Summarising and synthesising regression coefficients through systematic review and meta-analysis for improving...

Area of Science:

• Cardiovascular Medicine
• Epidemiology
• Biostatistics

Background:

• Hypertension is a prevalent condition and a significant risk factor for severe health outcomes like heart attack, stroke, kidney disease, and mortality.
• Accurate risk stratification is crucial for identifying individuals who would benefit from targeted interventions, including lifestyle changes and medical treatments, to prevent hypertension development.
• Existing hypertension prediction models can be enhanced through metamodel updating techniques, integrating new data with established models for improved accuracy.

Purpose of the Study:

• To conduct a systematic review and meta-analysis of hypertension prediction models.
• To identify known risk factors associated with high blood pressure.
• To quantify the magnitude of association between identified risk factors and hypertension.

Main Methods:

• A comprehensive systematic search of multiple databases (MEDLINE, Embase, Web of Science, Scopus) and grey literature.
• Focus on studies predicting hypertension risk in the general population, using 'hypertension' and 'risk prediction' as key concepts.
• Employ random-effect meta-analysis to pool regression coefficients from individual studies, assessing heterogeneity, publication bias, and study quality using the Prediction Model Risk of Bias Assessment Tool.

Main Results:

• The review will synthesize findings from existing hypertension prediction models.
• Pooled estimates of risk factor associations will be generated.
• An assessment of the quality and potential biases of included studies will be provided.

Conclusions:

• The findings will contribute to a better understanding of hypertension risk factors.
• Improved risk prediction models can facilitate timely and effective interventions.
• This research supports evidence-based strategies for hypertension prevention and management.