• Cardiology
  • Will the new Apple watch be a big hit?

    Apple is renowned for technological advancement and innovation.  During their annual product launch this year, Apple announced the new series 4 watch.  It’s not your average wearable.  This watch boasts mHealth features such as electrocardiogram (ECG) monitoring and fall detection, and allows you to share this data with your healthcare practitioner.

    These features are impressive.  Apple is snapping up opportunities to break into the rapidly expanding mHealth market, from fitness tracking to a health app and now wearable ECG.  But there’s a nagging concern too.  What impact will this wearable ECG have on the healthcare system?  Could Apple’s new innovation spur an increase in unnecessary healthcare utilisation?

    These are concerns that I seem to share with other healthcare practitioners who worry that consumers may incorrectly attempt to diagnose complex cardiac conditions themselves.  There are also concerns about the sensitivity and specificity of the device, which if not great, could spark a mass of panic-stricken consumers due to false positives.

    While this new innovation is an important movement towards better patient management, it is vital that the counsel of healthcare practitioners is not diminished.  Consumers must be advised to use it cautiously to augment their healthcare management rather than replace professional management. 

    Similarly, the medical community needs to work more closely with companies like Apple who drive much needed innovation.

  • Stethee reinvents the stethoscope with AI

    The worlds first Al enabled stethoscope system has been launched by M3DICINE Inc.

    The design itself is revolutionary and operates as easily as the traditional stethoscope. However, it allows users to listen to the lung and heart sounds with a more sophisticated amplification and filtering technology. Heart and respiratory sounds captured are sent via Bluetooth to the Stethee Android or iOS App which enables a wider range of diagnostic capabilities.

    The Stethee system comes in three core products:

    FDA cleared Stethee Pro for medical and healthcare professionalsStethee Vet for veterinarians and animal professionalsStethee Edu developed specifically as an education and research tool

    The technology platform behind the Stethee AI engine , named “Aida” can analyze the heart and lung sounds to build a unique personal biometric signature.  In addition to this, Aida automatically tags geo-location and environment data to each sample in real time.  This offers a completely new dimension of data analytics for public health planning by allowing one to understand what effects environmental factors such as pollution, temperature or humidity have on our heart and lungs.

    Aida also analyzes this encrypted and anonymised data in order to learn and report back quantitative clinically actionable data to vets, doctors and other healthcare professionals. Not only does it identify and analyze heart sounds and respiratory activity but also patterns that may indicate a disease condition. The data is represented in real time in the Stethee App, therefore making it easy to understand vital signs.

    The potential for the Stethee to be used in remote rural areas is quite vast because its relatively easy to use and results can be shared and analyzed promptly by a medical specialist anywhere in the world. This is invaluable to the improvement of patient care, more especially to remote rural areas where access to screening services or a cardiologist is very difficult.

  • Wearable heart rate monitors don’t tick the box

    A cynical insight from Napoleon Bonaparte was “If you wish to be a success in the world, promise everything, deliver nothing.” I doesn’t fit wearable mHealth, where reliable results are everything.

    Research in the Journal of Medical Internet Research (JMIR) says some wearables have considerable promise, but have to do much better at delivery.

    It sees an important role for wearable sensor technology in clinical research and healthcare. Before it can, it must undergo rigorous evaluation prior to market launch and its performance should be supported by evidences. The researchers found that match between three heart rate monitoring devices and an electrocardiography (ECG) reference was weak.

    Many studies have tried to validate wrist-worn photoplethysmography (PPG) heart rate monitors, but contrasting results question their utility. A big problem’s inadequate methodologies.

    Validation strategies should consider the nature of data provided by both the investigational and reference devices. There must be uniformity in the statistical approach to the analyses too. Investigators should test the technology in user populations and in appropriate settings for the planned uses. Developers, suppliers and scientific communities need robust standards to validate new wearable sensor technology. 

    There’s a lot more to do before wearables can become mainstream clinical devices. The findings and recommendations should be considered be Africa’s health systems as they advance their mHealth strategies and plans.

  • EU’s BigData@Heart aims to improve heart disease treatments

    A report on Cardiovascular Disease (CVD) published by Springer says CVD prevalence in sub-Saharan Africa’s increasing. Limited access to prevention and continuing care are seen as constraints to improvements. The EU’s BigData@Heart project may contribute to the development of treatments for heart disease patients. It has lessons for Africa’s health systems. 

