Failure Risk Stratification Research Articles

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

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

Cardiac evaluation of renal transplant candidates with heart failure.

Patients with advanced kidney disease are at elevated risk of developing heart failure and appropriate risk stratification is important to permit them to receive kidney transplantation. The American Heart Association and American College of Cardiology joint statement provides guidance on risk stratification for the major cause of heart failure for these patients in its recommendations for coronary heart disease. Herein we provide an overview of the available literature on risk stratification for nonischemic heart failure and functional heart disease states such as pulmonary hypertension. Many of these options for optimizing these patients before transplant include optimizing their volume status, often with more aggressive ultrafiltration. Kidney transplantation remains the treatment of choice for patients with advanced kidney disease and cardiac disease, the correction of the azotemic substances with kidney transplantation has been associated with improved survival than remaining on dialysis long-term. The findings in the studies reviewed here are expected to help clinicians refine current strategies for evaluating potential kidney transplant recipients.

Abstract 4135726: Prospective validation and implementation pilot study of an emergency department heart failure risk stratification tool: STRIDE-HF

Background: We previously found the STRIDE-HF emergency department (ED) risk tool accurately predicted risk of a 30-day serious adverse event (SAE), including 30-day mortality, cardiopulmonary resuscitation, intra-aortic balloon pump insertion, intubation, new dialysis, myocardial infarction, or coronary revascularization. Research question: We sought to prospectively validate STRIDE-HF and describe safety of the deployed model in a clinical pilot. We hypothesized that performance would decrease slightly with prospective evaluation and that real-time risk display would contribute to safe disposition decisions. Goals: 1) To prospectively validate KPCARES-HF across 21 community EDs among ED patients with acute heart failure (AHF) from January 1, 2023 – December 31 st , 2023; and 2) to describe outcomes among patients predicted to be very low risk and discharged home in the clinical pilot. Methods: For prospective validation, we report 30-day mortality rates by risk strata. We assess model performance using area under the receiver operator curve (AUROC) and sensitivity at a key clinical threshold. We describe rates of 30-day SAE among patients predicted to be very low risk and discharged after ED or observation unit care in the clinical pilot. Results: There were 13,274 ED patients in the prospective validation; median age was 76, 50.8% were female, and 44.5% were non-White. We found 11.4%, 24.8%, 31.9%, and 31.9% of patients were very low, low, moderate, and high risk, respectively, while 21.6%, 15.2% and 63.0% were discharged, observed, and admitted, respectively. Among discharged patients, 29.2% and 13.3% were moderate or high risk, respectively, and 30-day mortality rates among these patients were 2.2% and 8.4%, respectively. Among 8,363 admitted patients, 27.7% were very low or low risk. Conclusions: STRIDE-HF maintained high predictive accuracy with prospective validation in this diverse, multi-center cohort. Our findings suggest that use of STRIDE-HF might help better align risk with admission decision and can be safely used to assist providers in identifying patients who may be stable for outpatient care.

Risk Stratification and Management of Acute Respiratory Failure in Patients With Neuromuscular Disease.

Guillain-Barré syndrome (GBS) and myasthenia gravis (MG) are the most common causes of acute neuromuscular respiratory failure resulting in ICU admission. This synthetic narrative review summarizes the evidence for the prediction and management of acute neuromuscular respiratory failure due to GBS and MG. We searched PubMed for relevant literature and reviewed bibliographies of included articles for additional relevant studies. English-language publications were reviewed. Data regarding study methodology, patient population, evaluation metrics, respiratory interventions, and clinical outcomes were qualitatively assessed. No single tool has sufficient sensitivity and specificity for the prediction of acute neuromuscular respiratory failure requiring mechanical ventilation. Multimodal assessment, integrating history, examination maneuvers (single breath count, neck flexion strength, bulbar weakness, and paradoxical breathing) and pulmonary function testing are ideal for risk stratification. The Erasmus GBS Respiratory Insufficiency Score is a validated tool useful for GBS. Noninvasive ventilation can be effective in MG but may not be safe in early GBS. Airway management considerations are similar across both conditions, but dysautonomia in GBS requires specific attention. Extubation failure is common in MG, and early tracheostomy may be beneficial for MG. Prolonged ventilatory support is common, and good functional outcomes may occur even when prolonged ventilation is required. Multimodal assessments integrating several bedside indicators of bulbar and respiratory muscle function can aid in evidence-based risk stratification for respiratory failure among those with neuromuscular disease. Serial evaluations may help establish a patient's trajectory and to determine timing of respiratory intervention.

