Evidence map›Paper›PMID 37881167›Full record

ArticleRisk management and healthcare policy2023

Risk Factors for Granulomatous Mastitis and Establishment and Validation of a Clinical Prediction Model (Nomogram).

Yifei Zeng, Dongxiao Zhang, Na Fu, Wenjie Zhao, Qiao Huang, Jianchun Cui, Yunru Chen, Zhaolan Liu, Xiaojun Zhang, Shiyun Zhang and 1 more

Open access · goldAbstract read
In one paragraph

Article in Risk management and healthcare policy, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.

0numbers the graph read from it
0cells of the map it votes in
17citing papers in PubMed
4.1field-weighted citation impact, top 5% 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

17 citing papers in PubMed, 13 citations in OpenAlex.

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  16. Clinical review and drug sensitivity test ofFrontiers in microbiology · 2024
    Article
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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

11 authors at 7 institutions in 1 country.

Yifei ZengDepartment of Galactophore, Beijing Hospital of Traditional Chinese Medicine, Capital Medical University, Beijing, People's Republic of China.
Dongxiao ZhangDepartment of Galactophore, Beijing Hospital of Traditional Chinese Medicine, Capital Medical University, Beijing, People's Republic of China.
Na FuDepartment of Galactophore, Beijing Hospital of Traditional Chinese Medicine, Capital Medical University, Beijing, People's Republic of China.
Wenjie ZhaoDepartment of Galactophore, Beijing Hospital of Traditional Chinese Medicine, Capital Medical University, Beijing, People's Republic of China.
Qiao HuangDepartment of Galactophore, Beijing Hospital of Traditional Chinese Medicine, Capital Medical University, Beijing, People's Republic of China.
Jianchun CuiLiaoning Provincial People's Hospital (Department of Thyroid and Breast Surgery, People's Hospital of China Medical University), Shenyang, People's Republic of China.
Yunru ChenCentre for Evidence-Based Chinese Medicine, School of Traditional Chinese Medicine, Beijing University of Chinese Medicine, Beijing, People's Republic of China.
Zhaolan LiuCentre for Evidence-Based Chinese Medicine, School of Traditional Chinese Medicine, Beijing University of Chinese Medicine, Beijing, People's Republic of China.
Xiaojun ZhangXiyuan Hospital of China Academy of Chinese Medical Sciences, Beijing, People's Republic of China.
Shiyun ZhangGuang' Anmen Hospital, China Academy of Chinese Medical Science, Beijing, People's Republic of China.
Khattak Mazher MansoorLiaoning Provincial People's Hospital (Department of Thyroid and Breast Surgery, People's Hospital of China Medical University), Shenyang, People's Republic of China.
Beijing University of Chinese Medicine · CNCapital Medical University · CNBeijing Hospital of Traditional Chinese Medicine · CNChina Medical University · CNGuang’anmen Hospital · CNLiaoning Provincial People's Hospital · CNXiyuan Hospital · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: This study aimed to explore the risk factors and clinical characteristics of granulomatous mastitis (GM) using a case-control study and establish and validate a clinical prediction model (nomogram). Methods: This retrospective case-control study was conducted in three hospitals in China from June 2017 to December 2021. A total of 1634 GM patients and 186 healthy women during the same period were included and randomly divided into the modeling and validation groups in a 7:3 ratio. To identify the independent risk factors of GM, univariate and multivariate logistic analyses were conducted and used to develop a nomogram. The prediction model was internally and externally validated using the Bootstrap technique and validation cohort. The receiver operating characteristic (ROC) curve and calibration curve were used to evaluate the discrimination and calibration of the prediction model. Decision curve analysis (DCA) and clinical impact curve (CIC) were used to evaluate the clinical significance of the model. Results: The average age of GM patients was 33.14 years (mainly 20-40). The incidence was high within five years from delivery and mainly occurred in the unilateral breast. The majority of the patients exhibited local skin alterations, while some also presented with systemic symptoms. On multivariate logistic analysis, age, high prolactin level, sex hormone intake, breast trauma, nipple discharge or invagination, and depression were independent risk factors for GM. The mean area under the curve (AUC) in the modeling and validation groups were 0.899 and 0.889. The internal and external validation demonstrated the model's predictive ability and clinical value. Conclusion: Lactation-related factors are the main risk factors of GM, leading to milk stasis or increased ductal secretion. Meanwhile, hormone disorders could affect the secretion and expansion of mammary ducts. All these factors can obstruct or injure the duct, inducing inflammatory reactions and immune responses. Additionally, blunt trauma, depressed mood, and diet preference can accelerate the process. The nomogram can effectively predict the risk of GM.

Indexed as

case-control studygranulomatous mastitisnomogramprediction modelrisk factors

Identifiers

PMID37881167
PMCPMC10596285
OpenAlexW4387824268

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
Read underepoch 390

Registered trials

None linked

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.