Feature | Artificial Intelligence | March 16, 2021

Global Longitudinal Strain Echo and AI Can Aid Fight Against Heart Failure

Cloud AI software-as-a-service (SaaS) can help streamline workflows and increase throughput, enabling echocardiographers to better measure global longitudinal strain (GLS) more routinely without impacting productivity. This is an example of the Ultromics EchoGo Core artificial intelligence algorithm with fully automates GLS.

Cloud artificial intelligence (AI) software-as-a-service (SaaS) can help streamline workflows and increase throughput, enabling echocardiographers to better measure global longitudinal strain (GLS) more routinely without impacting productivity. This is an example of the Ultromics EchoGo Core artificial intelligence algorithm, which fully automates GLS. Learn more at www.ultromics.com.

Heart failure (HF) is a prevalent yet silent epidemic, affecting 26 million people and costing global healthcare systems an estimated $65 billion every year. While heart attacks are often sudden and can be symptomatic, heart failure is often considered a silent killer as it can build silently over time and treatment is often delayed to when the systolic burden has been compromised beyond reprieve.

Global longitudinal strain (GLS) echocardiography measurment are increasingly recognized as a more effective technique than conventional ejection fraction (EF) in detecting changes in left ventricular (LV) systolic function and predicting heart failure outcomes. However, introducing GLS cardiac ultrasound into routine clinical practice has been fraught with challenges — including inter-operator variability, time and resource constraints.

Artificial intelligence (AI) can help overcome these challenges and move the benefits of GLS analysis from the pages of the journals to the front line in the fight against HF.

Artificial Intelligence May Aid Heart Failure Epidemic 

The global public health burden of HF is not only significant, but also continuously increasing. In the United States, around 5.7 million people have HF, a figure that is expected to rise to 8 million by 2030.[1] 

It has a huge impact. In Europe, the Rotterdam Study showed a death rate of 11% at one year and 41% at year five.[3] In the U.S., the impact is even greater, the Framingham Heart Study found mortality rates of 20-30% at one year and 45-60% at five years.[2] These statistics have not changed in decades. In 2012, HF was responsible for an estimated health expenditure of around $31 billion, more than 10% of the U.S. total cardiovascular disease budget. As prevalence increases, so will the costs – as much as $69 billion by 2030.[1]

However, as cardiology communities the world over know, overcoming the challenge of HF is not easy, not least because of the lack of high-quality markers and predictors.[4] 

Traditionally, teams have used EF to measure left ventricular (LV) systolic function which only represents a measure of systolic heart failure. LVEF is far from perfect and far from predictive of events. Changes in LVEF lag far behind effectively managed disease progression. This is evident in the growing number of patients with heart failure with preserved ejection fraction (HFpEF), now accounting for more than half of all patients with clinically diagnosed HF.

The need for more advanced, accurate measures and markers has never been more apparent. AI may be able to help by greatly simplifying and automating GLS measurements.

The Evidence for Global Longitudinal Strain and the Evaluation of Heart Failure

There is now an irrefutable and growing body of evidence to suggest that GLS is a significantly more useful HF metric than LVEF. A paper published in the European Journal of Heart Failure in 2017 concluded that impaired GLS was indicative of diastolic dysfunction and elevated left ventricular filling pressures which is common in people with HFpEF.[5] The authors, who also reported LV GLS was associated with biomarkers of wall stress, collagen synthesis, and diastolic dysfunction, said the findings indicated the presence of systolic and diastolic dysfunction even when LVEF was normal.

Later that year, a team from Duke University Medical Center reported a high prevalence of patients hospitalized with acute HFpEF had abnormal LV GLS. In addition, they discovered that abnormal LV GLS was associated with worse clinical outcomes at 30 days, though it is worth noting that this was no longer statistically significant at one year.[6]

“LV GLS may be a useful tool for identifying a cohort of HFpEF patients with more overt myocardial dysfunction who are at risk for worse outcomes following a hospitalization for HF,” concluded the authors of the Duke University Medical Center study. 

This role of GLS in predicting disease severity was further highlighted in a study of almost 2,200 patients, published last month.[7] The observational, prospective cohort study looked at the GLS, as measured during an echo, of 2,186 people with stages A to D HF according to the American Heart Association (AHA) criteria.[6] The team found that GLS was not only associated with HF severity, but during a median follow up period of just over three years, was predictive of all-cause mortality and cardiac death.[6]

A 2019 review of the evidence concluded that GLS had “greater prognostic value” than LVEF.[8] “GLS adds incremental value to EF in the prediction of adverse outcomes,” the study authors wrote.

“Comprehensive evaluation of GLS in HF patients provides additive prognostic information and can play a significant role in improving risk stratification in chronic systolic heart failure.”
But while they said they were “hopeful” that global longitudinal strain would become the standard measurement for HF in the future, they also acknowledged the challenges of bringing it into routine practice.

