Evidence map›Paper›PMID 39321638›Full record

ArticlePsychiatry research2024

Clinical associations with treatment resistance in depression: An electronic health record study.

Brandon J Coombes, Jorge A Sanchez-Ruiz, Brian Fennessy, Vanessa K Pazdernik, Prakash Adekkanattu, Nicolas A Nuñez, Lauren Lepow, Lindsay M Melhuish Beaupre, Euijung Ryu, Ardesheer Talati and 6 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Psychiatry research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

16 authors.

Brandon J CoombesDepartment of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, USA. Electronic address: coombes.brandon@mayo.edu.
Jorge A Sanchez-RuizDepartment of Psychiatry & Psychology, Mayo Clinic, Rochester, MN, USA.
Brian FennessyDepartment of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
Vanessa K PazdernikDepartment of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, USA.
Prakash AdekkanattuDepartment of Population Health Sciences, Weill Cornell Medicine, New York, NY, USA; Clinical and Translational Science Center, Weill Cornell Medicine, New York, NY, USA.
Nicolas A NuñezDepartment of Psychiatry & Psychology, Mayo Clinic, Rochester, MN, USA.
Lauren LepowDepartment of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
Lindsay M Melhuish BeaupreDepartment of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, USA.
Euijung RyuDepartment of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, USA.
Ardesheer TalatiDepartment of Psychiatry, Vagelos College of Physicians and Surgeons Columbia University & NY State Psychiatric Institute, New York, NY, USA.
J John MannDepartment of Psychiatry, Vagelos College of Physicians and Surgeons Columbia University & NY State Psychiatric Institute, New York, NY, USA.
Myrna M WeissmanDepartment of Psychiatry, Vagelos College of Physicians and Surgeons Columbia University & NY State Psychiatric Institute, New York, NY, USA.
Mark OlfsonDepartment of Psychiatry, Vagelos College of Physicians and Surgeons Columbia University & NY State Psychiatric Institute, New York, NY, USA.
Jyotishman PathakDepartment of Population Health Sciences, Weill Cornell Medicine, New York, NY, USA; Department of Psychiatry, Weill Cornell Medicine, New York, NY, USA.
Alexander W CharneyDepartment of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA; Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA; Mount Sinai Clinical Intelligence Center, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
Joanna M BiernackaDepartment of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, USA; Department of Psychiatry & Psychology, Mayo Clinic, Rochester, MN, USA. Electronic address: biernacka.joanna@mayo.edu.

Funding

Training the next generation of clinical neuroscientistsT32MH122394 · NIMH · ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI · PI Rene S. Kahn, ANTONIA S NEW · 2020 to 2026
$2.5M
3/4: Leveraging EHR-linked biobanks for deep phenotyping, polygenic risk score modeling, and outcomes analysis in psychiatric disordersR01MH121923 · NIMH · ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI · PI CHARNEY, ALEXANDER W · 2019 to 2023
$2.1M
4/4: Leveraging EHR-linked biobanks for deep phenotyping, polygenic risk score modeling, and outcomes analysis in psychiatric disordersR01MH121922 · NIMH · WEILL MEDICAL COLL OF CORNELL UNIV · PI PATHAK, JYOTISHMAN · 2019 to 2023
$2.0M
2/4: Leveraging EHR-linked biobanks for deep phenotyping, polygenic risk score modeling, and outcomes analysis in psychiatric disordersR01MH121924 · NIMH · MAYO CLINIC ROCHESTER · PI BIERNACKA, JOANNA M · 2019 to 2024
$2.0M
1/4 Leveraging EHR-linked biobanks for deep phenotyping, polygenic risk score modeling, and outcomes analysis in psychiatric disordersR01MH121921 · NIMH · NEW YORK STATE PSYCHIATRIC INSTITUTE DBA RESEARCH FOUNDATION FOR MENTAL HYGIENE, INC · PI MANN, JOSEPH JOHN, OLFSON, MARK · 2019 to 2023
$1.3M
NIMH NIH HHS R01 MH121921NIMH NIH HHS R01 MH121922NIMH NIH HHS R01 MH121923NIMH NIH HHS R01 MH121924NIMH NIH HHS T32 MH122394
6 · The paper itself

Abstract

Treatment resistance is common in major depressive disorder (MDD), yet clinical risk factors are not well understood. Using a discovery-replication design, we conducted phenome-wide association studies (PheWASs) of MDD treatment resistance in two electronic health record (EHR)-linked biobanks. The PheWAS included participants with an MDD diagnosis in the EHR and at least one antidepressant (AD) prescription. Participant lifetime diagnoses were mapped to phecodes. PheWASs were conducted for three treatment resistance outcomes based on AD prescription data: number of unique ADs prescribed, ≥1 and ≥2 CE switches. Of the 180 phecodes significantly associated with these outcomes in the discovery cohort (n = 12,558), 71 replicated (n = 8,206). In addition to identifying known clinical factors for treatment resistance in MDD, the total unique AD prescriptions was associated with additional clinical variables including irritable bowel syndrome, gastroesophageal reflux disease, symptomatic menopause, and spondylosis. We calculated polygenic risk of specific-associated conditions and tested their association with AD outcomes revealing that genetic risk for many of these conditions is also associated with the total unique AD prescriptions. The number of unique ADs prescribed, which is easily assessed in EHRs, provides a more nuanced measure of treatment resistance, and may facilitate future research and clinical application in this area.

Indexed as

Antidepressive AgentsDepressive Disorder, Treatment-ResistantElectronic Health RecordsMajor Depressive DisorderAdultAgedFemaleGenome-Wide Association StudyHumansMaleMiddle AgedAntidepressive AgentsAntidepressantsComorbidityMDDPheWASPRSTRD

Identifiers

PMID39321638
PMCPMC11617277

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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.