Warfarin maintenance dose prediction for Chinese after heart valve replacement by a feedforward neural network with equal stratified sampling.

Patients requiring low-dose warfarin are more likely to suffer bleeding due to overdose. The goal of this work is to improve the feedforward neural network model's precision in predicting the low maintenance dose for Chinese in the aspect of training data construction. We built the model from a resampled dataset created by equal stratified sampling (maintaining the same sample number in three dose-groups with a total of 3639) and performed internal and external validations. Comparing to the model trained from the raw dataset of 19,060 eligible cases, we improved the low-dose group's ideal prediction percentage from 0.7 to 9.6% and maintained the overall performance (76.4% vs. 75.6%) in external validation. We further built neural network models on single-dose subsets to invest whether the subsets samples were sufficient and whether the selected factors were appropriate. The training set sizes were 1340 and 1478 for the low and high dose subsets; the corresponding ideal prediction percentages were 70.2% and 75.1%. The training set size for the intermediate dose varied and was 1553, 6214, and 12,429; the corresponding ideal prediction percentages were 95.6, 95.1%, and 95.3%. Our conclusion is that equal stratified sampling can be a considerable alternative approach in training data construction to build drug dosing models in the clinic.

The Prediction Model of Warfarin Individual Maintenance Dose for Patients Undergoing Heart Valve Replacement, Based on the Back Propagation Neural Network.

BACKGROUND AND OBJECTIVE: Because of the narrow therapeutic window and huge inter-individual variation, the individual precision on anticoagulant therapy of warfarin is challenging. In our study, we aimed to construct a Back Propagation Neural Network (BPNN) model to predict the individual warfarin maintenance dose among Chinese patients who have undergone heart valve replacement, and validate its prediction accuracy. METHODS: In this study, we analyzed 13,639 eligible patients extracted from the Chinese Low Intensity Anticoagulant Therapy after Heart Valve Replacement database, which collected data on patients using warfarin after heart valve replacement from 15 centers all over China. Ten percent of patients who were finally enrolled in the database were used as the external validation, while the remaining were randomly divided into the training and internal validation groups at a ratio of 3:1. Input variables were selected by univariate analysis of the general linear model; 2.0, the mean value of the international normalized ratio (INR) range 1.5-2.5, was used as the mandatory variable. The BPNN model and the multiple linear regression (MLR) model were constructed by the training group and validated through comparisons of the mean absolute error (MAE), mean squared error (MSE), root mean squared error (RMSE), and ideal predicted percentage. RESULTS: Finally, 10 input variables were selected and a three-layer BPNN model was constructed. In the BPNN model, the value of MAE (0.688 mg/day and 0.740 mg/day in internal and external validation, respectively), MSE (0.580 mg/day and 0.599 mg/day in internal and external validation, respectively), and RMSE (0.761 mg/day and 0.774 mg/day in internal and external validation, respectively) were achieved. Ideal predicted percentages were high in both internal (63.0%) and external validation (59.7%), respectively. Compared with the MLR model, the BPNN model showed a higher ideal prediction percentage in the external validation group (59.7% vs. 56.6%), and showed the best prediction accuracy in the intermediate-dose subgroup (internal validation group: 85.2%; external validation group: 84.7%) and a high predicted percentage in the high-dose subgroup (internal validation group: 36.2%; external validation group: 39.8%), but poor performance in the low-dose subgroup (internal validation group: 0%; external validation group: 0.3%). Meanwhile, the BPNN model showed better ideal prediction percentage in the high-dose group than the MLR model (internal validation: 36.2% vs. 31.6%; external validation: 42.8% vs. 37.8%). CONCLUSION: The BPNN model shows promise for predicting the warfarin maintenance dose after heart valve replacement.

Regression of pathological cardiac hypertrophy: signaling pathways and therapeutic targets.

Pathological cardiac hypertrophy is a key risk factor for heart failure. It is associated with increased interstitial fibrosis, cell death and cardiac dysfunction. The progression of pathological cardiac hypertrophy has long been considered as irreversible. However, recent clinical observations and experimental studies have produced evidence showing the reversal of pathological cardiac hypertrophy. Left ventricle assist devices used in heart failure patients for bridging to transplantation not only improve peripheral circulation but also often cause reverse remodeling of the geometry and recovery of the function of the heart. Dietary supplementation with physiologically relevant levels of copper can reverse pathological cardiac hypertrophy in mice. Angiogenesis is essential and vascular endothelial growth factor (VEGF) is a constitutive factor for the regression. The action of VEGF is mediated by VEGF receptor-1, whose activation is linked to cyclic GMP-dependent protein kinase-1 (PKG-1) signaling pathways, and inhibition of cyclic GMP degradation leads to regression of pathological cardiac hypertrophy. Most of these pathways are regulated by hypoxia-inducible factor. Potential therapeutic targets for promoting the regression include: promotion of angiogenesis, selective enhancement of VEGF receptor-1 signaling pathways, stimulation of PKG-1 pathways, and sustention of hypoxia-inducible factor transcriptional activity. More exciting insights into the regression of pathological cardiac hypertrophy are emerging. The time of translating the concept of regression of pathological cardiac hypertrophy to clinical practice is coming.