Evidence map›Paper›PMID 39501728›Full record

ArticleClinical and applied thrombosis/hemostasis : official journal of the International Academy of Clinical and Applied Thrombosis/Hemostasis

Development and Validation of a Blood-Biomarker-Based Predictive Model for HBV-Associated Hepatocellular Carcinoma.

Yafeng Tan, Wei Xia, Fenglan Sun, Bing Mei, Yaoling Ouyang, Linyun Li, Zhenxia Chen, Song Wu, Jufang Tan, Zhaxi Pubu and 2 more

Abstract readValidation Study
In one paragraph

Article in Clinical and applied thrombosis/hemostasis : official journal of the International Academy of Clinical and Applied Thrombosis/Hemostasis. 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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0citing papers in PubMed
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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

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

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5 · Who and what money

Authors and funding

12 authors.

Yafeng TanDepartment of Laboratory medicine, Jingzhou Hospital Affiliated to Yangtze University, Jingzhou, China.ORCID 0000-0002-0852-9190
Wei XiaDepartment of Laboratory medicine, Jingzhou Hospital Affiliated to Yangtze University, Jingzhou, China.ORCID 0000-0001-7198-0825
Fenglan SunDepartment of Laboratory medicine, The First People's Hospital of Tianmen City, Tianmen, China.
Bing MeiDepartment of Laboratory medicine, Jingzhou Hospital Affiliated to Yangtze University, Jingzhou, China.
Yaoling OuyangDepartment of Laboratory medicine, Jingzhou Hospital Affiliated to Yangtze University, Jingzhou, China.
Linyun LiDepartment of Laboratory medicine, Jingzhou Hospital Affiliated to Yangtze University, Jingzhou, China.
Zhenxia ChenDepartment of Gynaecology, Jingzhou Hospital Affiliated to Yangtze University, Jingzhou, China.
Song WuDepartment of Laboratory medicine, Jingzhou Hospital Affiliated to Yangtze University, Jingzhou, China.
Jufang TanDepartment of Pediatrics, Jingzhou Hospital Affiliated to Yangtze University, Jingzhou, China.
Zhaxi PubuDepartment of pediatrics, Lozha County People's Hospital, Shannan, China.
Bu SangDepartment of Laboratory Medicine, Lozha County People's Hospital, Shannan, China.
Tao JiangDepartment of Laboratory medicine, Jingzhou Hospital Affiliated to Yangtze University, Jingzhou, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveThis study aims to explore the optimal predictors of HBV-associated HCC using Lasso, and establish a prediction model.

methodsA retrospective analysis was conducted on patients who underwent CBC and CRP testing between January 2016 and March 2024. The study population comprised 5441 cases divided into three cohorts: non-HBV-infected (1333 cases), HBV-infected (1023 cases), and HBV-associated HCC (3085 cases). A value of CRP <10 mg/L was used to exclude cases of acute bacterial infections. Baseline data and blood parameters were compared across the three groups (control group (n = 1049), the HBV-infected group (n = 789), and the HBV-associated HCC group (n = 1367)). HBV-infected group and the HBV-associated HCC group were used as modeling subjects which 70% were classified as training set (n = 1512) and 30% were classified as validation set (n = 644). Lasso regression and logistic regression were employed to identify the most effective predictors of HBV-associated HCC, which were subsequently incorporated into a predictive model by training set.

resultsSignificant variations in age, gender, and blood parameter indices were observed between individuals with acute bacterial infections and non-infections in the study population, and also between three groups. The optimal predictors identified for HBV-associated HCC included gender, age, MONO, EO%, MCHC, MPV, and PCT.

conclusionsThe study highlights the significant impact of acute bacterial infections on immune status, erythrocyte system, and platelet system. After excluding acute bacterial infections, factors such as gender, age, MONO, EO%, MCHC, MPV, and PCT are effective predictors for clinical prediction of HCC development in HBV-infected patients.

Indexed as

Carcinoma, HepatocellularLiver NeoplasmsAdultBiomarkersFemaleHepatitis BHepatitis B virusHumansMaleMiddle AgedRetrospective StudiesBiomarkersblood parametersCRPHBV-associated hepatocellular carcinomaimmune statusLasso regressionnomogramprediction model

Identifiers

PMID39501728
PMCPMC11539192

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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.