Evidence map›Paper›PMID 41877109›Full record

ArticleBMC pulmonary medicine2026

Risk factors for secondary pulmonary hypertension in patients with silicosis.

Ying Zhen, Xiu-Fang Zhang, Wei-Feng Guo, Jun Ye, Can-Qiang Qiu, Jun Wu

Abstract read
In one paragraph

Article in BMC pulmonary medicine, 2026. 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

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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

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3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

6 authors.

Ying ZhenDepartment of Respiratory, The Second Hospital of Nanping, No.10 Huanghua Mountain Road, Jianyang District, Nanping, 354200, China.
Xiu-Fang ZhangDepartment of Stomatology, The Second Hospital of Nanping, Nanping, 354200, China.
Wei-Feng GuoDepartment of Respiratory, The First Quanzhou Hospital of Fujian Medical University, Quanzhou, 362000, China.
Jun YeDepartment of Respiratory, The Second Hospital of Nanping, No.10 Huanghua Mountain Road, Jianyang District, Nanping, 354200, China.
Can-Qiang QiuDepartment of Respiratory, The Second Hospital of Nanping, No.10 Huanghua Mountain Road, Jianyang District, Nanping, 354200, China.
Jun WuDepartment of Respiratory, The Second Hospital of Nanping, No.10 Huanghua Mountain Road, Jianyang District, Nanping, 354200, China. wujun116119@163.com.

Funding

Natural Science Foundation of Nanping N2023J038
6 · The paper itself

Abstract

backgroundThe primary objective of this study is to identify independent risk factors associated with secondary pulmonary hypertension (PH) in silicosis patients through retrospective statistical analysis and to develop a clinically applicable short-term predictive model.

methodsWe retrospectively analyzed the clinical data of 199 silicosis patients, among whom 98 had PH and 101 did not. PH was defined as an estimated systolic pulmonary artery pressure (sPAP) > 40 mmHg. A short-term clinical prediction model for PH in hospitalized silicosis patients was constructed by identifying independent risk factors through logistic regression analysis.

resultsLogistic regression analysis identified the modified Medical Research Council (mMRC) score, silicosis stage, forced vital capacity (FVC), oxygenation index, and eosinophil ratio as independent risk factors for the development of pulmonary hypertension in silicosis patients. A clinical prediction model for male patients was constructed using binary multifactorial logistic regression incorporating these factors: P = ex/(ex+1), where X=-14.958 + 0.943 ⋅ silicosis stage + 0.479 ⋅ mMRC score + 0.471 ⋅ eosinophil ratio + 2.549 ⋅ oxygenation index + 0.863 ⋅ FVC. The Hosmer-Lemeshow test indicated a good fit for this model (P = 0.231). The plotted receiver operating characteristic curve yielded an area under the curve of 0.908, accompanied by a Youden’s index of 0.704, a specificity of 87.9%, and a sensitivity of 82.5%.

conclusionsThe model demonstrates strong predictive performance for predicting the risk of PH in male patients with silicosis. In clinical practice, paying attention to key factors—including mMRC, silicosis stage, FVC, oxygenation index, and eosinophil ratio—along with the implementation of targeted interventions for high-risk patients, is essential to improving quality of life and long-term prognosis.

Indexed as

Hypertension, PulmonarySilicosisAgedEosinophilsFemaleHumansLogistic ModelsMaleMiddle AgedRetrospective StudiesRisk FactorsROC CurveSeverity of Illness IndexVital CapacityPrediction modelPulmonary hypertensionRisk factorSilicosis

Identifiers

PMID41877109
PMCPMC13134084

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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.