression analysis showed that an irisin level of ≤7.2 ng/mL was an independent predictor of cumulative clinical endpoint. Moreover, patients with levels of irisin > 7.2 ng/mL had a better Kaplan-Meier survival rate than those with a lower serum irisin level (≤7.2 ng/mL).

CONCLUSIONS

Multivariable analysis showed that an age ≥ 64 years; the presence of atrial fibrillation, diabetes mellitus, CKD stages 1-3 and dilated cardiomyopathy; an LAVI ≥ 39 mL/m2; and serum levels of hs-CRP ≥ 6.10 mg/L, irisin ≤ 7.2 ng/mL, and visfatin ≤ 1.1 ng/mL were predictors of poor clinical outcomes in HFpEF with low levels of NT-proBNP. A serum level of irisin ≤ 7.2 ng/mL could emerge as valuable biomarker for predicting long-term prognosis among HFpEF patients with low or near-normal levels of NT-proBNP.

Impact of first-line antihypertensive drug class and intensity on NT-proBNP improvement and cardiovascular outcomes among hypertensive patients with pre-heart failure: findings from SPRINT trial

Five first-line classes of antihypertensive drugs are recommended for hypertension treatment. However, it is unclear which class should be chosen for hypertensive patients with pre-heart failure (pre-HF). The study aimed to investigate the association between antihypertensive drug classes and intensity with probability of NT-proBNP (N-terminal pro-B-type natriuretic peptide) improvement and risk of cardiovascular events among pre-HF hypertensive patients. Utilizing the data from SPRINT, we included pre-HF hypertensive patients, identified by NT-proBNP ≥125 pg/mL at baseline. NT-proBNP improvement is defined as a reduction of ≥50% to a level below 125 pg/mL. A total of 3293 patients (mean age: 71.9 years; female: 43.8%) were included. NT-proBNP improvement was observed in 415 patients (12.6%) over 1-year follow up. Thiazide-type diuretics users were associated with a higher likelihood of NT-proBNP improvement (odds ratio [OR], 1.33; 95% confidence interval [CI], 1.05-1.70), a lower risk of HF (hazard ratio [HR], 0.54; 95% CI, 0.37-0.78) and primary composite outcome (HR, 0.72; 95% CI, 0.57-0.89). ACEI/ARB users were only associated with a lower risk of primary composite outcome (HR, 0.80; 95% CI, 0.63-0.99). In contrast, beta-blockers users were associated with a lower likelihood of NT-proBNP improvement (OR, 0.43; 95% CI, 0.34-0.55), while a higher risk of HF (HR, 1.79; 95% CI, 1.21-2.64) and primary composite outcome (HR, 1.48; 95% CI, 1.18-1.87). These associations varied across subgroups of different drug intensities. This post hoc analysis supports the use of thiazide-type diuretics and ACEI/ARB for prevention of cardiovascular events. The use of beta-blockers is associated with an increased risk of HF and primary outcomes, which requires further validation. Association between antihypertensive drug classes and intensity with NT-proBNP improvement and long-term clinical outcome.

Prediction of cardiac deterioration in acute heart failure patients: Evaluation of the efficacy of single laboratory indicator models versus comprehensive models

