Aims: Lipid point-of-care testing (POCT) analysers are being used to screen target populations to identify individuals at high risk of developing cardiovascular disease (CVD) as part of the National Health Service (NHS) Health Checks programme. We evaluated the performance of the Cholestech LDX and CardioChek PA POCT analysers against laboratory methods in CVD risk assessment.
Methods: Ten-year QRISK2, Joint British Societies' II (JBSII), and Framingham CVD risk scores were calculated for subjects recruited from Wolverhampton City PCT community NHS Health Check clinics. CVD risk scores derived using POCT capillary whole blood total cholesterol and HDL-cholesterol measurements were compared with those derived from the laboratory analysis of paired venous serum samples. Data from subjects with diabetes, overt CVD, and those who did not meet the risk algorithm age criteria were excluded.
Results: All subjects classified as high risk (risk score >20%) by the three risk algorithms on the basis of the laboratory results were correctly identified by the LDX. One (2.2%) and four (7.0%) moderate-risk subjects were misclassified by the LDX as high risk, using the JBSII and Framingham risk algorithms, respectively. The CardioChek identified all subjects classed as high risk by QRISK2, but failed to identify 6/31 (19.4%) and 3/19 (15.8%) of subjects classed as high risk by the Framingham and JBSII algorithms, respectively. The CardioChek, however, did not misclassify any moderate-risk subjects as high risk.
Conclusions: Identification of subjects at risk of CVD depends on the cardiovascular risk algorithm and also on the performance of the POCT device.
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http://dx.doi.org/10.1136/jclinpath-2013-202123 | DOI Listing |
Genet Med
January 2025
Genomics Ethics, and Translational Research Program, RTI International, Research Triangle Park, NC; Department of Translational and Applied Genomics, Kaiser Permanente Center for Health Research, Portland, OR. Electronic address:
Purpose: Limited evidence evaluates parents' perceptions of their child's clinical genomic sequencing (GS) results, particularly among individuals from medically underserved groups. Five Clinical Sequencing Evidence-Generating Research (CSER) consortium studies performed GS in children with suspected genetic conditions with high proportions of individuals from underserved groups to address this evidence gap.
Methods: Parents completed surveys of perceived understanding, personal utility, and test-related distress after GS result disclosure.
Int J Offender Ther Comp Criminol
January 2025
Diana R. Garland School of Social Work, Baylor University, Houston, TX, USA.
Most studies on the impact of maternal incarceration on adolescent health risk behaviors have focused on singular, separated behaviors, even though these behaviors often cluster and co-occur. This study used the FFCWS dataset to examine the association between maternal incarceration and the aggregation of health risk behaviors among adolescents. Latent class analysis suggested the four-class model had the optimal model fit.
View Article and Find Full Text PDFObjective: This quality improvement initiative aimed to increase the rate of provider screening and documentation of contraception use for reproductive-aged women seen in an academic rheumatology fellows' clinic to >50% by 24 weeks, with sustained improvement at one year.
Methods: With a multidisciplinary team, we devised and implemented six interventional cycles over 24 weeks informed by key stakeholder survey responses. The primary outcome measure was the percentage of eligible visits with contraception information documented in the structured electronic health record field.
Circ Genom Precis Med
January 2025
Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT (A.A., L.S.D., E.K.O., R.K.).
Background: While universal screening for Lp(a; lipoprotein[a]) is increasingly recommended, <0.5% of patients undergo Lp(a) testing. Here, we assessed the feasibility of deploying Algorithmic Risk Inspection for Screening Elevated Lp(a; ARISE), a validated machine learning tool, to health system electronic health records to increase the yield of Lp(a) testing.
View Article and Find Full Text PDFJ Hand Surg Eur Vol
January 2025
Clinical Scientific Computing, Guy's and St Thomas' NHS Foundation Trust, London, UK.
This paper discusses the current literature surrounding the potential use of artificial intelligence and machine learning models in the diagnosis of acute obvious and occult scaphoid fractures. Current studies have notable methodological flaws and are at high risk of bias, precluding meaningful comparisons with clinician performance (the current reference standard). Specific areas should be addressed in future studies to help advance the meaningful and clinical use of artificial intelligence for radiograph interpretation.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!