Data Management And Sharing System of China National Clinical Research Center for Neurological Diseases

Journal： Stroke & Vascular Neurology

Publication type: Article

IF(IF): 4.765

PMID: 33468639 Link To Pubmed Full text Download

Hypertension has been identified as the first leading modifiable risk factor for stroke. It is widely accepted that 24-hour ambulatory blood pressure (ABP) monitoring is superior to clinic and self-measurement of blood pressure (BP) in the diagnosis and prognosis of hypertension and risk assessment of cardiovascular and cerebrovascular disease. The conventional ABP parameters includes 24h mean BP, daytime and nighttime BP, dipping status, morning surge (MS), nocturnal hypertension (NH), BP variability (BPV) et al. Extensive studies have shown that above conventional ABP parameters are associated with stroke and its clinical outcome.However, individual ABP parameter might only represent a specific characteristic of the whole ABP profiles, and different parameter play distinct role on stroke prognosis. For example, disturbed dipping status, especially reverse dippers, the long-lasting high BP level could cause impairment of the arterial wall structure and endothelium leading to acceleration of vascular remodeling and atherosclerosis of the artery, which promote subsequent stroke events.An exaggerated morning BP surge could induce plaque instability through increased inflammatory reaction and leading to plaque rupture triggered by increased mechanical pressure and shear stress of an exaggerated fluctuation of blood flow on the vessel wall. Moreover, despite extensive existing data on individual ABP parameters in relation to the risk of developing stroke, the literature is relatively limited regarding the association between ABP profiles and stroke outcome. Most previous studies that focused on analyses of individual ABP parameters neglected the temporal nature of the 24h ABP data and the additive effects of multiple ABP characteristics. Profile analysis of 24h ABP, incorporating all the ABP parameters with the minimum information lost, may be better for evaluating its performance on stroke outcome. A piecewise linear random-effects model has been recently proposed to identify 24-hour ABP profile clusters;26 however, this model has not been used for clinical evaluation of stroke patients. In this study, we aimed to identify clusters of stroke patients with similar ABP profile patterns, characterize the circadian rhythm parameters in the profile groups and examine the association of 24-hour ABP profiles with the clinical outcome measured as stroke recurrence.

The functional clustering method was implemented to group the cluster of BP profiles. We treated each resulting BP curve as a function of time and assumed that the profiles in the same cluster share the same mean value. Then we utilized the Fourier expansion in combination with the iterative k-centers clustering method. Essentially, the algorithm was developed to minimize the errors between observed curves and their cluster sample means. Since wrong clustering would lead to larger errors than the true one did, the minimization algorithm forced the estimated clusters approaching the underlying truth when the sample size increased. The associations of ambulatory SBP parameters and profiles with the clinical outcome were then examined using a Cox proportional hazards model with adjustment for confounding variables.