Use of Repeated Blood Pressure and Cholesterol Measurements to Improve Cardiovascular Disease Risk Prediction: An Individual-Participant-Data Meta-Analysis

Ellie Paige; Jessica Barrett; Lisa Pennells; Michael Sweeting; Peter Willeit; Emanuele Di Angelantonio; Vilmundur Gudnason; Børge G Nordestgaard; Bruce M Psaty; Uri Goldbourt; Lyle G Best; Gerd Assmann; Jukka T Salonen; Paul J Nietert; W M Monique Verschuren; Eric J Brunner; Richard A Kronmal; Veikko Salomaa; Stephan J L Bakker; Gilles R Dagenais; Shinichi Sato; Jan-Håkan Jansson; Johann Willeit; Altan Onat; Agustin Gómez de la Cámara; Ronan Roussel; Henry Völzke; Rachel Dankner; Robert W Tipping; Tom W Meade; Chiara Donfrancesco; Lewis H Kuller; Annette Peters; John Gallacher; Daan Kromhout; Hiroyasu Iso; Matthew Knuiman; Edoardo Casiglia; Maryam Kavousi; Luigi Palmieri; Johan Sundström; Barry R Davis; Inger Njølstad; David Couper; John Danesh; Simon G Thompson; Angela Wood

doi:10.1093/aje/kwx149

Use of Repeated Blood Pressure and Cholesterol Measurements to Improve Cardiovascular Disease Risk Prediction: An Individual-Participant-Data Meta-Analysis

Ellie Paige, Jessica Barrett, Lisa Pennells, Michael Sweeting, Peter Willeit, Emanuele Di Angelantonio, Vilmundur Gudnason, Børge G Nordestgaard, Bruce M Psaty, Uri Goldbourt, Lyle G Best, Gerd Assmann, Jukka T Salonen, Paul J Nietert, W M Monique Verschuren, Eric J Brunner, Richard A Kronmal, Veikko Salomaa, Stephan J L Bakker, Gilles R DagenaisShinichi Sato, Jan-Håkan Jansson, Johann Willeit, Altan Onat, Agustin Gómez de la Cámara, Ronan Roussel, Henry Völzke, Rachel Dankner, Robert W Tipping, Tom W Meade, Chiara Donfrancesco, Lewis H Kuller, Annette Peters, John Gallacher, Daan Kromhout, Hiroyasu Iso, Matthew Knuiman, Edoardo Casiglia, Maryam Kavousi, Luigi Palmieri, Johan Sundström, Barry R Davis, Inger Njølstad, David Couper, John Danesh, Simon G Thompson, Angela Wood

Department of Clinical Medicine

16 Citations (Scopus)

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Abstract

The added value of incorporating information from repeated blood pressure and cholesterol measurements to predict cardiovascular disease (CVD) risk has not been rigorously assessed. We used data on 191,445 adults from the Emerging Risk Factors Collaboration (38 cohorts from 17 countries with data encompassing 1962-2014) with more than 1 million measurements of systolic blood pressure, total cholesterol, and high-density lipoprotein cholesterol. Over a median 12 years of follow-up, 21,170 CVD events occurred. Risk prediction models using cumulative mean values of repeated measurements and summary measures from longitudinal modeling of the repeated measurements were compared with models using measurements from a single time point. Risk discrimination (C-index) and net reclassification were calculated, and changes in C-indices were meta-analyzed across studies. Compared with the single-time-point model, the cumulative means and longitudinal models increased the C-index by 0.0040 (95% confidence interval (CI): 0.0023, 0.0057) and 0.0023 (95% CI: 0.0005, 0.0042), respectively. Reclassification was also improved in both models; compared with the single-time-point model, overall net reclassification improvements were 0.0369 (95% CI: 0.0303, 0.0436) for the cumulative-means model and 0.0177 (95% CI: 0.0110, 0.0243) for the longitudinal model. In conclusion, incorporating repeated measurements of blood pressure and cholesterol into CVD risk prediction models slightly improves risk prediction.

Original language	English
Journal	American Journal of Epidemiology
Volume	186
Issue number	8
Pages (from-to)	899-907
Number of pages	9
ISSN	0002-9262
DOIs	https://doi.org/10.1093/aje/kwx149
Publication status	Published - 15 Oct 2017

Keywords

Adult
Aged
Blood Pressure
Blood Pressure Determination
Cardiovascular Diseases/epidemiology
Cholesterol/blood
Female
Humans
Middle Aged
Risk Assessment/methods
Risk Factors

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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Use of Repeated Blood Pressure and Cholesterol Measurements to Improve Cardiovascular Disease Risk PredictionFinal published version, 200 KBLicence: CC BY

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Paige, E., Barrett, J., Pennells, L., Sweeting, M., Willeit, P., Di Angelantonio, E., Gudnason, V., Nordestgaard, B. G., Psaty, B. M., Goldbourt, U., Best, L. G., Assmann, G., Salonen, J. T., Nietert, P. J., Verschuren, W. M. M., Brunner, E. J., Kronmal, R. A., Salomaa, V., Bakker, S. J. L., ... Wood, A. (2017). Use of Repeated Blood Pressure and Cholesterol Measurements to Improve Cardiovascular Disease Risk Prediction: An Individual-Participant-Data Meta-Analysis. American Journal of Epidemiology, 186(8), 899-907. https://doi.org/10.1093/aje/kwx149