This study aims to compare the efficacy of single-indicator models versus comprehensive models in predicting cardiac deterioration events in patients with acute heart failure (AHF), providing a more precise predictive tool for clinical practice. This retrospective cohort study included 484 patients with AHF treated at our hospital between June 2018 and January 2023. Patients were categorized into a deterioration group and a non-deterioration group based on the occurrence of cardiac deterioration events within 1 year, defined as cardiogenic shock, cardiac arrest, or the need for mechanical circulatory support. We collected clinical data, laboratory markers, and imaging indicators for analysis. Both single-indicator models and comprehensive models (clinical data + indicators) were constructed and evaluated using the area under the receiver operating characteristic (ROC) curve (AUC) to assess their predictive performance. Among the 484 AHF patients, 121 were in the deterioration group and 363 were in the non-deterioration group. Among the single indicators, WBC had the highest AUC of 0.683. The indicator model (WBC, NOMO, Cr, BUN, Troponin, NT-proBNP, D-Dimer, LVEF, and RVFAC) achieved an AUC of 0.886 in the training set and 0.876 in the validation set. The comprehensive model (age, time from onset to admission, heart failure type, WBC, NOMO, Cr, BUN, troponin, NT-proBNP, LA, D-dimer, fibrinogen, and RVFAC) had an AUC of 0.940 in the training set and 0.925 in the validation set. In the training set, the comprehensive model had a significantly higher AUC than the indicator model (P < .05), while no significant difference was observed between the 2 in the validation set (P > .05). Furthermore, decision curve analysis (DCA) and calibration curve analysis indicated that the comprehensive model provided greater clinical benefits and better predictive accuracy in clinical applications. The comprehensive model demonstrates superior predictive capability for cardiac deterioration events in AHF patients, significantly outperforming both single-indicator and indicator models. This suggests that a comprehensive assessment can more accurately identify high-risk patients, offering a more reliable basis for clinical decision-making.

Post-Acute COVID-19 Syndrome: Prevalence of Peripheral Microvascular Endothelial Dysfunction and Associations with NT-ProBNP Dynamics

BACKGROUND

Post-acute COVID-19 syndrome (PACS) has been linked to microvascular endothelial dysfunction as a potential underlying pathomechanism and can manifest even following a mild course of the initial infection. Prevalence of microvascular endothelial dysfunction and circulating natriuretic peptides in such PACS patients remains unknown.

METHODS

This prospective, cross-sectional cohort study enrolled 92 patients (82% females, median age 48 years) with PACS. Reactive hyperemia index (RHI) was evaluated with peripheral arterial tonometry, where <1.67 was defined as microvascular endothelial dysfunction, 1.67-2.0 as impaired function, and >2 normal endothelial function, on average 31 months after the acute infection. N-terminal pro-B-type natriuretic peptide (NT-proBNP) levels were collected at 2 different time points within over a 1-year span.

RESULTS

In total, 41% of PACS subjects had microvascular endothelial dysfunction and 20% had impaired RHI. No major differences in clinical characteristics, routine chemistry laboratory testing, or symptom burden were observed across the groups. Only subjects with microvascular endothelial dysfunction and impaired endothelial function had a significant increase in NT-proBNP levels over time, and those with larger increase in NT-proBNP had significantly lower RHI. There was a significant correlation between relative or absolute increase in NT-proBNP and RHI, which remained significant in a multivariable adjusted linear regression.

CONCLUSIONS

Peripheral microvascular endothelial dysfunction was prevalent in a symptomatic PACS population long after recovery from a mild acute infection. Increases in NT-proBNP levels were associated with microvascular endothelial dysfunction, suggesting a link between, and providing a foundation for, future studies on post viral microvascular endothelial dysfunction in PACS.

Low Levels of Adropin Predict Adverse Clinical Outcomes in Outpatients with Newly Diagnosed Prediabetes after Acute Myocardial Infarction

