Clinical Prediction Model for Time in Therapeutic Range While on Warfarin in Newly Diagnosed Atrial Fibrillation

Abstract

BackgroundThough warfarin has historically been the primary oral anticoagulant for stroke prevention in newly diagnosed atrial fibrillation (AF), several new direct oral anticoagulants may be preferred when anticoagulation control with warfarin is expected to be poor. This study developed a prediction model for time in therapeutic range (TTR) among newly diagnosed AF patients on newly initiated warfarin as a tool to assist decision making between warfarin and direct oral anticoagulants.Methods and ResultsThis electronic medical record–based, retrospective study included newly diagnosed, nonvalvular AF patients with no recent warfarin exposure receiving primary care services through a large healthcare system in rural Pennsylvania. TTR was estimated as the percentage of time international normalized ratio measurements were between 2.0 and 3.0 during the first year following warfarin initiation. Candidate predictors of TTR were chosen from data elements collected during usual clinical care. A TTR prediction model was developed and temporally validated and its predictive performance was compared with the SAMe‐TT 2R2 score (sex, age, medical history, treatment, tobacco, race) using R 2 and c‐statistics. A total of 7877 newly diagnosed AF patients met study inclusion criteria. Median (interquartile range) TTR within the first year of starting warfarin was 51% (32, 67). Of 85 candidate predictors evaluated, 15 were included in the final validated model with an R 2 of 15.4%. The proposed model showed better predictive performance than the SAMe‐TT 2R2 score (R 2=3.0%).ConclusionsThe proposed prediction model may assist decision making on the proper mode of oral anticoagulant among newly diagnosed AF patients. However, predicting TTR on warfarin remains challenging.

Impact of Aspirin on Warfarin Control as Measured by Time in Therapeutic Range

“…Similar to this, Williams et al . . recently found aspirin to be a strong predictor of TTR and proposed predicting a decrease of around 5% TTR for newly initiated warfarin patients on aspirin.…”

Prognostic value of the SAMe-TT2R2 index and Charlson Comorbidity Index for the non-target anticoagulation and risk of thrombotic events in warfarin-treated patients (results of 10-years prospective study)

“…Также очевидна связь коморбидности с полипрагмазией, что, несомненно, важно при терапии варфарином. С данными нашего исследования о достоверной ассоциации индекса коморбидности Charlson с развитием тромботических событий согласуются результаты других работ [35,[38][39][40], показавших ассоциации полиморбидности больных с ФП, получающих терапию варфарином, с развитием определяющих прогноз неблагоприятных событий.…”

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

“…[13][14][15][16][17] Various bleeding risk scores feature a TTR component to enhance accuracy, 18 and TTR has predictive power for thrombotic and bleeding events. 19,20 However, information on serial changes in PT-INR during early-phase VKA therapy, which may reflect many occult clinical characteristics of patients such as genotype, 21,22 concomitant medications, 23 and lifestyle, 24 were not included in these TTR-based models. Advances in artificial intelligence (AI) using recurrent neural networks (RNN) allow the identification and translation of multi-dimensional data including time-series data directly into meaningful models.…”

“… 13–17 Various bleeding risk scores feature a TTR component to enhance accuracy, 18 and TTR has predictive power for thrombotic and bleeding events. 19 , 20 However, information on serial changes in PT-INR during early-phase VKA therapy, which may reflect many occult clinical characteristics of patients such as genotype, 21 , 22 concomitant medications, 23 and lifestyle, 24 were not included in these TTR-based models.…”