    It’s a large-scale, five-year, €19 million project. Its aim’s to use data and advanced analytics to develop a translational research platform of phenotypical resolution to improve patient outcomes and reduce societal burdens of atrial fibrillation (AF), heart failure (HF) and acute coronary syndrome (ACS). Data sources include real-world evidence, best-practices in drug development and personalised medicines. 

    Four outputs are:

    New universal, computable, definitions of diseases and outcomes relevant for patients, clinicians, industry and regulatorsInformatics platforms linking, visualising and harmonising data sources, completeness and structuresData science techniques to develop new definitions of disease, identify new phenotypes, and construct personalised predictive modelsGuidelines that allow for cross-border use of big data sources acknowledging ethical and legal constraints and data security.

    It can have considerable value for Africa. The continents health systems and cardiologists could move their CVD services ahead in the wake of BigData@Heart’s progress.

  • Informatics and EHRs can prevent strokes and improve monitoring

    Increasing responses to strokes and their after effects are important health priorities. A report in the US National Library of Medicine has estimated that in 2015, strokes were the second-leading cause of death worldwide after ischaemic heart disease. In 2010, strokes caused 5.3 million deaths globally, 10% of all deaths. Trends include increasing stroke mortality and lost Disability Adjusted Life Years (DALYs) in low- and middle-income countries and a dire estimate of the global economic impact unless effective preventive measures are implemented.

    Another study identified the aged-standardised incidence of stroke in Africa as 316 per 100,000, 0.3% population, and age-standardised prevalence rates of up to 981 per 100,000, almost 1%. Stroke incidence’s increasing, but the study said the “peculiar factors responsible for the substantial disparities in incidence velocity, ischaemic stroke proportion, mean age and case fatality compared to high-income countries remain unknown.” This is despite the incidence being lower than higher-income countries. A study in Sage Journals estimated the incidence of stroke, adjusted to the WHO World standard population, in 51 countries. It ranges from 76 to 199 per 100,000 population.

    Atrial fibrillation (AF) an irregular and often very fast heart rate may cause symptoms like heart palpitations, fatigue and shortness of breath. Treating it’s important because it may cause a stroke, with resulting adverse DALYs. After a stroke, AF needs monitoring. A study in Cardiology, published by Karger, aimed to identify the characteristics of atrial fibrillation (AF) in post-cryptogenic stroke. It’s a stroke with an unknown origin.

    The US Stroke Association has an estimate that cryptogenic strokes (CS) may be between 25% and 45% of ischemic strokes, so about 30%. They are where blood supply to part of the brain is interrupted or severely reduced, depriving brain tissue of oxygen and nutrients. Within minutes, brain cells begin to die. Ischemic strokes are about 87% of all types.

    The team’s project included Transient Ischemic Attacks (TIA). Mayo Clinic has a simple description of a TIA. It produces similar symptoms to a stroke, but usually lasting only a few minutes and causing no permanent damage. Often called a mini-stroke, a TIA may be a warning of worse to come.

    The team of cardiologists and informatics researchers from the Department of Medicine and Division of Cardiology at Santa Clara Valley Medical Center, the Biomedical Informatics Training Program, Stanford University, the Center for Biomedical Informatics Research, Stanford University School of Medicine and the University of California San Francisco, stratified a cohort of stroke patients by risk factors. It used data from EHRs.

    These included obesity, congestive heart failure, hypertension, coronary artery disease, peripheral vascular disease and valve disease. A risk-scoring model applied seven clinical variables that assigned patients into three groups. The risk-score’s measures of AF risk and may be used to select patients who need extended AF monitoring, especially home monitoring.

    The study’s an example of the value of doctors, informaticians and analysts working together to exploit the value of data in EHRs. It’s a model for Africa’s health systems and universities to work towards.

  • KardioPro helps to tackle cardiometabolic disease

    Cardiometabolic disease, a cluster of inter-related risk factors that can lead to atherosclerotic vascular disease and type 2 diabetes, is the world’s leading cause of morbidity and mortality. It kills more people than AIDS and malaria combined and places tremendous strain on healthcare resources and costs. Currently, the epidemic of cardiometabolic disease worldwide is being diagnosed, treated and managed in separate silos. Healthcare systems rely on repetitive, duplicated tests and services, which inevitably leads to reduced patient outcomes and increased costs. To address this challenge, the Kardiogroup, a connected health company, has developed the first comprehensive cardiovascular risk reduction and treatment approach.