Adropin-a multifunctional peptide with tissue-protective capacity that regulates energy homeostasis, sensitivity to insulin and inflammatory response-seems to show an inverse association with the presence of cardiovascular and renal diseases, obesity and diabetes mellitus in the general population. The purpose of the study is to elucidate whether adropin may be a plausible predictive biomarker for clinical outcomes in post-ST elevation of myocardial infarction (STEMI) patients with newly diagnosed prediabetes according to the American Diabetes Association criteria. A total of 1214 post-STEMI patients who received percutaneous coronary intervention were identified in a local database of the private hospital "Vita Center" (Zaporozhye, Ukraine). Between November 2020 and June 2024, we prospectively enrolled 498 patients with prediabetes in this open prospective cohort study and followed them for 3 years. The combined clinical endpoint at follow-up was defined as cardiovascular death due to acute myocardial infarction, heart failure, sudden death due to arrhythmia or cardiac surgery, and/or all-cause death. We identified 126 clinical events and found that serum levels of adropin < 2.15 ng/mL (area under the curve = 0.836; 95% confidence interval = 0.745-0.928; sensitivity = 84.9%; specificity = 72.7%; likelihood ratio = 3.11; p = 0.0001) predicted clinical outcomes. Multivariate logistic regression showed that a Gensini score ≥ 32 (Odds ratio [OR] = 1.07; p = 0.001), adropin ≤ 2.15 ng/mL (OR = 1.18; p = 0.001), use of SGLT2i (OR = 0.94; p = 0.010) and GLP-1 receptor agonist (OR = 0.95; p = 0.040) were independent predictors of clinical outcome. Kaplan-Meier plots showed that patients with lower adropin levels (≤2.15 ng/mL) had worse clinical outcomes compared to patients with higher adropin levels (>2.15 ng/mL). In conclusion, low levels of adropin (≤2.15 ng/mL) independently predicted clinical outcomes in post-STEMI patients with newly detected prediabetes and improved the discriminative ability of the Gensini score for 3-year follow-up events. Future clinical studies are needed to clarify whether adropin is a promising molecule to be incorporated into conventional risk scores for the prediction of MACCEs after STEMI.

Association of N-terminal pro-brain natriuretic peptide with survival among US cancer survivors

BACKGROUND

N-terminal pro-brain natriuretic peptide (NT-proBNP) is a cardiac biomarker associated with the risk of heart failure and death in the general population, but it has not been explored in cancer survivors.

METHODS

Using a US nationally representative sample of adults 20 years of age and older from the National Health and Nutrition Examination Survey from 1999 to 2004, this study compared NT-proBNP levels between adults without cancer (n = 12 574) and adult cancer survivors (n = 787). It examined the association of NT-proBNP with all-cause and cause-specific mortality among cancer survivors.

RESULTS

Cancer survivors had higher NT-proBNP levels than adults without cancer (median [interquartile range] = 125.4 pg/mL [52.4-286.0] vs 43.2 pg/mL [20.3-95.0]). In particular, survivors of breast, prostate, and colorectal cancers had higher NT-proBNP levels than adults without cancer (multivariable-adjusted P < .05). In total, 471 survivors died (141 from cancer; 95 from cardiac disease) during a median follow-up period of 13.4 years (9393 person-years). Among cancer survivors, higher NT-proBNP levels were statistically associated with increased risks of all-cause death (hazard ratio [HR] = 1.31, 95% confidence interval [CI] = 1.18 to 1.46) and cardiac death (HR = 1.55, 95% CI = 1.21 to 2.00) but not with death from cancer (HR = 1.10, 95% CI = 0.92 to 1.32]). Higher NT-proBNP levels were associated with elevated overall mortality in survivors of prostate cancer (HR = 1.49, 95% CI = 1.22 to 1.81) and colorectal cancer (HR = 1.78, 95% CI = 1.00 to 3.16) (P = .169 for interaction). Nonlinear dose-response relationships were observed between NT-proBNP and mortality, with statistically significant relationships emerging above 125 pg/mL.

CONCLUSIONS

Cancer survivors had higher NT-proBNP levels than adults without cancer, and elevated NT-proBNP levels were associated with higher risks of all-cause and cardiac mortality in cancer survivors.