Proteomics for heart failure risk stratification: a systematic review

BackgroundHeart failure (HF) is a complex clinical syndrome with persistently high mortality. High-throughput proteomic technologies offer new opportunities to improve HF risk stratification, but their contribution remains to be clearly defined. We aimed to systematically review prognostic studies using high-throughput proteomics to identify protein signatures associated with HF mortality.MethodsWe searched four databases and two clinical trial registries for articles published from 2012 to 2023. HF proteomics studies measuring high numbers of proteins using aptamer or antibody-based affinity platforms on human plasma or serum with outcomes of all-cause or cardiovascular death were included. Two reviewers independently screened articles, extracted data, and assessed the risk of bias. A third reviewer resolved conflicts. We assessed the risk of bias using the Risk Of Bias In Non-randomized Studies—of Exposure tool.ResultsOut of 5131 unique articles identified, nine articles were included in the review. The nine studies were observational; three used the aptamer platform, and six used the antibody platform. We found considerable heterogeneity across studies in measurement panels, HF definitions, ejection fraction categorization, follow-up duration, and outcome definitions, and a lack of risk estimates for most protein associations. Hence, we proceeded with a systematic review rather than a meta-analysis. In two comparable aptamer studies in patients with HF with reduced ejection fraction, 21 proteins were identified in common for the association with all-cause death. Among these, one protein, WAP four-disulfide core domain protein 2 was also reported in an antibody study on HFrEF and for the association with CV death. We proposed standardized reporting criteria to facilitate the interpretation of future studies.ConclusionsIn this systematic review of nine studies evaluating the association of proteomics with mortality in HF, we identified a limited number of proteins common across several studies. Heterogeneity across studies compromised drawing broad inferences, underscoring the importance of standardized approaches to reporting.

Natriuretic peptides, body mass index and heart failure risk: Pooled analyses of SAVOR-TIMI 53, DECLARE-TIMI 58 and CAMELLIA-TIMI 61.

N-terminal pro-B-type natriuretic peptide (NT-proBNP) concentrations are lower in patients with obesity. The interaction between body mass index (BMI) and NT-proBNP with respect to heart failure risk remains incompletely defined. Data were pooled across three randomized clinical trials enrolling predominantly patients who were overweight or obese with established cardiometabolic disease: SAVOR-TIMI 53, DECLARE-TIMI 58 and CAMELLIA-TIMI 61. Hospitalization for heart failure (HHF) was examined across strata of baseline BMI and NT-proBNP. The effect of dapagliflozin versus placebo was assessed for a treatment interaction across BMI categories in patients with or without an elevated baseline NT-proBNP (≥125 pg/ml). Among 24 455 patients, the median NT-proBNP was 96 (interquartile range [IQR]: 43-225) pg/ml and the median BMI was 33 (IQR 29-37) kg/m2, with 68% of patients having a BMI ≥30 kg/m2. There was a significant inverse association between NT-proBNP and BMI which persisted after adjustment for all clinical variables (p < 0.001). Within any range of NT-proBNP, those at higher BMI had higher risk of HHF at 2 years (comparing BMI <30 vs. ≥40 kg/m2 for NT-proBNP ranges of <125, 125-<450 and ≥450 pg/ml: 0.0% vs. 0.6%, 1.3% vs. 4.0%, and 8.1% vs. 13.8%, respectively), which persisted after multivariable adjustment (adjusted hazard ratio [HRadj] 7.47, 95% confidence interval [CI] 3.16-17.66, HRadj 3.22 [95% CI 2.13-4.86], and HRadj 1.87 [95% CI 1.35-2.60], respectively). In DECLARE-TIMI 58, dapagliflozin versus placebo consistently reduced HHF across BMI categories in those with an elevated NT-proBNP (p-trend for HR across BMI = 0.60), with a pattern of greater absolute risk reduction (ARR) at higher BMI (ARR for BMI <30 to ≥40 kg/m2: 2.2% to 4.7%; p-trend = 0.059). The risk of HHF varies across BMI categories for any given range of circulating NT-proBNP. These findings showcase the importance of considering BMI when applying NT-proBNP for heart failure risk stratification, particularly for patients with low-level elevations in NT-proBNP (125-<450 pg/ml) where there appears to be a clinically meaningful absolute and relative risk gradient.

