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

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http://hdl.handle.net/10138/228257

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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 , Dagenais , G R , Sato , S , Jansson , J-H , Willeit , J , Onat , A , de la Camara , A G , Roussel , R , Volzke , H , Dankner , R , Tipping , R W , Meade , T W , Donfrancesco , C , Kuller , L H , Peters , A , Gallacher , J , Kromhout , D , Iso , H , Knuiman , M , Casiglia , E , Kavousi , M , Palmieri , L , Sundstrom , J , Davis , B R , Njolstad , I , Couper , D , Danesh , J , Thompson , S G & 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

Title: Use of Repeated Blood Pressure and Cholesterol Measurements to Improve Cardiovascular Disease Risk Prediction : An Individual-Participant-Data Meta-Analysis
Author: Paige, Ellie; Barrett, Jessica; Pennells, Lisa; Sweeting, Michael; Willeit, Peter; Di Angelantonio, Emanuele; Gudnason, Vilmundur; Nordestgaard, Borge 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-Hakan; Willeit, Johann; Onat, Altan; de la Camara, Agustin Gomez; Roussel, Ronan; Volzke, 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; Sundstrom, Johan; Davis, Barry R.; Njolstad, Inger; Couper, David; Danesh, John; Thompson, Simon G.; Wood, Angela
Contributor: University of Helsinki, Clinicum
Date: 2017-10-15
Language: eng
Number of pages: 9
Belongs to series: American Journal of Epidemiology
ISSN: 0002-9262
URI: http://hdl.handle.net/10138/228257
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.
Subject: cardiovascular disease
longitudinal measurements
repeated measurements
risk factors
risk prediction
CORONARY-HEART-DISEASE
MULTIPLE
VARIABILITY
MODELS
3142 Public health care science, environmental and occupational health
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