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Using AI to make precision heart failure detection possible

According to literature, up to 75% of heart failure patients are missed. [1]

High rates in missed cases are associated with an increase in hospitalizations, high readmission rates, unnecessary procedures, and often death.

To improve and extend lives, and decrease associated costs, we need to detect heart failure earlier and more accurately. This is especially true for patients with heart failure with preserved ejection fraction (HFpEF).

We must improve our detection and risk-stratification of heart failure

Precision heart failure detection

Ultromics combines echocardiography - the most common and cost-effective cardiac imaging modality - with AI-derived insights that go beyond what the human eye can detect.

Ultromics’ AI specializes in early and accurate heart failure detection and risk stratification, including HFpEF, to reduce hospitalizations and help improve patient outcomes.

The need to improve heart failure costs is critical

$25,000

The annual median medical costs for heart failure care per patient.2

$25,000

80%

The number of direct heart failure costs attributed to inpatient stays.

80%

> 3 million

The number of admissions of patients over 65 years of age. As the population of this age group increases, so too will readmissions. 3
> 3 million

#1 driver

The number one cause of Medicare costs relative to any other diagnosis.
#1 driver

Health Insurance providers play a pivotal role in addressing the expanding heart failure crisis

More focus needed on HFpEF

Heart failure with preserved ejection fraction (HFpEF) is the single greatest unmet need in cardiovascular medicine. [3]

HFpEF is more costly than the more commonly diagnosed heart failure with reduced ejection fraction (HFrEF) and represents a greater proportion of heart failure costs. [4]

HFpEF diagnosis is complex and replies on often inconclusive diagnostic algorithms and invasive testing – such as catheterization – which contributes to it being missed up to 75% of the time. [1]

Why precision matters

New treatments make it more important than ever to quickly and accurately diagnose heart failure


Sodium-glucose cotransporter 2 inhibitors (SGLT2is) and other new drug therapies have been shown to improve both HFrEF and HFpEF patient outcomes. [6]

These SGLT2is can reduce death and hospitalizations by 20-40%. [5]

Effective and fast diagnoses lead to earlier treatment with these new classes of drugs, and can have a significant outcome when combined with effective patient management.

Echocardiography remains the safest and most accessible option

 

Echocardiography is already the most common means of diagnosing heart failure, with over 30 million exams being performed each year. [7]

Echocardiography is low-cost, accessible, portable, and safe for patients, and with AI automating insights, it removes manual time, subjectivity and variability in calculating analysis, and provides accurate heart failure diagnostics to any care setting.

The benefits of AI for payers

AI-driven insights consistently deliver accurate, reproducible results that reduce the costs associated with rehospitalization and invasive procedures.

Guidelines

 

Ultromics regularly invites industry leaders to summarize guidelines for echocardiography and heart failure.

GuidelinesPic

Publications

 

Discover our publications and studies that showcase the clinical application of our AI solutions.

Researchpapers

Discover our AI solutions

EchoGo Heart Failure

EchoGo Core

  

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With Ultromics as a partner in your cardiac care assessment, your organization can make improved and better-informed decisions when detecting heart failure. Care becomes faster and more precise with accurate AI-driven data, helping to reduce costs and improve outcomes for patients.

 

  

References:

  1. Sanders-van Wijk S, et al. Eur J Heart Fail. 2020;23:838-840
  2. Urbich M, et al.PharmacoEconomics 2020;38:1219–1236
  3. Shah S, et al. Circulation. 2020;141:1001–1026
  4. Nichols GA, et al. J Am Coll Cardiol. 2022;79: (9 Supplement):283
  5. Cunningham JW, et al. J Am Coll Cardiol 2022;80:1302–1310
  6. Heidenreich PA, et al. JACC. 2022;79:263-421
  7. Deloittes Proprietary Data