Evidence map›Paper›PMID 37065571›Full record

ArticleJournal of thoracic disease2023

Prediction of postoperative pulmonary complications in older patients undergoing lobectomy for lung cancer based on skeletal muscle mass.

Seung-Wan Hong, Song-Am Lee, Seong-Hyop Kim

Open access · diamondAbstract read
In one paragraph

Article in Journal of thoracic disease, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
1.2field-weighted citation impact, top 22% 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

6 citing papers in PubMed, 7 citations in OpenAlex.

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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

3 authors at 2 institutions in 1 country.

Seung-Wan HongDepartment of Anesthesiology and Pain Medicine, Konkuk University Medical Center, Konkuk University School of Medicine, Seoul, Korea.
Song-Am LeeDepartment of Thoracic Surgery, Konkuk University Medical Center, Konkuk University School of Medicine, Seoul, Korea.
Seong-Hyop KimDepartment of Anesthesiology and Pain Medicine, Konkuk University Medical Center, Konkuk University School of Medicine, Seoul, Korea.
Konkuk University Medical Center · KRKonkuk University · KR

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: This retrospective study was designed to evaluate preoperative pulmonary function test (PFT) results and skeletal muscle mass, represented by the erector spinae muscle (EM), as predictors of postoperative pulmonary complications (PPCs) in older patients undergoing lobectomy for lung cancer. Methods: The medical records, including preoperative PFT, chest computed tomography (CT) and PPCs, of patients older than 65 years undergoing lobectomy for lung cancer were retrospectively examined at Konkuk University Medical Center from January 2016 to December 2021. The sum of cross-sectional areas (CSAs) of the right and left EMs at the level of the spinous process with the 12 Results: Data from a total of 197 patients were included in the analyses. In total, 55 patients had PPCs. The preoperative functional vital capacity (FVC) and forced expiratory volume in 1 second (FEV1) showed significantly poorer values and the CSA Conclusions: PPCs in older patients undergoing lobectomy for lung cancer were associated with lower preoperative FVC and FEV1 values and a lower skeletal muscle mass. Skeletal muscle mass, represented by the EM, was significantly correlated with the preoperative FVC and FEV1. Therefore, skeletal muscle mass may be useful for the prediction of PPCs in patients undergoing lobectomy for lung cancer.

Indexed as

lobectomylung cancerolder patientPulmonary function test (PFT)skeletal muscle mass

Identifiers

PMID37065571
PMCPMC10089878
OpenAlexW4323339069

What OpenQuestion holds

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Registered trials

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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.