Evidence map›Paper›PMID 36675764›Full record

ArticleJournal of personalized medicine2023

Nomogram for Predicting Portal Vein Thrombosis in Cirrhotic Patients: A Retrospective Cohort Study.

Jingnuo Ding, Fazhi Zhao, Youhan Miao, Yunnuo Liu, Huiting Zhang, Weifeng Zhao

Open access · goldAbstract read
In one paragraph

Article in Journal of personalized medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
1.4field-weighted citation impact, top 19% 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

7 citing papers in PubMed, 7 citations in OpenAlex.

  1. Article
  2. Article
  3. Review
  4. [Research progress on predictive models of portal vein thrombosis in liver cirrhosis].Zhonghua gan zang bing za zhi = Zhonghua ganzangbing zazhi = Chinese journal of hepatology · 2025
    Review
  5. Article
  6. Article
  7. Towards Personalized Medicine for Chronic Liver Disease.Journal of personalized medicine · 2023
    Article
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

6 authors at 4 institutions in 1 country.

Jingnuo DingDepartment of Infectious Diseases, The First Affiliated Hospital of Soochow University, No. 188 Shizi Street, Gusu District, Suzhou 215000, China.
Fazhi ZhaoDepartment of Stomach Surgery, Sichuan Cancer Hospital & Institute, Chengdu 610041, China.
Youhan MiaoDepartment of Infectious Diseases, The Third Affiliated Hospital of Nantong University, Nantong 226006, China.
Yunnuo LiuDepartment of Infectious Diseases, The First Affiliated Hospital of Soochow University, No. 188 Shizi Street, Gusu District, Suzhou 215000, China.
Huiting ZhangDepartment of Infectious Diseases, The First Affiliated Hospital of Soochow University, No. 188 Shizi Street, Gusu District, Suzhou 215000, China.
Weifeng ZhaoDepartment of Infectious Diseases, The First Affiliated Hospital of Soochow University, No. 188 Shizi Street, Gusu District, Suzhou 215000, China.
Soochow University · CNFirst Affiliated Hospital of Soochow University · CNSichuan Cancer Hospital · CNWuxi Third People's Hospital · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

aimPortal vein thrombosis (PVT) is a common complication in cirrhotic patients and will aggravate portal hypertension, thus leading to a series of severe complications. The aim of this study was to develop a nomogram based on a simple and effective model to predict PVT in cirrhotic patients.

methodsClinical data of 656 cirrhotic patients with or without PVT in the First Affiliated Hospital of Soochow University and The Third Affiliated Hospital of Nantong University from January 2017 to March 2022 were retrospectively collected, and all patients were divided into training, internal and external validation cohorts. SPSS and R software were used to identify the independent risk factors and construct a predictive model. We evaluated the predictive value of the model by receiver operating characteristic (ROC) curve, calibration curve, and decision curve analyses. The feasibility of the model was further validated in the internal and external cohorts. All enrolled patients were followed up to construct the survival curves and calculate the incidence of complications.

resultsThe predictors of PVT included serum albumin, D-dimer, portal vein diameter, splenectomy, and esophageal and gastric varices. Based on the clinical and imaging findings, the final model served as a potential tool for predicting PVT in cirrhotic patients, with an AUC of 0.806 (0.766 in the internal validation cohort and 0.845 in the external validation cohort). The decision curve analysis revealed that the model had a high level of concordance between different medical centers. There was a significant difference between the PVT and non-PVT groups in survival analyses, with

conclusionThe nomogram based on our model provides a simple and convenient method for predicting PVT in cirrhotic patients. Cirrhotic patients with PVT had a shorter survival time and were prone to recurrent EGVB compared with those in the non-PVT group.

Indexed as

cirrhosisnomogramportal vein thrombosispredictive modelsurvival curve

Identifiers

PMID36675764
PMCPMC9864963
OpenAlexW4313389042

What OpenQuestion holds

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LicenceCC BY
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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.