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

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dc.contributor.authorPaige, Ellie
dc.contributor.authorBarrett, Jessica
dc.contributor.authorPennells, Lisa
dc.contributor.authorSweeting, Michael
dc.contributor.authorWilleit, Peter
dc.contributor.authorDi Angelantonio, Emanuele
dc.contributor.authorGudnason, Vilmundur
dc.contributor.authorNordestgaard, Børge G
dc.contributor.authorPsaty, Bruce M
dc.contributor.authorGoldbourt, Uri
dc.contributor.authorBest, Lyle G
dc.contributor.authorAssmann, Gerd
dc.contributor.authorSalonen, Jukka T
dc.contributor.authorNietert, Paul J
dc.contributor.authorVerschuren, W M Monique
dc.contributor.authorBrunner, Eric J
dc.contributor.authorKronmal, Richard A
dc.contributor.authorSalomaa, Veikko
dc.contributor.authorBakker, Stephan J L
dc.contributor.authorDagenais, Gilles R
dc.contributor.authorSato, Shinichi
dc.contributor.authorJansson, Jan-Håkan
dc.contributor.authorWilleit, Johann
dc.contributor.authorOnat, Altan
dc.contributor.authorde la Cámara, Agustin Gómez
dc.contributor.authorRoussel, Ronan
dc.contributor.authorVölzke, Henry
dc.contributor.authorDankner, Rachel
dc.contributor.authorTipping, Robert W
dc.contributor.authorMeade, Tom W
dc.contributor.authorDonfrancesco, Chiara
dc.contributor.authorKuller, Lewis H
dc.contributor.authorPeters, Annette
dc.contributor.authorGallacher, John
dc.contributor.authorKromhout, Daan
dc.contributor.authorIso, Hiroyasu
dc.contributor.authorKnuiman, Matthew
dc.contributor.authorCasiglia, Edoardo
dc.contributor.authorKavousi, Maryam
dc.contributor.authorPalmieri, Luigi
dc.contributor.authorSundström, Johan
dc.contributor.authorDavis, Barry R
dc.contributor.authorNjølstad, Inger
dc.contributor.authorCouper, David
dc.contributor.authorDanesh, John
dc.contributor.authorThompson, Simon G
dc.contributor.authorWood, Angela
dc.date.accessioned2018-02-08T12:48:38Z
dc.date.available2018-02-08T12:48:38Z
dc.date.issued2017-10-15
dc.description.abstractThe 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.
dc.identifier.citationUse of Repeated Blood Pressure and Cholesterol Measurements to Improve Cardiovascular Disease Risk Prediction: An Individual-Participant-Data Meta-Analysis. 2017, 186 (8):899-907 Am. J. Epidemiol.en
dc.identifier.doi10.1093/aje/kwx149
dc.identifier.issn1476-6256
dc.identifier.journalAm J Epidemiol 2017; 186(8):899-907en
dc.identifier.pmid28549073
dc.identifier.urihttp://hdl.handle.net/10029/621375
dc.language.isoenen
dc.rightsArchived with thanks to American journal of epidemiologyen
dc.subject.meshAdult
dc.subject.meshAged
dc.subject.meshBlood Pressure
dc.subject.meshBlood Pressure Determination
dc.subject.meshCardiovascular Diseases
dc.subject.meshCholesterol
dc.subject.meshFemale
dc.subject.meshHumans
dc.subject.meshMiddle Aged
dc.subject.meshRisk Assessment
dc.subject.meshRisk Factors
dc.titleUse of Repeated Blood Pressure and Cholesterol Measurements to Improve Cardiovascular Disease Risk Prediction: An Individual-Participant-Data Meta-Analysis.en
dc.typeArticleen
dspace.entity.typePublication
html.description.abstractThe 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.
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