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

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.

SUBMITTER: Goto S 

PROVIDER: S-EPMC7556811 | biostudies-literature | 2020 Sep

REPOSITORIES: biostudies-literature