Predictive significance of modern echocardiographic parameters, stress ultrasound of the lungs in patients with acute myocardial infarction and percutaneous coronary intervention

Abstract Introduction Improving the EF can reduce the risk of cardiovascular complications (CVC) in postinfarction period. Short-term periprocedural dynamics (PPD) of EF and its prognostic value in patients with first acute myocardial infarction (AMI) and percutaneous coronary intervention (PCI) have not been studied. Stress lung ultrasound (LUS) can be used as an initial step in heart failure (HF) risk stratification. Speckle tracking Echo with assessment of myocardial work (MW) is promising in relation to the prediction of CVC in patients with AMI. The predictive abilities of 3D Echo deformity and rotation in patients with AMI are not well studied. Purpose Determination of the value of PPD EF, subclinical pulmonary congestion (PC) in stress LUS, parameters of MW and 3D-Echo in the prognosis of the development of CVC in long-term period in patients with the first AMI and PCI. Methods In 131 patients (89 men, mean age: 61.7±11.45 years, Me EF admission: 48%, multivessel coronary artery disease 66.4%) with a first MI without a history of HF with successful PCI, EF was assessed before PCI and at discharge, MW parameters, 3D-Echo parameters were determined, and stress LUS with physical activity was performed. An increase in EF&amp;gt;5% and an achievement of 50% or more were taken as a criterion for a positive PPD. After 3 months, the dynamics of 2- and 3D-Echo were evaluated. The observation period was 550 days. Hospitalization with HF, CV death, and composite point were assessed. Results Upon admission baseline EF&amp;lt;50% was detected in 56.5% of cases. 47.3% of patients from the group with baseline EF &amp;lt;50% did not have PPD EF after PCI, which is associated with an increased risk of hospitalization with HF (HR=7.13 p=0.004). An increase in PPD by 1% reduces the risks of hospitalization with HF by 1.31 times (p=0.013). According to the stress LUS, subclinical PC was detected in 99.1% of patients. In patients with baseline EF &amp;lt;50%, the sum of B-lines ≥10 during exercise increases the risks of hospitalization with HF by 2.4 times, and its combination with the absence of PPD - by 7.45 times during 1.5 years. With GLS&amp;lt;13.7%, the risk of hospitalization with HF increases by 2.34 times (p=0.048), GWW&amp;gt;161.5 mmHg% - by 2.65 times (p=0.028). The model for calculating the risk of hospitalization with HF included GLS (OR 0.75; p=0.006), GCW (OR 0.85; p=0.036) and ΔEF (OR 0.88; p=0.040). Patients hospitalized with HF after AMI showed a significant increase in the LV sphericity index after 3 months. The predictors of hospitalization with HF according to 3D Echo were indicators of circumflexial strain (CS)(OR 0.76; p=0.026), areal strain (AS)(OR 0.60; p=0.012), radial strain (RS)(OR 0.91; p=0.039), area strain dynamics over 3 months (HR 0.702; р=0.025). Conclusion In patients with the first AMI, it is advisable to conduct a comprehensive Echo with an assessment of MW, stress LUS to identify high-risk patients for the development of HF in the postinfarction period.

What are the Data for Current Prognostic Tools Used to Determine the Risk of Short-Term Adverse Events in Patients with Acute Heart Failure?

