Current state of artificial intelligence-based algorithms for hospital admission prediction in patients with heart failure: a scoping review. Eur Heart J Digit Health 2022;3:415-425. [PMID: 36712159 PMCID: PMC9707890 DOI: 10.1093/ehjdh/ztac035]

A systematic review on the associations between the built environment and adult's physical activity in global tropical and subtropical climate regions

BACKGROUND

Physical inactivity is a major public health concern, exacerbated in countries with a (sub)tropical climate. The built environment can facilitate physical activity; however, current evidence is mainly from North American and European countries with activity-friendly climate conditions. This study explored associations between built environment features and physical activity in global tropical or subtropical dry or desert climate regions.

METHODS

A systematic review of four major databases (Web of Science, Scopus, PubMed, and SportDISCUS) was performed. To be included, studies had to investigate associations between perceived or objective built environment characteristics and adult's physical activity and had to be conducted in a location with (sub)tropical climate. Each investigated association was reported as one case and results were synthesized based upon perceived and objectively assessed environment characteristics as well as Western and non-Western countries. Study quality was evaluated using a tool designed for assessing studies on built environment and physical activity.

RESULTS

Eighty-four articles from 50 studies in 13 countries with a total of 2546 built environment-physical activity associations were included. Design (connectivity, walking/cycling infrastructure), desirability (aesthetics, safety), and destination accessibility were the built environment characteristics most frequently associated with physical activity across the domains active transport, recreational physical activity, total walking and cycling, and moderate-to-vigorous physical activity, particularly if multiple attributes were present at the same time. Very few studies assessed built environment attributes specifically relevant to physical activity in (sub)tropical climates. Most studies were conducted in Western countries, with results being largely comparable with non-Western countries. Findings were largely generalizable across gender and age groups. Results from natural experiments indicated that relocating to an activity-friendly neighborhood impacted sub-groups differently.

CONCLUSIONS

Built environment attributes, including destination accessibility, connectivity, walking and cycling infrastructure, safety, and aesthetics, are positively associated with physical activity in locations with (sub)tropical climate. However, few studies focus on built environment attributes specifically relevant in a hot climate, such as shade or indoor recreation options. Further, there is limited evidence from non-Western countries, where most of the urban population lives in (sub)tropical climates. Policy makers should focus on implementing activity-friendly environment attributes to create sustainable and climate-resilient cities.

Weight telemonitoring of heart failure versus standard of care in a real-world setting: Results on mortality and hospitalizations in a 6-month nationwide matched cohort study

AIMS

Evaluating the benefit of telemonitoring in heart failure (HF) management in real-world settings is crucial for optimizing the healthcare pathway. The aim of this study was to assess the association between a 6-month application of the telemonitoring solution Chronic Care Connect™ (CCC) and mortality, HF hospitalizations, and associated costs compared with standard of care (SOC) in patients with a diagnosis of HF.

METHODS AND RESULTS

From February 2018 to March 2020, a retrospective cohort study was conducted using the largest healthcare insurance system claims database in France (Système National des Données de Santé) linked to the CCC telemonitoring database of adult patients with an ICD-10-coded diagnosis of HF. Patients from the telemonitoring group were matched with up to two patients from the SOC group based on their high-dimensional propensity score, without replacement, using the nearest-neighbour method. A total of 1358 telemonitored patients were matched to 2456 SOC patients. The cohorts consisted of high-risk patients with median times from last HF hospitalization to index date of 17.0 (interquartile range: 7.0-66.0) days for the telemonitoring group and 27.0 (15.0-70.0) days for the SOC group. After 6 months, telemonitoring was associated with mortality risk reduction (hazard ratio [HR] 0.71, 95% confidence interval [CI] 0.56-0.89), a higher risk of first HF hospitalization (HR 1.81, 95% CI 1.55-2.13), and higher HF healthcare costs (relative cost 1.38, 95% CI 1.26-1.51). Compared with the SOC group, the telemonitoring group experienced a shorter average length of overnight HF hospitalization and fewer emergency visits preceding HF hospitalizations.

CONCLUSION

The results of this nationwide cohort study highlight a valuable role for telemonitoring solutions such as CCC in the management of high-risk HF patients. However, for telemonitoring solutions based on weight and symptoms, consideration should be given to implement additional methods of assessment to recognize imminent worsening of HF, such as impedance changes, as a way to reduce mortality risk and the need for HF hospitalizations. Further studies are warranted to refine selection of patients who could benefit from a telemonitoring system and to confirm long-term benefits in high-risk and stable HF patients.

Machine learning methods, applications and economic analysis to predict heart failure hospitalisation risk: a scoping review protocol

INTRODUCTION

Machine learning (ML) has emerged as a powerful tool for uncovering patterns and generating new information. In cardiology, it has shown promising results in predictive outcomes risk assessment of heart failure (HF) patients, a chronic condition affecting over 64 million individuals globally.This scoping review aims to synthesise the evidence on ML methods, applications and economic analysis to predict the HF hospitalisation risk.

