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    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, Ellie
    Barrett, 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
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    Type
    Article
    Language
    en
    
    Metadata
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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.
    Published in
    Am J Epidemiol 2017; 186(8):899-907
    Publiekssamenvatting
    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.
    DOI
    10.1093/aje/kwx149
    PMID
    28549073
    URI
    http://hdl.handle.net/10029/621375
    ae974a485f413a2113503eed53cd6c53
    10.1093/aje/kwx149
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