Plasma Biomarkers for Hypertension-Mediated Organ Damage Detection: A Narrative Review

Hypertension (HT) is a disease that poses a serious threat to human health, mediating organ damage such as the cardiovascular (CV) system, kidneys, central nervous system (CNS), and retinae, ultimately increasing the risk of death due to damage to the entire vascular system. Thus, the widespread prevalence of hypertension brings enormous health problems and socioeconomic burdens worldwide. The goal of hypertension management is to prevent the risk of hypertension-mediated organ damage and excess mortality of cardiovascular diseases. To achieve this goal, hypertension guidelines recommend accurate monitoring of blood pressure and assessment of associated target organ damage. Early identification of organ damage mediated by hypertension is therefore crucial. Plasma biomarkers as a non-invasive test can help identify patients with organ damage mediated by hypertension who will benefit from antihypertensive treatment optimization and improved prognosis. In this review, we provide an overview of some currently available, under-researched, potential plasma biomarkers of organ damage mediated by hypertension, looking for biomarkers that can be detected by simple testing to identify hypertensive patients with organ damage, which is of great significance in clinical work. Natriuretic peptides (NPs) can be utilized as a traditional biomarker to detect hypertension-mediated organ damage, especially for heart failure. Nevertheless, we additionally may need to combine two or more plasma biomarkers to monitor organ damage in the early stages of hypertension.

Scalable Risk Stratification for Heart Failure Using Artificial Intelligence applied to 12-lead Electrocardiographic Images: A Multinational Study

Background

Current risk stratification strategies for heart failure (HF) risk require either specific blood-based biomarkers or comprehensive clinical evaluation. In this study, we evaluated the use of artificial intelligence (AI) applied to images of electrocardiograms (ECGs) to predict HF risk.

Methods

Across multinational longitudinal cohorts in the integrated Yale New Haven Health System (YNHHS) and in population-based UK Biobank (UKB) and Brazilian Longitudinal Study of Adult Health (ELSA-Brasil), we identified individuals without HF at baseline. Incident HF was defined based on the first occurrence of an HF hospitalization. We evaluated an AI-ECG model that defines the cross-sectional probability of left ventricular dysfunction from a single image of a 12-lead ECG and its association with incident HF. We accounted for the competing risk of death using the Fine-Gray subdistribution model and evaluated the discrimination using Harrel's c-statistic. The pooled cohort equations to prevent HF (PCP-HF) were used as a comparator for estimating incident HF risk.

Results

Among 231,285 individuals at YNHHS, 4472 had a primary HF hospitalization over 4.5 years (IQR 2.5-6.6) of follow-up. In UKB and ELSA-Brasil, among 42,741 and 13,454 people, 46 and 31 developed HF over a follow-up of 3.1 (2.1-4.5) and 4.2 (3.7-4.5) years, respectively. A positive AI-ECG screen portended a 4-fold higher risk of incident HF among YNHHS patients (age-, sex-adjusted HR [aHR] 3.88 [95% CI, 3.63-4.14]). In UKB and ELSA-Brasil, a positive-screen ECG portended 13- and 24-fold higher hazard of incident HF, respectively (aHR: UKBB, 12.85 [6.87-24.02]; ELSA-Brasil, 23.50 [11.09-49.81]). The association was consistent after accounting for comorbidities and the competing risk of death. Higher model output probabilities were progressively associated with a higher risk for HF. The model's discrimination for incident HF was 0.718 in YNHHS, 0.769 in UKB, and 0.810 in ELSA-Brasil. Across cohorts, incorporating model probability with PCP-HF yielded a significant improvement in discrimination over PCP-HF alone.

Conclusions

An AI model applied to images of 12-lead ECGs can identify those at elevated risk of HF across multinational cohorts. As a digital biomarker of HF risk that requires just an ECG image, this AI-ECG approach can enable scalable and efficient screening for HF risk.

