Early Detection Of Heart Failure Research Articles

Abstract WMP16: Race, Gender, And Cognition In Heart Failure

Background: Cognitive impairment is prevalent in the heart failure (HF) population, eventually complicating the disease course in over 50%. Disease management and risk factor control are thought to play a role. Disparities in healthcare utilization and access may also contribute. We hypothesize gender, ethnic and racial disparities in HF contribute to accelerated cognitive decline. Methods: We performed a cross-sectional analysis of study participants age ≥ 60 years in the biannual NHANES survey. Medical history, clinical variables, and Digit Symbol Substitution Test (DSST) scores from cycles 1999, 2001, 2011, and 2013 were extracted. The DSST assesses visuomotor, attention, and processing speed, and in prior studies was found to be strongly associated with vascular-related cognitive impairment. Survey-weighted regression models adjusting for age, race, gender, education, vascular risk factors and history of stroke were used to generate nationally representative estimates. Results: 6,651 study participants representing 51,841,223 adults ≥ 60 years were included, 7% with prevalent HF. Participants with HF were older (73.8 vs 70.0, P <0.001) but there were no significant differences in the proportion of women or in the racial/ethnic makeup of participants with and without HF. Stroke was 3 times more prevalent in the HF population (20.1% vs 6.5%, P <0.001). Individuals with HF had significantly lower DSST scores (mean 39.6 [0.97] vs 50.2 [0.45], P<0.001). We found a significant interaction between HF and sex (P <0.10). Among men, prevalent HF was associated with a 2.95 (95% CI: -4.75, -1.16) decrease in DSST. In women, larger decreases in cognition were observed (-5.18, 95% CI: -7.34, -3.02). There was no interaction between heart failure and race. Conclusions: Women with HF experience greater levels of HF-related cognitive impairment. These differences may be a consequence of known differential rates of early HF detection and disease maintenance across genders.

ECG Signal-Enabled Automatic Diagnosis Technology of Heart Failure.

Usually, heart failure occurs when heart-related diseases are developed and continue to deteriorate veins and arteries. Heart failure is the final stage of heart disease, and it has become an important medical problem, particularly among the aging population. In medical diagnosis and treatment, the examination of heart failure contains various indicators such as electrocardiogram. It is one of the relatively common ways to collect heart failure or attack related information and is also used as a reference indicator for doctors. Electrocardiogram indicates the potential activity of patient's heart and directly reflects the changes in it. In this paper, a deep learning-based diagnosis system is presented for the early detection of heart failure particularly in elderly patients. For this purpose, we have used two datasets, Physio-Bank and MIMIC-III, which are publicly available, to extract ECG signals and thoroughly examine heart failure. Initially, a heart failure diagnosis model which is based on attention convolutional neural network (CBAM-CNN) is proposed to automatically extract features. Additionally, attention module adaptively learns the characteristics of local features and efficiently extracts the complex features of the ECG signal to perform classification diagnosis. To verify the exceptional performance of the proposed network model, various experiments were carried out in the realistic environment of hospitals. Influence of signal preprocessing on the performance of model is also discussed. These results show that the proposed CBAM-CNN model performance is better for both classifications of ECG signals. Likewise, the CBAM-CNN model is sensitive to noise, and its accuracy is effectively improved as soon as signal is refined.

The Analysis Performance of Heart Failure Classification by Using Machine Learning Techniques

Heart failure means that the heart is not pumping well as normal as it should be. A congestive heart failure is a form of heart failure that involves seeking timely medical care, although the two terms are sometimes used interchangeably. Heart failure happens when the heart muscle does not pump blood as well as it can, often referred to as congestive heart failure. Some disorders, such as heart's narrowed arteries (coronary artery disease) or high blood pressure, eventually make the heart too weak or rigid to fill and pump effectively. Early detection of heart failure by using data mining techniques has gained popularity among researchers. This research uses some classification techniques for heart failure classification from medical data. This research analyzed the performance of some classification algorithms, namely Support Vector Machine (SVM), Decision Forest (DF), and Boosted Decision Tree (BDT), to classify accurately heart failure risk data as input. The best algorithm among the three is discovered for heart failure classification at the end of this research.

