Eko Low Ejection Fraction Tool (ELEFT)

K233409

Eko Health, Inc. · cleared 2024-03-28 · product code QYE · Cardiovascular

Premarket evidence — what FDA accepted

Device typesamd
source quote (p.5)
Eko Low Ejection Fraction Tool (ELEFT) is an algorithm that is intended to aid clinicians to identify individuals with Left Ventricular Ejection Fraction (LVEF) less than or equal to 40%. ELEFT takes as input ECG and heart sounds from patients at risk for heart failure. The software uses signal processing as well as machine learning algorithms, to analyze the electrocardiogram (ECG) and heart sound/phonocardiogram (PCG) recording signals generated by FDA-cleared Eko Stethoscopes and saved as .WAV file recordings in the Eko Cloud. ELEFT is a machine learning based notification software which employs machine learning techniques to suggest the likelihood of LVEF
Algorithmmachine learning based notification software, deep convolutional neural network models
source quote (p.5)
Eko Low Ejection Fraction Tool (ELEFT) is an algorithm that is intended to aid clinicians to identify individuals with Left Ventricular Ejection Fraction (LVEF) less than or equal to 40%. ELEFT takes as input ECG and heart sounds from patients at risk for heart failure. The software uses signal processing as well as machine learning algorithms, to analyze the electrocardiogram (ECG) and heart sound/phonocardiogram (PCG) recording signals generated by FDA-cleared Eko Stethoscopes and saved as .WAV file recordings in the Eko Cloud. ELEFT is a machine learning based notification software which employs machine learning techniques to suggest the likelihood of LVEF ≤ 40% for further referral or diagnostic follow-up. Otherwise, deep convolutional neural network models are used to classify ECG and heart sound as “Normal Ejection Fraction” or “Low Ejection Fraction".
Adaptive (vs locked)FDA source did not state this
PCCPNo
Cybersecurity addressedYes
source quote (p.9)
The performance characteristics for the Eko Low Ejection Fraction Tool (ELEFT) have been evaluated with the following non-clinical testing: software unit, integration and system level verification testing consistent with the IEC 62304 standard, and cybersecurity testing.

Validation studies (2)

Retrospective clinical

n=3,456 patients · 5 site(s)

endpoints: Low EF Detection (LVEF <= 40%)

Bench

sample size not stated

endpoints: software unit testing; integration testing; system level verification testing; cybersecurity testing

standards: IEC 62304

Reported performance (4 observations)

sensitivity74.7CI 95% CI: 69.4-79.6
source quote (p.9)
74.7 (95% CI: 69.4-79.6)
specificity77.5CI 95% CI: 75.9-79.0
source quote (p.9)
77.5 (95% CI: 75.9-79.0)
ppvas written: “Positive Predictive Value25.7CI 95%CI: 22.8-28.7
source quote (p.10)
positive predictive value (PPV) of 25.7% (95%CI: 22.8-28.7)
npvas written: “Negative Predictive Value96.7CI 95%CI: 95.9 to 97.4
source quote (p.10)
negative predictive value (NPV) of 96.7% (95%CI: 95.9 to 97.4)

Each value carries its own analysis unit and task — never compare or pool across devices. Source: 510(k) summary PDF.

Predicate network

Postmarket — what happened after clearance

0
recalls in product code, 24mo
0
MAUDE reports in code, 12mo
vs code's own 3-yr baseline
0
drift signals on this device

Recall and MAUDE counts are product-code-level (reports aren't reliably attributable to one device); a recall is shown as device-attributed only when the recall record itself lists this clearance number. Signals are descriptive observables with sources — never a judgment that the device is unsafe or drifting. Snapshot 2026-07-08.

Reimbursement — how devices like this got paid

Not yet tracked — no payment pathway indexed for this clearance (the reimbursement corpus is a growing seed set).

Applicable FDA guidance — what the submission is measured against

FDA guidance documents and guiding principles applicable to 510(k) AI/ML devices in the Cardiovascular panel. A curated reference index, not legal or regulatory advice — each item states its own status, and a draft is never binding.

Applicability is derived from the device's FDA advisory panel and pathway — cross-cutting guidances apply to every AI/ML device; panel-specific ones are flagged. Titles, dates, and links verified against fda.gov as of July 2026.

Constat Precedent · public FDA/CMS data · descriptive decision-support, not regulatory or reimbursement advice. Share this page: constat.dev/precedent/device/K233409