Early Detection Of Heart Failure Research Articles

Towards Point-of-Care Heart Failure Diagnostic Platforms: BNP and NT-proBNP Biosensors.

Heart failure is a class of cardiovascular diseases that remains the number one cause of death worldwide with a substantial economic burden of around $18 billion incurred by the healthcare sector in 2017 due to heart failure hospitalization and disease management. Although several laboratory tests have been used for early detection of heart failure, these traditional diagnostic methods still fail to effectively guide clinical decisions, prognosis, and therapy in a timely and cost-effective manner. Recent advances in the design and development of biosensors coupled with the discovery of new clinically relevant cardiac biomarkers are paving the way for breakthroughs in heart failure management. Natriuretic neurohormone peptides, B-type natriuretic peptide (BNP) and N-terminal prohormone of BNP (NT-proBNP), are among the most promising biomarkers for clinical use. Remarkably, they result in an increased diagnostic accuracy of around 80% owing to the strong correlation between their circulating concentrations and different heart failure events. The latter has encouraged research towards developing and optimizing BNP biosensors for rapid and highly sensitive detection in the scope of point-of-care testing. This review sheds light on the advances in BNP and NT-proBNP sensing technologies for point-of-care (POC) applications and highlights the challenges of potential integration of these technologies in the clinic. Optical and electrochemical immunosensors are currently used for BNP sensing. The performance metrics of these biosensors—expressed in terms of sensitivity, selectivity, reproducibility, and other criteria—are compared to those of traditional diagnostic techniques, and the clinical applicability of these biosensors is assessed for their potential integration in point-of-care diagnostic platforms.

CASE STUDY EXEMPLAR OF DETECTING SEVERE DIASTOLIC DYSFUNCTION USING BALLISTOCARDIOGRAM

The specific aim of this case study was to describe how monitoring ballistocardiogram (BCG) waveforms can detect early heart failure (HF) changes. HF significantly impairs quality of life and is the principal cause for hospital readmissions in older adults. HF prevalence in American adults aged 65 years and older is expected to increase over 70% by 2030. Detecting worsening HF is challenging. Invasive arterial waveforms display blood pressure changes with each heartbeat; BCG waveforms display repetitive body motions resulting from ejection of blood into the great vessels. BCG waveforms change as cardiac function changes. Currently, BCG signals can be captured non-invasively using sensors placed under a bed mattress and provide heart and respiratory rates. We have developed a new way to analyze the BCG waveform using an innovative closed-loop physiological model of the cardiovascular system. The subject, a 94-year old female with hypertension, presented to her physician with symptoms associated with a new diagnosis of acute mixed congestive HF. Mean heart and respiratory rate trends obtained from her bed sensor in the prior two months did not indicate HF. We simulated cardiac cycles using normal cardiac function data, mildly impaired diastolic function data, and the subject’s echocardiography data. The results demonstrated BCG waveform changes that correlated with decreasing cardiac output related to worsening diastolic function. New methods for clinically interpreting BCG waveforms present a significant opportunity for improving early HF detection and improving outcomes. Working on a clinical problem from an engineering perspective merges two disciplines, creating a new methodology.

Magnetic resonance hyperpolarisation imaging detects early myocardial dysfunction in a porcine model of right ventricular heart failure

BackgroundEarly detection of heart failure is important for timely treatment. During development of heart failure adaptive, intracellular processes that evolve prior to macro‐anatomic pathology could provide an early signal of impending heart failure. Therefore, metabolic alterations following cardiac dysfunction would appear early in disease progression. We hypothesised that metabolic imaging with hyperpolarised magnetic resonance can detect early development of heart failure before conventional echocardiography detects cardiac dysfunction.Methods and ResultsFive 8.5 kg piglets were subjected to pulmonary banding and subsequently examined by [1‐13C]pyruvate hyperpolarisation, conventional magnetic resonance imaging, echocardiography, and blood samples every four weeks for 16 weeks. They were compared with a weight matched, healthy control group. Conductance catheter examination at the end of the study showed impaired right ventricular systolic function along with compromised left ventricular diastolic function. After 16 weeks we saw a significant decrease in the left ventricular conversion ratio of pyruvate/bicarbonate from 0.13 (0.04) in controls to 0.07 (0.02) in animals with pulmonary banding, and a significant increase in the lactate/bicarbonate ratio to 3.47 (1.57) versus 1.34 (0.81) in controls. NT‐proBNP was increased by more than 300%, while cardiac index was reduced to 2.8 (0.95) l/min/m2 versus 3.9(0.95)in controls. Echocardiography revealed no changes.ConclusionHyperpolarisation detected a shift towards anaerobic metabolism in early stages of right ventricular dysfunction as evident by an increased lactate/bicarbonate ratio. Dysfunction was confirmed with conductance catheter assessment, but could not be detected by echocardiography. Hyperpolarisation holds a promising future in clinical assessment of heart failure in both acquired and congenital heart disease.Support or Funding InformationThe study is funded by the Danish Children Heart Foundation.This abstract is from the Experimental Biology 2019 Meeting. There is no full text article associated with this abstract published in The FASEB Journal.