BackgroundAcute heart failure (AHF) is a common condition evaluated in the emergency department (ED). Patients may present with a wide range of signs and symptoms, comorbidities, exacerbating factors, and ability to follow-up. Having a decision tool to objectively assess the risk of near-term events would help guide disposition decisions in these patients. Clinical QuestionWhat are the data for current tools used to determine the short-term risk of adverse events of patients with AHF in the ED setting? Evidence ReviewStudies retrieved included six prospective studies and three retrospective cohort studies that evaluated the following five different risk scores that may predict the risk of serious adverse events in those with AHF: Ottawa Heart Failure Risk Score (OHFRS), Emergency Heart Failure Mortality Risk Grade (EHMRG), EHMRG at 30 days with addition of an ST depression variable (EHMRG30-ST), Multiple Estimation of Risk Based on the Emergency Department Spanish 40 Score in Patients with AHF Score (MEESSI-AHF), and the Improving Heart Failure Risk Stratification in the ED (STRATIFY) tool. ConclusionsBased on the available literature, risk scores, including the OHFRS; EHMRG; EHMRG30-ST; MEESSI-AHF; and STRATIFY, can help identify short-term risk of adverse events, but are insufficient in isolation. Clinicians should use these tools in conjunction with other factors, such as the patient's symptom trajectory, hemodynamics, and access to follow-up care.

Optimizing Arteriovenous Fistula Care in Kidney Transplant Recipients: A Complex Task.

Optimizing Arteriovenous Fistula Care in Kidney Transplant Recipients: A Complex Task.

HLA amino acid Mismatch-Based risk stratification of kidney allograft failure using a novel Machine learning algorithm

ObjectiveWhile associations between HLA antigen-level mismatches (Ag-MM) and kidney allograft failure are well established, HLA amino acid-level mismatches (AA-MM) have been less explored. Ag-MM fails to consider the substantial variability in the number of MMs at polymorphic amino acid (AA) sites within any given Ag-MM category, which may conceal variable impact on allorecognition. In this study we aim to develop a novel Feature Inclusion Bin Evolver for Risk Stratification (FIBERS) and apply it to automatically discover bins of HLA amino acid mismatches that stratify donor-recipient pairs into low versus high graft survival risk groups. MethodsUsing data from the Scientific Registry of Transplant Recipients, we applied FIBERS on a multiethnic population of 166,574 kidney transplants between 2000 and 2017. FIBERS was applied (1) across all HLA-A, B, C, DRB1, and DQB1 locus AA-MMs with comparison to 0-ABDR Ag-MM risk stratification, (2) on AA-MMs within each HLA locus individually, and (3) using cross validation to evaluate FIBERS generalizability. The predictive power of graft failure risk stratification was evaluated while adjusting for donor/recipient characteristics and HLA-A, B, C, DRB1, and DQB1 Ag-MMs as covariates. ResultsFIBERS’s best-performing bin (on AA-MMs across all loci) added significant predictive power (hazard ratio = 1.10, Bonferroni adj. p < 0.001) in stratifying graft failure risk (where low-risk is defined as zero AA-MMs and high-risk is one or more AA-MMs) even after adjusting for Ag-MMs and donor/recipient covariates. The best bin also categorized more than twice as many patients to the low-risk category, compared to traditional 0-ABDR Ag mismatching (∼24.4% vs ∼ 9.1%). When HLA loci were binned individually, the bin for DRB1 exhibited the strongest risk stratification; relative to zero AA-MM, one or more MMs in the bin yielded HR = 1.11, p < 0.005 in a fully adjusted Cox model. AA-MMs at HLA-DRB1 peptide contact sites contributed most to incremental risk of graft failure. Additionally, FIBERS points to possible risk associated with HLA-DQB1 AA-MMs at positions that determine specificity of peptide anchor residues and HLA-DQ heterodimer stability. ConclusionFIBERS’s performance suggests potential for discovery of HLA immunogenetics-based risk stratification of kidney graft failure that outperforms traditional assessment.

NT-proBNP point-of-care measurement as a screening tool for heart failure and CVD risk in type 2 diabetes with hypertension

