Improving cardiovascular risk prediction through more accurate and alternative methods of blood ...

337 Pages·2017·8.53 MB·English

by  Sarah Stevens

Improving cardiovascular risk prediction
through more accurate and alternative
methods of blood pressure measurement

Sarah Louise Stevens
Nuffield Department of Primary Care Health Sciences
Green Templeton College
University of Oxford
A thesis submitted for the degree of Doctor of Philosophy
Trinity Term, 2017

Acknowledgements
I would like to acknowledge my supervisors Professors Richard McManus and Richard
Stevens both for encouraging me to undertake this thesis and for guiding me throughout. I
also want to thank the members of the Statistics and DPhil groups within the Nuffield
Department of Primary Care Health Sciences for their support over the last 3 years and for
making the department a happy place to be.
Special thanks go to my husband, James, for his belief in me and for bearing the brunt of
the emotional rollercoaster that completing this thesis has been at times. You’re the best!
Finally, I would not have been in a position to undertake this thesis without the
unwavering support of, and encouragement from, my parents. Thank you for all you have
done and sacrificed for me over the last 29 years.

This thesis presents independent research funded by the National Institute for Health
Research School for Primary Care Research (NIHR SPCR). The views expressed are those
of the author and not necessarily those of the NHS, the NIHR or the Department of
Health.

Abstract
Background
Cardiovascular risk scores are used to estimate absolute risk of disease and identify patients
who will benefit most from treatments to lower risk. As a key risk factor for cardiovascular
disease, blood pressure is accounted for in many risk scores, but is inherently variable and
may be influenced by both biological and measurement factors. This thesis aims to
determine how routinely collected blood pressure measurements should best be used for
accurate estimation of cardiovascular risk.
Methods
This thesis describes four main studies. A patient survey and prospective study establish
the current practice of blood pressure measurement. Secondary analyses of data from
blood pressure monitoring trials determine how risk estimates may be affected by the use
of different summary measures of blood pressure. A systematic review evaluates the
evidence of an association between blood pressure variability and cardiovascular risk.
Finally, a cohort study in the Clinical Practice Research Datalink determines if inclusion of
blood pressure variability in cardiovascular risk scores may improve risk estimation.
Results
Current practice of blood pressure measurement may differ from that in risk score
derivation studies. However, these differences have limited effects on cardiovascular risk
estimates with few patients reclassified across risk thresholds. Increased long-term
variability in blood pressure is in itself a risk factor for cardiovascular disease over and
above mean blood pressure but its inclusion in a cardiovascular risk score does not
materially improve the accuracy of risk estimates.
Conclusions
Healthcare professionals should continue to estimate risk for primary prevention of
cardiovascular disease using the blood pressure measurements available to them, whether
measured at home or in the clinic. There is also no additional benefit of considering
measures of long-term blood pressure variability in risk estimation.
Statement of originality
I can confirm that I wrote this thesis and carried out all of the research described within it
myself between October 2014 and August 2017. This thesis has not previously been
submitted for a degree at another university and to the best of my knowledge contains no
previously published work, except where referenced.
Specifically, my contribution to each chapter and any relevant contributions by others were
as follows:
Chapter 1: I carried out the literature review and wrote the chapter.
Chapter 2: I designed the survey, obtained ethical approval, piloted and tested the
online survey system, managed the survey data collection process, carried out the
statistical analysis and wrote the chapter. The programming to set up the online
survey system was conducted by two clinical trials programmers working in the
Nuffield Department of Primary Care Health Sciences Clinical Trials Unit (David
Judge and Luis Castello).
Chapter 3: I determined the analysis plan, carried out the data cleaning and analysis
and wrote the chapter. Data used in this chapter were collected prior to the start of
this thesis by teams led by Professor Richard McManus and Professors Willem
Verberk and Peter de Leeuw.
Chapter 4: I developed the systematic review protocol, screened eligible studies,
extracted data, carried out the statistical analysis and wrote the chapter. The search
strategy was developed by my supervisor, Professor Richard McManus and
colleagues Professor Paul Glasziou and Dr Sally Wood, prior to the beginning of
this thesis. My involvement began after an initial search was completed and some
data on included study characteristics had been extracted. However I subsequently
modified the inclusion/ exclusion criteria and was responsible for re-screening
studies based on the modified criteria and all subsequent work. Article screening
and data extraction was carried out in duplicate with assistance from Professor
Richard McManus, Professor Richard Stevens, Dr Sally Wood, Constantinos
Koshiaris and Dr Kathryn Law.
Chapter 5: I designed the study, obtained ethical approval, carried out the data
cleaning and statistical analysis and wrote the chapter.
Chapter 6: I carried out the literature review and wrote the chapter.
Abbreviations
ABPM Ambulatory blood pressure monitoring
ARV Average real variability
BMI Body mass index
BP Blood pressure
CHD Coronary heart disease
CIMT Carotid intima media thickness
CPRD Clinical Practice Research Datalink
CV Coefficient of variation
CVD Cardiovascular disease
DBP Diastolic blood pressure
GP General practitioner
HDL High density lipoprotein
HES Hospital Episodes Statistics
hsCRP High sensitivity C-reactive protein
IDI Integrated discrimination improvement
IMD Index of multiple deprivation
LDL Low density lipoprotein
LVH Left ventricular hypertrophy
MI Myocardial infarction
MINAP Myocardial Infarction National Audit Project
NHS National Health Service
NICE National Institute for Health and Care Excellence
NRI Net reclassification improvement
ONS Office for National Statistics
QOF Quality and Outcomes Framework
RMSE Root mean squared error
SBP Systolic blood pressure
SD Standard deviation
TIA Transient ischaemic attack
TC Total cholesterol
VIM Variation independent of mean