What are the costs associated with diagnosing heart failure using traditional methods?

BackgroundIdentifying heart failure patients most likely to suffer poor outcomes is an essential part of delivering interventions to those most likely to benefit. We sought a comprehensive account of heart failure events and their cumulative economic burden by examining patient characteristics that predict increased cost or poor outcomes.MethodsWe collected electronic medical data from members of a large HMO who had a heart failure diagnosis and an echocardiogram from 1999–2004, and followed them for one year. We examined the role of demographics, clinical and laboratory findings, comorbid disease and whether the heart failure was incident, as well as mortality. We used regression methods appropriate for censored cost data.ResultsOf the 4,696 patients, 8% were incident. Several diseases were associated with significantly higher and economically relevant cost changes, including atrial fibrillation (15% higher), coronary artery disease (14% higher), chronic lung disease (29% higher), depression (36% higher), diabetes (38% higher) and hyperlipidemia (21% higher). Some factors were associated with costs in a counterintuitive fashion (i.e. lower costs in the presence of the factor) including age, ejection fraction and anemia. But anemia and ejection fraction were also associated with a higher death rate.ConclusionsClose control of factors that are independently associated with higher cost or poor outcomes may be important for disease management. Analysis of costs in a disease like heart failure that has a high death rate underscores the need for economic methods to consider how mortality should best be considered in costing studies.

Background:The incidence of heart failure is continuing to rise, and the mortality rate is high. Chest X-ray (CXR) has irreplaceable advantages in diagnosing heart failure, such as fast, low risk, and cheap. However, excessive CXR images place a huge burden on physicians and create data imbalance problems. Traditional methods, such as random under-sampling, are used to solve the problems. However, the under-sampling method can destroy the integrity of the data distribution. So, it is necessary to have a method that can address data imbalance problems and assist overburdened healthcare systems. Objective:This study establishes an automatic heart failure diagnosis system based on deep learning from imbalance datasets. Methods:To address the data imbalance problem based on the publicly available CheXpert dataset, this study proposes a method combining under-sampling and instance selection to ensure the integrity of the data distribution. To help physicians better treat heart failure, this study proposes an end-to-end multi-level classification method to diagnose the specific causes of heart failure. Results:On the testing set, our method improves the average accuracy by 3.78% compared to the traditional random under-sampling method, and the accuracy of our end-to-end multi-class classification experiment is 84.44%. Conclusions:The heart failure automated diagnostic system is more efficient and accurate in diagnosing heart failure compared to state-of-the-art methods.

Diagnosing heart failure is often a challenge for the healthcare providers due to it's non-specific and usually subtle physical presentations. The outcomes for treatment are strongly related to the stage of the disease. Considering the importance of early and accurate diagnosis, it is important to have an easy, inexpensive, non-invasive, reliable and reproducible method for diagnosis of heart failure. Recent advancement in radiology and cardiology are supporting the emerging technique of lung ultrasound through B-line evaluation for identifying extravascular lung water. To establish lung ultrasound as an easy, inexpensive, non-invasive, reliable and reproducible method for diagnosing Acute Decompensated Heart Failure (ADHF) in emergency department. The study was a cross-sectional, prospective, observational, diagnostic validation study of lung ultrasound for diagnosis of acute heart failure in an emergency department and was performed at Amrita Institute of Medical Science, Kochi, Kerala, India. A total of 42 patients presenting with symptoms suggestive of acute decompensated heart failure were evaluated by plasma B-type Natriuretic Peptide (BNP), Echocardiography (ECHO) and X-ray. Lung ultrasound was done to look for the presence of B-lines. Sensitivity, specificity and predictive value of diagnostic modalities were calculated using Mc Nemar's Chi-square test for the presence and absence of heart failure. Lung ultrasound showed a sensitivity of 91.9% and a specificity of 100% in diagnosing acute heart failure comparable to plasma BNP which had a sensitivity of 100% and a specificity of 60%. It was also superior to other methods of diagnosing ADHF namely X-ray and ECHO and showed a good association. Lung ultrasound and its use to detect ultrasonographic B-lines is an early, sensitive and an equally accurate predictor of ADHF in the emergency setting as compared to BNP.

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.

Heart failure is a clinical syndrome associated with high rates of morbidity and mortality and associated healthcare costs. The burden of heart failure is likely to increase with time, but effective treatments that improve quality of life and survival are available. Accurate and timely diagnosis is crucial to ensure patients receive appropriate treatment and avoid hospital admissions. However, diagnosing heart failure can be difficult as symptoms and signs commonly overlap with other conditions. A chest X-ray can be useful to identify evidence of heart failure or other lung pathology; however, a normal result does not rule out a diagnosis of heart failure. An electrocardiogram (ECG) is often abnormal in patients with heart failure, although up to 10% of patients may have a normal ECG. Natriuretic peptides are a useful biomarker for heart failure and a negative result can rule out the diagnosis. This can be helpful in determining who should be referred for echocardiogram. A new clinical-decision rule (CDR) could help clinicians to achieve a more timely and accurate diagnosis of heart failure.