Association of High Sensitive C - Reactive Protein with Severity of the Left Ventricular Systolic Dysfunction in Acute Anterior ST Elevation Myocardial Infarction

High Sensitive C-reactive protein (hs- CRP) is an established risk marker in coronary artery disease. It is a marker of inflammation activated early after Acute Myocardial Infarction (AMI) and its quantity depends upon extent of myocardial damage. Release of inflammatory marker occur after acute myocardial infarction leading to cardiac remodeling which clinically manifests as Heart failure (HF). Heart failure is a common complication after acute anterior myocardial infarction (AMI). The prevalence of post-infarct Left Ventricular Systolic Dysfunction (LVSD) ranges from 27 to 60 % and half of patients having early post-infarct LVSD subsequently develop chronic heart failure. The purpose of this study is to show association between hs-CRP with LVSD in AMI and early detection of HF. This was a cross-sectional analytical study in which hs-CRP was done among all the study subjects between 24-48 hours after onset of AMI. The study population was categorized into groups I, II, II according to the lowest to highest hs-CRP level. Transthoracic echocardiography was done between 24-48 hours of anterior ST Elevation Myocardial Infarction (STEMI). Then LVSD was assessed between those three groups and searched for association. Severely reduced ejection fraction (EF) was found in patients of group III (highest hs-CRP tertile) only. Severe and moderately reduced EF and FS was found significantly more in group III and II than group I (mid and lowest hs-CRP tertile) (p<0.001). High level of hs-CRP in patient of acute anterior STEMI patients was associated with moderate to severe reduction in EF and Fractional Shortening (FS). So hs- CRP may be a prognostic marker in acute anterior STEMI complicating LVSD and early management would improved the short and long term prognosis.

Early Detection of Heart Failure Using Electronic Health Records: Practical Implications for Time Before Diagnosis, Data Diversity, Data Quantity, and Data Density

Background: Using electronic health records (EHR) data to predict events and onset of diseases is increasingly common. Relatively little is known, though, about the tradeoffs between data requirements and model utility.

Specificity of the Framingham Heart Failure Signs and Symptoms: Bridging the Gap Between Epidemiological Research and Clinical Practice

Background: While the Framingham Heart Failure Signs and Symptoms (FHFSS) have been around for decades, little is known about the specificity of the relation between presence of each FHFSS and early detection of heart failure. The FHFSS continue to be used in epidemiological research; however, their clinical relevance has been criticized. We examined variation in the ability of individual FHFSS to predict heart failure diagnosis by assertion or denial of each feature, by counts, and by a composite score.

Diagnostic significance of serum galectin-3 levels in heart failure with preserved ejection fraction.

Galectin-3, reflecting cardiac fibrosis, is a promising biomarker in early detection of heart failure with preserved ejection fraction (HFpEF). We aimed to clarify the clinical utility of galectin-3 levels in the diagnosis of HFpEF and to compare galectin-3 with N-terminal pro-brain-type natriuretic peptide (NT-proBNP) levels. The study included 44 HFpEF patients (mean age 60 ± 6.78 years, 24 men) and 38 control subjects (mean age 57 ± 8.98 years, 20 men). Galectin-3 and NT-proBNP levels were assessed by the ELISA kits. The receiver operating characteristics (ROC) curve was used to examine the diagnostic performance of galectin-3 and NT-proBNP in HFpEF. Galectin-3 and NT-proBNP levels were significantly increased in patients with HFpEF compared to controls [5.35 ng/ml (0.86 – 14.90) vs 0.51 ng/ml (0.15 – 1.71) P < 0.0001, 617.75 ± 271.30 pg/ml vs 66.35 ± 54.01 pg/ml P < 0.0001, respectively]. Galectin-3 correlated with NT-proBNP, left atrial volume index, left ventricular mass index, and E/E’ (r = 0.90, P < 0.0001; r = 0.75, P = 0.0001; r = 0.86, P = 0.0001; r = 0.80, P = 0.0001; respectively). The area under the ROC curve was 0.98 for galectin-3 and 1.0 for NT-proBNP. Our results support that, in addition to NT-proBNP, galectin-3 is also a valuable biomarker for the diagnosis of patients with HFpEF.

