CLINICO-EPIDEMIOLOGICAL ALGORITHM FOR PREDICTING SYSTEMIC ARTERIAL HYPERTENSION AT HIGH ALTITUDE THROUGH MATHEMATICAL...

Area of Science:

• Cardiovascular disease research
• Preventive medicine
• Biostatistics

Background:

• Hypertension is a significant public health concern, affecting cardiovascular health globally.
• Predictive models for hypertension are crucial for early intervention and risk stratification.
• High-altitude environments may present unique physiological challenges influencing blood pressure.

Purpose of the Study:

• To develop and validate a simple clinical algorithm for predicting the individual probability of developing hypertension.
• To identify key risk factors associated with hypertension development in a specific population.
• To provide a practical tool for risk assessment in clinical settings.

Main Methods:

• A population-based hybrid study design combining cohort and cross-sectional elements was employed.
• Systematic random sampling was used to study 3615 initially normotensive soldiers.
• Multiple logistic regression and Receiver Operating Characteristics (ROC) curve analysis were utilized for model development.

Main Results:

• Age, Body Mass Index (BMI), tobacco smoking, and alcohol consumption were significantly associated with hypertension development.
• A predictive rule was established: Age + (BMI × 3.86) + (5.53 if smoker) + (19.81 if alcohol consumer).
• A total score exceeding 142 indicates a high risk for hypertension, with 68.2% sensitivity and 78.5% specificity.

Conclusions:

• The developed algorithm offers a straightforward method for predicting hypertension risk.
• The algorithm effectively integrates modifiable and non-modifiable risk factors for practical application.
• This tool can aid healthcare providers in identifying at-risk individuals for targeted preventive strategies.