Machine learning for predicting the outcomes and risks of cardiovascular diseases in patients with hypertension: results of ESSE-RF in the Primorsky Krai

Aim. To assess the prospects of using artificial intelligence technologies in predicting the outcomes and risks of cardiovascular diseases (CVD) in patients with hypertension (HTN).

Material and methods. A software application was created for data mining from respondent profiles in a semi-automatic mode; libraries with data preprocessing were analyzed. We analyzed the main and additional parameters (35) of CVD risk factors in 2131 people as a part of ESSE-RF study (2014-2019). To create a forecasting model, a high-level language Python 2.7 was used using object-oriented programming and exception handling with multithreading support. Using randomization, learning (n=488) and test (n=245) samples were formed, which included data from patients with an established diagnosis of HTN.

Results. The prevalence of HTN among subjects was 34,39%. There were following significant factors for predicting CVD: anthropometric parameters, smoking, biochemical profile (total cholesterol, ApoA, ApoB, glucose, D-dimer, C-reactive protein). As a result of a 5-year follow-up, CVD was found in 235 people (32,06%) with HTN and 187 people (13,38%) without HTN; mortality rates were 1,27% in subjects with HTN and 1,12% — without HTN. The absolute mortality risk among participants with HTN (0,037) was significantly higher (p<0,05) than in patients without HTN (0,017). To create a neural network (NN), the basic Sequential model from the Keras library was used. During machine learning, 26 variables important for the CVD development were used as input and 9 neurons — as output, which corresponded to the number of established cardiovascular events. The created NN had a predictive value of up to 97,9%, which exceeded the SCORE value (34,9%).

Conclusion. The data obtained indicate the importance of risk factor phenotyping using anthropometric markers and biochemical profile for determining their significance in the top 20 predictors of CVD. The Python-based machine learning provides CVD prediction according to standard risk assessments.

1. Siontis GC, Tzoulaki I, Siontis KC, et al. Comparisons of established risk prediction models for cardiovascular disease: systematic review. BMJ. 2012;344:e3318. doi:10.1136/bmj.e3318.

2. Weng SF, Reps J, Kai J, et al. Can machine-learning improve cardiovascular risk prediction using routine clinical data?. PLoS One. 2017;12(4):e0174944. Published 2017 Apr 4. doi:10.1371/journal.pone.0174944.

3. Ahmad T, Lund LH, Rao P, et al. Machine Learning Methods Improve Prognostication, Identify Clinically Distinct Phenotypes, and Detect Heterogeneity in Response to Therapy in a Large Cohort of Heart Failure Patients. Journal of the American Heart Association. 2018;7(8):e008081. doi:10.1161/JAHA.117.008081.

4. Plekhova NG, Nevzorova VA, Rodionova LV, et al. Scale of Binary Variables for Predicting Cardiovascular Risk Scale for predicting cardiovascular risk. Proceedings of the 2018 3rd Russian-Pacific Conf. on computer technology and applications (RPC). 2018. doi:10.1109/RPC.2018.8482216.

5. The Scientific and Organizing Committee of the project of the ESSE-RF. Epidemiology of cardiovascular diseases in various regions of Russia (ESSE-RF). Rationale and design of the study. Prophylactic medicine. 2013;6:25-34. (In Russ.)

6. Mancia G, Fagard R, Narkiewicz K, et al. Recommendations for the treatment of arterial hypertension. ESH/ESC 2013. Russian Journal of Cardiology. 2014;(1):7-94. (In Russ.) doi:10.15829/1560-4071-2014-1-7-94.

7. Plekhova NG, Nevzorova VA, Rodionova LV, et al. Indicators of lipoprotein metabolism in young patients with arterial hypertension. Bulletin of modern clinical medicine. 2019;4:44-51. (In Russ.) doi:10.20969/VSKM.2019.12(4).44-51.

8. Ambale-Venkatesh B, Yang X, Wu CO, et al. Cardiovascular Event Prediction by Machine Learning: The Multi-Ethnic Study of Atherosclerosis. Circ Res. 2017;121(9):1092-101. doi:10.1161/CIRCRESAHA.117.311312.