Navigating Heart Failure: Unveiling Sex Disparities in Guideline-Directed Medical Therapy Combinations

Major heart failure (HF) trials remain insufficient in terms of assessing the differences in clinical characteristics, biomarkers, treatment efficacy, and safety because of the under-representation of women. The study aimed to present sex-related disparities in HF management, including differences in demographics, co-morbidities, cardiac biomarkers, prescribed medications, and treatment outcomes. The study utilized anonymized data from the Turkish Ministry of Health's National Electronic Database between January 1, 2016, and December 31, 2022. The cohort analysis included 2,501,231 adult patients with HF. Specific therapeutic combinations were analyzed using a Cox regression model to obtain relative risk reduction for all-cause death. The primary end point was all-cause mortality. In the cohort, 48.7% (n = 1,218,911) were male, whereas 51.3% (n = 1,282,320) were female. Female patients exhibited a higher median age (71 vs 68 years) and manifested higher prevalence of diabetes mellitus, anemia, atrial fibrillation, anxiety, and ischemic stroke. Male patients demonstrated higher rates of previous myocardial infarction, dyslipidemia, chronic obstructive pulmonary disease, and chronic kidney disease. Higher concentrations of natriuretic peptides were observed in female patients. Renin-angiotensin aldosterone inhibitor, β blockers, mineralocorticoid receptor antagonists, sodium/glucose cotransporter 2 inhibitor (SGLT2i), and ivabradine were more commonly prescribed in male patients, whereas loop diuretics, digoxin, and ferric carboxymaltose were more frequent in female patients. Male patients had higher rates of cardiac resynchronization therapy and implantable cardioverter defibrillator implantation rates. All-cause mortality and hospitalization rates were higher in male patients. Compared with monotherapy, all combinations, including SGLT2i, showed a beneficial effect on all-cause mortality in both female and male patients with HF. In hospitalized patients with HF, the addition of digoxin to renin-angiotensin aldosterone inhibitor, mineralocorticoid receptor antagonists, and β blockers was superior to monotherapy regarding all-cause mortality in female patients with HF compared with male patients with HF. In conclusion, this study highlights that sex-specific responses to HF medication combinations compared with monotherapy and differences in co-morbidities underscore the importance of tailored management strategies. Digoxin showed a contrasting effect on all-cause mortality between both sexes after hospitalization, whereas SGLT2i exhibited a consistent beneficial effect in both sexes when added to all combinations.

Characteristics and Prognosis of Type 2 Myocardial Infarction Through Worsening Renal Function and NT-proBNP in Older Adults with Pneumonia

Background

Type 2 myocardial infarction (MI) is becoming more recognized. This study aimed to assess the factors linked to type 2 MI in older adults with pneumonia and further determine the predictive factors of 90-day adverse events (refractory heart failure, cardiogenic shock, and all-cause mortality).

Methods

A single-center retrospective analysis was conducted among older adults with pneumonia. The primary outcome was the prevalence of type 2 MI. The secondary objective was to assess the adverse events in these patients with type 2 MI within 90 days.

Results

A total of 2618 patients were included. Of these, 361 patients (13.8%) suffered from type 2 MI. Multivariable predictors of type 2 MI were chronic kidney disease (CKD), age-adjusted Charlson comorbidity index (ACCI) score, and NT-proBNP > 4165pg/mL. Moreover, the independent predictive factors of 90-day adverse events included NT-proBNP > 4165pg/mL, age, ACCI score, and CKD. The Kaplan-Meier adverse events curves revealed that the type 2 MI patients with CKD and NT-proBNP > 4165pg/mL had a higher risk than CKD or NT-proBNP > 4165pg/mL alone.

Conclusion

Type 2 MI in older pneumonia hospitalization represents a heterogeneous population. Elevated NT-proBNP level and prevalence of CKD are important predictors of type 2 MI and 90-day adverse events in type 2 MI patients.

Prognostic Impact of Repeated NT-proBNP Measurements in Patients With Heart Failure With Reduced Ejection Fraction

BACKGROUND

Although clinical studies have demonstrated the association between a single N-terminal pro-B-type natriuretic peptide (NT-proBNP) measurement and clinical outcomes in chronic heart failure, the biomarker is frequently measured serially in clinical practice.

OBJECTIVES

The aim of this study was to determine the added prognostic value of repeated NT-proBNP measurements compared with single measurements alone for chronic heart failure patients.