    The Kardio Ecosystem links connected health devices as a Technology Enabled Care (TEC) to validated Point of Care (POC) blood tests. It provides accurate and validated risk analyses, links to emergency care and access to treatment protocols informed by local and international guidelines.

     

    KardioPro, an mHeath app, is part of the ecosystem. It integrates with diagnostic tools, including a cardiolabs to measure patients’ blood pressure and Ankle-Brachial Index (ABI), a pulse oximeter, a professional wireless core body scale, and a glucometer.  Path Pro’s part of the configuration too. It provides the Alere Affinion Machine and the Abbott Istat POC pathology diagnostic equipment.

    Healthcare workers can use KardioPro to take measurements, connect to the KardioPro app from iPads or Androids, then visualise, track and share the results. It performs tests in 15 – 20 minutes, stores and organises results, simplifies patient monitoring and edits reports in PDF format so they can be shared by treatment teams. It also helps with the interaction of healthcare workers and patients to:

     

    •   Improve adherence

    •   Reassess treatments

    •   Reassure patients and explain to them the evolution of their health status

    •   Fix goals for patients

     

    The App:

    •   Is simple and easy to use

    •   Provides accurate risk analysis

    •   Has multi step reporting

    •   Provides treatment suggestions based on guidelines

    •   Delivers secure cloud based data capturing

     

    Tests performed by the app includes:

    HBA1C - Glycated Haemoglobin - This is used to test the 3 month average glucose of patients. It is used for screening for diabetes and used to monitor diabetic patients.   Lipogram - This is a full cholesterol panel which is one of the important components in cardiovascular disease. It measures the different types of cholesterol in the body which is important in assessing cardiovascular risk in patients Crp - known as C-Reactive Protein - This is an inflammatory marker test can be used to determine if antibiotic therapy is required in patients who are ill. Urine ACR - known as Albumin to Creatinine Ratio - These are the two key markers to test for chronic kidney disease.  U&E - Urea and Electrolytes - This is an important and common type of biochemistry test. It is used to assess Renal Function in Diabetic patients and are important screening test for patients with hypertension.  

    All health data generated by the device is secured and stored in an approved secure healthcare database. This is increasingly important with the rise in cyber-security threats.

    KardioPro is currently being used by 40 practitioners in South Africa. The solution has the potential to benefit resource poor communities across the continent. KardioPro is looking to expand internationally with interest to collaborate with international partners. 

  • AI predicts heart failure

    Heart failure’s not always easy to predict. CADence, described in eHNA post, shows how difficult it can be and how eHealth can help. Now, Artificial Intelligence (AI) can help too. It can save lives by identifying patients who need more aggressive treatment, says the UK's Medical Research Council (MRC) team in an article in BBC News. Technology’s advanced so much that AI can now calculate accurately when patients with heart disorders will die. The software can to do this by analysing blood tests and scans of beating hearts to spot signs that show they’re about to fail.  

     Researchers, at the MRC London Institute of Medical Sciences, were investigating patients with pulmonary hypertension, raised blood pressure in arteries supplying the lungs. Results indicate that high blood pressure in the lungs damages part of the heart, and about a third of patients die within five years of being diagnosed.  

    The AI software helps doctors to predict how long the patients will live. The data improves informed clinical decisions, leading to more precisely prescribed and individually tailored intensive treatments, such as drugs, injections into blood vessels or in extreme cases, lung transplants if necessary.  

    The AI was given MRI scans of 256 patients' hearts, and blood test results. The software measured movement of 30,000 different points in the hearts’ structures during each heartbeat. Data was coupled with eight years of patient’s health records. AI learned which abnormalities predicted when patients would die.

    It estimated up to approximately five years into the future, with an 80% potential to predict whether people would live beyond a year.  This’s a third better than doctors performance of 60% accuracy.  Researches want to use AI for other forms of heart failure such as cardiomyopathy, diseased of heart muscles, to see who needs pacemakers or supplementary treatments. They also hope to test AI on other patients in several hospitals to assess whether it should be widely available to doctors.

    eHNA’s previously posted about AI revolutionising healthcare. Pulmonary hypertension might be a good place for Africa’s health systems to start.

  • Med-e-Tel’s conference’s 5 to 7 April

    Telecardiology, social media and beyond are Med-e-Tel’s annual conference themes for 5 to 7 April 2017. It’ll include sessions on:

    Pharmacy and m and eHealth, presented with the Pharmaceutical Group of the European Union ( PGEU)Primary careMental healthTelenursingWomen's and maternal healthDisease management and remote monitoringEducation and eLearning

    Agence eSanté Luxembourg will present on:

    Artificial intelligence for decision supportMultidisciplinary consultation meetings and coordination in oncologyQuality of mHealth apps, with Med-e-Tel.