The Comprehensive Machine Learning Analytics for Heart Failure.

Background: Early detection of heart failure is the basis for better medical treatment and prognosis. Over the last decades, both prevalence and incidence rates of heart failure have increased worldwide, resulting in a significant global public health issue. However, an early diagnosis is not an easy task because symptoms of heart failure are usually non-specific. Therefore, this study aims to develop a risk prediction model for incident heart failure through a machine learning-based predictive model. Although African Americans have a higher risk of incident heart failure among all populations, few studies have developed a heart failure risk prediction model for African Americans. Methods: This research implemented the Least Absolute Shrinkage and Selection Operator (LASSO) logistic regression, support vector machine, random forest, and Extreme Gradient Boosting (XGBoost) to establish the Jackson Heart Study’s predictive model. In the analysis of real data, missing data are problematic when building a predictive model. Here, we evaluate predictors’ inclusion with various missing rates and different missing imputation strategies to discover the optimal analytics. Results: According to hundreds of models that we examined, the best predictive model was the XGBoost that included variables with a missing rate of less than 30 percent, and we imputed missing values by non-parametric random forest imputation. The optimal XGBoost machine demonstrated an Area Under Curve (AUC) of 0.8409 to predict heart failure for the Jackson Heart Study. Conclusion: This research identifies variations of diabetes medication as the most crucial risk factor for heart failure compared to the complete cases approach that failed to discover this phenomenon.

Early diagnosis of myocardial fibrosis in patients with epicardial obesity

It has been proven that about half of patients with heart failure (HF) have a preserved the left ventricle (LV) ejection fraction (EF), which complicates early detection of HF. Currently, there is a search for non-invasive methods for diagnosing myocardial fibrosis at the preclinical heart failure to prevent its progression and the appearance of clinical symptoms.Aim. To study the relationship of LV mechanics with the level of serum myocardial fibrosis markers in patients with epicardial obesity (EO).Material and methods. The study included 110 men with general obesity. Depending on echocardiographic data, the patients were divided into 2 groups: EO (+) with epicardial fat thickness (EFT) ≥7 mm (n=70); EO (-) with EFT <7 mm (n=40). All patients were studied for serum profibrotic markers (MMP-3, collagen I, collagen III, TGF-β, VEGFA, PICP) using enzyme-linked immunosorbent assay. Speckle-tracking echocardiography was used to study LV mechanics (LV twisting, LV twisting rate, time to peak twist, LV untwisting rate, time to peak untwist). The exclusion criteria were the presence of coronary artery disease, hypertension, type 2 diabetes.Results. In the group of patients with EO (+), a significant increase in the level of all studied profibrotic markers was revealed. According to the results of speckletracking echocardiography in the EO (+) group, an increase in the LV untwisting rate to -128,31 (-142,0; -118,0) deg/s-1 (p=0,002) and an increase in the time to peak untwist to 476,44 (510,0; 411,0) ms compared to the EO (-) group (p=0,03). A weak significant effect of EFT on LV untwisting rate was revealed in the EO (+) group (r=0,24; p=0,04). In addition, a significant relationship was found between the LV untwisting rate and markers of myocardial fibrosis: MMP-3 (r=0,21; p=0,04) and type III collagen (r=0,26; p=0,03).Conclusion. Thus, the obtained data suggest that patients with EO have signs of preclinical LV diastolic dysfunction, which are characterized by an increase in LV untwisting rate and level of serum profibrotic factors.

