Keywords
Youth hypertension
Anxiety
Prediction model
Submitted : 2025-03-30
Accepted : 2025-04-14
Published : 2025-04-29
Abstract
Objective: To analyze the risk factors of anxiety in young hypertensive patients and build a prediction model to provide a scientific basis for clinical diagnosis and treatment. Methods: According to the research content, young hypertensive patients admitted to the hospital from January 2022 to December 2024 were selected as the research object and at least 950 patients were included according to the sample size calculation. According to the existence of anxiety, 950 patients were divided into control group (n = 650) and observation group (n = 300), and the clinical data of all patients were collected for univariate analysis and multivariate Logistic regression analysis to get the risk factors of hypertension patients complicated with anxiety in. All patients were randomly divided into a training set (n = 665) and a test set (n = 285) according to the ratio of 7:3, and the evaluation efficiency of different prediction models was obtained by using machine learning algorithm. To evaluate the clinical application effect of the prediction model. Results: (1) Univariate analysis showed that age, BMI, education background, marital status, smoking, drinking, sleep disorder, family history of hypertension, history of diabetes, history of hyperlipidemia, history of cerebral infarction, and TC were important risk factors for young hypertensive patients complicated with anxiety. (2) Multivariate Logistic regression analysis showed that hypertension history, drinking history, coronary heart disease history, diabetes history, BMI, TC, and TG are important independent risk factors for young hypertensive patients complicated with anxiety. (3) Extra Trees has the highest predictive power for young people with hypertension complicated with anxiety, while Decision-Tree has the lowest predictive power. Conclusion: Hypertension history, drinking history, coronary heart disease history, diabetes history, BMI, TC, and TG are important independent risk factors that affect the anxiety of young hypertensive patients. Extra Trees model has the best prediction efficiency among different groups of models.
References
Zhang C, Wang S, Wang X, et al., 2024, Study on the Prevalence and Risk Factors of Hypertension Among the Elderly in Liangyuan District, Shangqiu. China Health Statistics, 41(4): 543–550.
Zhang Y, Wang L, Huang Y, et al., 2024, Prevalence of Hypertension, Depression and Anxiety Disorder and Related Factors. Chinese Mental Health Journal, 38(12): 1021–1027.
Shen J, Yang X, Niu W, et al., 2024, Construction of Nomogram Based on Risk Factors to Assess the Risk of Target Organ Damage in Patients With Hypertension and Unexplained Hypokalemia. China Cardiovascular Research, 22(11): 1052–1056.
Ye Q, Wang Y, Li L, et al., 2023, Study on the Prevalence and Influencing Factors of Mild Cognitive Impairment in Young and Middle-Aged Hypertensive Inpatients. China General Medicine, 26(2): 154–167.
Liu D, Wang M, 2023, Analysis of Risk Factors in Young Patients With Hypertensive Cerebral Hemorrhage. Chinese Sci-tech Journal Database (Abstract Edition) Medicine and Health, 2023(5): 42–44.
Liu X, 2023, Analysis of Clinical Characteristics and Risk Factors of Middle-Aged and Young People With Hypertension. Modern Diagnosis and Treatment, 34(23): 3590–3592.
Chen Y, Wang Y, Li B, et al., 2024, Study on the Relationship Between LMR and Anxiety in Elderly Patients With Essential Hypertension. Laboratory Medicine and Clinic, 21(1): 20–23.
Li J, Zheng M, 2023, Investigation on Health Behavior Status of Young and Middle-Aged Patients With Hypertension and Analysis of Influencing Factors. Clinical Medical Research and Practice, 8(15): 5–8.
Yang F, Han B, Xu X, et al., 2024, Study on Sleep Quality of Young and Middle-Aged Hypertensive Inpatients and Its Influencing Factors. Shanxi Medical Journal, 53(7): 519–522.
Zeng H, Tian B, Yuan H, et al., 2024, Prediction Model of Chronic Kidney Disease With Hypertension or Diabetes by Machine Learning Algorithm. Journal of Kunming Medical University, 45(3): 99–105..
Xiao H, Yang K, 2024, To Explore the Predictive Value of Machine Learning Model for Young People With Hypertension Complicated With Anxiety. Electronic Journal of Modern Medicine and Health Research, 8(10): 119–124.
Wei S, Zhang Z, Wang Y, et al., 2024, Systematic Evaluation of Constructing Hypertension Risk Prediction Model Based on Machine Learning. Journal of Mudanjiang Medical College, 45(5): 55–61.
Cui W, Lin P, Liu X, et al., 2022, Cardiovascular Risk Prognosis Model of Essential Hypertension Based on Machine Learning. China Journal of Gerontology, 42(15): 3625–3629.
Qin W, Gan F, Yin B, et al., 2023, Risk Prediction Model of Hypertension Complicated With Retinopathy Based on Machine Learning Algorithm. Journal of Nanchang University (Medical Edition), 63(5): 49–80.
Liu J, Zheng W, Fang F, et al., 2024, Building an Evaluation and Prediction Model of Anxiety and Depression of the Elderly in the Community Based on Machine Learning. Chinese Journal of Geriatrics, 43(2): 234–239.
Liu T, Zhu Q, Xu L, et al., 2022, Risk Prediction Model of Essential Hypertension Complicated With Cerebral Infarction Based on Machine Learning. Journal of Naval Medical University, 43(3): 258–265.
Zeng Q, Jiang W, 2023, Research Progress of Hypertension Risk Prediction Model Based on Machine Learning. Chinese Sci-tech Journal Database (Abstract Edition), 2023(3): 122–125.
Guan Y, Zhu B, Ma J, et al., 2024, Predictive Value of Different Machine Learning Algorithms on the Risk of Senile Essential Hypertension. Chinese Medicine Guides, 21(18): 49–52.
Ji J, Du T, Wang X, et al., 2024, Influencing Factors of Anxiety and Depression in Middle-Aged and Elderly Hypertensive Patients in Rural Areas of Northern Henan. Journal of Cardiovascular and Cerebrovascular Diseases of Integrated Traditional Chinese and Western Medicine, 22(16): 3050–3055.
Wang C, Bian L, Li X, 2023, Investigation on Depression and Risk Factors of Elderly Patients With Hypertension in Yuetan Community of Beijing. Basic Medicine and Clinic, 43(5): 798–803.