Evidence map›Paper›PMID 40261652›Full record

ArticleJAMA network open2025

Patterns in Symptoms Preceding Acute Care in Patients With Cancer.

Chichi Chang, Jie Jane Chen, Jean Feng, Isabel Friesner, Somya Mohindra, Lauren Boreta, Michael W Rabow, Steve E Braunstein, Ryzen Benson, Julian C Hong

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in JAMA network open, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. 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

10 authors.

Chichi ChangBakar Computational Health Sciences Institute, University of California, San Francisco.
Jie Jane ChenDepartment of Radiation Oncology, University of California, San Francisco.
Jean FengDepartment of Epidemiology and Biostatistics, University of California, San Francisco.
Isabel FriesnerBakar Computational Health Sciences Institute, University of California, San Francisco.
Somya MohindraBakar Computational Health Sciences Institute, University of California, San Francisco.
Lauren BoretaDepartment of Radiation Oncology, University of California, San Francisco.
Michael W RabowDivision of Palliative Medicine, Department of Internal Medicine, University of California, San Francisco.
Steve E BraunsteinDepartment of Radiation Oncology, University of California, San Francisco.
Ryzen BensonBakar Computational Health Sciences Institute, University of California, San Francisco.
Julian C HongBakar Computational Health Sciences Institute, University of California, San Francisco.

Funding

Multi-institutional validation of a multi-modal machine learning algorithm to predict and reduce acute care during cancer therapyR01CA277782 · NCI · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI Julian Clint Hong · 2023 to 2026
$1.6M
NCI NIH HHS R01 CA277782
6 · The paper itself

Abstract

Importance: Patients with cancer frequently experience unplanned acute care with emergency department visits and hospitalization due to disease or treatment complications, which impacts outcomes, quality of life, and health care costs. There remains a knowledge gap in understanding patterns of symptoms that precede acute care events. Natural language processing (NLP) may enable greater understanding of the symptoms and identify differences across patient and cancer characteristics. Objective: To characterize symptoms preceding acute care in patients with cancer and quantify differences in symptom documentation across sociodemographic and cancer histologic subgroups. Design, Setting, and Participants: A cohort study in a single tertiary-care institution, including all acute care (emergency department and hospitalization) encounters for patients aged 18 years or older with a primary cancer diagnosis identified between January 1, 2013, and December 31, 2023. Main Outcomes and Measures: Natural language processing was used to identify routine clinical documentation to characterize symptoms documented in the 30 days preceding acute care. Logistic regression analyses was used to examine the possible association between sex, age, race and ethnicity, insurance coverage, cancer histologic characteristics, and reported symptoms. Results: Overall, 28 708 patients with cancer had 70 606 acute care visits with 854 830 associated preceding documented symptoms. Median age was 61 (IQR, 48-70) years. Men (37 861 encounters [53.62%]) and patients of White race (39 989 encounters [56.64%]) accounted for most acute care encounters. Pain (7.54% of documented symptoms), nausea (6.74%), and vomiting (5.79%) were the most frequently documented symptoms. Acute care encounters with patients who were female (adjusted odds ratio [AOR], 1.14; 95% CI, 1.10-1.18; P < .001), Asian (AOR, 1.22; 1.17-1.28; P < .001), Black (AOR, 1.17; 95% CI, 1.10-1.25; P < .001), American Indian or Alaska Native (AOR, 1.21; 95% CI, 1.01-1.44; P = .04), or Medicaid-insured (AOR, 1.10; 95% CI, 1.05-1.14; P < .001) were associated with a high documented symptom burden (>10 unique symptoms) preceding acute care visits. Patients aged 65 years or older (AOR, 0.96; 95% CI, 0.92-1.00; P = .04) or uninsured (AOR, 0.58; 95% CI, 0.45-0.76; P < .001) were less likely to have a high symptom burden documented before acute care events. Conclusions and Relevance: The findings of this study highlight common symptoms preceding acute care as well as the need for further research on interventions to reduce patient burden, improve quality of life, and reduce the use of acute care in patients with cancer.

Indexed as

Emergency Service, HospitalHospitalizationNeoplasmsSymptom AssessmentAgedCohort StudiesFemaleHumansMaleMiddle AgedNatural Language Processing

Identifiers

PMID40261652
PMCPMC12015675

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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.