Company: TEM
Filing Date: 2025-02-24
Form Type: 10-K
Source: 0000950170-25-025603
Chunk: 29

Company: Tempus AI, Inc.
Filing Date: 2025-02-24
Form: 10-K
Item: Item 1
Chunk 29
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 than 11% of American adults diagnosed with heart disease and millions of patients suffering from undiagnosed, life-threatening, yet highly treatable conditions such as AFib, cardiomyopathy, and valvular heart disease, to name a few. Tempus is working on solutions to find, diagnose, and help treat these patients earlier in order to improve patient outcomes, using routinely generated clinical data, such as data from a 12-lead ECG, a widely used and easily acquired medical test that measures the electrical activity of the heart, to screen patients who might be at high risk and help navigate them to the appropriate interventional therapy.

In cardiology, we ingest multimodal data and use approximately 60 algorithms to identify potential care gaps and continuously monitor patient data to find at-risk patients who may be falling through a care gap unbeknownst to their physician, and automatically notify care teams of any needed follow-up or disease progression. More than 100 hospitals nationwide are currently powered by Tempus Next and more than 45,000 patients are screened per month.

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We are also developing algorithmic models that aid clinicians in identifying patients at increased risk of developing atrial fibrillation, or AFib, along with a variety of other cardiac conditions. These Algos are trained using a de-identified subset from approximately 3.5 million ECGs, across more than 800,000 patients, with decades of longitudinal clinical data, including outcome and response data. The FDA granted Tempus breakthrough status for our first ECG software device, which employs a diagnostic algorithm designed to identify patients at high risk of developing AFib in certain populations (patients 40 years of age and older, without pre-existing or concurrent AFib or atrial flutter, and who are at elevated risk of stroke based on a commonly used clinical stroke risk assessment tool (i.e., CHA2DS2-VASc score of ≥4)).

    Algo 
    LaunchYear 
    Description 

    Cardiology

    Atrial Fibrillation Test
    2023 (inclinicaltrialsetting)
    •	We have developed an algorithm designed to predict AFib from a normal ECG for certain populations. •	About 3.5% of patients who receive ECGs appear not to have AFib but will develop AFib, acute coronary syndrome, or similar condition within one year. This Algo is designed to predict major cardiac trauma and stroke risk from these normal ECG results. •	The Tempus AFib test received FDA