    The preliminary conference programme’s now available. Join and be a part of this exciting program and networking event. Hear from and meet with colleagues from around the world, and develop new partnerships and collaboration. You can register now.

  • Algorithms identify heart failure risk

    Lateral thinking was devised by Edward de Bono. Born in Malta, one of his opinions is “Dealing with complexity is an inefficient and unnecessary waste of time, attention and mental energy.” Cardiologists don’t see it quite that way. Africa’s health systems should follow the cardiologists lead.

    Algorithms using clinical data from EHRs offer opportunities for better healthcare. Cardiology services are taking advantage of these new analytic techniques, A study in the Journal of the American Medical Association (JAMA) Cardiology describes how cardiologists are develop algorithms that use readily available clinical data to identify hospital patients with heart failure. They’re diagnosing heart failure diagnosis based on discharge diagnosis and their review of sampled EHRs.

    It’s leading them to better, real-time case identification so they can target interventions to improve quality and outcomes for hospital patients with heart failure. Problem lists aren’t good enough for the task. They’re useful for case identification, but often inaccurate or incomplete. Machine-learning’s seen as a way to improve accuracy, but have drawbacks too, such as implementation complexities.

    The team completed a retrospective study of random 75% of hospital admissions of patients over 18 months at New York University Langone Medical Center. Data included demographics, laboratory results, vital signs, problem list diagnoses and medications to treat heart failure. Five algorithms for identifying heart failure were developed using data from EHRs.

    Heart failure on problem listsPresence of at least one of three characteristics: heart failure on problem list, inpatient loop diuretic or brain natriuretic peptide level of 500 pg/mL or higherLogistic regression of 30 clinically relevant structured data elementsMachine-learning, using unstructured notes with over 1,118 data items in the modelMachine-learning using structured and unstructured data, with 947 data items.

    The problem list algorithm identified about half the patients with heart failure. It’s insufficient for real-time identification. The next two had better results, but the machine-learning ones had the best predictive accuracy because they relied on free text notes and reports.

    However, it’s not a simple decision for Africa’s health systems to opt for machine learning algorithms. The research team says they’re difficult to implement because they rely on unstructured data and may need special expertise and resources. Instead, the researchers suggest that investment choices may depend on cardiologists’ clinical and operational needs. For Africa’s cardiology services, it may depend on the availability of data from EHRs too.

    De Bono went on to say “There is never any justification for things being complex when they could be simple.” Selecting an algorithm to identify heart failure may not be simple. The team says there may be a trade-off between costs benefits. Complex and simple aren’t binary choices. For Africa, the five methods may offer an investment ladder to eventually reach complexity and maximise benefits.

  • mHealth for cardiology may not be mature yet

    As mHealth marches on, the front line may not be as straight as a military model, and cardiology may be lagging behind, or perhaps slightly out of step. An article by a team from Stanford University School of Medicine and Center for Digital Health in the American Medical Association’s (AMA) JAMA Cardiology, authors from Stanford University School of Medicine and Center for Digital Health sets out the challenges.

    While cardiovascular care has been at the centre of healthcare transformation, including using EHRs, there’s much more to do. Displaying data in EHRs is seen as effective, but associated with information overload. Expanding the data by transferring more from wearables and remote monitoring will exacerbate the problem. Investment in sifting data and using it effectively’s needed too. It’s not just clinicians who face this challenge, but patients too, as they have more access to their own data.

    As mHealth apps keep cropping up in various locations, they’ve not been accompanied with good evidence about their therapeutic value. Oversight and management by healthcare teams are needed to ensure patients remain engaged, so they have to be integrated into overall healthcare models.

    Gathering and assembling rigorous evidence is becoming increasingly important. Rigorous studies of validation, effectiveness and implementation in actual healthcare settings for new digital technologies is vital. Studies randomised trials show effectiveness need supplementing with patient-level data and results. Sharing results between hospitals’s needed too, alongside implementation case studies and increased investment for training clinicians in eHealth.

    The authors say healthcare organisations and vendors are collaborating more to work out these problems. While this is welcome, there’s a big workload looming. Africa’s health systems should take heed of the USA’s experiences before expanding mHealth in their cardiology services.