METHODS

In the GUIDE-IT (Guiding Evidence Based Therapy Using Biomarker Intensified Treatment in Heart Failure) study, 894 study participants with chronic heart failure with reduced ejection fraction were enrolled at 45 outpatient sites in the United States and Canada. Repeated NT-proBNP levels were measured over a 2-year study period. Associations between repeated NT-proBNP measurements and trial endpoints were assessed using a joint longitudinal and survival model.

RESULTS

After adjustment for baseline covariates, each doubling of the baseline NT-proBNP level was associated with a HR of 1.17 (95% CI: 1.08-1.28; P = 0.0003) for the primary trial endpoint of cardiovascular death or heart failure hospitalization. Serial measurements increased the adjusted HR for the primary trial endpoint to 1.66 (95% CI: 1.50-1.84; P < 0.0001), and a similar increased risk was observed across secondary trial endpoints. In joint modeling, an increase in NT-proBNP occurred weeks before the onset of adjudicated events.

CONCLUSIONS

Repeated NT-proBNP measurements are a strong predictor of outcomes in heart failure with reduced ejection fraction with an increase in concentration occurring well before event onset. These results may support routine NT-proBNP monitoring to assist in clinical decision making. (Guiding Evidence Based Therapy Using Biomarker Intensified Treatment in Heart Failure [GUIDE-IT]; NCT01685840).

Identification of novel endogenous control miRNAs in heart failure for normalization of qPCR data

MicroRNAs (miRNAs), a class of non-coding RNAs, are utilized as biomarkers for a wide range of disorders. Circulating miRNAs are proposed as potential markers in the clinical identification of heart failure (HF). However, identifying miRNA biomarkers in HF requires identification of robust endogenous control miRNAs for normalization in differential expression analysis. Hence, this study aimed to identify circulating miRNAs that can be utilized as endogenous controls in HF. We evaluated the expression of eight miRNAs, which were previously reported as endogenous controls in different pathological conditions. Total RNA, including miRNA, was extracted from the serum samples of 30 HF patients (15 HFrEF and 15 HFpEF) and their matched controls (n = 15). We used quantitative PCR to determine the miRNA expression. The stability of the selected endogenous miRNAs was assessed and compared using a standard set of criteria with the RefFinder software. Six of the eight miRNAs analyzed showed consistent expression among all sample groups. Stability analysis ranked hsa-let-7i-5p, hsa-miR-148b-3p, and hsa-miR-484 as the most stable miRNAs, indicating their potential as reliable endogenous controls.

Biomarkers for Heart Failure Prediction and Prevention

Heart failure (HF) is a global pandemic affecting over 64 million people worldwide. Its prevalence is on an upward trajectory, with associated increasing healthcare expenditure. Organizations including the American College of Cardiology (ACC) and the American Heart Association (AHA) have identified HF prevention as an important focus. Recently, the ACC/AHA/Heart Failure Society of America (HFSA) Guidelines on heart failure were updated with a new Class IIa, Level of Evidence B recommendation for biomarker-based screening in patients at risk of developing heart failure. In this review, we evaluate the studies that have assessed the various roles and contributions of biomarkers in the prediction and prevention of heart failure. We examined studies that have utilized biomarkers to detect cardiac dysfunction or abnormality for HF risk prediction and screening before patients develop clinical signs and symptoms of HF. We also included studies with biomarkers on prognostication and risk prediction over and above existing HF risk prediction models and studies that address the utility of changes in biomarkers over time for HF risk. We discuss studies of biomarkers to guide management and assess the efficacy of prevention strategies and multi-biomarker and multimodality approaches to improve risk prediction.

