气象参数与高血压危机风险:用于开发预测模型的纵向研究

A. D. Fesyun, O. V. Yurova, I. Grishechkina, M. Yakovlev, M. Nikitin, Tatyana A. Knyazeva, E. A. Valtseva
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引用次数: 0

摘要

简介。将气候疗法纳入针对不同地貌的动脉高血压的疗养胜地疗法中,如果用于目标群体并预防包括高血压危机(HC)在内的变态反应的发生,则有可能产生积极的效果。虽然以前已经研究过自然疗法因素对人体的影响,但利用现代数学方法开发高血压模型可以准确预测并及时预防恶劣天气期间的高血压。目的分析公开的气象数据时间序列,根据气候因素对动脉高血压患者的影响,构建一个预测高危情况的数学模型。该模型将确定全年入住疗养地的高血压患者的不利时期,以便在这些时期及时采取治疗和预防措施,防止高血压的发生。材料和方法:研究从 2019 年 1 月 1 日至 2020 年 10 月 31 日,在位于高加索黑海沿岸的著名度假胜地格连吉克和新罗西斯克进行,为期 22 个月。这些地区属于干燥的亚热带气候。气象数据来自 Gelendzhik 和 Novorossiysk 气象站,救护车呼叫数据来自 Gelendzhik(12,268 次)和 Novorossiysk(12,226 次),共计 24,494 次救护车呼叫。模型采用最大似然法通过非线性对数回归计算得出。模型的关键因素包括气候1 和地磁条件2 的主要指标。逻辑回归法的灵敏度为 56.0%,特异度为 77.3%,总体准确率为 76.0%。结果根据开发的预测模型,冬季与低高血压风险相关的天数不超过 75.0%,春季降至 59.0%。然而,这一比例在夏季增至 89.0%,在秋季达到 77.0%。模型充分性检查显示相关性很高,Q(模型质量)介于 +0.64 和 -0.117 之间,P 为 0.3。结论。所开发的逻辑回归模型能更准确地计算出个人患高血压并发症的风险,并为患者制定个人策略提供了机会。这些模型为气候疗法领域做出了贡献,加深了人们对气候因素对高血压患者影响的理解,有助于采取有针对性的干预措施,改善对高血压危机的管理。
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Meteorological Parameters and Hypertensive Crisis Risk: a Longitudinal Study for Prediction Model Developing
INTRODUCTION. Integrating climatotherapy into health resort therapy for arterial hypertension in diverse landscapes has the potential to yield positive effects, if used in target groups and preventing the occurrence of meteopathic reactions, including a hypertensive crisis (HC). While the impact of natural healing factors on the human body has been previously studied, the utilization of modern mathematical approaches in developing HC models has enabled accurate predictions and timely prevention of HC during adverse weather periods. AIM. To analyze publicly available meteorological data time series to construct a mathematical model for predicting high-risk situations of HC based on the influence of climatic factors on patients with arterial hypertension. This model would identify unfavorable periods for hypertensive patients staying in health resorts throughout the year, allowing for timely therapeutic and preventive measures to prevent HC during these periods. MATERIALS AND METHODS. The study was conducted over a 22-month period, from January 1, 2019 to October 31, 2020, in Gelendzhik and Novorossiysk, renowned resort destinations located on the Black Sea coast of the Caucasus. These regions have a dry and subtropical climate. Meteorological data were obtained from Gelendzhik and Novorossiysk weather stations, and ambulance calls data were collected from Gelendzhik (12,268 calls) and Novorossiysk (12,226 calls), resulting in a total of 24,494 ambulance calls. The model was calculated using the maximum likelihood method through nonlinear logit regression. Key factors for the model included the main indicators of climate1 and geomagnetic conditions2. The logistic regression method exhibited a sensitivity of 56.0 % and a specificity of 77.3 %, with an overall accuracy of 76.0 %. RESULTS. According to the developed predictive model, the winter season has no more than 75.0 % of days associated with a low risk of hypertension, decreasing to 59.0 % in spring. However, the proportion increases to 89.0 % in summer and reaches 77.0 % in autumn. Model adequacy checks indicated a high degree of relevance, with Q (model quality) ranging between +0.64 and –0.117, and p 0.3. CONCLUSION. The developed logistic regression models provide more accurate calculations of individual risks for developing complications of hypertension and offer the opportunity to formulate individual strategies for patients. These models contribute to the field of climatotherapy and enhance the understanding of the impact of climatic factors on hypertensive patients, facilitating targeted interventions and improved management of hypertensive crises.
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