Evidence map›Paper›PMID 41152968›Full record

ArticleThrombosis journal2025

Risk factor analysis and development of nomogram prediction model for central venous catheter-related thrombosis in burn patients: a retrospective observational study.

Qimeng Li, Yangping Wang, Xiaorong Zhang, Jingyuan Lang, Xue Heng, Ning Li, Siwei Miao, Chuan Hu, Gaoxing Luo, Haisheng Li

Abstract read
In one paragraph

Article in Thrombosis journal, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

10 authors.

Qimeng Li *Institute of Burn Research, State Key Laboratory of Trauma and Chemical Poisoning, Southwest Hospital, Third Military Medical University (Army Medical University), Gaotanyan Street, Shapingba District, Chongqing, 400038, China.
Yangping Wang *Institute of Burn Research, State Key Laboratory of Trauma and Chemical Poisoning, Southwest Hospital, Third Military Medical University (Army Medical University), Gaotanyan Street, Shapingba District, Chongqing, 400038, China.
Xiaorong Zhang *Institute of Burn Research, State Key Laboratory of Trauma and Chemical Poisoning, Southwest Hospital, Third Military Medical University (Army Medical University), Gaotanyan Street, Shapingba District, Chongqing, 400038, China.
Jingyuan LangInstitute of Burn Research, State Key Laboratory of Trauma and Chemical Poisoning, Southwest Hospital, Third Military Medical University (Army Medical University), Gaotanyan Street, Shapingba District, Chongqing, 400038, China.
Xue HengInstitute of Burn Research, State Key Laboratory of Trauma and Chemical Poisoning, Southwest Hospital, Third Military Medical University (Army Medical University), Gaotanyan Street, Shapingba District, Chongqing, 400038, China.
Ning LiInstitute of Burn Research, State Key Laboratory of Trauma and Chemical Poisoning, Southwest Hospital, Third Military Medical University (Army Medical University), Gaotanyan Street, Shapingba District, Chongqing, 400038, China.
Siwei MiaoCentre for Medical Big Data and Artificial Intelligence, Southwest Hospital, Third Military Medical University (Army Medical University), Chongqing, 400038, China.
Chuan HuCentre for Medical Big Data and Artificial Intelligence, Southwest Hospital, Third Military Medical University (Army Medical University), Chongqing, 400038, China.
Gaoxing LuoInstitute of Burn Research, State Key Laboratory of Trauma and Chemical Poisoning, Southwest Hospital, Third Military Medical University (Army Medical University), Gaotanyan Street, Shapingba District, Chongqing, 400038, China.
Haisheng LiInstitute of Burn Research, State Key Laboratory of Trauma and Chemical Poisoning, Southwest Hospital, Third Military Medical University (Army Medical University), Gaotanyan Street, Shapingba District, Chongqing, 400038, China. lihaisheng@tmmu.edu.cn.

Funding

Luo Gaoxing Expert Workstation in Yunnan Province 202405AF140056Natural Science Foundation of Chongqing 2024NSCQ-MSX3318Southwest Hospital Grants 2024BQTJ-5
6 · The paper itself

Abstract

backgroundCentral venous catheter (CVC)-related thrombosis (CRT) has become a common iatrogenic complication in burn patients. Early and precise prediction is the foundations of effective prevention of CRT. However, there is not specific tool to predict CRT in burn populations. This study aimed to investigate the risk factors of CRT and develop a prediction model for CRT in burn populations.

methodsThis retrospective observational study was conducted at a large burn center in Southwest China from January 2018 to December 2022. All adult patients with burn injuries undergoing central venous catheterization were included in the cohort. The clinical data, thrombosis profile, and catheter management were collected and analyzed.

resultsA total of 271 burn patients (mean burn area: 53.29 ± 23.65%) with 797 CVCs were finally included. The incidence of CRT was 13.28%, with a mean time from burn injuries to CRT onset of 25.96 ± 19.00 days. Half of the thrombotic events occurred between 15 and 30 days following burn trauma. Notably, 91.07% and 87.5% of CRT cases had diameters less than 5 mm and lengths shorter than 7 mm, respectively. Compared with the non-CRT cohort, the CRT cohort exhibited significantly larger burn area, more insertion times, higher proportion of blood purification procedures, and longer in-bed durations. Logistic regression and LASSO regression analyses identified burn index and continuous blood purification as independent risk factors for CRT. Consequently, a predictive nomogram model for CRT was successfully developed, achieving an AUROC of 0.75 (95% CI: 0.675-0.834) and a mean absolute error of the calibration curve of 0.029.

conclusionThe incidence of CRT was relatively high in burn populations. The established nomogram could provide a straightforward, quantitative and effective strategy for identifying patients at high-risk for CRT. Clinicians can utilize this assessment tool periodically to facilitate the early identification of individuals at high risk for CRT.

Indexed as

BurnsCatheter-related thrombosisNomogramPrediction model

Identifiers

PMID41152968
PMCPMC12570734

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.