Evidence map›Paper›PMID 34611633›Full record

ArticleAmerican journal of preventive cardiology2021

A simplified approach to identification of risk status in patients with atherosclerotic cardiovascular disease.

Aparna Sajja, Hsin-Fang Li, Kateri J Spinelli, Amir Ali, Salim S Virani, Seth S Martin, Ty J Gluckman

Open access · goldAbstract read
In one paragraph

Article in American journal of preventive cardiology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
1.5field-weighted citation impact, top 19% 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

6 citing papers in PubMed, 10 citations in OpenAlex.

  1. Article
  2. A Real-World Cohort Study of the Risks of Kidney Failure and Death in Diabetes.Clinical journal of the American Society of Nephrology : CJASN · 2026
    Article
  3. Article
  4. Article
  5. Article
  6. 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

7 authors at 3 institutions in 1 country.

Aparna SajjaCiccarone Center for the Prevention of Cardiovascular Disease, Division of Cardiology, Department of Medicine, Johns Hopkins University School of Medicine, 1800 Orleans Street, Baltimore, MD 21287, United States.
Hsin-Fang LiCenter for Cardiovascular Analytics, Research and Data Science (CARDS), Providence Heart Institute, Providence Research Network, Portland, OR, United States.
Kateri J SpinelliCenter for Cardiovascular Analytics, Research and Data Science (CARDS), Providence Heart Institute, Providence Research Network, Portland, OR, United States.
Amir AliEvaluation and Research, Providence Research Network, Renton, WA, United States.
Salim S ViraniSection of Cardiology, Michael E. DeBakey Veterans Affairs Medical Center and Section of Cardiovascular Research, Baylor College of Medicine, Houston, TX, United States.
Seth S MartinCiccarone Center for the Prevention of Cardiovascular Disease, Division of Cardiology, Department of Medicine, Johns Hopkins University School of Medicine, 1800 Orleans Street, Baltimore, MD 21287, United States.
Ty J GluckmanCenter for Cardiovascular Analytics, Research and Data Science (CARDS), Providence Heart Institute, Providence Research Network, Portland, OR, United States.
Providence College · USJohns Hopkins University · USMichael E. DeBakey VA Medical Center · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveThe 2018 American Heart Association/American College of Cardiology (AHA/ACC) Blood Cholesterol Guideline recommendation to classify patients with atherosclerotic cardiovascular disease (ASCVD) as very high-risk (VHR) vs not-VHR (NVHR) has important implications for escalation of medical therapy. We aimed to define the prevalence and clinical characteristics of these two groups within a large multi-state healthcare system and develop a simpler means to assist clinicians in identifying VHR patients using classification and regression tree (CART) analysis.

methodsWe performed a retrospective analysis of all patients in a 28-hospital US healthcare system in 2018. ICD-10 codes were used to define the ASCVD population. Per the AHA/ACC Guideline, VHR status was defined by ≥2 major ASCVD events or 1 major ASCVD event and ≥2 high-risk conditions. CART analysis was performed on training and validation datasets. A random forest model was used to verify results.

resultsOf 180,669 ASCVD patients identified, 58% were VHR. Among patients with a history of myocardial infarction (MI) or recent acute coronary syndrome (ACS), 99% and 96% were classified as VHR, respectively. Both CART and random forest models identified recent ACS, ischemic stroke, hypertension, peripheral artery disease, history of MI, and age as the most important predictors of VHR status. Using five rules identified by CART analysis, fewer than 50% of risk factors were required to assign VHR status.

conclusionCART analysis helped to streamline the identification of VHR patients based on a limited number of rules and risk factors. This approach may help improve clinical decision making by simplifying ASCVD risk assessment at the point of care. Further validation is needed, however, in more diverse populations.

Indexed as

ASCVDCholesterolLipidSecondary prevention

Identifiers

PMID34611633
PMCPMC8387292
OpenAlexW3159456516

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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 OpenQuestion graph.