Evidence map›Paper›PMID 31821482›Full record

ArticleEuropean heart journal. Cardiovascular pharmacotherapy2020

New artificial intelligence prediction model using serial prothrombin time international normalized ratio measurements in atrial fibrillation patients on vitamin K antagonists: GARFIELD-AF.

Shinichi Goto, Shinya Goto, Karen S Pieper, Jean-Pierre Bassand, Alan John Camm, David A Fitzmaurice, Samuel Z Goldhaber, Sylvia Haas, Alexander Parkhomenko, Ali Oto and 6 more

Open access · bronzeAbstract readMulticenter StudyValidation Study
PubMed Full text at PMC Publisher
In one paragraph

Article in European heart journal. Cardiovascular pharmacotherapy, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
24citing papers in PubMed, 3 pooled it
3.7field-weighted citation impact, top 6% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

24 citing papers in PubMed, 3 syntheses or guidelines pooled it, 50 citations in OpenAlex.

  1. Pooled it
  2. Comprehensive comparison of stroke risk score performance: a systematic review and meta-analysis among 6 267 728 patients with atrial fibrillation.Europace : European pacing, arrhythmias, and cardiac electrophysiology : journal of the working groups on cardiac pacing, arrhythmias, and cardiac cellular electrophysiology of the European Society of Cardiology · 2022
    Pooled it
  3. Guideline
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  7. Artificial intelligence in clinical thrombosis and hemostasis: A review.Research and practice in thrombosis and haemostasis · 2025
    Review
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  16. Safety of antithrombotic therapy in East Asian patients.Internal and emergency medicine · 2021
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

16 authors at 15 institutions in 9 countries.

Shinichi GotoDepartment of Cardiology, Keio University School of Medicine, Shinanomachi 35, Shinjuku 160-8582, Tokyo, Japan.
Shinya GotoDepartment of Medicine (Cardiology), Tokai University School of Medicine, 143 Shimokasuya, Isehara, Kanagawa 259-1193, Japan.
Karen S PieperDepartment of Clinical Research, Thrombosis Research Institute, Emmanuel Kaye Building, Manresa Road, Chelsea, London SW3 6LR, UK.
Jean-Pierre BassandDepartment of Clinical Research, Thrombosis Research Institute, Emmanuel Kaye Building, Manresa Road, Chelsea, London SW3 6LR, UK.
Alan John CammCardiology Clinical Academic Group, Molecular & Clinical Sciences Institute, St. George's University of London, Cranmer Terrace, Tooting, London, UK.
David A FitzmauriceDepartment of Cardio-respiratory Primary Care, Warwick Medical School, University of Warwick, Coventry, CV4 7AL, UK.
Samuel Z GoldhaberDepartment of Medicine, Harvard Medical School, Brigham and Women's Hospital, 75 Francis Street, Boston, MA 02115, USA.
Sylvia HaasFormerly Klinikum rechts der Isar, Technical University of Munich, Normannenstr. 34a, Munich 80333, Germany.
Alexander ParkhomenkoNational Scientific Center, Strazhesko Institute of Cardiology, 5 Narodnogo Opolcheniya Street, Kiev 03680, Ukraine.
Ali OtoDepartment of Cardiology, Memorial Ankara Hospital, Sihhiye, 06100, Ankara, Turkey.
Frank MisselwitzTherapeutic areas Thrombosis & Hematology, Bayer AG, Müllerstraße 178, 13353 Berlin, Germany.
Alexander G G TurpieDepartment of Medicine, McMaster University, 237 Barton St E Hamilton, Ontario L8L 2X2, Canada.
Freek W A VerheugtDepartment of Cardiology, Onze Lieve Vrouwe Gasthuis (OLVG), Oosterpark 9, NL-1091-AC Amsterdam, Netherlands.
Keith A A FoxEdinburgh Centre for Cardiovascular Science, University of Edinburgh, Queen's Medical Research Institute, 47 Little France Crescent, Edinburgh EH16 4TJ, UK.
Bernard J GershDepartment of Cardiovascular Medicine, Mayo Clinic College of Medicine, Rochester, MN, USA.
Ajay K KakkarDepartment of Clinical Research, Thrombosis Research Institute, Emmanuel Kaye Building, Manresa Road, Chelsea, London SW3 6LR, UK.
Thrombosis Research Institute · GBBayer (Germany) · DEHarvard University · USKeio University · JPMayo Clinic · USMcMaster University · CAMemorial Ankara Hospital · TRNational Scientific Center "M.D. Strazhesko Institute of Cardiology" · UAOLVG · NLSt George's, University of London · GBTechnical University of Munich · DEThe Queen's Medical Research Institute · GBTokai University · JPUniversity College London · GBUniversity of Warwick · GB

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

aimsMost clinical risk stratification models are based on measurement at a single time-point rather than serial measurements. Artificial intelligence (AI) is able to predict one-dimensional outcomes from multi-dimensional datasets. Using data from Global Anticoagulant Registry in the Field (GARFIELD)-AF registry, a new AI model was developed for predicting clinical outcomes in atrial fibrillation (AF) patients up to 1 year based on sequential measures of prothrombin time international normalized ratio (PT-INR) within 30 days of enrolment. METHODS AND

resultsPatients with newly diagnosed AF who were treated with vitamin K antagonists (VKAs) and had at least three measurements of PT-INR taken over the first 30 days after prescription were analysed. The AI model was constructed with multilayer neural network including long short-term memory and one-dimensional convolution layers. The neural network was trained using PT-INR measurements within days 0-30 after starting treatment and clinical outcomes over days 31-365 in a derivation cohort (cohorts 1-3; n = 3185). Accuracy of the AI model at predicting major bleed, stroke/systemic embolism (SE), and death was assessed in a validation cohort (cohorts 4-5; n = 1523). The model's c-statistic for predicting major bleed, stroke/SE, and all-cause death was 0.75, 0.70, and 0.61, respectively.

conclusionsUsing serial PT-INR values collected within 1 month after starting VKA, the new AI model performed better than time in therapeutic range at predicting clinical outcomes occurring up to 12 months thereafter. Serial PT-INR values contain important information that can be analysed by computer to help predict adverse clinical outcomes.

Indexed as

Drug MonitoringDrug Therapy, Computer-AssistedInternational Normalized RatioNeural Networks, ComputerProthrombin TimeAdministration, OralAgedAged, 80 and overAnticoagulantsAtrial FibrillationBlood CoagulationDatabases, FactualFemaleHemorrhageHumansMaleAnticoagulantsVitamin KArtificial intelligenceAtrial fibrillationMachine learning

Identifiers

PMID31821482
PMCPMC7556811
OpenAlexW2996098367

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