Advancing Clinical Practice With Global Longitudinal Strain With the Help of AI

However, effectively utilizing global longitudinal strain for the measurement of HF is not without its challenges. Most strain software still requires at least some level of manual approval and contouring, leaving calculations vulnerable to human error, and there exists differences in GLS measurements across the different ultrasound equipment vendors.
Echocardiographers are also faced with time constraints. The average echo exam can take approximately 20-30 minutes of imaging time, so the addition of (strain) measurements to the standard protocol along with the time taken to acquire them — particularly as teams attempt to clear COVID-related backlogs — can be problematic.

Cost is another major barrier to the wide-scale adoption of global longitudinal strain analysis and reporting. Most vendors offer strain modules for their ultrasound machines, but these can be prohibitively expensive when considering the number of devices and software licenses required in relation to the practical application to patient populations. Additionally, those that have started to perform strain have discovered the cost burden of decreased productivity when staff perform strain routinely on all exams. 

But AI can help on many fronts. AI can automate the parts of the process that lead to inter-observer variability. Combining the latest knowledge of GLS with the innovation of cloud-based AI technology may be the key to improving outcomes and reducing the costs associated with the silent challenging epidemic of HF. 

 

Related Artificial intelligence Application in Echo and Cardiology:

New Cardiac Diagnostic Tool Helps Automate Point-of-Care Ultrasound Screening

JAMA Cardiology Article Shows Effectiveness of AI-Guided Ultrasound Software 

Mayo Clinic Uses Artificial Intelligence to Help Assess Cardiac Danger From COVID-19

Northwestern Medicine Introduces Artificial Intelligence to Improve Ultrasound Imaging

FDA Clears Ultromics Echocardiography AI for Automated Identification of Coronary Artery Disease

 

TomTec Releases New Echocardiography Technologies With AI and Auto Quantification

VIDEO: AI-Assisted Automatic Ejection Fraction for Point-of-Care Ultrasound

New Cardiac Diagnostic Tool Helps Automate Point-of-Care Ultrasound Screening

FDA Clears Caption Health AI-assisted Point-of-care Ejection Fraction Evaluation

WEBINAR: Intelligently Efficient Adult Echo Structured Reporting

 

What is New in Cardiology? A Review of All Major Emerging Technologies for Heart Diseases

Expanded AI-based Echocardiography Analysis and Reporting Added to Konica Minolta Exa Cardio PACS 

FDA Clears First AI-Powered Algorithm Suite for Personal ECG Monitoring

More Than 50,000 Children Screened for Congenital Heart Defects Using AI-enabled Stethoscopes
 

References:

1.  Gianluigi Savarese and Lars H Lund. Global Public Health Burden of Heart Failure. Card Fail Rev. 2017 Apr; 3(1): 7–11. doi: 10.15420/cfr.2016:25:2.

2. Ibadete Bytyçi and Gani Bajraktari. Mortality in heart failure patients. Anatol J Cardiol. 2015 Jan; 15(1): 63–68. Published online 2014 Aug 19. doi: 10.5152/akd.2014.5731.

3. Albert Hofman, Monique M. B. Breteler, Cornelia M. van Duijn, et al. The Rotterdam Study: objectives and design update. Eur J Epidemiol. 2007 Nov; 22(11): 819–829. Published online 2007 Oct 23. doi: 10.1007/s10654-007-9199-x.

4. Ramachandran S. Vasan, Vanessa Xanthakis, Asya Lyass, et al. Epidemiology of Left Ventricular Systolic Dysfunction and Heart Failure in the Framingham Study: An Echocardiographic Study Over 3 Decades. J Am Coll Cardiol Cardiovasc Imaging. 2018 Jan, 11 (1) 1–11.

5. Adam D DeVore, Steven McNulty, Fawaz Alenezi, et al. Impaired left ventricular global longitudinal strain in patients with heart failure with preserved ejection fraction: insights from the RELAX trial. Eur J Heart Fail. 2017 Jul;19(7):893-900. doi: 10.1002/ejhf.754. Epub 2017 Feb 14.

6. Jonathan Buggey,  Fawaz Alenezi,  Hyun Ju Yoon, et al. Left ventricular global longitudinal strain in patients with heart failure with preserved ejection fraction: outcomes following an acute heart failure hospitalization. ESC Heart Failure. First published 20 April 2017. https://doi.org/10.1002/ehf2.12159.

7. Sven-Oliver Tröbs, Jürgen H. Prochaska, Sören Schwuchow-Thonke, et al. Association of Global Longitudinal Strain With Clinical Status and Mortality in Patients With Chronic Heart Failure. JAMA Cardiol. Published online February 3, 2021. doi:10.1001/jamacardio.2020.7184.

8. Kumar Ashisha, Mohammed Faisaluddinb, Dhrubajyoti Bandyopadhyay, et al. Prognostic value of global longitudinal strain in heart failure subjects: A recent prototype. IJC Heart & Vasculature. Volume 22, March 2019, Pages 48-49.

