Abstract
Warfarin is the most recommended anticoagulant drug for patients undergoing heart valve replacement. However, due to the narrow therapeutic window and individual dose, the use of warfarin needs more advanced technology. We used the data collected from a multi-central registered clinical system all over China about the patients who have undergone heart valve replacement, subsequently divided into three groups (training group: 10673 cases; internal validation group: 3558 cases; external validation group: 1463 cases) in order to construct a hybrid model with genetic algorithm and Back-Propagation neural network (BP-GA), For testing the model’s prediction accuracy, we used Mean absolute error (MAE), Root mean squared error (RMSE) and the ideal predicted percentage of total and dose subgroups. In results, whether in internal or in external validation group, the total ideal predicted percentage was over 58% while the intermediate dose subgroup manifested the best. Moreover, it showed higher prediction accuracy, lower MAE value and lower RMSE value in the external validation group than that in the internal validation group (p < 0.05). In conclusion, BP-GA model is promising to predict warfarin maintenance dose.
Highlights
Warfarin is a commonly used oral anticoagulant in heart valve replacement[1]
BackPropagation neural network (BP-GA), a hybrid artificial intelligent algorithm with genetic algorithm and Back-Propagation (BP) neural network, may be exactly what we look for
Our study has three important features: firstly, our study was based on a clinical registered system of 27012 cases using warfarin after heart valve replacement; secondly, we used BP-GA, an artificial intelligence method, to build a model based on 15694 eligible patients from the database; thirdly, the average warfarin maintenance dose was 2.73 mg/d, which was less than the previous IWPC7 maintenance dose of 4 mg/d
Summary
Warfarin is a commonly used oral anticoagulant in heart valve replacement[1]. warfarin has the following three limitations: the first is the narrow therapeutic window, which means that the effective dose is very close to the threshold dose and even a small dose variation can cause serious bleeding events; the second is the obvious variation of individual dose. The complex nonlinear relationship between the factors mentioned above and warfarin dose made the MLR model an inappropriate method that can predict warfarin maintenance dose accurately[10]. BP-GA, a hybrid artificial intelligent algorithm with genetic algorithm and Back-Propagation (BP) neural network, may be exactly what we look for. As it combines the benefit of the two algorithms, which can identify nonlinear relationships by its adaptive learning features[13,14,15], in the meantime, it is not easy to fall into locally optimal solution[16]. This study was aimed to construct the BP-GA model to predict individual warfarin maintenance dose and to evaluate its prediction accuracy
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