Evidence map›Paper›PMID 40696117›Full record

ArticleCommunications medicine2025

Comorbidities associated with a clinically-recognized delirium diagnosis in the hospital using real world data.

Lay Kodama, Sarah R Woldemariam, Alice S Tang, Yaqiao Li, John Kornak, Isabel Elaine Allen, Eva Raphael, Tomiko T Oskotsky, Marina Sirota

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Communications medicine, 2025. 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

5 · Who and what money

Authors and funding

9 authors.

Lay KodamaBakar Computational Health Sciences Institute, University of California San Francisco, San Francisco, CA, USA.
Sarah R WoldemariamBakar Computational Health Sciences Institute, University of California San Francisco, San Francisco, CA, USA.ORCID http://orcid.org/0000-0003-1873-8575
Alice S TangBakar Computational Health Sciences Institute, University of California San Francisco, San Francisco, CA, USA.ORCID http://orcid.org/0000-0003-4745-0714
Yaqiao LiBakar Computational Health Sciences Institute, University of California San Francisco, San Francisco, CA, USA.
John KornakDepartment of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, CA, USA.
Isabel Elaine AllenDepartment of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, CA, USA.
Eva RaphaelDepartment of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, CA, USA.
Tomiko T OskotskyBakar Computational Health Sciences Institute, University of California San Francisco, San Francisco, CA, USA.ORCID http://orcid.org/0000-0001-7393-5120
Marina SirotaBakar Computational Health Sciences Institute, University of California San Francisco, San Francisco, CA, USA. marina.sirota@ucsf.edu.ORCID http://orcid.org/0000-0002-7246-6083

Funding

Community-onset urinary tract infections caused by extended-spectrum beta-lactamase-producing Escherichia coli in women of diverse backgroundsK23AI166030 · NIAID · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI Eva Raphael · 2022 to 2026
$1.1M
Foundation for the National Institutes of Health (Foundation for the National Institutes of Health, Inc.) R01AG060393NIAID NIH HHS K23 AI166030
6 · The paper itself

Abstract

backgroundDelirium is a mental condition defined as fluctuating disturbances in attention, awareness, and cognition. It is often seen in older, hospitalized patients and is currently hard to predict, with long- and short-term outcomes being detrimental to patients.

methodsWe leveraged electronic health records (EHR) to identify 7492 UCSF patients and 19,417 UC health system patients with an inpatient delirium diagnosis and the same number of control patients without delirium. We used the Fisher's exact test with multiple corrections for the association studies and the Cox regression model for the longitudinal analyses.

resultsHere we show significant associations between comorbidities or laboratory values and an inpatient delirium diagnosis, including metabolic abnormalities and psychiatric diagnoses. Some associations are sex-specific, including dementia subtypes and infections. We further explore the associations with anemia and bipolar disorder by conducting longitudinal analyses from the time of first diagnosis to development of delirium, demonstrating a significant relationship across time. Finally, we show that an inpatient delirium diagnosis leads to increased risk of mortality.

conclusionsThese results demonstrate the powerful application of the EHR to shed insights into prior diagnoses and laboratory values that could help predict development of inpatient delirium and the importance of sex when making these assessments.

Identifiers

PMID40696117
PMCPMC12284072

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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.