Ambulatory Heart Failure Monitoring: A Systemic Review.

Heart failure (HF) is one of the leading causes of morbidity and mortality and has a large effect on the country’s economy. Although there have been major advances in HF monitoring, including more advanced pharmacological management and device-based therapy, HF-related mortality remains high. It is important to monitor HF so that HF-related hospitalization and mortality can be prevented. Due to the lower sensitivity of clinical features and biochemical markers, as well as the failure of telemonitoring in early detection of HF, more advanced techniques have been sought to more accurately predict impending HF, in order to address timely pharmacological management and prevent heart failure hospitalization (HFH). Device-based therapy has passed through various stages and culminated in the recently introduced CardioMEMSTM (St. Jude Medical, Inc., Saint Paul, Minnesota). CardioMEMSTM is a wireless pulmonary artery pressure (PAP) monitoring device, which continuously monitors PAP and transmits data to a healthcare provider. It rapidly identifies changes in intracardiac pressure and allows timely pharmacological management. CardioMEMSTM showed a higher reduction of HFH compared to any other devices.

NT-proBNP levels in β-thalassemia major patients without cardiac hemosiderosis

Abstract Introduction Heart failure due to hemosiderosis is frequent in beta-thalassemia major (β-TM) patients. Magnetic resonance imaging (MRI) is used in the early detection of heart failure. Amino-terminal pro-brain natriuretic peptide (NT-proBNP) is a very sensitive marker in the diagnosis of heart failure. In this study, we aimed to investigate the efficacy of NT-proBNP levels in thalassemia patients, who are thought to have no cardiac iron deposition according to T2* scoring system (CMRT2*&gt;20 msn), in early identification of cardiac failure. Methods NT-proBNP levels of 31 patients, who have T2*&gt;20 ms, and of 25 healthy population were measured by chemoluminescence method. Results NT-proBNP levels were not different in thalassemic patients [median: 33 (IQR: 28–94) pg/mL] compared to control group [median: 41 (IQR: 28–59) pg/mL]. We found that NT-proBNP level was above cut-off value in six patients. Conclusion NT-proBNP is a cheaper, reachable, and noninvasive method compared to MRI technique, it can be easily used in monthly controls. Detection of high NT-proBNP levels above cut-off values in patients whose T2* values are normal indicates that measurement of NT-pro-BNP is a more sensitive marker in early detection of cardiac failure.

Early Detection of Heart Failure Using Electronic Health Records: Practical Implications for Time Before Diagnosis, Data Diversity, Data Quantity, and Data Density.

Using electronic health records data to predict events and onset of diseases is increasingly common. Relatively little is known, although, about the tradeoffs between data requirements and model utility. We examined the performance of machine learning models trained to detect prediagnostic heart failure in primary care patients using longitudinal electronic health records data. Model performance was assessed in relation to data requirements defined by the prediction window length (time before clinical diagnosis), the observation window length (duration of observation before prediction window), the number of different data domains (data diversity), the number of patient records in the training data set (data quantity), and the density of patient encounters (data density). A total of 1684 incident heart failure cases and 13 525 sex, age-category, and clinic matched controls were used for modeling. Model performance improved as (1) the prediction window length decreases, especially when <2 years; (2) the observation window length increases but then levels off after 2 years; (3) the training data set size increases but then levels off after 4000 patients; (4) more diverse data types are used, but, in order, the combination of diagnosis, medication order, and hospitalization data was most important; and (5) data were confined to patients who had ≥10 phone or face-to-face encounters in 2 years. These empirical findings suggest possible guidelines for the minimum amount and type of data needed to train effective disease onset predictive models using longitudinal electronic health records data.

Physician Documentation Behaviors in Electronic Health Records as a Potential Source of Noise for Early Detection of Heart Failure

Physician Documentation Behaviors in Electronic Health Records as a Potential Source of Noise for Early Detection of Heart Failure

Evaluation of the appropriateness of the preclinical phase (stage A and stage B) of heart failure Management in Outpatient clinics in Italy rationale and design of the 'VASTISSIMO' study.

