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 regulators
- Informatics platforms linking, visualising and harmonising data sources, completeness and structures
- Data science techniques to develop new definitions of disease, identify new phenotypes, and construct personalised predictive models
- Guidelines 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.
- 539 views
- August 29, 2017
- Tom Jones
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.
- 382 views
- August 21, 2017
- Tom Jones
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
• 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.
- 477 views
- July 11, 2017
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.
- 929 views
- March 15, 2017
- Gontse Ramela
Med-e-Tel’s conference’s 5 to 7 April
- Pharmacy and m and eHealth, presented with the Pharmaceutical Group of the European Union ( PGEU)
- Primary care
- Mental health
- Women's and maternal health
- Disease management and remote monitoring
- Education and eLearning
Agence eSanté Luxembourg will present on:
- Artificial intelligence for decision support
- Multidisciplinary consultation meetings and coordination in oncology
- Quality 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.
- 649 views
- February 24, 2017
- Lesley Dobson
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 lists
- Presence 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 higher
- Logistic regression of 30 clinically relevant structured data elements
- Machine-learning, using unstructured notes with over 1,118 data items in the model
- Machine-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.
- 391 views
- November 11, 2016
- Tom Jones
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.
- 470 views
- September 27, 2016
- Tom Jones
Apple Watch can identify your stroke risk
Atrial Fibrillation (AFib) can cause the heart to beat irregularly. While people with the disorder don't necessarily feel any symptoms, they're at risk of a stroke or heart failure. If a device close to a patient, such as an Apple Watch, could identify people at risk, that would be a great advantage.
UC San Francisco and Cardiogram experts are collaborating in the Health eHeart study to investigate whether sensors in the Apple Watch can be used to identify people at risk of strokes. mRhythm, the observational study aims to "Contribute your data and save lives." Brandon Ballinger, a cofounder of Cardiogram, tells Fast Company that researchers have two major goals for the study:
- Quantify the accuracy of the Apple Watch sensor for clinical research
- Collect evidence that the Cardiogram app and algorithm can detect AFib.
Experts are not convinced that a smart-watches have yet proven their ability to monitor and track heart rates safely enough to support making medical decisions. A chest strap that closely emulates an electrocardiogram (ECG) is more accurate than wrist-worn devices. Perspiration and rapid movement, for instance, can affect a smartwatch's ability to measure heart rate accurately.
Ballinger is more upbeat. "We are trying to understand the distribution of errors when you're sitting or walking, or male versus female’" He’s hoping to recruit 10,000 patients for the study. His team will test other devices that track heart rate, such as Fitbit.
Medical device startup Alivecor has created a medical-grade ECG band for Apple Watch. It measures electrical activity in hearts and analyses it for problems. The company claims it’s the first wearable to provide instant ECG analysis approved by the US Food and Drug Administration (FDA). Kardia band has an app that displays the EKG in real time. When users touch their Kardia Band's sensor, it sends information to the app. The algorithm then looks for signs of AFib. The system also includes a Normal Detector that indicates if heart rates and rhythms are in the normal range. Another feature notifies users when they need to retake their ECGs. Users can share their data with a health professional at any time.
AliveCor spokeswoman Rebecca Phillips says the Kardia Band only needs good contact between the electrode and skin, "anywhere on the forearm to the fingertip." Neither sweat nor tattos diminish the ECG results. Kardia’s intended for patients diagnosed with AFib. It could make a big difference for these patients in Africa too.
- 1,003 views
- June 30, 2016
- Sean Broomhead
Heart failure apps need improving
Few apps for Heart Failure (HF) conditions meet the required criteria for quality, content, or functionality. This’s the finding of a study by a team from Columbia University, its College of Physicians and Surgeons and New York-Presbyterian/Columbia University Medical Center, and reported in the Journal of Medical Internet Research (JMIR). The team proposed that the apps need refining and mapping to evidence-based guidelines to meet the need for overall quality improvement in monitoring HF symptoms and self-care.
HF’s a common, complex, and costly cardiovascular condition. A study by Banerjee and Shanthi Mendis in Current Cardiology Reviews identified a need for a global health perspective because “HF is likely to grow as a major clinical and public health challenge due to demographic changes as well as rising prevalence of causative risk factors in aging patients, including hypertension, coronary artery disease, degenerative valve disease and obesity.” Africa’s stretched health systems will have to deal with this challenge, and effective mHealth should offer a viable solution. It seems that it’s some way off.
The Columbia study’s the first to undertake a comprehensive, independent review and evaluation of commercially available apps for HF symptom monitoring and self-care. Disappointingly, and despite the HF patients’ needs, most mHealth apps support healthy living rather than chronic disease management. However, many apps are used with minimal knowledge of their functionality and ability to integrate data into health care systems, and with limited efficacy for improving patient or clinical outcomes.
Consequently, many HF apps have not yet been readily adopted into routine clinical management. They need further development to support comprehensive symptom management for HF patient. These findings are important for cardiologists and their patients in Africa. mHealth for HF needs rigorous assessment and business case before a decision to proceed.
- 661 views
- June 28, 2016
- Sean Broomhead
A web-based tool estimates these heart patients’ risk
- 2013 ACC/AHA Guideline on the Assessment of Cardiovascular Risk
- 2013 ACC/AHA Guideline on the Treatment of Blood Cholesterol to Reduce Atherosclerotic Cardiovascular Risk in Adults.
There’s now an ASCVD Risk Estimator as a companion tool to the guidelines. It’s for cardiologists and other health workers caring for patinets with atherosclerotic Cardiovascular Disease (ASCVD). ASCVD includes coronary death, nonfatal myocardial infarction, or fatal or nonfatal stroke. It relies on the Pooled Cohort Equations and Lifetime Risk Prediction tools.
Healthcare providers can use the Risk Estimator for patients with a Low-Density Lipoprotein (LDL) cholesterol level of <190 Milligrams per Decilitre (mg/dl) to assess the ten-year and lifetime risks for ASCVD. It’s not designed for patients with LDL levels of >190 mg.dl.
ASCVD risk factors include age, sex, race, total cholesterol, HDL cholesterol, systolic blood pressure, blood pressure lowering medication use, diabetes status, and smoking status. Measures of the ten-year ASCVD risk are derived from data from multiple community-based populations.
Estimates of lifetime risk for ASCVD are provided for adults aged between 19 to 60.They’re shown as a lifetime ASCVD risk for a 50-year old without ASCVD who has the risk factor values entered in the Estimator.
As Non-Communicable Disease (NSD) rates increase in Africa, the Risk Estimator can have an expanding value. It, and equivalent tools should be part of countries’ latest eHealth strategies.
Image from naplesperiodontist.com
- 542 views
- June 24, 2016
- Sean Broomhead
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