2015
DOI: 10.1161/hypertensionaha.115.04808
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Prognosis in Relation to Blood Pressure Variability

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Cited by 86 publications
(26 citation statements)
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“…Second, the number of required readings in our current analyses should not be extrapolated to studies with a focus on blood pressure variability or on the diurnal rhythmicity of blood pressure. However, compared with blood pressure level, blood pressure variability is a substantially weaker predictor of cardiovascular risk [37][38][39]. Third, for sake of generalizability, we reported the minimum number of daytime and night-time readings for investigators applying short fixed time intervals excluding the transition periods in the morning and evening when blood pressure usually changes rapidly [22].…”
Section: Discussionmentioning
confidence: 99%
“…Second, the number of required readings in our current analyses should not be extrapolated to studies with a focus on blood pressure variability or on the diurnal rhythmicity of blood pressure. However, compared with blood pressure level, blood pressure variability is a substantially weaker predictor of cardiovascular risk [37][38][39]. Third, for sake of generalizability, we reported the minimum number of daytime and night-time readings for investigators applying short fixed time intervals excluding the transition periods in the morning and evening when blood pressure usually changes rapidly [22].…”
Section: Discussionmentioning
confidence: 99%
“…VIM was calculated as the SD divided by the within-individual mean to the power p and multiplied by the average value of systolic BP in the cohort to the power p . 11, 25 The power p is obtained by fitting a curve of SD against within-individual mean systolic BP using the model SD=a times mean p , where p was derived by nonlinear regression analysis as implemented in the SAS PROC NLIN procedure. 11, 25 VIM was shown to correlate highly with other indices of BP variability 26 while its correlation with mean BP level is almost zero.…”
Section: Methodsmentioning
confidence: 99%
“…11, 25 The power p is obtained by fitting a curve of SD against within-individual mean systolic BP using the model SD=a times mean p , where p was derived by nonlinear regression analysis as implemented in the SAS PROC NLIN procedure. 11, 25 VIM was shown to correlate highly with other indices of BP variability 26 while its correlation with mean BP level is almost zero. 11, 27 VIM allows assessing the association of BP variability with outcomes while removing the confounding effect of BP level.…”
Section: Methodsmentioning
confidence: 99%
“…[7][8][9][10][11][12][13][14][15][16][17] However, the degree to which variability improves the prediction of cardiovascular risk is controversial. [18][19][20] Previous studies have shown that nighttime BP is generally a better predictor of cardiovascular outcomes than daytime BP in patients with hypertension, and diminished nocturnal decline in BP is associated with or predictive of organ damage and cardiovascular events. [21][22][23][24][25][26] Only a few studies have analyzed whether different phenotypes on the hypertension spectrum such as normotension, white-coat hypertension, masked hypertension, and sustained hypertension are associated with BP variability independent of mean BP.…”
Section: Introductionmentioning
confidence: 99%