Hypertension remains in 2017 a leading cause of mortality and disability worldwide. A number of issues related to the determinants of cardiovascular risk in hypertensive patients and to the strategies for better hypertension control are still pending. In such a context, aims of my research program were: 1. To investigate the contribution of blood pressure variability to the risk of cardiovascular mortality in hypertensive patients. In this setting, different methods for assessing blood pressure variability and different models exploring the link between blood pressure variability and outcome were investigated. 2. To assess the possibility that a hypertension management strategy based on hemodynamic assessment of patients through impedance cardiography might lead to a better hypertension control over 24 hours than a conventional approach only based on blood pressure measurement during clinic visits. To these aims, this thesis summarizes data obtained by performing a). An in-depth analysis of a study conducted in the Dublin hypertensive population, including 11492 subjects, and b). The analysis of longitudinal data collected in the frame of BEAUTY (BEtter control of blood pressure in hypertensive pAtients monitored Using the hoTman® sYstem) study. In Dublin study, the proportional hazard Cox model and accelerated failure time models have been used to estimate the additional effect of blood pressure variability on cardiovascular mortality over and above the effect of increased mean BP levels, with an attempt to identify the best threshold values for risk stratification. On the other hand, in BEAUTY study, mixed model and generalized estimation equation are used for the longitudinal data analysis.

(2017). Managing Cardiovascular Risk in Hypertension: Methodological Issues in Blood Pressure Data Analysis. (Tesi di dottorato, Università degli Studi di Milano-Bicocca, 2017).

Managing Cardiovascular Risk in Hypertension: Methodological Issues in Blood Pressure Data Analysis

LIU, XIAOQIU
2017

Abstract

Hypertension remains in 2017 a leading cause of mortality and disability worldwide. A number of issues related to the determinants of cardiovascular risk in hypertensive patients and to the strategies for better hypertension control are still pending. In such a context, aims of my research program were: 1. To investigate the contribution of blood pressure variability to the risk of cardiovascular mortality in hypertensive patients. In this setting, different methods for assessing blood pressure variability and different models exploring the link between blood pressure variability and outcome were investigated. 2. To assess the possibility that a hypertension management strategy based on hemodynamic assessment of patients through impedance cardiography might lead to a better hypertension control over 24 hours than a conventional approach only based on blood pressure measurement during clinic visits. To these aims, this thesis summarizes data obtained by performing a). An in-depth analysis of a study conducted in the Dublin hypertensive population, including 11492 subjects, and b). The analysis of longitudinal data collected in the frame of BEAUTY (BEtter control of blood pressure in hypertensive pAtients monitored Using the hoTman® sYstem) study. In Dublin study, the proportional hazard Cox model and accelerated failure time models have been used to estimate the additional effect of blood pressure variability on cardiovascular mortality over and above the effect of increased mean BP levels, with an attempt to identify the best threshold values for risk stratification. On the other hand, in BEAUTY study, mixed model and generalized estimation equation are used for the longitudinal data analysis.
VALSECCHI, MARIA GRAZIA
Survival data analysis; Longitudinal data analysis; Clinical trial; Blood pressure variability; Hypertension management
MED/01 - STATISTICA MEDICA
English
7-giu-2017
EPIDEMIOLOGIA E BIOSTATISTICA - 64R
28
2015/2016
open
(2017). Managing Cardiovascular Risk in Hypertension: Methodological Issues in Blood Pressure Data Analysis. (Tesi di dottorato, Università degli Studi di Milano-Bicocca, 2017).
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/154475
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