Evidence map›Paper›PMID 33483911›Full record

ArticleHealth care management science2021

Optimal cholesterol treatment plans and genetic testing strategies for cardiovascular diseases.

Wesley J Marrero, Mariel S Lavieri, Jeremy B Sussman

Abstract read
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In one paragraph

Article in Health care management science, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
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

2 citing papers in PubMed.

  1. Article
  2. Review
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.

Wesley J MarreroIndustrial & Operations Engineering, University of Michigan, 1891 IOE Building 1205 Beal Ave, Ann Arbor, MI, 48109, USA. wmarrero@umich.edu.ORCID http://orcid.org/0000-0002-7092-2292
Mariel S LavieriIndustrial & Operations Engineering, University of Michigan, 1891 IOE Building 1205 Beal Ave, Ann Arbor, MI, 48109, USA.
Jeremy B SussmanDepartment of Internal Medicine, University of Michigan, 1500 E Medical Center Dr, Ann Arbor, MI, 48109, USA.

Funding

National Science Foundation CMMI-1552545National Science Foundation DGE 1256260U.S. Department of Veterans Affairs VA HSR&D CDA13-021U.S. Department of Veterans Affairs VA HSR&D IIR 15-432
6 · The paper itself

Abstract

Atherosclerotic cardiovascular disease (ASCVD) is among the leading causes of death in the US. Although research has shown that ASCVD has genetic elements, the understanding of how genetic testing influences its prevention and treatment has been limited. To this end, we model the health trajectory of patients stochastically and determine treatment and testing decisions simultaneously. Since the cholesterol level of patients is one controllable risk factor for ASCVD events, we model cholesterol treatment plans as Markov decision processes. We determine whether and when patients should receive a genetic test using value of information analysis. By simulating the health trajectory of over 64 million adult patients, we find that 6.73 million patients undergo genetic testing. The optimal treatment plans informed with clinical and genetic information save 5,487 more quality-adjusted life-years while costing $1.18 billion less than the optimal treatment plans informed with clinical information only. As precision medicine becomes increasingly important, understanding the impact of genetic information becomes essential.

Indexed as

Genetic TestingAdultAnticholesteremic AgentsAtherosclerosisCardiovascular DiseasesComputer SimulationFemaleGenetic Predisposition to DiseaseHumansHypercholesterolemiaMaleMarkov ChainsMiddle AgedQuality-Adjusted Life YearsAnticholesteremic AgentsCardiovascular diseaseGenetic testingMarkov decision processOperations researchStochastic simulationValue of information

Identifiers

What OpenQuestion holds

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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.