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

European Heart Journal - Cardiovascular Pharmacotherapy, ISSN: 2055-6845, Vol: 6, Issue: 5, Page: 301-309
2020
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Article Description

Aims: Most 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 results: Patients 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. Conclusions: Using 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.

Bibliographic Details

Goto, Shinichi; Goto, Shinya; Pieper, Karen S; Bassand, Jean-Pierre; Camm, Alan John; Fitzmaurice, David A; Goldhaber, Samuel Z; Haas, Sylvia; Parkhomenko, Alexander; Oto, Ali; Misselwitz, Frank; Turpie, Alexander G G; Verheugt, Freek W A; Fox, Keith A A; Gersh, Bernard J; Kakkar, Ajay K

Oxford University Press (OUP)

Medicine

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