A Photoplethysmography-Based Machine Learning Algorithm for Early Atrial Fibrillation Detection: A Prospective Validation Study (HeartCore AF)
Prospective Validation of a Machine-Learning Algorithm Using Photoplethysmography Signals for Early Detection of Atrial Fibrillation During Remote Telemonitoring
Studieoversigt
Status
Status
Betingelser
Betingelser
Intervention / Behandling
Intervention / Behandling
Detaljeret beskrivelse
Atrial fibrillation (AF) and heart failure (HF) frequently coexist and share a bidirectional causal relationship; their concurrence is associated with worse clinical outcomes. Early detection of AF may enable timely intervention and improve outcomes. This study is prospectively validating a machine-learning algorithm for AF detection from PPG signals, intended for integration into the Seerlinq remote monitoring platform. This algorithm builds on the same core PPG signal-processing technology as Seerlinq's HeartCore device (a CE-certified, Class IIb device under the EU MDR that monitors left ventricular filling pressures in heart failure patients). It is a stand-alone algorithm designed specifically to detect clinically relevant (≥ 30s) atrial fibrillation.
Validation of the algorithm will proceed in three stages: (1) internal cross-validation; (2) external validation against an independent cohort with paired PPG-ECG recordings, to confirm generalizability; and (3) validation in a cohort of patients with paroxysmal atrial fibrillation and frequent sinus-AF transitions, to assess performance during clinically challenging rhythm changes.
The study is enrolling toward an estimated 1,000 unique PPG recordings. A 12-lead ECG is used to confirm cardiac rhythm classification (gold standard) as the reference for evaluating algorithm performance.
Undersøgelsestype
Undersøgelsestype
Tilmelding (Anslået)
Tilmelding
Kontakter og lokationer
Studiekontakt
Studiekontakt
- Navn: Marta Kollárová, MSc., PhD.
- Telefonnummer: +421 950 896 026
- E-mail: marta.kollarova@premedix.org
Studiesteder
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Bratislava, Slovakiet
- Rekruttering
- PreMedix
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Kontakt:
- Allan Bohm, M.D., MSc. PhD.
- Telefonnummer: +421 907 411 499
- E-mail: allan.bohm@premedix.org
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Ledende efterforsker:
- Allan Bohm, M.D., MSc., PhD.
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Deltagelseskriterier
Berettigelseskriterier
Berettigelseskriterier
Aldre berettiget til at studere
- Voksen
- Ældre voksen
Tager imod sunde frivillige
Prøveudtagningsmetode
Studiebefolkning
Beskrivelse
Inclusion Criteria:
- Adults ≥18 years with a diagnosis of heart failure (HFrEF, HFmrEF, or HFpEF)
- 12-lead ECG performed to confirm cardiac rhythm classification (AF vs. non-AF)
Exclusion Criteria:
- Missing a valid PPG recording
Studieplan
Hvordan er undersøgelsen tilrettelagt?
Design detaljer
Antal grupper/kohorter
Kohorter og interventioner
Gruppe / kohorteGruppe / kohorte |
Intervention / BehandlingIntervention / Behandling |
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Documented AF
HF patients with a history of permanent/paroxysmal AF and AF documented on 12-lead ECG at enrollment
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The PPG-based atrial fibrillation detection algorithm is a non-invasive signal processing approach that analyzes photoplethysmographic waveforms obtained during remote monitoring.
The algorithm evaluates pulse-to-pulse variability, waveform characteristics, and signal quality parameters to identify irregular rhythm patterns associated with atrial fibrillation and provide early detection of potential arrhythmic events.
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Non-AF
HF patients in sinus rhythm on the index 12-lead ECG with no prior documented AF episodes
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The PPG-based atrial fibrillation detection algorithm is a non-invasive signal processing approach that analyzes photoplethysmographic waveforms obtained during remote monitoring.
The algorithm evaluates pulse-to-pulse variability, waveform characteristics, and signal quality parameters to identify irregular rhythm patterns associated with atrial fibrillation and provide early detection of potential arrhythmic events.
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Hvad måler undersøgelsen?
Primære resultatmål
Primære resultatmål
Resultatmål |
Tidsramme |
|---|---|
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Diagnostic accuracy (area under the ROC curve) of the PPG-based machine-learning algorithm for detecting clinically relevant AF (≥ 30s), compared with gold-standard 12-lead ECG
Tidsramme: Through study completion (estimated November 2026)
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Through study completion (estimated November 2026)
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Sekundære resultatmål
Sekundære resultatmål
Resultatmål |
Foranstaltningsbeskrivelse |
Tidsramme |
|---|---|---|
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Sensitivity and specificity of the algorithm at the Youden-optimal threshold
Tidsramme: Through study completion (estimated November 2026)
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Through study completion (estimated November 2026)
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Positive predictive value and negative predictive value
Tidsramme: Through study completion (estimated November 2026)
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Through study completion (estimated November 2026)
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Average precision
Tidsramme: Through study completion (estimated November 2026)
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area under the precision-recall curve
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Through study completion (estimated November 2026)
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Model calibration
Tidsramme: Through study completion (estimated November 2026)
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e.g., calibration curve / Brier score
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Through study completion (estimated November 2026)
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Matthews correlation coefficient
Tidsramme: Through study completion (estimated November 2026)
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Through study completion (estimated November 2026)
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Overall classification accuracy
Tidsramme: Through study completion (estimated November 2026)
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Through study completion (estimated November 2026)
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Specificity and false-positive rate in the subgroup with frequent atrial/ventricular extrasystoles
Tidsramme: Through study completion (estimated November 2026)
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Through study completion (estimated November 2026)
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Accuracy of AF detection during sinus-AF transitions at the individual patient level
Tidsramme: Through study completion (estimated November 2026)
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Through study completion (estimated November 2026)
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Samarbejdspartnere og efterforskere
Sponsor
Sponsor
Samarbejdspartnere
Samarbejdspartnere
Datoer for undersøgelser
Studer store datoer
Studiestart (Faktiske)
Studiestart
Primær færdiggørelse (Anslået)
Primær færdiggørelse
Studieafslutning (Anslået)
Studieafslutning
Datoer for studieregistrering
Først indsendt
Først indsendt
Først indsendt, der opfyldte QC-kriterier
Først indsendt, der opfyldte QC-kriterier
Først opslået (Faktiske)
Først opslået
Opdateringer af undersøgelsesjournaler
Sidste opdatering sendt (Faktiske)
Sidste opdatering sendt
Sidste opdatering indsendt, der opfyldte kvalitetskontrolkriterier
Sidste opdatering indsendt, der opfyldte kvalitetskontrolkriterier
Sidst verificeret
Sidst verificeret
Mere information
Begreber relateret til denne undersøgelse
Nøgleord
Yderligere relevante MeSH-vilkår
Andre undersøgelses-id-numre
Andre undersøgelses-id-numre
- HeartCoreAF01
Plan for individuelle deltagerdata (IPD)
Planlægger du at dele individuelle deltagerdata (IPD)?
IPD-planbeskrivelse
Lægemiddel- og udstyrsoplysninger, undersøgelsesdokumenter
Studerer et amerikansk FDA-reguleret lægemiddelprodukt
Studerer et amerikansk FDA-reguleret enhedsprodukt
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