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Development and validation of QRISK3 risk prediction algorithms to estimate future risk of cardiovascular disease: prospective cohort study

BMJ 2017; 357 doi: https://doi.org/10.1136/bmj.j2099 (Published 23 May 2017) Cite this as: BMJ 2017;357:j2099

Rapid Response:

Blood pressure standard deviation in the new QRISK3 model

We note with interest the inclusion of a measure of blood pressure (BP) variability in the new QRISK3 algorithm by Hippisley-Cox and colleagues. As a recognised novel risk factor for cardiovascular disease (CVD),[1] the impact of including measures of long-term blood pressure variability in cardiovascular risk equations is the subject of ongoing work by ourselves and colleagues in the Clinical Practice Research Datalink.

Our experience leads us to question several of the decisions made in the current analysis:

Firstly, we question whether two repeat measurements – the bare minimum with which standard deviation (SD) can be calculated – is adequate to measure BP variability. Our experience is similar to that of Rothwell and colleagues, who found very different and increasing hazard ratios as more measurements were included in their measure of variability.[2] The current analysis reports significantly reduced hazard rations than would have been expected from our previous meta-analysis.[1]

Missing data is a significant issue in the utilisation of routine healthcare records and the investigators used five imputations as a “pragmatic” choice reflecting volume of data and availability of computing power. Our experience is that as many as 50 imputations are necessary when dealing with BP variability over two measures, in order to reduce the Monte Carlo error to adequate levels (e.g. Monte Carlo error of a coefficient test statistic should be approximately 0.1).[3]

Those using antihypertensive medication were included in the development of the risk algorithm by Hippisley-Cox and colleagues and this may have confounded the observed relationship between BP variability and outcomes.[4] Patterns of adherence to medication could also be expected to affect observed variation in blood pressure,[5] as could changing medications over time. Both of these are likely to be associated with cardiovascular risk, but the authors do not give details of the timing of measurements with respect to medication change.

The authors conclude that the model incorporating variability is “preferred” even though the reported c-statistics for models with and without variability are identical to three decimal places, and the few individuals whose risk would be classified differently are those whose risk is very close to the decision threshold according to either model.

Finally, the true performance of these new models cannot be reliably determined until they have undergone independent external validation in other populations. We look forward to future validation studies to confirm the results present by Hippisley-Cox and colleagues and further studies that more carefully consider the utility of BP variability in cardiovascular risk prediction.

Yours faithfully,

Sarah L Stevens and Richard J McManus

Competing interests: SS and the work described in this rapid response are funded by the National Institute for Health Research School for Primary Care Research (NIHR SPCR). The views expressed are those of the authors and not necessarily those of the NHS, the NIHR, or the Department of Health. RM has received grants and personal fees from Omron and grants from Lloyds Pharmacy, outside the described work.

1 Stevens SL, Wood S, Koshiaris C, et al. Blood pressure variability and cardiovascular disease: systematic review and meta-analysis. BMJ 2016;:i4098. doi:10.1136/bmj.i4098
2 Rothwell PM, Howard SC, Dolan E, et al. Prognostic significance of visit-to-visit variability, maximum systolic blood pressure, and episodic hypertension. Lancet 2010;375:895–905. doi:10.1016/S0140-6736(10)60308-X
3 White IR, Royston P, Wood AM. Multiple imputation using chained equations: Issues and guidance for practice. Stat Med 2011;30:377–99.
4 Rothwell PM, Howard SC, Dolan E, et al. Effects of β blockers and calcium-channel blockers on within-individual variability in blood pressure and risk of stroke. Lancet Neurol 2010;9:469–80. doi:10.1016/S1474-4422(10)70066-1
5 Muntner P, Levitan EB, Joyce C, et al. Association Between Antihypertensive Medication Adherence and Visit-to-Visit Variability of Blood Pressure. J Clin Hypertens 2013;15:112–7.

Competing interests: SS and the work described in this rapid response are funded by the National Institute for Health Research School for Primary Care Research (NIHR SPCR). The views expressed are those of the authors and not necessarily those of the NHS, the NIHR, or the Department of Health. RM has received grants and personal fees from Omron and grants from Lloyds Pharmacy, outside the described work.

14 June 2017
Sarah L Stevens
Statistician and DPhil student
Richard J McManus
Nuffield Department of Primary Care Health Sciences, University of Oxford
Floor 2, Gibson Building, Radcliffe Observatory Quarter, Woodstock Road, Oxford, OX2 6GG