ArticlePharmacogenomics and personalized medicine2021
No Association Between Pharmacogenomics Variants and Hospital and Emergency Department Utilization: A Mayo Clinic Biobank Retrospective Study.
Article in Pharmacogenomics and personalized medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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.
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.
Who cites it
4 citing papers in PubMed.
- Effect Modification by Social Determinants of Pharmacogenetic Medication Interactions on 90-Day Hospital Readmissions within an Integrated U.S. Healthcare System.Journal of personalized medicine · 2022Article
- The Contribution of Pharmacogenetic Drug Interactions to 90-Day Hospital Readmissions: Preliminary Results from a Real-World Healthcare System.Journal of personalized medicine · 2021Article
- Pathway to Ascertain the Role of Pharmacogenomics in Healthcare Utilization Outcomes [Letter].Pharmacogenomics and personalized medicine · 2021Article
- Pathway to Ascertain the Role of Pharmacogenomics in Healthcare Utilization Outcomes [Response to Letter].Pharmacogenomics and personalized medicine · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
Abstract
backgroundThe use of pharmacogenomics data is increasing in clinical practice. However, it is unknown if pharmacogenomics data can be used more broadly to predict outcomes like hospitalization or emergency department (ED) visit. We aim to determine the association between selected pharmacogenomics phenotypes and hospital utilization outcomes (hospitalization and ED visits).
methodsThis cohort study utilized 10,078 patients from the Mayo Clinic Biobank in the RIGHT protocol with sequence and interpreted phenotypes for 10 selected pharmacogenes including
resultsDuring the follow-up period (median [in years] = 7.3), 13% (n=1354) and 8% (n=813) of the subjects experienced hospitalization and ED visits, respectively. Compared to subjects who did not experience hospitalization, hospitalized patients were older (median age [in years]: 67 vs 65), poorer self-rated health (15% vs 4.7% for fair/poor), and higher disease burden (median number of chronic conditions: 7 vs 4) at baseline. There was no association of hospitalization with any of the pharmacogenomics phenotypes. The pharmacogenomics phenotypes were not associated with disease burden, a well-established risk factor for hospital utilization outcomes. Similar findings were observed for patients with ED visits during the follow-up period.
conclusionWe found no association of 10 well-established pharmacogenomics phenotypes with either hospitalization or ED visits in this relatively large biobank population and outside the context of specific drug use related to these genes. Traditional risk factors for hospitalization like age and self-rated health were much more likely to predict hospitalization and/or ED visits than this pharmacogenomics information.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.