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

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.