Natriuretic peptide testing strategies in heart failure: A 2023 update

Natriuretic peptides (NPs), including B-type natriuretic peptide (BNP) and N-terminal pro-BNP (NT-proBNP), have been recommended as standard biomarkers for diagnosing heart failure (HF), and one of the strongest risk predictors for mortality and HF hospitalization regardless of ejection fraction (EF) and etiology of HF. BNP is an active neurohormone opposing renin-angiotensin-aldosterone and sympathetic nervous system overactivated in HF, whereas NT-proBNP is an inactive prohormone released from cardiomyocytes in response to wall stress. Despite substantial advances in the development of guideline-directed medical therapy (GDMT) for HF with reduced EF, studies demonstrating direct benefits of NP-guided chronic HF therapy on mortality, HF hospitalization, and GDMT optimization have yielded conflicting results. However, accumulating evidence shows that achieving prespecified BNP or NT-proBNP target over time is significantly associated with favorable outcomes, suggesting that benefits of serially measured NPs may be limited to particular groups of HF patients, such as those with extreme levels of baseline BNP or NT-proBNP, which could represent severe phenotypes of HF associated with natriuretic peptide resistance or cardiorenal syndrome. Over the past decade, clinical utilization of BNP and NT-proBNP has been expanded, especially using serial NP measurements for guiding HF therapy, optimizing GDMT and identifying at-risk patients with HF phenotypes who may be minimally symptomatic or asymptomatic.

Use of 3D Printing Techniques to Fabricate Implantable Microelectrodes for Electrochemical Detection of Biomarkers in the Early Diagnosis of Cardiovascular and Neurodegenerative Diseases

This Review provides a comprehensive overview of 3D printing techniques to fabricate implantable microelectrodes for the electrochemical detection of biomarkers in the early diagnosis of cardiovascular and neurodegenerative diseases. Early diagnosis of these diseases is crucial to improving patient outcomes and reducing healthcare systems' burden. Biomarkers serve as measurable indicators of these diseases, and implantable microelectrodes offer a promising tool for their electrochemical detection. Here, we discuss various 3D printing techniques, including stereolithography (SLA), digital light processing (DLP), fused deposition modeling (FDM), selective laser sintering (SLS), and two-photon polymerization (2PP), highlighting their advantages and limitations in microelectrode fabrication. We also explore the materials used in constructing implantable microelectrodes, emphasizing their biocompatibility and biodegradation properties. The principles of electrochemical detection and the types of sensors utilized are examined, with a focus on their applications in detecting biomarkers for cardiovascular and neurodegenerative diseases. Finally, we address the current challenges and future perspectives in the field of 3D-printed implantable microelectrodes, emphasizing their potential for improving early diagnosis and personalized treatment strategies.

Natriuretic Peptides: It Is Time for Guided Therapeutic Strategies Based on Their Molecular Mechanisms

Natriuretic peptides (NPs) are the principal expression products of the endocrine function of the heart. They exert several beneficial effects, mostly mediated through guanylate cyclase-A coupled receptors, including natriuresis, diuresis, vasorelaxation, blood volume and blood pressure reduction, and regulation of electrolyte homeostasis. As a result of their biological functions, NPs counterbalance neurohormonal dysregulation in heart failure and other cardiovascular diseases. NPs have been also validated as diagnostic and prognostic biomarkers in cardiovascular diseases such as atrial fibrillation, coronary artery disease, and valvular heart disease, as well as in the presence of left ventricular hypertrophy and severe cardiac remodeling. Serial measurements of their levels may be used to contribute to more accurate risk stratification by identifying patients who are more likely to experience death from cardiovascular causes, heart failure, and cardiac hospitalizations and to guide tailored pharmacological and non-pharmacological strategies with the aim to improve clinical outcomes. On these premises, multiple therapeutic strategies based on the biological properties of NPs have been attempted to develop new targeted cardiovascular therapies. Apart from the introduction of the class of angiotensin receptor/neprilysin inhibitors to the current management of heart failure, novel promising molecules including M-atrial natriuretic peptide (a novel atrial NP-based compound) have been tested for the treatment of human hypertension with promising results. Moreover, different therapeutic strategies based on the molecular mechanisms involved in NP regulation and function are under development for the management of heart failure, hypertension, and other cardiovascular conditions.