Fewer Worsening Heart Failure Events With HeartLogic on top of Standard Care: a Propensity-Matched Cohort Analysis.

BACKGROUND: The implantable cardiac defibrillator-based HeartLogic algorithm aims to detect impending fluid retention in patients with heart failure (HF). Studies show that HeartLogic is safe to integrate into clinical practice. The current study investigates whether HeartLogic provides clinical benefit on top of standard care and device telemonitoring in patients with HF. METHODS: A multicenter, retrospective, propensity-matched cohort analysis was performed in patients with HF and implantable cardiac defibrillators, and it compared HeartLogic to conventional telemonitoring. The primary endpoint was the number of worsening HF events. Hospitalizations and ambulatory visits due to HF were also evaluated. RESULTS: Propensity score matching yielded 127 pairs (median age 68 years, 80% male). Worsening HF events occurred more frequently in the control group (2; IQR 0-4) compared to the HeartLogic group (1; IQR 0-3; P = 0.004). The number of HF hospitalization days was higher in controls than in the HeartLogic group (8; IQR 5-12 vs 5; IQR 2-7; P = 0.023), and ambulatory visits for diuretic escalation were more frequent in the control group than in the HeartLogic group (2; IQR 0-3 vs 1; IQR 0-2; P = 0.0001). CONCLUSION: Integrating the HeartLogic algorithm in a well-equipped HF care path on top of standard care is associated with fewer worsening HF events and shorter duration of fluid retention-related hospitalizations.

Central Arterial Dynamic Evaluation from Peripheral Blood Pressure Waveforms Using CycleGAN: An In Silico Approach.

Arterial stiffness is a major condition related to many cardiovascular diseases. Traditional approaches in the assessment of arterial stiffness supported by machine learning techniques are limited to the pulse wave velocity (PWV) estimation based on pressure signals from the peripheral arteries. Nevertheless, arterial stiffness can be assessed based on the pressure-strain relationship by analyzing its hysteresis loop. In this work, the capacity of deep learning models based on generative adversarial networks (GANs) to transfer pressure signals from the peripheral arterial region to pressure and area signals located in the central arterial region is explored. The studied signals are from a public and validated virtual database. Compared to other works in which the assessment of arterial stiffness was performed via PWV, in the present work the pressure-strain hysteresis loop is reconstructed and evaluated in terms of classical machine learning metrics and clinical parameters. Least-square GAN (LSGAN) and Wasserstein GAN with gradient penalty (WGAN-GP) adversarial losses are compared, yielding better results with LSGAN. LSGAN mean ± standard deviation of error for pressure and area pulse waveforms are 0.8 ± 0.4 mmHg and 0.1 ± 0.1 cm2, respectively. Regarding the pressure-strain elastic modulus, it is achieved a mean absolute percentage error of 6.5 ± 5.1%. GAN-based deep learning models can recover the pressure-strain loop of central arteries while observing pressure signals from peripheral arteries.

The Race for COVID-19 Vaccines: Accelerating Innovation, Fair Allocation and Distribution.

Racing to develop and distribute a vaccine against COVID-19 has proven to be a challenging endeavor. Not only has there been the enormous scientific-technical challenge of developing the world's first vaccines against a coronavirus, the subsequent ethical issues involved in vaccine allocation have been equally complex. This contribution focuses on the policy and strategic aspects of accelerating innovation, fair allocation, and distribution. We take a holistic approach to describing the various tasks and how they are interconnected. Through comparative analysis we explore the issues through the lens of multiple stakeholders: i.e., public health authorities, governments of industrialized and developing countries, and industry. At each step of the consecutive process-from vaccine development to delivery-common ground has to be found between global community stakeholders, to move the project to the next stage, until completion: (1) accelerated innovation, (2) centralized purchasing, (3) selecting suppliers, (4) equitable allocation, (5) global access and affordability, (6) compulsory vs. voluntary licensing, and (7) a universal pandemic treaty. Conclusions: Public-private partnership is essential with regards to inventing and allocating new vaccines to fight infectious disease pandemics. The nature of pandemics requires collaboration at both the national and international levels. Seven important lessons have been identified that we can learn from based on the experience gathered during the COVID-19 pandemic. We trust that these findings will be helpful in drafting preparedness guidelines and a global pandemic treaty to manage future pandemics.

Economic evaluation of an ultra-high density mapping system compared to non-ultra-high density mapping systems for radiofrequency catheter ablation procedures in patients with atrial fibrillation.