Research Progress on the Relationship between Serum Cystatin C and Heart Failure

Heart failure (HF) is caused by many factors that lead to myocardial damage, myocardial remodeling of ventricular overload, dysfunction of cardiac diastolic and systolic function, and insufficient blood volume of cardiac circulation. The process of myocardial remodeling is accompanied by myocardial cell ischemia, necrosis, apoptosis, progressive interstitial cell fibrosis and other pathological processes, which is a clinical syndrome in the final stage of various heart diseases, it has high morbidity and mortality, which is a serious threat to human health [1]. Early detection of HF and effective treatment to reduce its morbidity and mortality is particularly important. In recent years, it has been found that Cystatin C (Cys C) is closely related to the occurrence, development and prognosis of HF, and may be an independent predictor of HF. This article reviews the research progress on the relationship between serum Cys C and HF, as well as the pathogenesis, diagnosis, severity and prognosis of Cys C in HF.

Research Progress on the Relationship between Serum Cystatin C and Heart Failure

Heart failure (HF) is caused by many factors that lead to myocardial damage, myocardial remodeling of ventricular overload, dysfunction of cardiac diastolic and systolic function, and insufficient blood volume of cardiac circulation. The process of myocardial remodeling is accompanied by myocardial cell ischemia, necrosis, apoptosis, progressive interstitial cell fibrosis and other pathological processes, which is a clinical syndrome in the final stage of various heart diseases, it has high morbidity and mortality, which is a serious threat to human health [1]. Early detection of HF and effective treatment to reduce its morbidity and mortality is particularly important. In recent years, it has been found that Cystatin C (Cys C) is closely related to the occurrence, development and prognosis of HF, and may be an independent predictor of HF. This article reviews the research progress on the relationship between serum Cys C and HF, as well as the pathogenesis, diagnosis, severity and prognosis of Cys C in HF

HeartPredict algorithm: Machine intelligence for the early detection of heart failure

Heart failure (HF) is among the leading causes of death. Its prevalence is increasing dramatically causing considerable healthcare costs as well. Remote patient monitoring (RPM) is one of the solutions to enhance patient well-being. Taking advantage of new advances in artificial intelligence and RPM digital platforms, we propose HeartPredict: a novel machine learning algorithm for early detection of HF episodes and associated unplanned hospitalizations. The algorithm relies on a telemonitoring dataset from the largest European clinical study on HF. It uses balanced random forests on significant features extracted from patient weight time series, symptoms and socio-demographics. We benchmark HeartPredict with rules from medical guidelines and state of the art machine learning models. HeartPredict has better performance in terms of sensitivity (72% vs. guidelines: 53%) and specificity (94% vs. guidelines: 84%), with AUROC = 0.8. In addition, we introduce precocity as a new criterion to evaluate the ability of the algorithm to detect early HF risks, and therefore, enabling proactive medical actions. Finally, we show that HeartPredict prediction scores are consistent with HF risk levels, thereby limiting the risk of non-detection for patients.

Residual Learning: A New Paradigm to Improve Deep Learning-Based Segmentation of the Left Ventricle in Magnetic Resonance Imaging Cardiac Images.

Background:Recently, magnetic resonance imaging (MRI) has become a useful tool for the early detection of heart failure. A vital step of this process is a valid measurement of the left ventricle's properties, which seriously depends on the accurate segmentation of the heart in captured images. Although various schemes have been tested for this segmentation so far, the latest proposed methods have used the concept of deep learning to estimate the range of the left ventricle in cardiac MRI images. While deep learning methods can lead to better results than their classical alternatives, but unfortunately, the gradient vanishing and exploding problems may hamper their efficiency for the accurate segmentation of the left ventricle in MRI heart images.Methods:In this article, a new concept called residual learning is utilized to improve the performance of deep learning schemes against gradient vanishing problems. For this purpose, the Residual Network of Residual Network (i.e., Residual of Residual) substructure is utilized inside the main deep learning architecture (e.g., Unet), which provides more significant detection indexes.Results and Conclusion:The proposed method's performances and its alternatives were evaluated on Sunnybrook Cardiac Data as a reliable dataset in the left ventricle segmentation. The results show that the detection parameters are improved at least by 5%, 3.5%, 8.1%, and 11.4% compared to its deep alternatives in terms of Jaccard, Dice, precision, and false-positive rate indexes, respectively. These improvements were made when the recall parameter was reduced to a negligible value (i.e., approximately 1%). Overall, the proposed method can be used as a suitable tool for more accurate detection of the left ventricle in MRI images.

