Elements of artificial intelligence in a predictive personalized model of pharmacotherapy choice in patients with heart failure with mildly reduced ejection fraction of ischemic origin

Abstract

Aim. To create and train a neural network (NN) of a predictive personalized model of pharmacotherapy choice in patients with heart failure with mildly reduced ejection fraction (HFmrEF) of ischemic origin.Material and methods. The study included 170 people with HFmrEF of ischemic origin, who on the background standard pharmacotherapy, received a beta-blocker (BB) or BB+mineralocorticoid receptor antagonist eplerenone (EP): bisoprolol (BIS); BIS+EP; nebivolol (NEB); NEB+EP. Patients underwent echocardiography and were analyzed for serum aldosterone (AL), tumor necrosis factor-α (TNF-α), matrix metalloproteinase 9 (MMP-9). To create the NN model, the following approximate predictive function of parameters was used: age, AL, TNF-α, MMP-9, sphericity index (SI), type of pharmacotherapy. The result of this function is a parameter vector: AL, TNF-α, MMP-9, SI and quality of life (QOL). The designed NN model is implemented in the Matlab software package for solving machine learning and Data Science problems. The NN model is represented as a connected graph and NN function. Dichotomous analysis was used to compare the effect of treatment types in pairs. For intergroup comparison of therapy, the Wilcoxon W test method. The critical significance (p) was considered <0,05.Results. As a result of model inference, the predicted clinical parameters of patients were obtained, depending on the influence of pharmacotherapy type on the levels of AL, TNF-α, MMP-9, and SI. Function approximation of the distribution was constructed. Determination coefficient R2 of approximating functions was ≥0,92. The calculated values for the BIS therapy groups were obtained; BIS+EP — 169,59, 82,30, 15,26 and 52,92; NEB — 186,42, 87,65, 16,10 and 57,22; NEB+EP — 171,17, 71,90, 14,22 and 58,68, respectively. There were following mean values in the vector of initial states (before therapy): AL, MMP-9, TNF-α, and QOL — 205,84, 174,16, 18,32, and 50,71, respectively. The greatest negative changes of AL, MMP-9, TNF-α (p<0,05) was observed in the NEP+EP group.Conclusion. In the course of the study, using artificial intelligence, a predictive model of a personalized approach to pharmacotherapy choice in patients with HFmrEF of ischemic origin was developed and trained. It has been established that NEP+EP therapy has the greatest effect.

Keywords

• artificial intelligence
• neural network
• machine learning
• pharmacotherapy
• heart failure