PURPOSE: Advanced non-fluoroscopic mapping systems for radiofrequency ablation (RFA) have shown to be an effective treatment of atrial fibrillation. This study analyzes the resource usage and subsequent costs associated with the implementation of an ultra-high density mapping system (UHDMS) compared to non-ultra-high density mapping systems (NUHDMS). METHODS: This retrospective observational study included 120 patients (18 years or older) with paroxysmal or persistent atrial fibrillation who underwent RFA for de novo pulmonary vein isolation guided either by an UHDMS (n=63) or NUHDMS (n=57) for their index procedure. We compared patient characteristics, short- and long-term procedural outcomes, resource usage, and clinical outcomes followed up to 16 months between the two treatment groups. The cost analysis was conducted from the perspective of a single center in Spain (Clinica Universidad de Navarra). RESULTS: Neither baseline patient characteristics nor complication rate differed between groups. Repeat RFAs following recurrent arrhythmia at 16 months was lower in the UHDMS patient group than in the NUHDMS group (6 vs. 14, respectively; P=0.027). The average total cost per patient was €1,600 lower in the UHDMS group, compared to the NUHDMS group (€11,061 and €12,661, respectively; P=0.03). CONCLUSION: In patients treated with an NUHDMS, 25% had a repeat ablation for recurrent arrhythmia, whereas only 9% of patients treated with a UHDMS had one (61% relative risk reduction), resulting in an average cost saving per patient of €1,600.

A Delineator for Arterial Blood Pressure Waveform Analysis Based on a Deep Learning Technique.

A deep learning technique based on semantic segmentation was implemented into the blood pressure detection points field. Two models were trained and evaluated in terms of a reference detector. The proposed methodology outperforms the reference detector in two of the three classic benchmarks and on signals from a public database that were modified with realistic test maneuvers and artifacts. Both models differentiate regions with valid information and artifacts. So far, no other delineator had shown this capacity.

Blood Pressure Morphology Assessment from Photoplethysmogram and Demographic Information Using Deep Learning with Attention Mechanism.

Arterial blood pressure (ABP) is an important vital sign from which it can be extracted valuable information about the subject's health. After studying its morphology it is possible to diagnose cardiovascular diseases such as hypertension, so ABP routine control is recommended. The most common method of controlling ABP is the cuff-based method, from which it is obtained only the systolic and diastolic blood pressure (SBP and DBP, respectively). This paper proposes a cuff-free method to estimate the morphology of the average ABP pulse (ABPM¯) through a deep learning model based on a seq2seq architecture with attention mechanism. It only needs raw photoplethysmogram signals (PPG) from the finger and includes the capacity to integrate both categorical and continuous demographic information (DI). The experiments were performed on more than 1100 subjects from the MIMIC database for which their corresponding age and gender were consulted. Without allowing the use of data from the same subjects to train and test, the mean absolute errors (MAE) were 6.57 ± 0.20 and 14.39 ± 0.42 mmHg for DBP and SBP, respectively. For ABPM¯, R correlation coefficient and the MAE were 0.98 ± 0.001 and 8.89 ± 0.10 mmHg. In summary, this methodology is capable of transforming PPG into an ABP pulse, which obtains better results when DI of the subjects is used, potentially useful in times when wireless devices are becoming more popular.

Clinical and economic impact of HeartLogic™ compared with standard care in heart failure patients.

AIMS: The implantable cardiac defibrillator/cardiac resynchronization therapy with defibrillator-based HeartLogic™ algorithm has recently been developed for early detection of impending decompensation in heart failure (HF) patients; but whether this novel algorithm can reduce HF hospitalizations has not been evaluated. We investigated if activation of the HeartLogic algorithm reduces the number of hospital admissions for decompensated HF in a 1 year post-activation period as compared with a 1 year pre-activation period. METHODS AND RESULTS: Heart failure patients with an implantable cardiac defibrillator/cardiac resynchronization therapy with defibrillator with the ability to activate HeartLogic and willingness to have remote device monitoring were included in this multicentre non-blinded single-arm trial with historical comparison. After a HeartLogic alert, the presence of HF symptoms and signs was evaluated. If there were two or more symptoms and signs apart from the HeartLogic alert, lifestyle advices were given and/or medication was adjusted. After activation of the algorithm, patients were followed for 1 year. HF events occurring in the 1 year prior to activation and in the 1 year after activation were compared. Of the 74 eligible patients (67.2 ± 10.3 years, 84% male), 68 patients completed the 1 year follow-up period. The total number of HF hospitalizations reduced from 27 in the pre-activation period to 7 in the post-activation period (P = 0.003). The number of patients hospitalized for HF declined from 21 to 7 (P = 0.005), and the hospitalization length of stay diminished from average 16 to 7 days (P = 0.079). Subgroup analysis showed similar results (P = 0.888) for patients receiving cardiac resynchronization therapy during the pre-activation period or not receiving cardiac resynchronization therapy, meaning that the effect of hospitalizations cannot solely be attributed to reverse remodelling. Subanalysis of a single-centre Belgian subpopulation showed important reductions in overall health economic costs (P = 0.025). CONCLUSION: Activation of the HeartLogic algorithm enables remote monitoring of HF patients, coincides with a significant reduction in hospitalizations for decompensated HF, and results in health economic benefits.

