Evidence map›Paper›PMID 41261505›Full record

ArticleCancer reports (Hoboken, N.J.)2025

Development of a Predictive Model for Large-Volume Pleural Effusion After Separation Surgery in Patients With Spinal Metastatic Tumors.

Haochen Mou, Keyi Wang, Hao Qu, Yaling Jiang, Meng Liu, Xiaobo Yan, Xin Huang, Nong Lin, Zhaoming Ye

Abstract read
In one paragraph

Article in Cancer reports (Hoboken, N.J.), 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

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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

9 authors.

Haochen MouThe Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, Zhejiang, People's Republic of China.ORCID 0009-0004-0384-5524
Keyi WangThe Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, Zhejiang, People's Republic of China.ORCID 0009-0000-4370-5869
Hao QuThe Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, Zhejiang, People's Republic of China.ORCID 0000-0001-7633-7097
Yaling JiangThe Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, Zhejiang, People's Republic of China.ORCID 0000-0002-5268-1333
Meng LiuThe Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, Zhejiang, People's Republic of China.ORCID 0009-0008-7893-5099
Xiaobo YanThe Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, Zhejiang, People's Republic of China.ORCID 0000-0001-8509-869X
Xin HuangThe Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, Zhejiang, People's Republic of China.ORCID 0000-0002-6247-1946
Nong LinThe Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, Zhejiang, People's Republic of China.ORCID 0000-0003-4939-113X
Zhaoming YeThe Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, Zhejiang, People's Republic of China.ORCID 0000-0001-5951-5840

Funding

National Natural Science Foundation of China
6 · The paper itself

Abstract

backgroundSeparation surgery followed by radiotherapy has emerged as a prevalent approach for managing spinal metastatic tumors. However, large-volume postoperative pleural effusion (POPE) represents a challenging complication, as it potentially delays subsequent treatments and increases morbidity. This study aims to identify risk factors for large-volume POPE and develop a predictive model for early identification to improve patient prognosis.

methodsThis retrospective study analyzed 443 patients who underwent separation surgery for spinal metastases at our center between January 2014 and January 2022. High-resolution CT-based 3D modeling was utilized for accurate pleural effusion (PE) volume quantification. Variables including patient demographics, surgical details, and laboratory results were examined to identify risk factors associated with large-volume POPE (≥ 1000 mL). A predictive nomogram was developed based on the multivariate logistic regression analysis.

resultsOur findings indicated that advanced age, increased intraoperative blood loss, and decreased levels of preoperative serum albumin, postoperative serum protein, and hemoglobin were significant independent risk factors for large-volume POPE. The predictive nomogram demonstrated high accuracy, with a mean AUC value of 0.953 for the training dataset and 0.927 for the testing dataset, indicating reliable predictability for identifying patients at high risk for large-volume POPE.

conclusionOur study identified independent risk factors for large-volume PE following separation surgery in patients with spinal metastasis. The developed nomogram offers a practical tool for early identification of high-risk groups, enabling timely and targeted interventions. By reducing the risk of large POPE, this approach may shorten hospitalization and accelerate the resumption of postoperative treatment, ultimately improving patient prognosis.

Indexed as

Pleural EffusionPostoperative ComplicationsSpinal NeoplasmsAdultAgedAged, 80 and overFemaleHumansMaleMiddle AgedNomogramsPrognosisRetrospective StudiesRisk FactorsTomography, X-Ray Computednomogrampleural effusionprognostic factorspinal metastasis

Identifiers

PMID41261505
PMCPMC12629910

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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.