Evidence map›Paper›PMID 39679372›Full record

ArticleFrontiers in pharmacology2024

Effects of pharmacogenomics-guided treatment on medication adherence and the antidepressant switching rate in major depressive disorder.

Chaoli Chen, Yang Lun, Jing Yu, Xiaochuan Zhao, Shi Su, Mengqiang Zhao, Yuhang Yan, Jiaqi Wang, Ran Fu, Feiyue An and 9 more

Abstract read
In one paragraph

Article in Frontiers in pharmacology, 2024. 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
–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

4 citing papers in PubMed.

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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

19 authors.

Chaoli Chen *Department of Clinical Pharmacy, The First Hospital of Hebei Medical University, Shijiazhuang, China.
Yang Lun *Department of Clinical Pharmacy, The First Hospital of Hebei Medical University, Shijiazhuang, China.
Jing YuDepartment of Clinical Pharmacy, The First Hospital of Hebei Medical University, Shijiazhuang, China.
Xiaochuan ZhaoDepartment of Psychiatry, The First Hospital of Hebei Medical University, Shijiazhuang, China.
Shi SuDepartment of Clinical Pharmacy, The First Hospital of Hebei Medical University, Shijiazhuang, China.
Mengqiang ZhaoDepartment of Clinical Pharmacy, The First Hospital of Hebei Medical University, Shijiazhuang, China.
Yuhang YanDepartment of Clinical Pharmacy, The First Hospital of Hebei Medical University, Shijiazhuang, China.
Jiaqi WangDepartment of Clinical Pharmacy, The First Hospital of Hebei Medical University, Shijiazhuang, China.
Ran FuDepartment of Clinical Pharmacy, The First Hospital of Hebei Medical University, Shijiazhuang, China.
Feiyue AnPharmacy Department, The First Hospital of Hebei Medical University, Shijiazhuang, China.
Liguang DuanPharmacy Department, The First Hospital of Hebei Medical University, Shijiazhuang, China.
Leting YanDepartment of Psychiatry, The First Hospital of Hebei Medical University, Shijiazhuang, China.
Ruxing LiDepartment of Psychiatry, The First Hospital of Hebei Medical University, Shijiazhuang, China.
Jinxiao LiDepartment of Psychiatry, The First Hospital of Hebei Medical University, Shijiazhuang, China.
Ziyu LiuDepartment of Psychiatry, The First Hospital of Hebei Medical University, Shijiazhuang, China.
Xiaoying GengSchool of Pharmacy, Hebei Medical University, Shijiazhuang, China.
Jincheng WangDepartment of Psychiatry, The First Hospital of Hebei Medical University, Shijiazhuang, China.
Yuanyuan ZhaoKey Laboratory for Neuroimmunological Regulation and Mental Health of Hebei Province, Shijiazhuang, China.
Chunhua ZhouDepartment of Clinical Pharmacy, The First Hospital of Hebei Medical University, Shijiazhuang, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: In the treatment of depression, medication plays a crucial role. However, insufficient patient adherence to medication often results in unsatisfactory treatment outcomes, increasing both the recurrence and rehospitalization rates of depression, and consequently imposing a greater economic burden on the healthcare system. Objectives: Our objective was to examine the impact of pharmacogenomic testing on medication adherence and antidepressant switching rates among individuals diagnosed with depression. Methods: This retrospective cohort study encompassed patients diagnosed with depression who were admitted to the First Hospital of Hebei Medical University between April 2022 and September 2023. Patients were categorized into a pharmacogenomics-guided treatment (PGxT) group and a treatment as usual (TAU) group based on whether pharmacogenetic testing was conducted. The primary outcome measures included the proportion of patients exhibiting medication adherence greater than 80% at three and 6 months post-discharge, as well as the proportion of patients experiencing changes in their prescribed medication types. Results: A total of 310 patients in the PGxT group and TAU group were obtained through propensity score matching. Among the 620 patients in both groups, 57.42% demonstrated good adherence (≥80%) at 3 months; this percentage dropped to 31.45% at 6 months. At 3 months of observation, the percentages of patients demonstrating good adherence were significantly different between the groups (64.52% in the PGxT group vs. 50.32% in the TAU group; Conclusion: The findings of this study indicate that pharmacogenomics testing positively influences treatment adherence and may decrease the need to switch medications among patients with depression.

Indexed as

adherenceantidepressantdepressionpharmacogenomicsswitching

Identifiers

PMID39679372
PMCPMC11639597

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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.