A report on the status of vaccination in Europe.

Vaccine policy, decision processes and outcomes vary widely across Europe. The objective was to map these factors across 16 European countries by assessing (A) national vaccination strategy and implementation, (B) attributes of healthcare vaccination systems, and (C) outcomes of universal mass vaccination (UMV) as a measure of how successful the vaccination policy is. A. Eleven countries use standardised assessment frameworks to inform vaccine recommendations. Only Sweden horizon scans new technologies, uses standard assessments, systematic literature and health economic reviews, and publishes its decision rationale. Time from European marketing authorisation to UMV implementation varies despite these standard frameworks. Paediatric UMV recommendations (generally government-funded) are relatively comparable, however only influenza vaccine is widely recommended for adults. B. Fourteen countries aim to report annually on national vaccine coverage rates (VCRs), as well as have target VCRs per vaccine across different age groups. Ten countries use either electronic immunisation records or a centralised registry for childhood vaccinations, and seven for other age group vaccinations. C. National VCRs for infant (primary diphtheria tetanus pertussis (DTP)), adolescent (human papillomavirus (HPV)) and older adult (seasonal influenza) UMV programmes found ranges of: 89.1% to 98.2% for DTP-containing vaccines, 5% to 85.9% for HPV vaccination, and 4.3% to 71.6% for influenza vaccine. Regarding reported disease incidence, a wide range was found across countries for measles, mumps and rubella (in children), and hepatitis B and invasive pneumococcal disease (in all ages). These findings reflect an individual approach to vaccination by country. High VCRs can be achieved, particularly for paediatric vaccinations, despite different approaches, targets and reporting systems; these are not replicated in vaccines for other age groups in the same country. Additional measures to improve VCRs across all age groups are needed and could benefit from greater harmonisation in target setting, vaccination data collection and sharing across EU countries.

Cost-Utility Analysis of Lipegfilgrastim Compared to Pegfilgrastim for the Prophylaxis of Chemotherapy-Induced Neutropenia in Patients with Stage II-IV Breast Cancer.

Background: Lipegfilgrastim (Lonquex®) has demonstrated to be non-inferior to pegfilgrastim (Neulasta®) in reducing the duration of severe neutropenia (SN) in patients with stage II-IV breast cancer. Compared to pegfilgrastim, lipegfilgrastim also demonstrated statistically significant lower time to ANC recovery in cycles 1-3, lower incidence of SN in cycle 2 and lower depth of absolute neutrophil count (ANC) nadir in cycles 2 and 3. The aim of this study was to quantify the cost utility of lipegfilgrastim compared to pegfilgrastim in stage II-IV breast cancer patients, taking the perspective of the Belgian payer over a lifetime horizon. Methods: Two Markov models were developed to track on- and post-chemotherapy related complications, including SN, febrile neutropenia (FN), chemotherapy dose delay, chemotherapy relative dose intensity of less than 85%, infection, death rates, and quality-adjusted life years (QALYs). Data on costs (2015 value) and effects were obtained from literature, national references, and complemented by a survey of clinical experts using a modified Delphi method. Both deterministic and probabilistic sensitivity analyses were carried out. Outcomes measures included costs, QALYs and life-years (LY). Results: At current equivalent price of €1,169, treatment with lipegfilgrastim was associated with overall costs of €9,845 vs. €10,208 for pegfilgrastim and overall QALYs of 13.977 vs. 13.925 for pegfilgrastim. Life expectancy was increased by 21 days (or 0.058 LY gained). The difference in costs stem from avoided infection, SN and FN cases in the lipegfilgrastim compared to the pegfilgrastim group. Similarly, the difference in QALYs was explained by the difference in the number of patients in the chemotherapy/G-CSF Markov state followed by infection and FN between lipegfilgrastim and pegfilgrastim. The probability of lipegfilgrastim to be cost-effective compared to pegfilgrastim was 68, 79, and 83% at the willingness-to-pay thresholds (WTP) of €10,000, €30,000 and €50,000 per QALY gained, respectively. At a WTP threshold of €30,000 per QALY gained, lipegfilgrastim was cost-effective up to €1,500 across all age bands and cancer stages, compared to the current price. Conclusions: Lipegfilgrastim is a cost-effective use of health care resources in patients with stage II-IV breast cancer.

