Evidence map›Paper›PMID 36529406›Full record

ArticleJournal of affective disorders2023

Comorbidity and healthcare utilization in patients with treatment resistant depression: A large-scale retrospective cohort analysis using electronic health records.

Prakash Adekkanattu, Mark Olfson, Leah C Susser, Braja Patra, Veer Vekaria, Brandon J Coombes, Lauren Lepow, Brian Fennessy, Alexander Charney, Euijung Ryu and 9 more

Open access · greenAbstract read
In one paragraph

Article in Journal of affective disorders, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.

0numbers the graph read from it
0cells of the map it votes in
14citing papers in PubMed
3.6field-weighted citation impact, top 6% 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

14 citing papers in PubMed, 24 citations in OpenAlex.

  1. Trial
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  3. Changes in leukocytes and CRP in different treatments of major depression.Journal of neural transmission (Vienna, Austria : 1996) · 2026
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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 at 6 institutions in 1 country.

Prakash AdekkanattuWeill Cornell Medicine, New York, NY, USA. Electronic address: pra2008@med.cornell.edu.
Mark OlfsonColumbia University and New York State Psychiatric Institute, New York, NY, USA.
Leah C SusserWeill Cornell Medicine, New York, NY, USA.
Braja PatraWeill Cornell Medicine, New York, NY, USA.
Veer VekariaWeill Cornell Medicine, New York, NY, USA.
Brandon J CoombesMayo Clinic, Rochester, MN, USA.
Lauren LepowIcahn School of Medicine at Mount Sinai, New York, NY, USA.
Brian FennessyIcahn School of Medicine at Mount Sinai, New York, NY, USA.
Alexander CharneyIcahn School of Medicine at Mount Sinai, New York, NY, USA.
Euijung RyuMayo Clinic, Rochester, MN, USA.
Kurt D MillerMayo Clinic, Rochester, MN, USA.
Lifang PanColumbia University and New York State Psychiatric Institute, New York, NY, USA.
Tenzin YangchenColumbia University and New York State Psychiatric Institute, New York, NY, USA.
Ardesheer TalatiColumbia University and New York State Psychiatric Institute, New York, NY, USA.
Priya WickramaratneColumbia University and New York State Psychiatric Institute, New York, NY, USA.
Myrna WeissmanColumbia University and New York State Psychiatric Institute, New York, NY, USA.
John MannColumbia University and New York State Psychiatric Institute, New York, NY, USA.
Joanna M BiernackaMayo Clinic, Rochester, MN, USA.
Jyotishman PathakWeill Cornell Medicine, New York, NY, USA.
New York State Psychiatric InstituteMayo Clinic in Arizona · USWeill Cornell Medicine · USIcahn School of Medicine at Mount Sinai · USColumbia University · USCornell University · US

Funding

National Infrastructure for Standardized and Portable EHR Phenotyping AlgorithmsR01GM105688 · NIGMS · WEILL MEDICAL COLL OF CORNELL UNIV · PI LUO, YUAN, PATHAK, JYOTISHMAN · 2013 to 2020
$5.7M
Linking VA and non-VA data to study the risk of suicide in chronic pain patients.R01MH121907 · NIMH · WEILL MEDICAL COLL OF CORNELL UNIV · PI OSLIN, DAVID W., PATHAK, JYOTISHMAN · 2020 to 2024
$3.6M
Using claims data to study the association between Alzheimer's disease and suicidal behaviorsR01MH119177 · NIMH · WEILL MEDICAL COLL OF CORNELL UNIV · PI PATHAK, JYOTISHMAN · 2019 to 2022
$3.3M
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
Risk modeling and shared decision making for postpartum depressionR41MH124581 · NIMH · IRIS OB HEALTH INC. · PI LASKOFF, MICHAEL B., PATHAK, JYOTISHMAN · 2021 to 2022
$1.0M
NIGMS NIH HHS R01 GM105688NIMH NIH HHS R01 MH119177NIMH NIH HHS R01 MH121907NIMH NIH HHS R01 MH121921NIMH NIH HHS R01 MH121922NIMH NIH HHS R01 MH121923NIMH NIH HHS R01 MH121924NIMH NIH HHS R41 MH124581
6 · The paper itself

Abstract

backgroundMedical comorbidity and healthcare utilization in patients with treatment resistant depression (TRD) is usually reported in convenience samples, making estimates unreliable. There is only limited large-scale clinical research on comorbidities and healthcare utilization in TRD patients.

methodsElectronic Health Record data from over 3.3 million patients from the INSIGHT Clinical Research Network in New York City was used to define TRD as initiation of a third antidepressant regimen in a 12-month period among patients diagnosed with major depressive disorder (MDD). Age and sex matched TRD and non-TRD MDD patients were compared for anxiety disorder, 27 comorbid medical conditions, and healthcare utilization.

resultsOut of 30,218 individuals diagnosed with MDD, 15.2 % of patients met the criteria for TRD (n = 4605). Compared to MDD patients without TRD, the TRD patients had higher rates of anxiety disorder and physical comorbidities. They also had higher odds of ischemic heart disease (OR = 1.38), stroke/transient ischemic attack (OR = 1.57), chronic kidney diseases (OR = 1.53), arthritis (OR = 1.52), hip/pelvic fractures (OR = 2.14), and cancers (OR = 1.41). As compared to non-TRD MDD, TRD patients had higher rates of emergency room visits, and inpatient stays. In relation to patients without MDD, both TRD and non-TRD MDD patients had significantly higher levels of anxiety disorder and physical comorbidities. LIMITATIONS: The INSIGHT-CRN data lack information on depression severity and medication adherence.

conclusionsTRD patients compared to non-TRD MDD patients have a substantially higher prevalence of various psychiatric and medical comorbidities and higher health care utilization. These findings highlight the challenges of developing interventions and care coordination strategies to meet the complex clinical needs of TRD patients.

Indexed as

Depressive Disorder, Treatment-ResistantMajor Depressive DisorderCohort StudiesComorbidityElectronic Health RecordsHealth Care CostsHumansPatient Acceptance of Health CareRetrospective StudiesElectronic health recordsHealthcare utilizationMajor depressive disorderMedical comorbiditiesTreatment resistant depression

Identifiers

PMID36529406
PMCPMC10327872
OpenAlexW4311513230

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.