Artificial intelligence in the diagnosis and detection of heart failure: the past, present, and future.

Artificial Intelligence (AI) performs human intelligence-dependant tasks using tools such as Machine Learning, and its subtype Deep Learning. AI has incorporated itself in the field of cardiovascular medicine, and increasingly employed to revolutionize diagnosis, treatment, risk prediction, clinical care, and drug discovery. Heart failure has a high prevalence, and mortality rate following hospitalization being 10.4% at 30-days, 22% at 1-year, and 42.3% at 5-years. Early detection of heart failure is of vital importance in shaping the medical, and surgical interventions specific to HF patients. This has been accomplished with the advent of Neural Network (NN) model, the accuracy of which has proven to be 85%. AI can be of tremendous help in analyzing raw image data from cardiac imaging techniques (such as echocardiography, computed tomography, cardiac MRI amongst others) and electrocardiogram recordings through incorporation of an algorithm. The use of decision trees by Rough Sets (RS), and logistic regression (LR) methods utilized to construct decision-making model to diagnose congestive heart failure, and role of AI in early detection of future mortality and destabilization episodes has played a vital role in optimizing cardiovascular disease outcomes. The review highlights the major achievements of AI in recent years that has radically changed nearly all areas of HF prevention, diagnosis, and management.

Artificial intelligence assessment for early detection of heart failure with preserved ejection fraction based on electrocardiographic features.

Although heart failure with preserved ejection fraction (HFpEF) is a rapidly emerging global health problem, an adequate tool to screen it reliably and economically does not exist. We developed an interpretable deep learning model (DLM) using electrocardiography (ECG) and validated its performance. This retrospective cohort study included two hospitals. 34 103 patients who underwent echocardiography and ECG within 1 week and indicated normal left ventricular systolic function were included in this study. A DLM based on an ensemble neural network was developed using 32 671 ECGs of 20 169 patients. The internal validation included 1979 ECGs of 1979 patients. Furthermore, we conducted an external validation with 11 955 ECGs of 11 955 patients from another hospital. The endpoint was to detect HFpEF. During the internal and external validation, the area under the receiver operating characteristic curves of a DLM using 12-lead ECG for detecting HFpEF were 0.866 (95% confidence interval 0.850-0.883) and 0.869 (0.860-0.877), respectively. In the 1412 individuals without HFpEF at initial echocardiography, patients whose DLM was defined as having a higher risk had a significantly higher chance of developing HFpEF than those in the low-risk group (33.6% vs. 8.4%, P < 0.001). Sensitivity map showed that the DLM focused on the QRS complex and T-wave. The DLM demonstrated high performance for HFpEF detection using not only a 12-lead ECG but also 6- single-lead ECG. These results suggest that HFpEF can be screened using conventional ECG devices and diverse life-type ECG machines employing the DLM, thereby preventing disease progression.

Multiple Feature Selection Strategies Identified Novel Cardiac Gene Expression Signature for Heart Failure.

Heart failure (HF) is a serious condition in which the support of blood pumped by the heart is insufficient to meet the demands of body at a normal cardiac filling pressure. Approximately 26 million patients worldwide are suffering from heart failure and about 17–45% of patients with heart failure die within 1-year, and the majority die within 5-years admitted to a hospital. The molecular mechanisms underlying the progression of heart failure have been poorly studied. We compared the gene expression profiles between patients with heart failure (n = 177) and without heart failure (n = 136) using multiple feature selection strategies and identified 38 HF signature genes. The support vector machine (SVM) classifier based on these 38 genes evaluated with leave-one-out cross validation (LOOCV) achieved great performance with sensitivity of 0.983 and specificity of 0.963. The network analysis suggested that the hub gene SMOC2 may play important roles in HF. Other genes, such as FCN3, HMGN2, and SERPINA3, also showed great promises. Our results can facilitate the early detection of heart failure and can reveal its molecular mechanisms.