AimsWe used N-terminal pro-B-type natriuretic peptide (NT-proBNP) point-of-care testing (POCT) for heart failure risk stratification of individuals with type 2 diabetes for >10 years and hypertension. MethodsOverall 259 participants aged 50 years or older with type 2 diabetes (duration of >10 years), hypertension, and no overt cardiovascular disease (CVD) were recruited at two study centers. Patients' data were acquired and NT-proBNP levels were measured using the CARDIAC proBNP+ test (Roche) and the cobas h232 instrument (Roche). Participants were clustered into two groups according to their NT-proBNP concentration value: with NT-proBNP <125 pg/ml and with NT-proBNP ≥125 pg/ml. ResultsMean age of the participants was 66.1 ± 9.2 years, 55.2 % were female, 60.6 % (n = 157) had a NT-proBNP <125 pg/ml and 39.4 % (n = 102 ≥ 125 pg/ml). Differences were observed among those with low and high NT-proBNP in mean age (63.4 ± 8.8 years vs. 70.1 ± 8.2 years, p < 0.001), diabetes duration (15.4 ± 5.9 years vs. 17.9 ± 7.3 years, p = 0.003), and estimated glomerular filtration rate (eGFR) (86 ± 16 ml/min/1.73 m2 vs. 76 ± 20 ml/min/1.73 m2, p < 0.001). ConclusionsNT-proBNP POCT is practical and can be pragmatically targeted for screening people with type 2 diabetes and hypertension for heart failure risk stratification in routine clinical practice.

Artificial intelligence predicts direct-acting antivirals failure among hepatitis C virus patients: A nationwide hepatitis C virus registry program.

Despite the high efficacy of direct-acting antivirals (DAAs), approximately 1-3% of hepatitis C virus (HCV) patients fail to achieve a sustained virological response. We conducted a nationwide study to investigate risk factors associated with DAA treatment failure. Machine-learning algorithms have been applied to discriminate subjects who may fail to respond to DAA therapy. We analyzed the Taiwan HCV Registry Program database to explore predictors of DAA failure in HCV patients. Fifty-five host and virological features were assessed using multivariate logistic regression, decision tree, random forest, eXtreme Gradient Boosting (XGBoost), and artificial neural network. The primary outcome was undetectable HCV RNA at 12 weeks after the end of treatment. The training (n=23,955) and validation (n=10,346) datasets had similar baseline demographics, with an overall DAA failure rate of 1.6% (n=538). Multivariate logistic regression analysis revealed that liver cirrhosis, hepatocellular carcinoma, poor DAA adherence, and higher hemoglobin A1c were significantly associated with virological failure. XGBoost outperformed the other algorithms and logistic regression models, with an area under the receiver operating characteristic curve of 1.000 in the training dataset and 0.803 in the validation dataset. The top five predictors of treatment failure were HCV RNA, body mass index, α-fetoprotein, platelets, and FIB-4 index. The accuracy, sensitivity, specificity, positive predictive value, and negative predictive value of the XGBoost model (cutoff value=0.5) were 99.5%, 69.7%, 99.9%, 97.4%, and 99.5%, respectively, for the entire dataset. Machine learning algorithms effectively provide risk stratification for DAA failure and additional information on the factors associated with DAA failure.

Relationship Between Regional Adiposity Distribution and Incident Heart Failure in General Populations without Cardiovascular Disease

BackgroundObesity is associated with a high risk of heart failure. However, the contribution of regional fat distribution evaluated using bioimpedance analysis toward heart failure risk in the general population without cardiovascular disease has rarely been studied. MethodsThis study included 483,316 participants without heart failure and cardiovascular disease from the UK Biobank study. The regional fat mass was determined by bioimpedance analysis and calculated by dividing the square of height in meters (kg/m2). This study evaluated the association of regional fat mass (arm fat index [AFI], trunk fat index [TFI], and leg fat index [LFI]) with the risk of incident heart failure and whether regional fat mass adds a further prognostic value for heart failure besides body mass index (BMI) in a large prospective cohort study. ResultsDuring the median 12.1 years, 3134 incident heart failure cases occurred. After adjustment for BMI and other confounding factors, each 1-standard deviation increase in LFI was associated with a 21% lower heart failure risk even after adjusting for BMI and other confounding factors (hazard ratio [HR] 0.79; 95% confidence interval [CI], 0.73-0.85). However, we did not observe heart failure-associated risks with AFI and TFI (HR 1.04; 95% CI, 0.99-1.09; HR 0.97, 95% CI, 0.91–1.04, respectively). Subgroup analysis demonstrated that the protective role of LFI was more prominent in the elderly and female participants (P < .01). ConclusionRegional fat measurement other than BMI can improve heart failure risk stratification; leg fat plays a protective role, yet arm and trunk fat do not, in the general population without cardiovascular disease.

