Evidence map›Paper›PMID 41473043›Full record

ArticleJTCVS open2025

Are there different phenotypes of thoracic surgery patients? A latent class analysis of pretreatment patient-reported outcomes.

Eagan J Peters, Brenden Dufault, Sadeesh K Srinathan, Gordon Buduhan, Lawrence Tan, Biniam Kidane

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Article in JTCVS open, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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4 · The record

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5 · Who and what money

Authors and funding

6 authors.

Eagan J PetersDepartment of Medicine, Temerty Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada.
Brenden DufaultDepartment of Community Health Sciences, Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, Manitoba, Canada.
Sadeesh K SrinathanSection of Thoracic Surgery, Department of Surgery, Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, Manitoba, Canada.
Gordon BuduhanDivision of Thoracic Surgery, Faculty of Medicine, University of British Columbia, Kelowna, British Columbia, Canada.
Lawrence TanSection of Thoracic Surgery, Department of Surgery, Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, Manitoba, Canada.
Biniam KidaneSection of Thoracic Surgery, Department of Surgery, Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, Manitoba, Canada.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Among patients undergoing thoracic surgery, quality of life is associated with multiple perioperative outcomes. Whether patients suffer reduced quality of life in certain areas compared to others is unclear. Knowing this could direct risk mitigation interventions for patients who share common symptoms. The objective of this study was to determine whether patients can be subdivided into groups based on preoperative quality of life. Methods: This is a secondary analysis of a retrospective cohort study of consecutive patients undergoing thoracic surgery between January 2018 and January 2019 at a Canadian tertiary center. Latent class analysis was conducted according to 3-level EuroQol-5 dimension (EQ-5D-3L) scores. The number of latent classes was selected by comparing different models using the Akaike information criterion, Bayesian information criterion, and Results: Among 482 patients, models with 2 to 5 classes were constructed. The 3-class model demonstrated the lowest Akaike and Bayesian information criterion values. The Conclusions: There is evidence that patients undergoing thoracic surgery can be divided into 3 latent classes based on EQ-5D-3L score: low symptom burden, mobility-pain complex, and pain predominant. By identifying patients using these latent classes, targeted supportive interventions may be offered in the pretreatment period to improve perioperative outcomes.

Indexed as

esophageal cancerEuroQol-5-dimension 3-levelfrailtyhealth-related quality-of-lifelatent class analysislung cancerperioperative pain

Identifiers

PMID41473043
PMCPMC12745110

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LicenceCC BY
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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.