Quantitative 4D Flow CMR analysis of intracardiac blood flow energetics in ischemic cardiomyopathy patients

Abstract Background Ischemic cardiomyopathy (ICM) is often associated with negative LV remodelling after myocardial infarction, sometimes resulting in impaired LV function and dilation (iDCM). 4D Flow CMR has been recently exploited to assess intracardiac hemodynamic changes in presence of LV remodelling. Purpose To quantify 4D Flow intracardiac kinetic energy (KE) and viscous energy loss (EL) and investigate their relation with LV dysfunction and remodelling. Methods Patients with prior anterior myocardial infarction underwent a CMR study with 4D Flow sequences acquisition; they were divided into ICM (n=10) and iDCM (n=10, EDV&amp;gt;208 ml and EF&amp;lt;40%). 10 controls were used for comparison. LV was semi-automatically segmented using short axis CMR stacks and co-registered with 4D Flow. Global KE and EL were computed over the cardiac cycle. NT-proBNP measurements were correlated with average and peak values, during systole and diastole. Results Both LV volume and EF significantly differ (P&amp;lt;0.0001) between iDCM (EDV=294±56 ml, EF=24±8%), ICM (EDV=181±32 ml, EF=34±6%) and controls (EDV=124±29 ml, EF=72±5%). If compared to controls, both ICM and iDCM showed significantly lower KE (P≤0.0008); though lower than controls, EL was higher in iDCM than ICM. Within the iDCM subgroup, diastolic mean KE and peak EL reported good inverse correlation with NT-proBNP (r=−0.75 and r=−0.69, respectively). EL indexed (ELI) to average KE during systole was higher in the entire ischemic group as compared to controls (ELI(ischemic) = 0.17 vs. ELI(controls) = 0.10, P=0.0054). Conclusions 4D Flow analyses effectively mapped post-ischemic LV energetic changes, highlighting the disproportionate intraventricular EL relative to produced KE; preliminary good correlation between LV energetic changes and NT-proBNP will deserve further investigation in order to contribute to early detection of heart failure. Funding Acknowledgement Type of funding source: Public grant(s) – National budget only. Main funding source(s): Italian Ministry of Health

Особенности выявления хронической сердечной недостаточности при хронической обструктивной болезни легких

Особенности выявления хронической сердечной недостаточности при хронической обструктивной болезни легких

A Structured Measurement of Highly Synchronous Real-Time Ballistocardiography Signal Data of Heart Failure Patients.

Ballistocardiography (BCG) has gained more attention due to the fundamental goal of medical intervention in diagnostics and follow-up. BCG is particularly suitable for the study of heart failure, which a recent study has shown. The results of this working group shall be validated and reproduced with another study trial. Therefore, acceleration sensor prototypes will be placed on various parts of the patient's body and be connected to a computer unit, which allows a high data quality and high signal resolution. A temporal shift of only 20 ns ensures real-time measurement of BCG parameters. The reference measurement will be done with a 12-channel ECG. The study will include patients with heart failure. All conducted tests take place as part of the diagnostic-therapeutic routine. The only change in the procedure concerns the additional equipment with the measuring sensors. The results will be the validation of the data from the other working group, as well as the information about the choice of sensors and clock frequency, the measuring points and the needed features for early detection of heart failure in BCG signals.

Early Detection of Heart Failure With Reduced Ejection Fraction Using Perioperative Data Among Noncardiac Surgical Patients: A Machine-Learning Approach.

