Predictive model for recommending home blood pressure monitoring in outpatients with arterial hypertension
M. V. Ionov,
I. V. Emelyanov,
V. V. Vashchenkov,
Y. P. Voronin,
E. E. Gaibaliev,
S. V. Kovalchuk,
A. O. Konradi https://doi.org/10.18705/2311-4495-2026-13-4-359-370
Abstract
Background. Home blood pressure monitoring (HBPM) is recommended both for confirming the diagnosis of hypertension (HTN) and for monitoring treatment efficacy. The recommendation of the treating physician plays a key role in promoting active and correct HBPM. Objective. To assess the frequency of HBPM recommendations by cardiologists for outpatients with HTN, identify factors associated with HBPM recommendation, and develop a machine learning–based predictive model of HBPM recommendation using electronic medical record (EMR) data. Materials and methods. A retrospective analysis of EMR data from 44,358 outpatients with HTN seen by cardiologists in 2018–2024 was conducted. A predictive model was developed using the LightGBM algorithm based on structured clinical and demographic parameters and text features from the “Diagnosis Description” field (TF-IDF). Model performance was evaluated on a test set (70 % of 2018–2023 data) and an out-of-time validation set (OOT, 2024 data). Topic modeling (LDA) was applied to analyze recommendation structure. Results. HBPM recommendation was present in 40,180 patients (90.6 %). The machine learning model demonstrated high performance: test set AUC-ROC = 0.809, accuracy = 0.954, sensitivity = 0.810; OOT set AUC-ROC = 0.840, accuracy = 0.979, sensitivity = 0.837. Text features from diagnosis descriptions made the highest contribution (gain = 11,315.1), followed by age (3,461.8) and ICD code (3,138.4). Excluding text reduced AUC-ROC to 0.655–0.673. Higher probability of HBPM prescription was associated with younger age, advanced HTN stage, and uncontrolled blood pressure. LDA identified 6 topics; HBPM was mentioned in most. Conclusion. Cardiologists recommend HBPM to most patients with HTN. The decision to recommend HBPM is associated with objective indicators of disease severity and age. The developed predictive model accurately identifies patients for whom HBPM is indicated and can serve as a foundation for clinical decision support systems.
Keywords
arterial hypertension,
electronic health records,
home blood pressure monitoring,
machine learning,
physician recommendations,
predictive model
Supporting agencies: The study was carried out as part of the implementation of the state assignment project «Development of a personalized comprehensive approach to the management of patients with uncontrolled essential arterial hypertension using direct methods of treatment adherence assessment and telemedicine technologies»
About the Authors
M. V. Ionov
Federal State Budgetary Institution “Almazov National Medical Research Centre” of the Ministry of Health
of the Russian Federation
Russian Federation
Competing Interests:
Russian Federation
Mikhail V. Ionov, MD, PhD, Researcher, Department for
Arterial Hypertension, Department for Arterial Hypertension
St. Petersburg
Competing Interests:
The authors declare no use of Generative AI in the preparation of this manuscript
I. V. Emelyanov
Federal State Budgetary Institution “Almazov National Medical Research Centre” of the Ministry of Health
of the Russian Federation
Russian Federation
Competing Interests:
Russian Federation
Igor V. Emelyanov, MD, PhD, Senior Researcher, Department for Arterial Hypertension, Department for Arterial Hypertension
St. Petersburg
Competing Interests:
The authors declare no use of Generative AI in the preparation of this manuscript
V. V. Vashchenkov
Federal State Budgetary Institution “Almazov National Medical Research Centre” of the Ministry of Health
of the Russian Federation
Russian Federation
Competing Interests:
Russian Federation
Vladislav V. Vashchenkov, MD, PhD, Associated Professor, Head of the Medical Information Systems Department, Information Technology Department
St. Petersburg
Competing Interests:
The authors declare no use of Generative AI in the preparation of this manuscript
Y. P. Voronin
Federal State Budgetary Institution “Almazov National Medical Research Centre” of the Ministry of Health
of the Russian Federation
Russian Federation
Competing Interests:
Russian Federation
Yakov P. Voronin, Engineer in the Medical Information Systems Department, Information Technology Department
St. Petersburg
Competing Interests:
The authors declare no use of Generative AI in the preparation of this manuscript
E. E. Gaibaliev
ITMO University
Russian Federation
Competing Interests:
Russian Federation
Emil E. Gaibaliev, 2nd year MSc student in the “Big Data and Machine Learning” program at the Faculty of AI Technologies
St. Petersburg
Competing Interests:
The authors declare no use of Generative AI in the preparation of this manuscript
S. V. Kovalchuk
ITMO University
Competing Interests:
Sergey V. Kovalchuk, PhD in Technical Sciences, Associate Professor at the Faculty of AI Technologies
St. Petersburg
Competing Interests:
The authors declare no use of Generative AI in the preparation of this manuscript
A. O. Konradi
Federal State Budgetary Institution “Almazov National Medical Research Centre” of the Ministry of Health
of the Russian Federation
Russian Federation
Competing Interests:
Russian Federation
Aleksandra O. Konradi, MD, DSc, Professor, Academician of the Russian Academy of Sciences, Head of Department for Arterial Hypertension, Deputy General Director on Research
St. Petersburg
Competing Interests:
The authors declare no use of Generative AI in the preparation of this manuscript
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For citations:
Ionov M.V., Emelyanov I.V., Vashchenkov V.V., Voronin Y.P., Gaibaliev E.E., Kovalchuk S.V., Konradi A.O. Predictive model for recommending home blood pressure monitoring in outpatients with arterial hypertension. Translational Medicine. 2026;13(4):359-370. (In Russ.) https://doi.org/10.18705/2311-4495-2026-13-4-359-370
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