AimsThe aim of this study is to characterize healthcare resource utilization and costs in patients with heart failure with reduced ejection fraction (HFrEF) following a worsening heart failure event.Methods and resultsThis was a retrospective observational cohort analysis. Patients with HFrEF were identified from the PINNACLE Registry and linked to a nationwide pharmacy and medical claims database. Worsening heart failure was defined as stable heart failure with a subsequent hospitalization and/or intravenous diuretic therapy. Healthcare resource use and costs in 2015 US dollars were analysed for dispensed prescriptions, outpatient encounters, and hospital encounters. Among 11 064 patients with HFrEF, 3087 (27.9%) experienced a worsening heart failure event during an average follow‐up of 973 days. During the first 30 days after the worsening event, 19.8% of patients had hospital readmissions with heart failure as the primary or secondary diagnosis. During that same time period, mean per patient heart failure‐related healthcare resource use included 1.3 prescriptions, 0.5 practitioner visits, and 0.5 hospital encounters (admissions, observations, or emergency care), for an average total medical cost of $8779 per patient including $5359 in heart failure‐related costs. During the first year following worsening heart failure onset, mean per patient total and heart failure‐related costs were $62 615 and $35 329, respectively.ConclusionsThe economic burden following a worsening heart failure event calls for further review of methods to prevent progressive disease, improve adherence to guideline‐directed therapy, and develop novel treatments and care strategies to moderate further progression.

The development of deep learning technology has enabled machines to achieve high-level accuracy in interpreting medical images. While many previous studies have examined the detection of pulmonary nodules in chest X-rays using deep learning, the application of this technology to heart failure remains rare. In this paper, we investigated the performance of a deep learning algorithm in terms of diagnosing heart failure using images obtained from chest X-rays. We used 952 chest X-ray images from a labeled database published by the National Institutes of Health. Two cardiologists verified and relabeled a total of 260 "normal" and 378 "heart failure" images, with the remainder being discarded because they had been incorrectly labeled. Data augmentation and transfer learning were used to obtain an accuracy of 82% in diagnosing heart failure using the chest X-ray images. Furthermore, heatmap imaging allowed us to visualize decisions made by the machine. Deep learning can thus help support the diagnosis of heart failure using chest X-ray images.

An enormous number of deaths occur every year as a result of heart disease, making it a major concern in world health. Improving patient outcomes and lowering death rates, early detection and correct diagnosis of cardiac disease play a key role. When the heart's arteries become blocked, oxygen-poor blood cannot reach the heart properly, resulting in coronary heart disease. Early detection of cardiac disease is viable because it reduces medical costs and potentially saves the patient's life. Recently presented methods have improved heart failure detection accuracy on testing data without sacrificing accuracy on training data, yet most of these algorithms are suffering from the issue of overfitting. Models that were created end up fitting the test data too well. In this study, we create a novel diagnostic system to address this issue, and the resulting system demonstrates high intelligence and excellent performance on both training and testing data. Machine learning (ML) algorithms have demonstrated promising potential in assisting healthcare professionals with timely and accurate diagnosis. In this paper, is based on supervised machine learning methods are decision tree (DT), random forest (RF), Support vector Machine (SVM), Principal Component Analysis(PCA). We compare their accuracy with each other by using bar plot.

Diagnosing heart failure is often difficult due to the non-specific nature of symptoms, which can be caused by a range of medical conditions. Natriuretic peptides (NPs) have been recognized as important biomarkers for diagnosing heart failure. This document from the Heart Failure Association examines the practical uses of N-terminal pro-B-type natriuretic peptide (NT-proBNP) in various clinical scenarios. The concentrations of NT-proBNP vary according to the patient profile and the clinical scenario, therefore values should be interpreted with caution to ensure appropriate diagnosis. Validated cut-points are provided to rule in or rule out acute heart failure in the emergency department and to diagnose de novo heart failure in the outpatient setting. We also coin the concept of 'heart stress' when NT-proBNP levels are elevated in an asymptomatic patient with risk factors for heart failure (i.e. diabetes, hypertension, coronary artery disease), underlying the development of cardiac dysfunction and further increased risk. We propose a simple acronym for healthcare professionals and patients, FIND-HF, which serves as a prompt to consider heart failure: Fatigue, Increased water accumulation, Natriuretic peptide testing, and Dyspnoea. Use of this acronym would enable the early diagnosis of heart failure. Overall, understanding and utilizing NT-proBNP levels will lead to earlier and more accurate diagnoses of heart failure ultimately improving patient outcomes and reducing healthcare costs.

IntroductionHeart failure (HF) is a major public health concern, prevalent in millions of people worldwide. The most widely-used HF diagnostic method, echocardiography, incurs a decreased diagnostic accuracy for heart failure disease progression when patients are asymptomatic compared to those who are symptomatic. The purpose of this study is to conduct a cost-effectiveness analysis of heart failure diagnosis comparing echocardiography to a novel myocardial strain assessment (Fast-SENC), which utilizes cardiac-tagged magnetic resonance imaging.MethodsWe develop two models, one from the perspective of payers and one from the perspective of purchasers (hospitals). The payer model is a cost-effectiveness model composed of a 1-year short-term model and a lifetime horizon model. The hospital/purchaser model is a cost impact model where expected costs are calculated by multiplying cost estimates of each subcomponent by the accompanying probability.ResultsThe payer model shows lower healthcare costs for Fast-SENC in comparison to ECHO ($24,647 vs. $39,097) and a lifetime savings of 37% when utilizing Fast-SENC. Similarly, the hospital model revealed that the total cost per HF patient visit is $184 for ECHO and $209 for Fast-SENC, which results in hospital contribution margins of $81 and $115, respectively.ConclusionsFast-SENC is associated with higher quality-adjusted life years and lower accumulated expected healthcare costs than echocardiogram patients. Fast-SENC also shows a significant short-term and lifetime cost-savings difference and a higher hospital contribution margin when compared to echocardiography. These results suggest that early discovery of heart failure with methods like Fast-SENC can be cost-effective when followed by the appropriate treatment.