Evidence map›Paper›PMID 37523052›Full record

ReviewCurrent atherosclerosis reports2023

Algorithms for Treating Dyslipidemia in Youth.

Grace K Kim, Jennifer K Yee, Nidhi Bansal

Abstract readReview
PubMed Publisher
In one paragraph

Review in Current atherosclerosis reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
1.9field-weighted citation impact, top 14% 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

5 citing papers in PubMed, 6 citations in OpenAlex.

  1. Review
  2. Transition of care from childhood/adolescence to adulthood in familial hypercholesterolemia.Journal of pediatric endocrinology & metabolism : JPEM · 2025
    Review
  3. Review
  4. Review
  5. Article
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 3 institutions in 1 country.

Grace K KimDivision of Diabetes and Endocrinology, Department of Pediatrics, Baylor College of Medicine, Texas Children's Hospital, 6701 Fannin St. Ste 1020, Houston, TX, 77030, USA.
Jennifer K YeeDivision of Endocrinology, Department of Pediatrics, Harbor-UCLA Medical Center, The Lundquist Institute of Biomedical Innovation at Harbor-UCLA, Torrance, CA, 90509, USA.
Nidhi BansalDivision of Diabetes and Endocrinology, Department of Pediatrics, Baylor College of Medicine, Texas Children's Hospital, 6701 Fannin St. Ste 1020, Houston, TX, 77030, USA. nxbansa1@texaschildrens.org.
Baylor College of Medicine · USHarbor–UCLA Medical Center · USTexas Children's Hospital · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purpose of reviewThe goal of this article is to review algorithms for treating dyslipidemia in youth, discuss pitfalls, propose enhanced algorithms to address pitfalls, and consider future directions. RECENT

findingsThe presence of modifiable and non-modifiable cardiovascular disease (CVD) risk factors during childhood is associated with CVD-related events in adulthood. Recent data has shown that childhood initiation of statin therapy in youth < 18 years of age with familial hypercholesterolemia reduces the risk of adult CVD. However, pediatric dyslipidemia remains undertreated in part due to a lack of primary health care providers with adequate understanding of screening guidelines and pediatric lipidologists with experience in treatment and follow-up of this unique population. Management algorithms have been published by the National Heart, Lung, and Blood Institute and American Heart Association as tools to empower clinicians to manage dyslipidemia. We propose enhanced algorithms, which incorporate recently approved pharmacotherapy to address the management gaps. Future algorithms based upon clinical risk scores may enhance treatment and improve outcomes. Algorithms for dyslipidemia management which target youth < 18 years of age are tools which empower clinicians to manage dyslipidemia in this unique population. Enhanced algorithms may help address pitfalls. We acknowledge the need for further risk assessment tools in pediatrics for tailored dyslipidemia management.

Indexed as

Cardiovascular DiseasesDyslipidemiasHyperlipoproteinemia Type IIAdolescentAdultChildHumansRisk AssessmentRisk FactorsUnited StatesAlgorithmsCardiovascular diseaseDyslipidemiaLipidsYouth

Identifiers

PMID37523052
OpenAlexW4385406516

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.