Ten-second central SBP variability predicts first and recurrent cardiovascular events

Area of Science:

• Cardiology
• Hypertension Research
• Predictive Analytics in Medicine

Background:

• Long-term blood pressure variability (BPV) is a known risk factor for cardiovascular events.
• However, the predictive value of short-term BPV, specifically beat-to-beat variability, has not been extensively studied.
• Understanding different types of BPV can refine risk assessment.

Purpose of the Study:

• To investigate whether 10-second central systolic blood pressure variability (SBPV) predicts cardiovascular events.
• To compare the predictive power of short-term SBPV with established long-term BPV metrics.
• To assess the utility of 10-second SBPV in reclassifying cardiovascular event risk.

Main Methods:

• Suprasystolic brachial pressure measurements were taken over approximately 10 seconds in 4999 adults (aged 50-84 years).
• Aortic pressure waveforms were derived, and various short-term BPV metrics were calculated, including average real variability (ARV) and standard deviation (SD).
• Participants were followed for a median of 4.6 years to record first and recurrent cardiovascular events.

Main Results:

• All calculated 10-second central SBPV parameters were significantly associated with an increased risk of first cardiovascular events (hazard ratios 1.25-1.29).
• Higher SBPV levels, particularly ARV and SD, were linked to a 1.92 to 2.19 times greater risk of first events and a 1.50 to 1.76 times greater risk of recurrent events.
• 10-second SBPV demonstrated significant net reclassification improvement for 5-year cardiovascular event risk, especially in intermediate-risk individuals.

Conclusions:

• Ten-second central systolic blood pressure variability is a significant independent predictor of both first and recurrent cardiovascular events.
• These findings suggest that beat-to-beat blood pressure fluctuations offer valuable prognostic information beyond traditional long-term BPV.
• Incorporating short-term SBPV into risk assessment models may improve cardiovascular event prediction.