Paige, E, Barrett, J, Pennells, L, Sweeting, M, Willeit, P, Di Angelantonio, E, Gudnason, V, Nordestgaard, BG, Psaty, BM, Goldbourt, U, Best, LG, Assmann, G, Salonen, JT, Nietert, PJ, Verschuren, WMM, Brunner, EJ, Kronmal, RA, Salomaa, V, Bakker, SJL, Dagenais, GR, Sato, S, Jansson, J-H, Willeit, J, Onat, A, de la Cámara, AG, Roussel, R, Völzke, H, Dankner, R, Tipping, RW, Meade, TW, Donfrancesco, C, Kuller, LH, Peters, A, Gallacher, J, Kromhout, D, Iso, H, Knuiman, M, Casiglia, E, Kavousi, M, Palmieri, L, Sundström, J, Davis, BR, Njølstad, I, Couper, D, Danesh, J, Thompson, SG & Wood, A 2017, 'Use of Repeated Blood Pressure and Cholesterol Measurements to Improve Cardiovascular Disease Risk Prediction: An Individual-Participant-Data Meta-Analysis', American Journal of Epidemiology, vol. 186, no. 8, pp. 899-907. https://doi.org/10.1093/aje/kwx149

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title = "Use of Repeated Blood Pressure and Cholesterol Measurements to Improve Cardiovascular Disease Risk Prediction: An Individual-Participant-Data Meta-Analysis",

abstract = "The added value of incorporating information from repeated blood pressure and cholesterol measurements to predict cardiovascular disease (CVD) risk has not been rigorously assessed. We used data on 191,445 adults from the Emerging Risk Factors Collaboration (38 cohorts from 17 countries with data encompassing 1962-2014) with more than 1 million measurements of systolic blood pressure, total cholesterol, and high-density lipoprotein cholesterol. Over a median 12 years of follow-up, 21,170 CVD events occurred. Risk prediction models using cumulative mean values of repeated measurements and summary measures from longitudinal modeling of the repeated measurements were compared with models using measurements from a single time point. Risk discrimination (C-index) and net reclassification were calculated, and changes in C-indices were meta-analyzed across studies. Compared with the single-time-point model, the cumulative means and longitudinal models increased the C-index by 0.0040 (95% confidence interval (CI): 0.0023, 0.0057) and 0.0023 (95% CI: 0.0005, 0.0042), respectively. Reclassification was also improved in both models; compared with the single-time-point model, overall net reclassification improvements were 0.0369 (95% CI: 0.0303, 0.0436) for the cumulative-means model and 0.0177 (95% CI: 0.0110, 0.0243) for the longitudinal model. In conclusion, incorporating repeated measurements of blood pressure and cholesterol into CVD risk prediction models slightly improves risk prediction.",

keywords = "Adult, Aged, Blood Pressure, Blood Pressure Determination, Cardiovascular Diseases/epidemiology, Cholesterol/blood, Female, Humans, Middle Aged, Risk Assessment/methods, Risk Factors",

author = "Ellie Paige and Jessica Barrett and Lisa Pennells and Michael Sweeting and Peter Willeit and {Di Angelantonio}, Emanuele and Vilmundur Gudnason and Nordestgaard, {B{\o}rge G} and Psaty, {Bruce M} and Uri Goldbourt and Best, {Lyle G} and Gerd Assmann and Salonen, {Jukka T} and Nietert, {Paul J} and Verschuren, {W M Monique} and Brunner, {Eric J} and Kronmal, {Richard A} and Veikko Salomaa and Bakker, {Stephan J L} and Dagenais, {Gilles R} and Shinichi Sato and Jan-H{\aa}kan Jansson and Johann Willeit and Altan Onat and {de la C{\'a}mara}, {Agustin G{\'o}mez} and Ronan Roussel and Henry V{\"o}lzke and Rachel Dankner and Tipping, {Robert W} and Meade, {Tom W} and Chiara Donfrancesco and Kuller, {Lewis H} and Annette Peters and John Gallacher and Daan Kromhout and Hiroyasu Iso and Matthew Knuiman and Edoardo Casiglia and Maryam Kavousi and Luigi Palmieri and Johan Sundstr{\"o}m and Davis, {Barry R} and Inger Nj{\o}lstad and David Couper and John Danesh and Thompson, {Simon G} and Angela Wood",

note = "{\textcopyright} The Author(s) 2017. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health.",

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doi = "10.1093/aje/kwx149",

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T1 - Use of Repeated Blood Pressure and Cholesterol Measurements to Improve Cardiovascular Disease Risk Prediction

