Evidence map›Paper›PMID 42444895›Full record

ArticleJournal of thoracic disease2026

Diagnostic value of ocular imaging biomarkers in chronic obstructive pulmonary disease: a multicenter prospective cross-sectional study.

Yunpeng Xu, Yanru Chen, Cuifang He, Lei Zhu, Jian Liu

Abstract read
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Article in Journal of thoracic disease, 2026. 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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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

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4 · The record

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5 · Who and what money

Authors and funding

5 authors.

Yunpeng Xu *The First Clinical Medical College of Lanzhou University, Lanzhou, China.ORCID https://orcid.org/0009-0002-4693-0222
Yanru Chen *The First Clinical Medical College of Lanzhou University, Lanzhou, China.
Cuifang HeThe First Clinical Medical College of Lanzhou University, Lanzhou, China.
Lei ZhuDepartment of ICU, The First Hospital of Lanzhou University, Lanzhou, China.
Jian LiuThe First Clinical Medical College of Lanzhou University, Lanzhou, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Chronic obstructive pulmonary disease (COPD) is a common heterogeneous respiratory disease with systemic involvement. Although pulmonary function testing remains essential for diagnosis, its use in primary care is limited. Optical coherence tomography angiography (OCTA), a noninvasive technique for assessing ocular microcirculation, may provide new biomarkers for the auxiliary diagnosis and early identification of COPD. This study aimed to evaluate the diagnostic value of OCTA-derived ocular imaging biomarkers in COPD. Methods: In this cross-sectional study, 150 patients with COPD and 150 healthy volunteers were recruited between May 2024 and May 2025. According to the Global Initiative for Chronic Obstructive Lung Disease (GOLD) 2023 criteria, COPD patients were categorized into a mild-to-moderate group (Group 1, n=101) and a severe-to-very severe group (Group 2, n=49), while healthy participants served as controls (Group 3, n=150). A total of 38 OCTA parameters were assessed and compared across groups. Multivariate logistic regression was used to construct predictive models, and model performance was evaluated using receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA). Results: Compared with healthy controls, COPD patients exhibited thinning of the retinal nerve fiber layer (RNFL), reduced vascular density in the deep capillary plexus (DCP) and radial peripapillary capillaries (RPC), as well as enlargement of the foveal avascular zone (FAZ) in both area and perimeter (P<0.01). The overall diagnostic model demonstrated excellent discrimination between COPD patients and healthy controls, with an area under the ROC curve (AUC) of 0.906, sensitivity of 87.5%, and specificity of 81.5%. The early identification model also showed good performance in distinguishing mild-to-moderate COPD from healthy subjects (AUC 0.812, sensitivity 83.7%, specificity 66.4%). Conclusions: OCTA provides a novel, noninvasive approach for evaluating COPD and may complement pulmonary function testing, particularly in primary care settings. As a simple and scalable imaging tool, OCTA may have potential value in the auxiliary diagnosis and early screening of COPD. Further validation in larger, multiethnic cohorts and integration with multimodal biomarkers are warranted to enhance its generalizability and translational potential.

Indexed as

biomarkersChronic obstructive pulmonary disease (COPD)diagnostic modelearly diagnosisoptical coherence tomography angiography (OCTA)

Identifiers

PMID42444895
PMCPMC13358618

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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.