Evidence map›Paper›PMID 39890672›Full record

ArticleClinical rheumatology2025

Evaluation of inflammatory-thrombosis panel as a diagnostic tool for vascular Behçet's disease.

Haoting Zhan, Linlin Cheng, Haizhen Chen, Yongmei Liu, Xinxin Feng, Haolong Li, Zhan Li, Yongzhe Li

Abstract read
PubMed Publisher
In one paragraph

Article in Clinical rheumatology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

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

2 citing papers in PubMed.

  1. Article
  2. Review
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

8 authors.

Haoting Zhan *Department of Clinical Laboratory, State key Laboratory of Complex, Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing, China.ORCID http://orcid.org/0000-0001-5934-9141
Linlin Cheng *Department of Clinical Laboratory, State key Laboratory of Complex, Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing, China.
Haizhen ChenDepartment of Laboratory Medicine, The First Hospital of Jilin University, Jilin, China.
Yongmei LiuDepartment of Clinical Laboratory, State key Laboratory of Complex, Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing, China.
Xinxin FengDepartment of Clinical Laboratory, State key Laboratory of Complex, Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing, China.
Haolong LiDepartment of Clinical Laboratory, State key Laboratory of Complex, Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing, China.
Zhan LiDepartment of Clinical Laboratory, State key Laboratory of Complex, Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing, China.
Yongzhe LiDepartment of Clinical Laboratory, State key Laboratory of Complex, Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing, China. yongzhelipumch@126.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectivesVascular Behçet's disease (VBD) is prevalent in 40% of BD, but lacks laboratory biomarker for timely diagnosis. We aimed to establish a diagnostic panel for discerning VBD and non-VBD patients and identify hemostatic-thrombotic markers most related to VBD pathogenesis using machine learning algorithm.

objectivesA total of 338 BD patients comprising 123 VBD and 215 non-VBD were enrolled. Twenty-six clinical and laboratory features selected from LassoCV were included in multiple classifier to choose the optimal model for VBD differentiation. The Shapley Additive exPlanations (SHAP) was employed to interpret the contribution of model features for VBD prediction. Logistic regression analysis and nomogram were conducted to screen risk factors of VBD.

resultsInflammatory (neutrophils%, NK cells, IL-6), hematological (hemoglobin, hemoglobin distribution width (HDW)) and thrombosis (activated partial thromboplastin clotting time (APTT), D-dimer) parameters were elevated in VBD. Then we chose top contributors from XGBoost model and performed ten-fold cross validation, the diagnostic accuracy of which exceeded 0.90. Utilizing SHAP method, we identified higher incidence of arterial thrombosis or aneurysm and deep vein thrombosis, upregulated NK cell count, HDW, APTT and D-dimer, downregulated reticulocyte%, B cell count, red blood cell distribution width, cellular hemoglobin (CH) and TNF-α would ultimately generate the phenotype of VBD. Severity, hemoglobin, mean corpuscular hemoglobin, CH, HDW, APTT and D-dimer were found as potential risk factors for vascular outcomes among BD.

resultsOur study developed a well-performed model leveraging clinical and laboratory parameters for differentiating VBD. Inflammatory and thrombotic risk factors are potential contributors to VBD. Key Points • Inflammatory (neutrophils%, NK cells, IL-6), hematological (HGB, HDW) and thrombosis (APTT, D-dimer) parameters were elevated in VBD. • We firstly developed an inflammatory-thrombosis model as a diagnostic tool for VBD. • HGB, MCH, CH, HDW, APTT and D-dimer are potential risk factors for VBD.

Indexed as

Behcet SyndromeThrombosisAdultBiomarkersFemaleFibrin Fibrinogen Degradation ProductsHumansInflammationMachine LearningMaleMiddle AgedPartial Thromboplastin TimeRisk FactorsBiomarkersFibrin Fibrinogen Degradation Productsfibrin fragment DBehçet’s diseaseMachine learningRisk factorThrombosisVascular involvement

Identifiers

PMID39890672

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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.