In European heart journal. Cardiovascular pharmacotherapy ; h5-index 0.0
BACKGROUND : 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 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 PT-INR within 30 days of enrolment.
METHODS AND RESULTS : Patients with newly diagnosed AF who were treated with vitamin K antagonists (VKA) and had at least 3 measurements of PT-INR taken over the first 30 days after prescription were analyzed. The AI model was constructed with multilayer neural network including long short-term memory (LSTM) 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/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 (TTR) at predicting clinical outcomes occurring up to 12 months thereafter. Serial PT-INR values contain important information that can be analyzed by computer to help predict adverse clinical outcomes.
Goto Shinichi, Goto Shinya, Pieper Karen S, Bassand Jean-Pierre, Camm A 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
artificial intelligence (AI), atrial fibrillation (AF), machine learning