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      Impact of Successive Office Blood Pressure Measurements During a Single Visit on Cardiovascular Risk Prediction: Analysis of CARTaGENE

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          Abstract

          BACKGROUND:

          Multiple office blood pressure (BP) readings correlate more closely with ambulatory BP than single readings. Whether they are associated with long-term outcomes and improve cardiovascular risk prediction is unknown. Our objective was to assess the long-term impact of multiple office BP readings.

          METHODS:

          We used data from CARTaGENE, a population-based survey comprising individuals aged 40 to 70 years. Three BP readings (BP 1 , BP 2 , and BP 3 ) at 2-minute intervals were obtained using a semiautomated device. They were averaged to generate BP 1-2 , BP 2-3 , and BP 1-2-3 for systolic BP (SBP) and diastolic BP. Cardiovascular events (major adverse cardiovascular event [MACE]: cardiovascular death, stroke, and myocardial infarction) during a 10-year follow-up were recorded. Associations with MACE were obtained using adjusted Cox models. Predictive performance was assessed with 10-year atherosclerotic cardiovascular disease scores and their associated C statistics.

          RESULTS:

          In the 17 966 eligible individuals, 2378 experienced a MACE during follow-up. Crude SBP values ranged from 122.5 to 126.5 mm Hg. SBP 3 had the strongest association with MACE incidence (hazard ratio, 1.10 [1.05–1.15] per SD) and SBP 1 the weakest (hazard ratio, 1.06 [1.01–1.10]). All models including SBP 1 (SBP 1 , SBP 1-2 , and SBP 1-2-3 ) were underperformed. At a given SBP value, the excess MACE risk conferred by SBP 3 was 2× greater than SBP 1 . In atherosclerotic cardiovascular disease scores, SBP 3 yielded the highest C statistic, significantly higher than most other SBP measures. In contrast to SBP, all diastolic BP readings yielded similar results.

          CONCLUSIONS:

          Cardiovascular risk prediction is improved by successive office SBP values, especially when the first reading is discarded. These findings reinforce the necessity of using multiple office BP readings.

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          Most cited references59

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          2018 ESC/ESH Guidelines for the management of arterial hypertension

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            Global burden of 87 risk factors in 204 countries and territories, 1990–2019: a systematic analysis for the Global Burden of Disease Study 2019

            Summary Background Rigorous analysis of levels and trends in exposure to leading risk factors and quantification of their effect on human health are important to identify where public health is making progress and in which cases current efforts are inadequate. The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2019 provides a standardised and comprehensive assessment of the magnitude of risk factor exposure, relative risk, and attributable burden of disease. Methods GBD 2019 estimated attributable mortality, years of life lost (YLLs), years of life lived with disability (YLDs), and disability-adjusted life-years (DALYs) for 87 risk factors and combinations of risk factors, at the global level, regionally, and for 204 countries and territories. GBD uses a hierarchical list of risk factors so that specific risk factors (eg, sodium intake), and related aggregates (eg, diet quality), are both evaluated. This method has six analytical steps. (1) We included 560 risk–outcome pairs that met criteria for convincing or probable evidence on the basis of research studies. 12 risk–outcome pairs included in GBD 2017 no longer met inclusion criteria and 47 risk–outcome pairs for risks already included in GBD 2017 were added based on new evidence. (2) Relative risks were estimated as a function of exposure based on published systematic reviews, 81 systematic reviews done for GBD 2019, and meta-regression. (3) Levels of exposure in each age-sex-location-year included in the study were estimated based on all available data sources using spatiotemporal Gaussian process regression, DisMod-MR 2.1, a Bayesian meta-regression method, or alternative methods. (4) We determined, from published trials or cohort studies, the level of exposure associated with minimum risk, called the theoretical minimum risk exposure level. (5) Attributable deaths, YLLs, YLDs, and DALYs were computed by multiplying population attributable fractions (PAFs) by the relevant outcome quantity for each age-sex-location-year. (6) PAFs and attributable burden for combinations of risk factors were estimated taking into account mediation of different risk factors through other risk factors. Across all six analytical steps, 30 652 distinct data sources were used in the analysis. Uncertainty in each step of the analysis was propagated into the final estimates of attributable burden. Exposure levels for dichotomous, polytomous, and continuous risk factors were summarised with use of the summary exposure value to facilitate comparisons over time, across location, and across risks. Because the entire time series from 1990 to 2019 has been re-estimated with use of consistent data and methods, these results supersede previously published GBD estimates of attributable burden. Findings The largest declines in risk exposure from 2010 to 2019 were among a set of risks that are strongly linked to social and economic development, including household air pollution; unsafe water, sanitation, and handwashing; and child growth failure. Global declines also occurred for tobacco smoking and lead exposure. The largest increases in risk exposure were for ambient particulate matter pollution, drug use, high fasting plasma glucose, and high body-mass index. In 2019, the leading Level 2 risk factor globally for attributable deaths was high systolic blood pressure, which accounted for 10·8 million (95% uncertainty interval [UI] 9·51–12·1) deaths (19·2% [16·9–21·3] of all deaths in 2019), followed by tobacco (smoked, second-hand, and chewing), which accounted for 8·71 million (8·12–9·31) deaths (15·4% [14·6–16·2] of all deaths in 2019). The leading Level 2 risk factor for attributable DALYs globally in 2019 was child and maternal malnutrition, which largely affects health in the youngest age groups and accounted for 295 million (253–350) DALYs (11·6% [10·3–13·1] of all global DALYs that year). The risk factor burden varied considerably in 2019 between age groups and locations. Among children aged 0–9 years, the three leading detailed risk factors for attributable DALYs were all related to malnutrition. Iron deficiency was the leading risk factor for those aged 10–24 years, alcohol use for those aged 25–49 years, and high systolic blood pressure for those aged 50–74 years and 75 years and older. Interpretation Overall, the record for reducing exposure to harmful risks over the past three decades is poor. Success with reducing smoking and lead exposure through regulatory policy might point the way for a stronger role for public policy on other risks in addition to continued efforts to provide information on risk factor harm to the general public. Funding Bill & Melinda Gates Foundation.
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              Multiple Imputation for Nonresponse in Surveys

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                Author and article information

                Contributors
                (View ORCID Profile)
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                Journal
                Hypertension
                Hypertension
                Ovid Technologies (Wolters Kluwer Health)
                0194-911X
                1524-4563
                October 2023
                October 2023
                : 80
                : 10
                : 2209-2217
                Affiliations
                [1 ]Department of Medicine, Université de Montréal, Canada (L.-C.D., A.-C.N.-F., F.M., R.G.).
                [2 ]Hopital Maisonneuve-Rosemont, Montreal, Canada (L.-C.D., A.-C.N.-F.).
                [3 ]Hopital du Sacré-Coeur de Montréal Research Center, Canada (F.M., R.G.).
                [4 ]Department of Medicine, Université Laval, Quebec City, Canada (M.A.).
                [5 ]CHU de Quebec - Université Laval, Quebec City, Canada (M.A.).
                Article
                10.1161/HYPERTENSIONAHA.123.21510
                c09031f7-45b3-4e40-ae9b-a331fcc2c874
                © 2023
                History

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