Barriers and Opportunities Regarding Implementation of a Machine Learning-Based Acute Heart Failure Risk Stratification Tool in the Emergency Department.

Hospital admissions for patients with acute heart failure (AHF) remain high. There is an opportunity to improve alignment between patient risk and admission decision. We recently developed a machine learning (ML)-based model that stratifies emergency department (ED) patients with AHF based on predicted risk of a 30-day severe adverse event. Prior to deploying the algorithm and paired clinical decision support, we sought to understand barriers and opportunities regarding successful implementation. We conducted semi-structured interviews with eight front-line ED providers and surveyed 67 ED providers. Audio-recorded interviews were transcribed and analyzed using thematic analysis, and we had a 65% response rate to the survey. Providers wanted decision support to be streamlined into workflows with minimal disruptions. Most providers wanted assistance primarily with ED disposition decisions, and secondarily with medical management and post-discharge follow-up care. Receiving feedback on patient outcomes after risk tool use was seen as an opportunity to increase acceptance, and few providers (<10%) had significant hesitations with using an ML-based tool after education on its use. Engagement with key front-line users on optimal design of the algorithm and decision support may contribute to broader uptake, acceptance, and adoption of recommendations for clinical decisions.

Silver Nanoparticle Labels for Electrochemical Detection of Bioassays

Electrochemistry plays a major role in immunosensor development because of its higher sensitivity, simplicity, portability, and rapidity compared to more common enzymatic, fluorescent and radioactive labelling methods. Silver nanoparticles (AgNPs) have been identified as potential electrochemical labels for such immunoassays. Characterizing the composition, size, shape, and surface modifications of the AgNP is important to achieve expected analytical performance of the bio assay. Here we are interested in studying the effect of nanoparticle shape towards the electrochemical detection of a metallo-immunoassay using Au-Ag galvanic exchange (GE) reaction followed by anodic stripping voltammetry. We observe that during GE between electrogenerated Au3+ and the Ag labels, a thin shell of Au forms on the surface of the NP and prevent further exchange. This shell is more porous when GE proceeds on silver nanocubes (AgNCs) compared to spherical silver nanoparticles (sAgNPs), and therefore, more exchange occurs when using AgNCs. More interestingly, optimizing the ratio of the two types of AgNP labels, we could able to decrease the LOD of a biological assay designed to detect a heart failure biomarker (NT-proBNP) without compromising the dynamic range compared to using either of the two labels independently. This made it possible to achieve the clinically relevant range for NT-proBNP analysis used by physicians for heart failure risk stratification.

Retrospective validation of acute heart failure risk stratification in the emergency department

BackgroundHeart Failure (HF) is a primary diagnosis for hospital admission from the Emergency Department (ED), although not all patients require hospitalization. The Emergency Heart Failure Mortality Risk Grade (EHMRG) estimates 7-day mortality in patients with acute HF in ED settings, but further validation is needed in the United States (US). ObjectivesTo validate EHMRG scores by risk-stratifying patients with acute HF in a large tertiary healthcare center in the US and analyze outcome measures to determine if EHMRG risk scores safely identify low-risk groups that may be discharged or managed in ED observation units (EDOUs). MethodsA retrospective cohort analysis of 304 patients with acute HF presenting to an ED at a large, tertiary healthcare center was completed. EHMRG scores were calculated to stratify patients according to published thresholds. Mortality and major adverse cardiac event (MACE) rates were analyzed. ResultsNo deaths occurred in very low and low-risk EHMRG groups at 7 days post discharge. 30-day mortality was significantly less in the lower risk groups (3.1%) when compared to all other patients (11.1%). MACE rates at 30 days in the very low risk group (15%) were significantly less when compared to all other patients (31.3%). Hospitalizations occurred in 23.4% of patients in lower risk groups. ConclusionsED risk stratification with EHMRG differentiates high-risk patients requiring hospitalization from lower risk patients who can be safely managed in alternative settings with good outcomes. Data supports improved pathways for patients with acute HF during a time of high hospital volumes.

