Long Short-Term Memory Network for Development and Simulation of Warfarin Dosing Model Based on Time Series Anticoagulant Data

Yun Kuang, Yaxin Liu, Qi Pei, Xiaoyi Ning, Yi Zou, Liming Liu, Long Song, Chengxian Guo, Yuanyuan Sun, Kunhong Deng, Chan Zou, Dongsheng Cao, Yimin Cui, Chengkun Wu, Guoping Yang, Yun Kuang, Yaxin Liu, Qi Pei, Xiaoyi Ning, Yi Zou, Liming Liu, Long Song, Chengxian Guo, Yuanyuan Sun, Kunhong Deng, Chan Zou, Dongsheng Cao, Yimin Cui, Chengkun Wu, Guoping Yang

Abstract

Background: Warfarin is an effective treatment for thromboembolic disease but has a narrow therapeutic index, and dosage can differ tremendously among individuals. The study aimed to develop an individualized international normalized ratio (INR) model based on time series anticoagulant data and simulate individualized warfarin dosing.

Methods: We used a long short-term memory (LSTM) network to develop an individualized INR model based on data from 4,578 follow-up visits, including clinical and genetic factors from 624 patients whom we enrolled in our previous randomized controlled trial. The data of 158 patients who underwent valvular surgery and were included in a prospective registry study were used for external validation in the real world.

Results: The prediction accuracy of LSTM_INR was 70.0%, which was much higher than that of MAPB_INR (maximum posterior Bayesian, 53.9%). Temporal variables were significant for LSTM_INR performance (51.7 vs. 70.0%, P < 0.05). Genetic factors played an important role in predicting INR at the onset of therapy, while after 15 days of treatment, we found that it might unnecessary to detect genotypes for warfarin dosing. Using LSTM_INR, we successfully simulated individualized warfarin dosing and developed an application (AI-WAR) for individualized warfarin therapy.

Conclusion: The results indicate that temporal variables are necessary to be considered in warfarin therapy, except for clinical factors and genetic factors. LSTM network may have great potential for long-term drug individualized therapy.

Trial registration: NCT02211326; www.chictr.org.cn:ChiCTR2100052089.

Keywords: application; long short-term memory network; modeling; time series; warfarin.

Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Copyright © 2022 Kuang, Liu, Pei, Ning, Zou, Liu, Song, Guo, Sun, Deng, Zou, Cao, Cui, Wu and Yang.

Figures

Figure 1
Figure 1
The modeling flow chart (A) including schematic representation of feedforward neural network (B) and long short-term memory network (C).
Figure 2
Figure 2
Prediction accuracy of different models. LSTM_INR_no_gene means LSTM_INR model without genotype data; LSTM_INR_no_time means LSTM_INR model without temporal data; MAPB_INR means INR model based on maximum posterior Bayesian. Two yellow lines show the range of 70–130% of true values. Plots above the line means overestimated, plots under the line means underestimated.
Figure 3
Figure 3
Sequence diagram of effect of genetic factors on LSTM_INR. *There are significant differences between two groups (P < 0.05).

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