Heart failure with reduced ejection fraction (HFrEF) is a condition imposing significant health care burden. Given its syndromic nature and often insidious onset, the diagnosis may not be made until clinical manifestations prompt further evaluation. Detecting HFrEF in precursor stages could allow for early initiation of treatments to modify disease progression. Granular data collected during the perioperative period may represent an underutilized method for improving the diagnosis of HFrEF. We hypothesized that patients ultimately diagnosed with HFrEF following surgery can be identified via machine-learning approaches using pre- and intraoperative data. Perioperative data were reviewed from adult patients undergoing general anesthesia for major surgical procedures at an academic quaternary care center between 2010 and 2016. Patients with known HFrEF, heart failure with preserved ejection fraction, preoperative critical illness, or undergoing cardiac, cardiology, or electrophysiologic procedures were excluded. Patients were classified as healthy controls or undiagnosed HFrEF. Undiagnosed HFrEF was defined as lacking a HFrEF diagnosis preoperatively but establishing a diagnosis within 730 days postoperatively. Undiagnosed HFrEF patients were adjudicated by expert clinician review, excluding cases for which HFrEF was secondary to a perioperative triggering event, or any event not associated with HFrEF natural disease progression. Machine-learning models, including L1 regularized logistic regression, random forest, and extreme gradient boosting were developed to detect undiagnosed HFrEF, using perioperative data including 628 preoperative and 1195 intraoperative features. Training/validation and test datasets were used with parameter tuning. Test set model performance was evaluated using area under the receiver operating characteristic curve (AUROC), positive predictive value, and other standard metrics. Among 67,697 cases analyzed, 279 (0.41%) patients had undiagnosed HFrEF. The AUROC for the logistic regression model was 0.869 (95% confidence interval, 0.829-0.911), 0.872 (0.836-0.909) for the random forest model, and 0.873 (0.833-0.913) for the extreme gradient boosting model. The corresponding positive predictive values were 1.69% (1.06%-2.32%), 1.42% (0.85%-1.98%), and 1.78% (1.15%-2.40%), respectively. Machine-learning models leveraging perioperative data can detect undiagnosed HFrEF with good performance. However, the low prevalence of the disease results in a low positive predictive value, and for clinically meaningful sensitivity thresholds to be actionable, confirmatory testing with high specificity (eg, echocardiography or cardiac biomarkers) would be required following model detection. Future studies are necessary to externally validate algorithm performance at additional centers and explore the feasibility of embedding algorithms into the perioperative electronic health record for clinician use in real time.

Evaluation of ST2 as an early marker of heart failure with a preserved left ventricular ejection fraction in patients with persistent atrial fibrillation

Aim. To assess the diagnostic value of soluble ST2 (sST2) for early detection of heart failure with a preserved ejection fraction (HFpEF) in patients with persistent atrial fibrillation (AF).Material and methods. A total of 165 patients hospitalized with AF paroxysm were examined. The inclusion criteria were the persistent AF and LVEF &gt;50% according to echocardiography. A cohort of 60 patients with persistent AF and preserved LVEF was formed. Patients were divided into 2 equal groups of 30 people depending on the HF presence, assessed on the basis of LV diastolic dysfunction with or without left atrial enlargement &gt;34 ml/m2 or left ventricular mass index &gt;110 g/m2 in men and 95 g/m2 in women, as well as an increase in the N-terminal prohormone of brain natriuretic peptide (NT-proBNP) &gt;125 pg/ml. LVEF was calculated using the Simpson method; assessment of LV diastolic function was carried out with determination of transmitral flow velocity characteristics and visualization of mitral annulus motion. The NT-proBNP concentration was determined in blood serum using the Biomedica Group reagent (Austria), sST2 — using Presage ST2 (Critical Diagnostics, USA) by enzyme-linked immunosorbent assay on an Immulite 1000 analyzer (DPC, USA).Results. In patients with HFpEF and persistent AF, the correlation analysis showed a direct strong relationship between NT-proBNP and sST2 (r=0,726; p&lt;0,05). To assess sST2 as a diagnostic criterion for HF, ROC curve was constructed. It was shown that with sST2 ≥16 ng/ml (AUC=0,89), the sensitivity of the method was 80%, the specificity — 83%.Conclusion. In patients with a persistent AF, serum sST2 concentration ≥16 ng/ml can be used as an alternative to the NT-proBNP criterion for early diagnosis of HFpEF.

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.