Goto, S; Goto, S; Pieper, KS; Bassand, J-P; Camm, AJ; Fitzmaurice, DA; Goldhaber, SZ; ... GARFIELD-AF Investigators; + view all Goto, S; Goto, S; Pieper, KS; Bassand, J-P; Camm, AJ; Fitzmaurice, DA; Goldhaber, SZ; Haas, S; Parkhomenko, A; Oto, A; Misselwitz, F; Turpie, AGG; Verheugt, FWA; Fox, KAA; Gersh, BJ; Kakkar, AK; GARFIELD-AF Investigators; - view fewer (2019) New AI Prediction Model Using Serial PT-INR Measurements in AF Patients on VKAs: GARFIELD-AF. European Heart Journal - Cardiovascular Pharmacotherapy 10.1093/ehjcvp/pvz076. (In press).

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Abstract

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.

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