Abstract 12189: The Ratio of Cyclic Guanosine Monophosphate to Brain Natriuretic Peptide (cGMP/BNP) Stratifies The Risk of Heart Failure Hospitalization in Cardiac Resynchronization Therapy (CRT) Patients

Background: Cardiac Resynchronization Therapy (CRT) is established treatment for patients with heart failure, however, there are various predictors of prognosis. Although CRT responder is reported to have a better prognosis than CRT non-responder, some cases have poor clinical outcomes even among CRT responders. Meanwhile, brain natriuretic peptide (BNP) and cyclic guanosine monophosphate (cGMP) are associated with the severity of heart failure. Little is known about the efficacy of prognostic value of BNP and cGMP in CRT patients especially among CRT responders. Methods: Among 246 consecutive CRT patients, we retrospectively analyzed 86 patients whose biomarkers were measured at the time of implantation. The patients were divided into two groups by the median value of cGMP/BNP (0.26). The endpoint was heart failure hospitalization, and the CRT responder was defined as an improvement of end-systolic volume (ESV) ≥ 15% or ejection fraction (EF) ≥ 10% at 6 months after CRT implantation. Results: The patients were 65.4 ± 13.6 years old, 64 (74%) patients were male, 17 (20%) patients were ischemic cardiomyopathy, and 47 (55%) patients were CRT responders. During a median follow-up period of 830 (interquartile range: 482-1246) days, 27 patients (31%) had heart failure hospitalization (high cGMP/BNP group: 8 vs low cGMP/BNP group 19, p = 0.016). Kaplan-Meier analysis revealed the risk of heart failure hospitalization was higher in high cGMP/BNP group than in low group (log-rank p = 0.0025), and the same result was observed even among CRT responders (log-rank p=0.018). cGMP/BNP increased in responders (p = 0.0045) but not in non-responders (p = 0.23) at 6 months after CRT implantation in Wilcoxon signed-rank test. Univariate analysis showed that BMI, chronic kidney disease, diabetes mellitus, hemoglobin, CRT responder, and cGMP/BNP were predictors of heart failure hospitalization. Conclusion: cGMP/BNP is useful for the heart failure risk stratification in CRT patients.

Dual-Shaped Silver Nanoparticle Labels for Electrochemical Detection of Bioassays.

In this paper we demonstrate the use of dual-shaped silver nanoparticles (AgNPs) as detection labels for electrochemical bioassays. The key finding is that by using AgNP labels having two different shapes simultaneously, the limit of detection (LOD) for the assays is lowered compared to using either of the two shapes separately. The two shapes were silver nanocubes (AgNCs) having edge lengths of 40 ± 4 nm and spherical AgNPs (sAgNPs) having diameters of 20 ± 3 nm. Two different bioassays were examined. In both cases the Ag labels were functionalized with antibodies. In the one assay, the labels are directly linked to a second antibody immobilized on magnetic beads. In the second assay, the antibodies on the AgNP labels and the antibodies on the magnetic beads are linked via a peptide. The peptide is N-terminal prohormone brain natriuretic peptide (NT-proBNP), which is a heart failure marker. The efficacy of the two electrochemical assays as a function of the ratio of the two labels was investigated using a galvanic exchange/anodic stripping voltammetry method. The key finding is that by optimizing the ratio of the two types of AgNP labels, it is possible to decrease the LOD of the assays without compromising the dynamic range compared to using either of the two labels independently. This made it possible to achieve the clinically relevant range for NT-proBNP analysis used by physicians for heart failure risk stratification.

External validation of H2ARDD score for the prediction of heart failure events in patients with atrial fibrillation

