Cumulative systolic blood pressure (BP) load may provide better predictions of major cardiovascular events for patients with type 2 diabetes than traditional BP measurements, according to a study published Sept. 17 in JACC.

Nelson Wang, MD, et al., conducted a post-hoc analysis of the ADVANCE-ON study to assess the association between cumulative systolic BP load and risk of cardiovascular events in patients with type 2 diabetes. Patients had systolic BP recorded six times at the 3, 4, 6, 12, 18 and 24-month periods. Cumulative systolic BP load was defined as the area under the curve for systolic BP values ≥130 mm Hg divided by the area under the curve for all measured systolic BP values over the course of 24 months. Heart rates for the association between cumulative systolic BP load with major cardiovascular events and death were estimated using the Cox models.

Results showed that cumulative systolic BP load outperformed mean systolic BP, time-below-targe systolic BP, and visit-to-visit systolic BP variability in predicting cardiovascular events and death. Of 9,338 patients with a median of 7.6 years of follow-up, 1,469 major cardiovascular events, 1,615 deaths, and 660 cardiovascular deaths were observed. Each 1-SD increase in cumulative systolic BP load was associated with a 14% increase in major cardiovascular events (HR: 1.14; 95% CI: 1.09-1.20), 13% increase in all-cause mortality (HR: 1.13; 95% CI: 1.13-1.18), and 21% increase in cardiovascular death (HR: 1.21; 95% CI: 1.13-1.29).

The authors note that “these findings reinforce the importance of the magnitude and duration of exposure to elevated systolic BP” in the prediction of major cardiovascular events among patients with type 2 diabetes. In addition, they point out that with the increasing digitalization of health care records, incorporating big data and improving risk prediction can have major impacts when scaled across larger, high-risk populations.

In an accompanying editorial comment, Donald M. Lloyd-Jones, MD, FACC, notes that “clinicians should leverage as much information on their patients as possible to understand their BP-related [cardiovascular disease] risk, to identify those who may be more likely have occult or emerging subclinical target organ damage, and to identify those who may have particular net benefit from earlier or more-intensive treatment.”

Keywords: Delivery of Health Care, Heart Rate, Diabetes Mellitus, Type 2, Big Data, Follow-Up Studies, Proportional Hazards Models, Cardiovascular Diseases, Blood Pressure

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