Early detection of heart failure, when still preclinical, is fundamental. Therefore, it is important to assess whether preclinical heart failure management by cardiologists is adequate. The VASTISSIMO study ('EValuation of the AppropriateneSs of The preclInical phase (Stage A and Stage B) of heart failure Management in Outpatient clinics in Italy') is a prospective nationwide study aimed to evaluate the appropriateness of diagnosis and management of preclinical heart failure (stages A and B) by cardiologists working in outpatient clinics in Italy. Secondary goals are to verify if an online educational course for cardiologists can improve management of preclinical heart failure, and evaluate how well cardiologists are aware of patients' adherence to medications. The study involves 80 outpatient cardiology clinics distributed throughout Italy, affiliated either to the Hospital Cardiologists Association or to the Regional Association of Outpatient Cardiologists, and is designed with two phases of consecutive outpatient enrolment each lasting 1 month. In phase 1, physicians' awareness of the risk of heart failure and their decision-making process are recorded. Subsequently, half of the cardiologists are randomized to undergo an online educational course aimed to improve preclinical heart failure management through implementation of guideline recommendations. At the end of the course, all cardiologists are evaluated (phase 2) to see whether changes in clinical management have occurred in those who underwent the educational program versus those who did not. Patients' adherence to prescribed medications will be assessed through the Morisky Self-report Questionnaire. This study should provide valuable information about cardiologists' awareness of preclinical heart failure and the appropriateness of clinical practice in outpatient cardiology clinics in Italy.

PT251 Strain Analysis by Speckle-Tracking Echocardiography in a Rheumatoid Arthritis Hispanic Cohort

Introduction: The leading cause of death in rheumatoid arthritis (RA) is atherosclerotic cardiovascular disease (ASCVD), predominantly acute myocardial infarction, stroke and congestive heart failure. Myocardial strain analysis by speckle-tracking echocardiography is a useful tool for the early detection of heart failure with preserved ejection fraction. Data regarding right and left ventricular strain in RA is limited. Objectives: To describe strain analysis by speckle-tracking echocardiography in a Hispanic RA Cohort. Methods: A cross-sectional, observational and descriptive study was designed. Patients with RA who fulfilled the 2010 ACR/EULAR criteria were consecutively recruited. Patients with prior ASCVD, overlap syndrome, younger than 40 years old or older than 75 years old were excluded. A standard echocardiography was performed. Patients with sub-optimal images for strain evaluation by speckle-tracking were eliminated from the study according to the ASE recommendations. Results: A total of 47 patients were included in the analysis. Population characteristics are shown in Table 1. The majority of the subjects had moderate disease activity according to their DAS28-CRP classification, with an average value of 3.5 1.38. Average longitudinal strain was -20.8 2.71. Analysis by individual segments is shown in Figure 1. There was a significant difference between segments (p<0.05).

Diagnostic significance of serum galectin-3 levels in heart failure with preserved ejection fraction

Objective Galectin-3, reflecting cardiac fibrosis, is a promising biomarker in early detection of heart failure with preserved ejection fraction (HFpEF). We aimed to clarify the clinical utility of galectin-3 levels in the diagnosis of HFpEF and to compare galectin-3 with N-terminal pro-brain-type natriuretic peptide (NT-proBNP) levels.Methods and results The study included 44 HFpEF patients (mean age 60 ± 6.78 years, 24 men) and 38 control subjects (mean age 57 ± 8.98 years, 20 men). Galectin-3 and NT-proBNP levels were assessed by the ELISA kits. The receiver operating characteristics (ROC) curve was used to examine the diagnostic performance of galectin-3 and NT-proBNP in HFpEF. Galectin-3 and NT-proBNP levels were significantly increased in patients with HFpEF compared to controls [5.35 ng/ml (0.86 – 14.90) vs 0.51 ng/ml (0.15 – 1.71) P< 0.0001, 617.75 ± 271.30 pg/ml vs 66.35 ± 54.01 pg/ml P< 0.0001, respectively]. Galectin-3 correlated with NT-proBNP, left atrial volume index, left ventricular mass index, and E/E’ (r = 0.90, P< 0.0001; r = 0.75, P= 0.0001; r = 0.86, P= 0.0001; r = 0.80, P= 0.0001; respectively). The area under the ROC curve was 0.98 for galectin-3 and 1.0 for NT-proBNP.Conclusions Our results support that, in addition to NT-proBNP, galectin-3 is also a valuable biomarker for the diagnosis of patients with HFpEF.

Extended prediction rule to optimise early detection of heart failure in older persons with non-acute shortness of breath: a cross-sectional study

ObjectivesThere is a need for a practical tool to aid general practitioners in early detection of heart failure in the elderly with shortness of breath. In this study, such a...