Making sense of big data in health research: Towards an EU action plan.

Medicine and healthcare are undergoing profound changes. Whole-genome sequencing and high-resolution imaging technologies are key drivers of this rapid and crucial transformation. Technological innovation combined with automation and miniaturization has triggered an explosion in data production that will soon reach exabyte proportions. How are we going to deal with this exponential increase in data production? The potential of "big data" for improving health is enormous but, at the same time, we face a wide range of challenges to overcome urgently. Europe is very proud of its cultural diversity; however, exploitation of the data made available through advances in genomic medicine, imaging, and a wide range of mobile health applications or connected devices is hampered by numerous historical, technical, legal, and political barriers. European health systems and databases are diverse and fragmented. There is a lack of harmonization of data formats, processing, analysis, and data transfer, which leads to incompatibilities and lost opportunities. Legal frameworks for data sharing are evolving. Clinicians, researchers, and citizens need improved methods, tools, and training to generate, analyze, and query data effectively. Addressing these barriers will contribute to creating the European Single Market for health, which will improve health and healthcare for all Europeans.

Pharmaceutical portfolio management: global disease burden and corporate performance metrics.

BACKGROUND: Biopharmaceutical companies face multiple external pressures. Shareholders demand a profitable company while governments, nongovernmental third parties, and the public at large expect a commitment to improving health in developed and, in particular, emerging economies. Current industry commercial models are inadequate for assessing opportunities in emerging economies where disease and market data are highly limited. OBJECTIVE: The purpose of this article was to define a conceptual framework and build an analytic decision-making tool to assess and enhance a company's global portfolio while balancing its business needs with broader social expectations. METHODS: Through a case-study methodology, we explore the relationship between business and social parameters associated with pharmaceutical innovation in three distinct disease areas. The global burden of disease-based theoretical framework using disability-adjusted life-years provides an overview of the burden associated with particular diseases. The social return on investment is expressed as disability-adjusted life-years averted as a result of the particular pharmaceutical innovation. Simultaneously, the business return on investment captures the research and development costs and projects revenues in terms of a profitability index. CONCLUSIONS: The proposed framework can assist companies as they strive to meet the medical needs of populations around the world for decades to come.

Delivering the promise of the Decade of Vaccines: opportunities and challenges in the development of high quality new vaccines.

The Decade of Vaccines (DoV) initiative, launched in 2010, has as its mission "to extend, by 2020 and beyond, the full benefits of immunization to all people, regardless of where they are born, who they are, or where they live". Through their life-saving vaccines, the research-based vaccine companies represented by the International Federation of Pharmaceutical Manufacturers & Associations (IFPMA) and the Biotechnology Industry Organization (BIO) make a major contribution toward this vision. In this article, we begin by summarizing progress made over the past three decades in research and development (R&D) of new and future vaccines, and identify the opportunities and challenges faced by the research-based vaccine industry. We then review the Global Vaccine Action Plan (GVAP) and provide IFPMA and BIO consensus perspectives on its six strategic objectives. Finally, we identify policy measures to support R&D of, and access to, high-quality, innovative vaccines.

Equity in Pharmaceutical Pricing and Reimbursement: Crossing the Income Divide in Asia Pacific.

