Use of Repeated Blood Pressure and Cholesterol Measurements to Improve Cardiovascular Disease Risk Prediction: An Individual-Participant-Data Meta-Analysis.
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Authors
Paige, EllieBarrett, Jessica
Pennells, Lisa
Sweeting, Michael
Willeit, Peter
Di Angelantonio, Emanuele
Gudnason, Vilmundur
Nordestgaard, Børge G
Psaty, Bruce M
Goldbourt, Uri
Best, Lyle G
Assmann, Gerd
Salonen, Jukka T
Nietert, Paul J
Verschuren, W M Monique
Brunner, Eric J
Kronmal, Richard A
Salomaa, Veikko
Bakker, Stephan J L
Dagenais, Gilles R
Sato, Shinichi
Jansson, Jan-Håkan
Willeit, Johann
Onat, Altan
de la Cámara, Agustin Gómez
Roussel, Ronan
Völzke, Henry
Dankner, Rachel
Tipping, Robert W
Meade, Tom W
Donfrancesco, Chiara
Kuller, Lewis H
Peters, Annette
Gallacher, John
Kromhout, Daan
Iso, Hiroyasu
Knuiman, Matthew
Casiglia, Edoardo
Kavousi, Maryam
Palmieri, Luigi
Sundström, Johan
Davis, Barry R
Njølstad, Inger
Couper, David
Danesh, John
Thompson, Simon G
Wood, Angela
Type
ArticleLanguage
en
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Use of Repeated Blood Pressure and Cholesterol Measurements to Improve Cardiovascular Disease Risk Prediction: An Individual-Participant-Data Meta-Analysis.Published in
Am J Epidemiol 2017; 186(8):899-907Publiekssamenvatting
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.PMID
28549073ae974a485f413a2113503eed53cd6c53
10.1093/aje/kwx149
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