Abstract 16364: Physician Documentation Behaviors in Electronic Health Records as a Potential Source of Noise for Early Detection of Heart Failure

Introduction: Electronic health records (EHRs) are a potentially rich source of data for developing predictive models for early detection of heart failure (HF). But, EHR documentation can vary both because of patient health and variation in clinical practice and behavior among physicians. Hypothesis: The “noise” contributed by variable physician behaviors, such as differences in the frequency and detail of documentation, could potentially confound predictive models for detection of HF. In this study, we characterized the documentation behaviors of primary care physicians (PCPs) in an effort to better understand this potential source of noise. Methods: We used longitudinal EHR data of a stratified random sample of 5,187 patients who were: 1) ≥50 years of age, and without a history of HF. PCPs (n=144) were identified and paired with the patients that they had treated for a minimum of 6 months. We derived 28 measures to characterize PCP behaviors - documentation frequencies of assertions and denials of selected Framingham HF signs and symptoms (FHFSS) in office visit encounter notes adjusted for patient comorbidities. Hierarchical clustering analyses (HCA) were performed on PCP documentation behaviors. Results: Based on HCA analyses, PCPs were clustered into 3 groups with distinct documentation behaviors. Group 1 PCPs (n=63) documented 10 out of 15 assertions, and 11 out of 13 denials of FHFSS significantly more frequently than Group 3 (n=20); while Group 2 PCPs (n=61) have significantly more frequent denial documentation behaviors than the other two (see Figure 1). No significant differences were found among patients’ chronic, episodic and cardio metabolic chronic disease counts (comorbidities) in each of the groups (p&lt;0.05). Conclusions: This study identified PCP groups with distinct documentation behaviors unrelated to patient complexity. This source of noise and potential confounder should be taken into account for predictive modeling.

P-325: Telemedicine systems used for early detection of heart failure in elderly

P-325: Telemedicine systems used for early detection of heart failure in elderly

Early detection of heart failure with varying prediction windows by structured and unstructured data in electronic health records.

Heart failure (HF) prevalence is increasing and is among the most costly diseases to society. Early detection of HF would provide the means to test lifestyle and pharmacologic interventions that may slow disease progression and improve patient outcomes. This study used structured and unstructured data from electronic health records (EHR) to predict onset of HF with a particular focus on how prediction accuracy varied in relation to time before diagnosis. EHR data were extracted from a single health care system and used to identify incident HF among primary care patients who received care between 2001 and 2010. A total of 1,684 incident HF cases were identified and 13,525 controls were selected from the same primary care practices. Models were compared by varying the beginning of the prediction window from 60 to 720 days before HF diagnosis. As the prediction window decreased, the performance [AUC (95% CIs)] of the predictive HF models increased from 65% (63%-66%) to 74% (73%-75%) for the unstructured, from 73% (72%-75%) to 81% (80%-83%) for the structured, and from 76% (74%-77%) to 83% (77%-85%) for the combined data.

The role of echocardiography in heart failure.

Data from echocardiography provide a cornerstone in the management of heart failure. All imaging techniques can provide an ejection fraction, but the versatility of echocardiography makes it unique in the provision of volumes, diastolic function, right ventricular function, hemodynamics, and valvular regurgitation. The early detection of heart failure has been facilitated by the assessment of global longitudinal strain, which is also useful in later heart failure for the assessment of left ventricular synchrony. The use of echocardiography has been associated with favorable outcomes, probably on the basis of facilitation of appropriate therapy. This review examines the evidence provided by echocardiography and its application in specific settings. Although the guidelines emphasize that no single test satisfies all imaging requirements in heart failure, and other modalities can provide additional information about specific questions (especially tissue characterization), echocardiography is indispensable in the management of heart failure.

Prediction of Heart Failure Decompensation Events by Trend Analysis of Telemonitoring Data.

This paper aims to assess the predictive value of physiological data daily collected in a telemonitoring study in the early detection of heart failure (HF) decompensation events. The main hypothesis is that physiological time series with similar progression (trends) may have prognostic value in future clinical states (decompensation or normal condition). The strategy is composed of two main steps: a trend similarity analysis and a predictive procedure. The similarity scheme combines the Haar wavelet decomposition, in which signals are represented as linear combinations of a set of orthogonal bases, with the Karhunen-Loève transform, that allows the selection of the reduced set of bases that capture the fundamental behavior of the time series. The prediction process assumes that future evolution of current condition can be inferred from the progression of past physiological time series. Therefore, founded on the trend similarity measure, a set of time series presenting a progression similar to the current condition is identified in the historical dataset, which is then employed, through a nearest neighbor approach, in the current prediction. The strategy is evaluated using physiological data resulting from the myHeart telemonitoring study, namely blood pressure, respiration rate, heart rate, and body weight collected from 41 patients (15 decompensation events and 26 normal conditions). The obtained results suggest, in general, that the physiological data have predictive value, and in particular, that the proposed scheme is particularly appropriate to address the early detection of HF decompensation.