Abstract Background Heart failure is an important consequence in patients with atrial fibrillation (AF) which is associated with worse prognosis. The H2ARDD score, calculated from 5 clinical risk factors, was reported as a predictor of heart failure events in patients with AF. However, this score has not been externally validated. Purpose The purpose of this study was to evaluate and validate the usefulness of the H2ARDD score for the prediction of heart failure events in AF patients. Methods We used prospective data of patients with AF followed up from 2007 to 2017 in our institute. Patients with active cancer were excluded according to the previous report. H2ARDD score was calculated as follows; history of heart disease=2 points, anemia=1 point, renal dysfunction=1 point, diabetes =1 point, diuretic use=1 point (range from 0 to 6 points). Outcome of interest was defined as heart failure events including new-onset heart failure and death with heart failure. Heart failure was ascertained based on the Framingham criteria. Univariable and multivariable Cox-proportional hazards model were used to assess the risk of heart failure events. Heart failure events-free survival was estimated with Kaplan-Meier methods, and the predictive accuracy of the H2ARDD score for the prediction of heart failure events was measured by the area under the receiver operating characteristic (ROC) curve. Results Of 562 AF patients, 522 (age 69±10 year–old, 64.9%men) met study criteria. Patients who had a history of heart disease was 185 (35%), diabetes mellitus was 135 (26%), anemia was 54 (10%), renal dysfunction was 221 (43%), and diuretic use was 193 (37%). The mean H2ARDD score was 1.88±1.57. Of all study patients, 84 (16.2%) developed heart failure events during a mean follow–up of 54±42 months. Patients who developed heart failure events in 1 year was 24 (4.6%). In multivariable Cox–proportional hazards model, H2ARDD score was shown as an significant predictor for heart failure events (hazard ratio: 1.56, 95% confidence interval: 1.36 - 1.79, P&amp;lt;0.0001), independently of age (per 10 years, hazard ratio: 1.35, 95% confidence interval: 1.03 – 1.78, P&amp;lt;0.05). In the Kaplan–Meier analyses stratified by H2ARDD score (0–1, 2–3, 4–6), patients who had a higher H2ARDD sore had significantly worse heart failure event-free survival (log-rank P&amp;lt;0.0001) (Figure 1). The area under the ROC curve for the prediction of heart failure events in 1-year was 0.812 (95% confidence interval: 0.737 – 0.887, P&amp;lt;0.0001), and the best cut-off value was ≥4 points (sensitivity: 67%, specificity: 83%) (Figure 2). Conclusion H2ARDD score was demonstrated as a significant independent predictor for the prediction of heart failure events, with high predictive accuracy. H2ARDD score may be useful for heart failure risk stratification of AF patients. Funding Acknowledgement Type of funding sources: None. Figure 1Figure 2

A multiparametric ICD algorithm for heart failure risk stratification and management: an analysis in clinical practice

Abstract Funding Acknowledgements Type of funding sources: None. Introduction The HeartLogic algorithm combines multiple implantable cardioverter defibrillator (ICD) sensors to identify patients at risk of heart failure (HF) events. Purpose We sought to evaluate the risk stratification ability of this algorithm in clinical practice. We also analyzed the alert management strategies adopted in the study group and their association with the occurrence of HF events. Methods The HeartLogic feature was activated in 366 ICD and cardiac resynchronization therapy ICD patients at 22 centers. The HeartLogic algorithm automatically calculates a daily HF index and identifies periods IN or OUT of an alert state on the basis of a configurable threshold (in this analysis set to 16). Results The HeartLogic index crossed the threshold value 273 times (0.76 alerts/patient-year) in 150 patients over a median follow-up of 11 months [25-75 percentile: 6-16]. Overall, the time IN the alert state was 11% of the total observation period. Patients experienced 36 HF hospitalizations and 8 patients died of HF (rate: 0.12 events/patient-year) during the observation period. Thirty-five events were associated with the IN alert state (0.92 events/patient-year versus 0.03 events/patient-year in the OUT of alert state). The hazard ratio in the IN/OUT of alert state comparison was (HR: 24.53, 95% CI: 8.55-70.38, p &amp;lt; 0.001), after adjustment for baseline clinical confounders. Alerts followed by clinical actions were associated with a lower rate of HF events (HR: 0.37, 95% CI: 0.14-0.99, p = 0.047). No differences in event rates were observed between in-office and remote alert management. By contrast, verification of HF symptoms during post-alert examination was associated with a higher risk of HF events (HR: 5.23, 95% CI: 1.98-13.83, p &amp;lt; 0.001). Conclusions This multiparametric ICD algorithm identifies patients during periods of significantly increased risk of HF events. The rate of HF events seemed lower when clinical actions were undertaken in response to alerts. Extra in-office visits did not seem to be required in order to effectively manage HeartLogic alerts, while post-alert verification of symptoms seemed useful in order to better stratify patients at risk of HF events.