27 research outputs found
Modified Treatment Approach Using Cardiovascular Disease Risk Calculator for Primary Prevention
<div><p>Background</p><p>The recent guidelines for preventing atherosclerotic cardiovascular events are an important advancement. For primary prevention, statins are recommended if the ten-year risk is ≥ 5% (consideration for therapy) or ≥ 7.5% (definitive treatment unless contraindication after discussion). We rationalized that a significant cohort with ten-year risk below the treatment thresholds would predictably surpass them within the recommended 4–6 year window for reassessing the ten-year risk. As atherosclerosis is a progressive disease, these individuals may therefore benefit with more aggressive therapies even at baseline.</p><p>Methods and Findings</p><p>We used publicly available NHANES dataset for ten-year risk calculation. There were 1805 participants. To evaluate the ten-year risk change at five years, we considered two scenarios: no change in the baseline parameters except increased age by five (No Change) and alternatively 10% improvement in systolic BP, total and HDL-c, no smoking with five-year increase in age (Reduced Risk Profile). Amongst non-diabetics with <5% risk at baseline, 35% reached or exceeded 5% risk in five years (5% reached or exceed the 7.5% risk) with No Change and 9% reached or exceeded 5% risk in five years (none reached 7.5% risk) with Reduced Risk Profile; furthermore, 94% of the non-diabetic cohort with baseline risk between 3.5%–5% would exceed the 5% and/or 7.5% boundary limit with No Change. Amongst non-diabetics with 5–7.5% baseline risks, 87% reached or exceeded 7.5% with No Change while 30% reached or exceeded 7.5% risk with Reduced Risk Profile.</p><p>Conclusions</p><p>A significant population cohort at levels below the treatment thresholds will predictably exceed these limits with time with or without improvement in modifiable risk factors and may benefit with more aggressive therapy at baseline. We provide an improved risk calculator that allows for integrating expected risk modification into discussion with an individual. This needs to be prospectively tested in clinical trials.</p></div
Pie chart demonstrating the impact of risk modification on predicted ten-year risk based on non-diabetic cohort.
<p><b>A</b>. At baseline, 29% of the non-diabetic cohort have predicted ten-year risk <5% (dark green slice). Without risk modification but increased age by 5 years (No Change scenario), 35% would exceed 5% risk limit (light green and red slices) and 5% would exceed 7.5% threshold (red slice). In comparison, under Reduced Risk Profile, 9% will exceed 5% threshold (light green slice); moreover, at baseline, 13% of the cohort have predicted ten-year risk 5–7.5% (light green). Under Reduced Risk Profile, 30% would exceed 7.5% risk boundary (red slice); while under No Change scenario, 87% would exceed 7.5% risk limit in 5 years (red slice). <b>B</b>. The 29% non-diabetic cohort with baseline risk <5% (shown in <b>A</b> 1<sup>st</sup> pie dark green slice) is further divided into three groups with baseline risk <3%, between 3–3.5% and 3.5–5%. For the non-diabetic cohort that has baseline risk between 3–3.5% (orange slice, 1<sup>st</sup> pie), 2% (light green slice, 2<sup>nd</sup> column top pie) vs 57% (light green slice, 3<sup>rd</sup> column top pie) would exceed 5% limit under reduced risk profile scenario vs no change scenario; none exceed the 7.5% boundary. For the cohort that has baseline risk between 3.5–5% (gray slice, 1<sup>st</sup> pie), 29% (light green slice, 2<sup>nd</sup> column bottom pie) would exceed the 5% limit under reduced risk profile scenario. Under no change scenario, 94% (light green and red slices, 3<sup>rd</sup> column bottom pie), would exceed the 5% limit and 18% would even exceed the 7.5% boundary.</p
Examples of using the new risk calculator in two cases.
<p>Case A indicates that if the patient quits smoking, and improves other risk factors by 10%, the calculated risk at age 45 years is below 5%. In contrast if the baseline risk variables remain the same, and age increases by 5 years, the absolute ten- year risk would be ≥ 7.5% Case B indicates that even if there is 10% improvement in total and HDL-c but no change in BP, the risk will exceed 7.5% regardless of the presumed risk modification.</p
<b>Compare ten-year risk in five years with and without change in modifiable risk factors.</b>
<p><b>Compare ten-year risk in five years with and without change in modifiable risk factors.</b></p
A cross-national comparison of the Twitter feeds of popular alcohol brands in India and Australia
<p><b>Aims:</b> To evaluate (i) the types of techniques alcohol marketers utilise to facilitate user engagement with content on leading Indian and Australian Twitter alcohol brand pages and (ii) the extent to which users engage with this content in two diverse national contexts.</p> <p><b>Methods:</b> The 10 alcohol brands per country with the greatest Twitter presence were identified based on the number of ‘followers’. Number of tweets, photos, and videos were collected and the type of content noted for each brand between 1 January 2016 and 29 February 2016. The data were analysed via an inductive coding approach using NVivo10.</p> <p><b>Results:</b> In total, the brands had accumulated up to 150,386 followers (Indian: 110,032; Australian: 40,354). The techniques utilised were a mix of those that differed by country (e.g. India: sexually suggestive content versus Australia: posts related to the brand’s tradition or heritage) and generic approaches (e.g. alcohol sponsorship of sport, music, and fashion; offering consumption suggestions; organising competitions; giveaways; and use of memes).</p> <p><b>Conclusions:</b> The flexibility of Twitter, which complements traditional marketing, allows brands to adapt and deliver their online alcohol content in specific national contexts and to capitalise on the cultural meanings users invoke in their interactions with the brands.</p
<b>Baseline characteristics of the NHANES data.</b>
<p><b>Baseline characteristics of the NHANES data.</b></p
Additional file 1: of Impact of clinical input variable uncertainties on ten-year atherosclerotic cardiovascular disease risk using new pooled cohort equations
Dataset: our detailed analysis dataset of NHANES data. (XLSX 319 kb
Additional file 2: Table S1. of Impact of clinical input variable uncertainties on ten-year atherosclerotic cardiovascular disease risk using new pooled cohort equations
Analysis of the impact of input variable variations in categorizing subjects based on ten-year risk threshold of 7.5 % (with Hispanics). (DOCX 15 kb
Additional file 3: Table S2. of Impact of clinical input variable uncertainties on ten-year atherosclerotic cardiovascular disease risk using new pooled cohort equations
Analysis of the impact of input variable variations in categorizing subjects based on ten-year risk threshold of 5 % (with Hispanics). (DOCX 14 kb
Demographic data and the clinical profile of the malaria patients recruited as participants for the study.
<p>Demographic data and the clinical profile of the malaria patients recruited as participants for the study.</p