Evidence map›Paper›PMID 36158745›Full record

ArticleAdvances in radiation oncology

Independent Predictors for Hospitalization-Associated Radiation therapy Interruptions.

Adam Hubler, Daniel V Wakefield, Lydia Makepeace, Matt Carnell, Ankur M Sharma, Bo Jiang, Austin P Dove, Wesley B Garner, Drucilla Edmonston, John G Little and 6 more

Abstract read
In one paragraph

Article in Advances in radiation oncology. 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.

Adam HublerDepartment of Radiation Oncology, University of Tennessee Health Science Center, Memphis, Tennessee.
Daniel V WakefieldDepartment of Radiation Oncology, University of Tennessee Health Science Center, Memphis, Tennessee.
Lydia MakepeaceDepartment of Radiation Oncology, University of Tennessee Health Science Center, Memphis, Tennessee.
Matt CarnellUniversity of Tennessee Health Science Center College of Medicine, Memphis, Tennessee.
Ankur M SharmaDepartment of Radiation Oncology, University of Tennessee Health Science Center, Memphis, Tennessee.
Bo JiangDepartment of Radiation Oncology, University of Tennessee Health Science Center, Memphis, Tennessee.
Austin P DoveDepartment of Radiation Oncology, Vanderbilt University Medical Center, Nashville, Tennesse.
Wesley B GarnerDepartment of Radiation Oncology, University of Tennessee Health Science Center, Memphis, Tennessee.
Drucilla EdmonstonDepartment of Radiation Oncology, University of Tennessee Health Science Center, Memphis, Tennessee.
John G LittleUniversity of Tennessee Health Science Center College of Medicine, Memphis, Tennessee.
Esra OzdenerolDepartment of Earth Sciences, University of Memphis, Memphis, Tennessee.
Ryan B HansonDepartment of Earth Sciences, University of Memphis, Memphis, Tennessee.
Michelle Y MartinDepartment of Preventive Medicine, University of Tennessee Health Science Center, Memphis, Tennessee.
Arash Shaban-NejadUTHSC-ORNL Center for Biomedical Informatics, University of Tennessee Health Science Center, Memphis, Tennesse.
Maria PisuDivision of Preventive Medicine, University of Alabama at Birmingham, Birmingham, Alabama.
David L SchwartzDepartment of Radiation Oncology, University of Tennessee Health Science Center, Memphis, Tennessee.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Purpose: Radiation treatment interruption associated with unplanned hospitalization remains understudied. The intent of this study was to benchmark the frequency of hospitalization-associated radiation therapy interruptions (HARTI), characterize disease processes causing hospitalization during radiation, identify factors predictive for HARTI, and localize neighborhood environments associated with HARTI at our academic referral center. Methods and Materials: This retrospective review of electronic health records provided descriptive statistics of HARTI event rates at our institutional practice. Uni- and multivariable logistic regression models were developed to identify significant factors predictive for HARTI. Causes of hospitalization were established from primary discharge diagnoses. HARTI rates were mapped according to patient residence addresses. Results: Between January 1, 2015, and December 31, 2017, 197 HARTI events (5.3%) were captured across 3729 patients with 727 total missed treatments. The 3 most common causes of hospitalization were malnutrition/dehydration (n = 28; 17.7%), respiratory distress/infection (n = 24; 13.7%), and fever/sepsis (n = 17; 9.7%). Factors predictive for HARTI included African-American race (odds ratio [OR]: 1.48; 95% confidence interval [CI], 1.07-2.06; Conclusions: HARTI disproportionately affected socioeconomically disadvantaged urban patients facing a high treatment burden in our catchment population. A complementary geospatial analysis also captured the risk experienced by middle-income suburban patients independent of race or insurance status. Confirmatory studies are warranted to provide scale and context to guide intervention strategies to equitably reduce HARTI events.

Identifiers

PMID36158745
PMCPMC9489733

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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.