 

Related Content

Ultromics will offer EchoGo Pro as a stress-echo module in the EchoGo suite alongside EchoGo Core, its AI solution for automated systolic function and strain analysis. The EchoGo suite is a cloud-based service that uses artificial intelligence to fully automate the pathway to diagnosis, providing near-instant reports for clinicians without any need for physical software on site.

Ultromics will offer its artificial intelligence driven EchoGo Pro as a stress-echo module in the EchoGo suite alongside EchoGo Core, its AI solution for automated systolic function and strain analysis. The EchoGo suite is a cloud-based service that uses artificial intelligence to fully automate the pathway to diagnosis, providing near-instant reports for clinicians without any need for physical software on site.

News | Artificial Intelligence | January 06, 2021
January 6, 2021 — The U.S.
An example of a body composition analysis of an abdominal CT slice with the subcutaneous fat in green, skeletal muscle red and visceral fat in yellow. This was automatically identified and analyzed via a deep learning algorithm to assess the risk for heart attack and stroke in more than 12,000 patients. #RSNA2020 #RSNA20 #RSNA

An example of a body composition analysis of an abdominal CT slice with the subcutaneous fat in green, skeletal muscle red and visceral fat in yellow. This was automatically identified and analyzed via a deep learning algorithm to assess the risk for heart attack and stroke in more than 12,000 patients.

Feature | Artificial Intelligence | December 02, 2020
December 2, 2020 – Automated deep learning analysis of abdominal...
The U.S. Food and Drug Administration (FDA) has cleared AliveCor's Kardia AI V2 next generation of interpretive artificial intelligence (AI)-based personal electrocardiogram (ECG) algorithms.

The U.S. Food and Drug Administration (FDA) has cleared AliveCor's Kardia AI V2 next generation of interpretive artificial intelligence (AI)-based personal electrocardiogram (ECG) algorithms.

News | Artificial Intelligence | November 24, 2020
November 24, 2020 — The U.S.
Dia's LVivo artificial intelligence software can help automate many features of echocardiograms to speed workflow and aid novice users. The software is now integrated into the Konica Minolta Exa PACS.

Dia's LVivo artificial intelligence software can help automate many features of echocardiograms to speed workflow and aid novice users. The software is now integrated into the Konica Minolta Exa PACS. 

News | Artificial Intelligence | November 12, 2020
November 12, 2020 – Konica Minolta Healthcare Americas Inc. and DiA Imaging Analysis Ltd.
The artificial intelligence-driven Caption Guidance software guides point of care ultrasound (POCUS) users to get optimal cardiac ultrasound images. The AI software is an example of a FDA-cleared software that is helping improve imaging, even when used by less experienced users.

The artificial intelligence-driven Caption Guidance software guides point of care ultrasound (POCUS) users to get optimal cardiac ultrasound images. The AI software is an example of a FDA-cleared software that is helping improve imaging, even when used by less experienced users.

Feature | Artificial Intelligence | September 29, 2020 | Joe Fornadel, J.D., and Wes Moran, J.D.
The number of Federal Drug Administration (FDA)-approved AI-based algorithms is significant and has grown at a steady
Selfied might be used with AI to identify patients with heart disease. Getty Images

Getty Images

News | Artificial Intelligence | August 24, 2020
August 24, 2020 — Sending a photo selfie to the doctor could be a cheap and simple way of detecting heart disease usi
aption Health a leader in medical AI technology, has received U.S. Food and Drug Administration (FDA) 510(k) clearance for an updated version of Caption Interpretation, which uses artificial intelligence (AI) to enable clinicians to obtain quick, easy and accurate measurements of cardiac ejection fraction (EF) at the point of care.

Caption Health a leader in medical AI technology, has received U.S. Food and Drug Administration (FDA) 510(k) clearance for an updated version of Caption Interpretation, which uses artificial intelligence (AI) to enable clinicians to obtain quick, easy and accurate measurements of cardiac ejection fraction (EF) at the point of care.

News | Artificial Intelligence | August 19, 2020
August 19, 2020 — Caption Health a leader in medical AI technology, has received U.S.
In February 2020, the U.S. Food and Drug Administration (FDA) cleared artificial intelligence software to assist in the acquisition of cardiac ultrasound images. The Caption Guidance software from Caption Health is an accessory to compatible diagnostic ultrasound systems and uses artificial intelligence to help the user capture images of a patient’s heart that are of acceptable diagnostic quality. It is aimed at point of care ultrasound (POCUS) exams, where users may not be regular sonographers.

In February 2020, the U.S. Food and Drug Administration (FDA) cleared artificial intelligence software to assist in the acquisition of cardiac ultrasound images. The Caption Guidance software from Caption Health is an accessory to compatible diagnostic ultrasound systems and uses artificial intelligence to help the user capture images of a patient’s heart that are of acceptable diagnostic quality. It is aimed at point of care ultrasound (POCUS) exams, where users may not be regular sonographers.

Feature | Artificial Intelligence | August 18, 2020 | Dave Fornell, Editor
The No.