OBJECTIVES: The article takes a three-dimensional approach (triangulation) in defining international pricing policy for pharmaceuticals using cost-effectiveness analysis (CEA), willingness-to-pay (WTP) analysis, and ability-to-pay (ATP) analysis. It attempts to find a balance between the various economic methods of which some focus on effectiveness while others are geared toward incorporating equity in the equation. METHODS: A critical review of the first two established economic methods and their ability to evaluate not only "efficacy" but also "fairness" in pricing decisions identifies a gap in the latter. Therefore, a third analytic method is presented that measures the ATP based on a country's score in the human development index of the United Nations Development Program for 120 countries. This approach allows practicing differential pricing among and within countries. To refine this equity-driven pricing concept, two additional parameters can be added to the model: the Oxford "Multidimensional Poverty Index" and the "Out-of-Pocket" or "Self Pay" health expenditure as reported by the World Bank. RESULTS: There is no hierarchy between the above three pricing methods. Because one method provides further insight into the other, however, it is recommended to start with CEA followed by WTP analysis. These types of analysis are closely linked in that the first provides the CE ratio for the compound investigated and the other sets the anticipated ceiling threshold of the payer's WTP (in a particular country). The ATP method provides a supplementary "equity" check and facilitates the process of equity-based differential pricing. CONCLUSIONS: A third method should be used in conjunction with the standard CEA and WTP analysis that measures the ATP with the human development index as yardstick to provide sustainable and equitable access to medicines. We recommend that ATP analysis becomes an additional practice in policy decision making and in defining international pricing strategies for pharmaceuticals.

Optimal decision rule with class-selective rejection and performance constraints.

The problem of defining a decision rule which takes into account performance constraints and class-selective rejection is formalized in a general framework. In the proposed formulation, the problem is defined using three kinds of criteria. The first is the cost to be minimized, which defines the objective function, the second are the decision options, determined by the admissible assignment classes or subsets of classes, and the third are the performance constraints. The optimal decision rule within the statistical decision theory framework is obtained by solving the stated optimization problem. Two examples are provided to illustrate the formulation and the decision rule is obtained.

Gene-based multiclass cancer diagnosis with class-selective rejections.

Supervised learning of microarray data is receiving much attention in recent years. Multiclass cancer diagnosis, based on selected gene profiles, are used as adjunct of clinical diagnosis. However, supervised diagnosis may hinder patient care, add expense or confound a result. To avoid this misleading, a multiclass cancer diagnosis with class-selective rejection is proposed. It rejects some patients from one, some, or all classes in order to ensure a higher reliability while reducing time and expense costs. Moreover, this classifier takes into account asymmetric penalties dependent on each class and on each wrong or partially correct decision. It is based on nu-1-SVM coupled with its regularization path and minimizes a general loss function defined in the class-selective rejection scheme. The state of art multiclass algorithms can be considered as a particular case of the proposed algorithm where the number of decisions is given by the classes and the loss function is defined by the Bayesian risk. Two experiments are carried out in the Bayesian and the class selective rejection frameworks. Five genes selected datasets are used to assess the performance of the proposed method. Results are discussed and accuracies are compared with those computed by the Naive Bayes, Nearest Neighbor, Linear Perceptron, Multilayer Perceptron, and Support Vector Machines classifiers.

Epidemiology, etiology, and impact of traveler's diarrhea in Jamaica

CONTEXT: Traveler's diarrhea (TD) can incapacitate travelers. Characteristics of TD could be helpful in identifying individuals who might benefit from a vaccine against TD. OBJECTIVE: To determine epidemiology, etiology, and impact of TD in Jamaica. DESIGN: Two-armed, cross-sectional survey conducted between March 1996 and May 1997. SETTING: To investigate epidemiology and impact, 30369 short-term visitors completed a questionnaire just before boarding their homebound aircrafts. To investigate etiology, 322 patients (hotel guests) with TD provided stool samples. MAIN OUTCOME MEASURES: Attack and incidence rates of reported diarrhea and of classically defined TD (> or = 3 unformed stool samples in 24 hours and > or = 1 accompanying symptom), incapacity, risk factors, and etiology. RESULTS: The attack rate for diarrhea was 23.6 percent overall, with 11.7 percent having classically defined TD. For a mean duration of stay of 4 to 7 days, the incidence rate was 20.9 percent (all TD) and 10.0 percent (classic TD). Among airport respondents, the incapacity lasted a mean of 11.6 hours. Less than 3 percent of all travelers avoided potentially high-risk food and beverages. The most frequently detected pathogens were enterotoxigenic Escherichia coli, Rotavirus, and Salmonella species. CONCLUSIONS: A realistic plan for reducing TD is needed. Preventive measures such as the improvement of hygienic conditions at the destination, and/or the development of vaccines against the most frequent pathogens associated with TD may contribute toward achieving this goal (Au)