Evidence map›Paper›PMID 37581773›Full record

ReviewCurrent cardiology reports2023

Race as a Component of Cardiovascular Disease Risk Prediction Algorithms.

Ramachandran S Vasan, Shreya Rao, Edwin van den Heuvel

Open access · greenAbstract readReview
PubMed Publisher
In one paragraph

Review in Current cardiology reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
5.8field-weighted citation impact, top 4% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed, 10 citations in OpenAlex.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors at 2 institutions in 2 countries.

Ramachandran S VasanUniversity of Texas School of Public Health and University of Texas Health Sciences Center, 8403 Floyd Curl Drive, Mail Code 7992, San Antonio, TX 78229, USA. vasan@uthscsa.edu.ORCID 0000-0001-7357-5970
Shreya RaoUniversity of Texas School of Public Health and University of Texas Health Sciences Center, 8403 Floyd Curl Drive, Mail Code 7992, San Antonio, TX 78229, USA.
Edwin van den HeuvelSection of Preventive Medicine and Epidemiology, Department of Medicine, Boston University School of Medicine, Boston, MA, USA.
Boston University · USThe University of Texas Health Science Center at San Antonio · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purpose of reviewSeveral prediction algorithms include race as a component to account for race-associated variations in disease frequencies. This practice has been questioned recently because of the risk of perpetuating race as a biological construct and diverting attention away from the social determinants of health (SDoH) for which race might be a proxy. We evaluated the appropriateness of including race in cardiovascular disease (CVD) prediction algorithms, notably the pooled cohort equations (PCE). RECENT

findingsIn a recent investigation, we reported substantial and biologically implausible differences in absolute CVD risk estimates upon using PCE for predicting CVD risk in Black and White persons with identical risk factor profiles, which might result in differential treatment decisions based solely on their race. We recommend the development of raceless CVD risk prediction algorithms that obviate race-associated risk misestimation and racializing treatment practices, and instead incorporate measures of SDoH that mediate race-associated risk differences.

Indexed as

Cardiovascular DiseasesAlgorithmsHumansRisk AssessmentRisk FactorsSocial Determinants of HealthCardiovascular diseaseCohort studiesPredictionRaceRisk

Identifiers

PMID37581773
OpenAlexW4385827664

What Socratic holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.