METHODS AND ANALYSIS

This scoping review will use the approach described by Arksey and O'Malley. This protocol will use the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) Protocol, and the PRISMA extension for scoping reviews will be used to present the results. PubMed, Scopus and Web of Science are the databases that will be searched. Two reviewers will independently screen the full-text studies for inclusion and extract the data. All the studies focusing on ML models to predict the risk of hospitalisation from HF adult patients will be included.

ETHICS AND DISSEMINATION

Ethical approval is not required for this review. The dissemination strategy includes peer-reviewed publications, conference presentations and dissemination to relevant stakeholders.

Artificial Intelligence Applied to Electrical and Non-Invasive Hemodynamic Markers in Elderly Decompensated Chronic Heart Failure Patients

OBJECTIVES

The first aim of this study was to assess the predictive power of Tend interval (Te) and non-invasive hemodynamic markers, based on bioimpedance in decompensated chronic heart failure (CHF). The second one was to verify the possible differences in repolarization and hemodynamic data between CHF patients grouped by level of left ventricular ejection fraction (LVEF). Finally, we wanted to check if repolarization and hemodynamic data changed with clinical improvement or worsening in CHF patients.

METHODS

Two hundred and forty-three decompensated CHF patients were studied by 5 min ECG recordings to determine the mean and standard deviation (TeSD) of Te (first study). In a subgroup of 129 patients (second study), non-invasive hemodynamic and repolarization data were recorded for further evaluation.

RESULTS

Total in-hospital and cardiovascular mortality rates were respectively 19 and 9%. Te was higher in the deceased than in surviving subjects (Te: 120 ± 28 vs. 100 ± 25 ms) and multivariable logistic regression analysis reported that Te was related to an increase of total (χ2: 35.45, odds ratio: 1.03, 95% confidence limit: 1.02-1.05, p < 0.001) and cardiovascular mortality (χ2: 32.58, odds ratio: 1.04, 95% confidence limit: 1.02-1.06, p < 0.001). Subjects with heart failure with reduced ejection fraction (HFrEF) reported higher levels of repolarization and lower non-invasive systolic hemodynamic data in comparison to those with preserved ejection fraction (HFpEF). In the subgroup, patients with the NT-proBNP reduction after therapy showed a lower rate of Te, heart rate, blood pressures, contractility index, and left ventricular ejection time in comparison with the patients without NT-proBNP reduction.

CONCLUSION

Electrical signals from ECG and bioimpedance were capable of monitoring the patients with advanced decompensated CHF. These simple, inexpensive, non-invasive, easily repeatable, and transmissible markers could represent a tool to remotely monitor and to intercept the possible worsening of these patients early by machine learning and artificial intelligence tools.

Early Diagnosis of Cardiovascular Diseases in the Era of Artificial Intelligence: An In-Depth Review

Cardiovascular diseases (CVDs) are significant health issues that result in high death rates globally. Early detection of cardiovascular events may lower the occurrence of acute myocardial infarction and reduce death rates in people with CVDs. Traditional data analysis is inadequate for managing multidimensional data related to the risk prediction of CVDs, heart attacks, medical image interpretations, therapeutic decision-making, and disease prognosis due to the complex pathological mechanisms and multiple factors involved. Artificial intelligence (AI) is a technology that utilizes advanced computer algorithms to extract information from large databases, and it has been integrated into the medical industry. AI methods have shown the ability to speed up the advancement of diagnosing and treating CVDs such as heart failure, atrial fibrillation, valvular heart disease, hypertrophic cardiomyopathy, congenital heart disease, and more. In clinical settings, AI has shown usefulness in diagnosing cardiovascular illness, improving the efficiency of supporting tools, stratifying and categorizing diseases, and predicting outcomes. Advanced AI algorithms have been intricately designed to analyze intricate relationships within extensive healthcare data, enabling them to tackle more intricate jobs compared to conventional approaches.

Clinical Predictive Modeling of Heart Failure: Domain Description, Models' Characteristics and Literature Review

This study attempts to identify and briefly describe the current directions in applied and theoretical clinical prediction research. Context-rich chronic heart failure syndrome (CHFS) telemedicine provides the medical foundation for this effort. In the chronic stage of heart failure, there are sudden exacerbations of syndromes with subsequent hospitalizations, which are called acute decompensation of heart failure (ADHF). These decompensations are the subject of diagnostic and prognostic predictions. The primary purpose of ADHF predictions is to clarify the current and future health status of patients and subsequently optimize therapeutic responses. We proposed a simplified discrete-state disease model as an attempt at a typical summarization of a medical subject before starting predictive modeling. The study tries also to structure the essential common characteristics of quantitative models in order to understand the issue in an application context. The last part provides an overview of prediction works in the field of CHFS. These three parts provide the reader with a comprehensive view of quantitative clinical predictive modeling in heart failure telemedicine with an emphasis on several key general aspects. The target community is medical researchers seeking to align their clinical studies with prognostic or diagnostic predictive modeling, as well as other predictive researchers. The study was written by a non-medical expert.

