WFRF:(Verschuren Monique)
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| Fältnamn | Indikatorer | Metadata |
|---|---|---|
| 000 | 05396naa a22010573a 4500 | |
| 001 | 22129913 | |
| 003 | SE-LIBR | |
| 005 | 20171218202708.0 | |
| 007 | cr|||||||||||| | |
| 008 | 171218s2017 sw |||| o |||| ||eng c | |
| 024 | 7 | a http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-1429122 uri |
| 024 | 7 | a urn:nbn:se:umu:diva-1429122 urn |
| 024 | 7 | a 10.1093/aje/kwx1492 doi |
| 040 | a S | |
| 041 | 0 | a eng |
| 042 | 9 EPLK | |
| 100 | 1 | a Paige, Ellie4 aut |
| 245 | 1 0 | a Use of Repeated Blood Pressure and Cholesterol Measurements to Improve Cardiovascular Disease Risk Predictionh [Elektronisk resurs]b An Individual-Participant-Data Meta-Analysis |
| 260 | b Oxford University Pressc 2017 | |
| 500 | a Published | |
| 506 | 0 | a gratis |
| 520 | a 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. | |
| 650 | 7 | a Medical and Health Sciences2 hsv |
| 650 | 7 | a Health Sciences2 hsv |
| 650 | 7 | a Public Health, Global Health, Social Medicine and Epidemiology2 hsv |
| 650 | 7 | a Medicin och hälsovetenskap2 hsv |
| 650 | 7 | a Hälsovetenskaper2 hsv |
| 650 | 7 | a Folkhälsovetenskap, global hälsa, socialmedicin och epidemiologi2 hsv |
| 653 | 0 | a cardiovascular disease |
| 653 | 0 | a longitudinal measurements |
| 653 | 0 | a repeated measurements |
| 653 | 0 | a risk factors |
| 653 | 0 | a risk prediction |
| 700 | 1 | a Barrett, Jessica4 aut |
| 700 | 1 | a Pennells, Lisa4 aut |
| 700 | 1 | a Sweeting, Michael4 aut |
| 700 | 1 | a Willeit, Peter4 aut |
| 700 | 1 | a Di Angelantonio, Emanuele4 aut |
| 700 | 1 | a Gudnason, Vilmundur4 aut |
| 700 | 1 | a Nordestgaard, Børge G.4 aut |
| 700 | 1 | a Psaty, Bruce M.4 aut |
| 700 | 1 | a Goldbourt, Uri4 aut |
| 700 | 1 | a Best, Lyle G.4 aut |
| 700 | 1 | a Assmann, Gerd4 aut |
| 700 | 1 | a Salonen, Jukka T.4 aut |
| 700 | 1 | a Nietert, Paul J.4 aut |
| 700 | 1 | a Verschuren, W. M. Monique4 aut |
| 700 | 1 | a Brunner, Eric J.4 aut |
| 700 | 1 | a Kronmal, Richard A.4 aut |
| 700 | 1 | a Salomaa, Veikko4 aut |
| 700 | 1 | a Bakker, Stephan J. L.4 aut |
| 700 | 1 | a Dagenais, Gilles R.4 aut |
| 700 | 1 | a Sato, Shinichi4 aut |
| 700 | 1 | a Jansson, Jan-Håkan4 aut |
| 700 | 1 | a Willeit, Johann4 aut |
| 700 | 1 | a Onat, Altan4 aut |
| 700 | 1 | a de la Cámara, Agustin Gómez4 aut |
| 700 | 1 | a Roussel, Ronan4 aut |
| 700 | 1 | a Völzke, Henry4 aut |
| 700 | 1 | a Dankner, Rachel4 aut |
| 700 | 1 | a Tipping, Robert W.4 aut |
| 700 | 1 | a Meade, Tom W.4 aut |
| 700 | 1 | a Donfrancesco, Chiara4 aut |
| 700 | 1 | a Kuller, Lewis H.4 aut |
| 700 | 1 | a Peters, Annette4 aut |
| 700 | 1 | a Gallacher, John4 aut |
| 700 | 1 | a Kromhout, Daan4 aut |
| 700 | 1 | a Iso, Hiroyasu4 aut |
| 700 | 1 | a Knuiman, Matthew4 aut |
| 700 | 1 | a Casiglia, Edoardo4 aut |
| 700 | 1 | a Kavousi, Maryam4 aut |
| 700 | 1 | a Palmieri, Luigi4 aut |
| 700 | 1 | a Sundström, Johan4 aut |
| 700 | 1 | a Davis, Barry R.4 aut |
| 700 | 1 | a Njølstad, Inger4 aut |
| 700 | 1 | a Couper, David4 aut |
| 700 | 1 | a Danesh, John4 aut |
| 700 | 1 | a Thompson, Simon G.4 aut |
| 700 | 1 | a Wood, Angela4 aut |
| 710 | 1 2 | a Umeå universitetb Medicinska fakulteten4 pbl0 268483 |
| 772 | 1 8 | i channel recordw 18813935 |
| 773 | 0 8 | i Värdpublikationt American Journal of Epidemiologyg 186:8, 899-907x 0002-9262 |
| 856 | 4 0 | u http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-142912 |
| 856 | 4 0 | u http://dx.doi.org/10.1093/aje/kwx149 |
| 856 | 4 0 | u http://umu.diva-portal.org/smash/get/diva2:1165548/FULLTEXT01 |
| 910 | 2 s | 6 710a Umeå universitet.b Medicinsk-odontologiska fakultetenu Umeå universitet.b Medicinska fakulteten |
| 910 | 2 s | 6 710a Medicinska fakulteten vid Umeå universitetu Umeå universitet.b Medicinska fakulteten |
| 841 | 5 APISa x ab 171218||0000|||||001||||||000000e 1 | |
| 024 | 7 | 5 APISa urn:nbn:se:umu:diva-1429122 urn |
| 852 | 5 APISb APIS | |
| 856 | 4 0 | 5 APISu http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-142912 |
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