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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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2017 (English)In: American Journal of Epidemiology, ISSN 0002-9262, E-ISSN 1476-6256, Vol. 186, no 8, p. 899-907Article in journal (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
2017. Vol. 186, no 8, p. 899-907
Keywords [en]
cardiovascular disease, longitudinal measurements, repeated measurements, risk factors, risk prediction
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Cardiac and Cardiovascular Systems
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URN: urn:nbn:se:uu:diva-344000DOI: 10.1093/aje/kwx149ISI: 000412798300001PubMedID: 28549073OAI: oai:DiVA.org:uu-344000DiVA, id: diva2:1187417
Available from: 2018-03-05 Created: 2018-03-05 Last updated: 2018-03-22Bibliographically approved

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