T2 - An Individual-Participant-Data Meta-Analysis

AU - Paige, Ellie

AU - Barrett, Jessica

AU - Pennells, Lisa

AU - Sweeting, Michael

AU - Willeit, Peter

AU - Di Angelantonio, Emanuele

AU - Gudnason, Vilmundur

AU - Nordestgaard, Børge G

AU - Psaty, Bruce M

AU - Goldbourt, Uri

AU - Best, Lyle G

AU - Assmann, Gerd

AU - Salonen, Jukka T

AU - Nietert, Paul J

AU - Verschuren, W M Monique

AU - Brunner, Eric J

AU - Kronmal, Richard A

AU - Salomaa, Veikko

AU - Bakker, Stephan J L

AU - Dagenais, Gilles R

AU - Sato, Shinichi

AU - Jansson, Jan-Håkan

AU - Willeit, Johann

AU - Onat, Altan

AU - de la Cámara, Agustin Gómez

AU - Roussel, Ronan

AU - Völzke, Henry

AU - Dankner, Rachel

AU - Tipping, Robert W

AU - Meade, Tom W

AU - Donfrancesco, Chiara

AU - Kuller, Lewis H

AU - Peters, Annette

AU - Gallacher, John

AU - Kromhout, Daan

AU - Iso, Hiroyasu

AU - Knuiman, Matthew

AU - Casiglia, Edoardo

AU - Kavousi, Maryam

AU - Palmieri, Luigi

AU - Sundström, Johan

AU - Davis, Barry R

AU - Njølstad, Inger

AU - Couper, David

AU - Danesh, John

AU - Thompson, Simon G

AU - Wood, Angela

N1 - © The Author(s) 2017. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health.

PY - 2017/10/15

Y1 - 2017/10/15

N2 - The added value of incorporating information from repeated blood pressure and cholesterol measurements to predict cardiovascular disease (CVD) risk has not been rigorously assessed. We used data on 191,445 adults from the Emerging Risk Factors Collaboration (38 cohorts from 17 countries with data encompassing 1962-2014) with more than 1 million measurements of systolic blood pressure, total cholesterol, and high-density lipoprotein cholesterol. Over a median 12 years of follow-up, 21,170 CVD events occurred. Risk prediction models using cumulative mean values of repeated measurements and summary measures from longitudinal modeling of the repeated measurements were compared with models using measurements from a single time point. Risk discrimination (C-index) and net reclassification were calculated, and changes in C-indices were meta-analyzed across studies. Compared with the single-time-point model, the cumulative means and longitudinal models increased the C-index by 0.0040 (95% confidence interval (CI): 0.0023, 0.0057) and 0.0023 (95% CI: 0.0005, 0.0042), respectively. Reclassification was also improved in both models; compared with the single-time-point model, overall net reclassification improvements were 0.0369 (95% CI: 0.0303, 0.0436) for the cumulative-means model and 0.0177 (95% CI: 0.0110, 0.0243) for the longitudinal model. In conclusion, incorporating repeated measurements of blood pressure and cholesterol into CVD risk prediction models slightly improves risk prediction.

AB - The added value of incorporating information from repeated blood pressure and cholesterol measurements to predict cardiovascular disease (CVD) risk has not been rigorously assessed. We used data on 191,445 adults from the Emerging Risk Factors Collaboration (38 cohorts from 17 countries with data encompassing 1962-2014) with more than 1 million measurements of systolic blood pressure, total cholesterol, and high-density lipoprotein cholesterol. Over a median 12 years of follow-up, 21,170 CVD events occurred. Risk prediction models using cumulative mean values of repeated measurements and summary measures from longitudinal modeling of the repeated measurements were compared with models using measurements from a single time point. Risk discrimination (C-index) and net reclassification were calculated, and changes in C-indices were meta-analyzed across studies. Compared with the single-time-point model, the cumulative means and longitudinal models increased the C-index by 0.0040 (95% confidence interval (CI): 0.0023, 0.0057) and 0.0023 (95% CI: 0.0005, 0.0042), respectively. Reclassification was also improved in both models; compared with the single-time-point model, overall net reclassification improvements were 0.0369 (95% CI: 0.0303, 0.0436) for the cumulative-means model and 0.0177 (95% CI: 0.0110, 0.0243) for the longitudinal model. In conclusion, incorporating repeated measurements of blood pressure and cholesterol into CVD risk prediction models slightly improves risk prediction.

KW - Adult

KW - Aged

KW - Blood Pressure

KW - Blood Pressure Determination

KW - Cardiovascular Diseases/epidemiology

KW - Cholesterol/blood

KW - Female

KW - Humans

KW - Middle Aged

KW - Risk Assessment/methods

KW - Risk Factors

U2 - 10.1093/aje/kwx149

DO - 10.1093/aje/kwx149

M3 - Review

C2 - 28549073

SN - 0002-9262

VL - 186

SP - 899

EP - 907

JO - American Journal of Epidemiology

JF - American Journal of Epidemiology

IS - 8

ER -

Use of Repeated Blood Pressure and Cholesterol Measurements to Improve Cardiovascular Disease Risk Prediction: An Individual-Participant-Data Meta-Analysis

Abstract

Keywords

UN SDGs

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