Leveraging Emerging Technologies to Expand Accessibility and Improve Precision in Rehabilitation and Exercise for People with Disabilities

Physical rehabilitation and exercise training have emerged as promising solutions for improving health, restoring function, and preserving quality of life in populations that face disparate health challenges related to disability. Despite the immense potential for rehabilitation and exercise to help people with disabilities live longer, healthier, and more independent lives, people with disabilities can experience physical, psychosocial, environmental, and economic barriers that limit their ability to participate in rehabilitation, exercise, and other physical activities. Together, these barriers contribute to health inequities in people with disabilities, by disproportionately limiting their ability to participate in health-promoting physical activities, relative to people without disabilities. Therefore, there is great need for research and innovation focusing on the development of strategies to expand accessibility and promote participation in rehabilitation and exercise programs for people with disabilities. Here, we discuss how cutting-edge technologies related to telecommunications, wearables, virtual and augmented reality, artificial intelligence, and cloud computing are providing new opportunities to improve accessibility in rehabilitation and exercise for people with disabilities. In addition, we highlight new frontiers in digital health technology and emerging lines of scientific research that will shape the future of precision care strategies for people with disabilities.

New technologies for the diagnosis, treatment, and monitoring of cardiovascular diseases

Telemedicine enables the remote provision of medical care through information and communication technologies, facilitating data transmission, patient participation, promotion of heart-healthy habits, diagnosis, early detection of acute decompensation, and monitoring and follow-up of cardiovascular diseases. Wearable devices have multiple clinical applications, ranging from arrhythmia detection to remote monitoring of chronic diseases and risk factors. Integrating these technologies safely and effectively into routine clinical practice will require a multidisciplinary approach. Technological advances and data management will increase telemonitoring strategies, which will allow greater accessibility and equity, as well as more efficient and accurate patient care. However, there are still unresolved issues, such as identifying the most appropriate technological infrastructure, integrating these data into medical records, and addressing the digital divide, which can hamper patients' adoption of remote care. This article provides an updated overview of digital tools for a more comprehensive approach to atrial fibrillation, heart failure, risk factors, and treatment adherence.

Digital Health Technologies for Post-Discharge Care after Heart Failure Hospitalisation to Relieve Symptoms and Improve Clinical Outcomes

The prevention of recurrent heart failure (HF) hospitalisations is of particular importance, as each such successive event may increase the risk of death. Effective care planning during the vulnerable phase after discharge is crucial for symptom control and improving patient prognosis. Many clinical trials have focused on telemedicine interventions in HF, with varying effects on the primary endpoints. However, the evidence of the effectiveness of telemedicine solutions in cardiology is growing. The scope of this review is to present complementary telemedicine modalities that can support outpatient care of patients recently hospitalised due to worsening HF. Remote disease management models, such as video (tele) consultations, structured telephone support, and remote monitoring of vital signs, were presented as core components of telecare. Invasive and non-invasive monitoring of volume status was described as an important step forward to prevent congestion-the main cause of clinical decompensation. The idea of virtual wards, combining these facilities with in-person visits, strengthens the opportunity for education and enhancement to promote more intensive self-care. Electronic platforms provide coordination of tasks within multidisciplinary teams and structured data that can be effectively used to develop predictive algorithms based on advanced digital science, such as artificial intelligence. The rapid progress in informatics, telematics, and device technologies provides a wide range of possibilities for further development in this area. However, there are still existing gaps regarding the use of telemedicine solutions in HF patients, and future randomised telemedicine trials and real-life registries are still definitely needed.

Most Recent Trials and Advances in Hypertension

Hypertension is one of the most prevalent cardiovascular risk factors and blood pressure control remains a clinical challenge, especially for patients with established cardiovascular disease. Late-breaking clinical trials and other evidence in hypertension have evolved to assess the most accurate ways to measure blood pressure, the use of combination therapies, considerations in special populations and evaluation of new techniques. Recent evidence supports the superiority of ambulatory or 24-hour blood pressure measurements, rather than office blood pressure measurements, for the assessment of cardiovascular risk. The use of fixed-dose combinations and polypills has been demonstrated to be valid and to provide clinical benefits beyond blood pressure control. There have also been advances in new approaches such as telemedicine, devices and the use of algorithms. Clinical trials have provided valuable data on blood pressure control in primary prevention, during pregnancy and in the elderly. The role of renal denervation remains unsolved but innovative techniques using ultrasound or alcohol injections are being explored. Current evidence and results of latest trials are summarised in this review.