Evidence map›Paper›PMID 40854987›Full record

ArticleCardiovascular and interventional radiology2025

A novel Artificial Intelligence Model for Predicting Totally Implantable Venous Access Device (TIVAD)-Related Infections.

Mengsu Zhang, Jie Zhang, Guangxin Jin, Fangqin Zhang, Mengjun Dai, Shengxi Xu, Xuebin Zhang, Jun Pu

Abstract read
PubMed Publisher
In one paragraph

Article in Cardiovascular and interventional radiology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Incidence and Timing of Venous Thromboembolism After Implantable Port Placement in Cancer Patients: A Single-Centre Cohort Study.Clinical and applied thrombosis/hemostasis : official journal of the International Academy of Clinical and Applied Thrombosis/Hemostasis
    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

8 authors.

Mengsu ZhangDepartment of Cardiology, Renji Hospital, Shanghai Jiao Tong University School of Medicine, 160 Pujian Road, Shanghai, 200127, China.
Jie ZhangDepartment of Interventional Oncology, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200127, China.
Guangxin JinDepartment of Interventional Oncology, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200127, China.
Fangqin ZhangDepartment of Interventional Oncology, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200127, China.
Mengjun DaiDepartment of Interventional Oncology, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200127, China.
Shengxi XuDepartment of Interventional Oncology, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200127, China.
Xuebin ZhangDepartment of Interventional Oncology, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200127, China. zhangxuebinwqy@163.com.
Jun PuDepartment of Cardiology, Renji Hospital, Shanghai Jiao Tong University School of Medicine, 160 Pujian Road, Shanghai, 200127, China. pujun310@hotmail.com.

Funding

Shanghai Science and Technology Innovation Fund 10441097000
6 · The paper itself

Abstract

purposeTo develop and validate a model for predicting totally implantable venous access device (TIVAD)-related infections. MATERIALS &

methodsPatients who underwent placement of TIVADs between 2014 and 2023 were included and divided into a training set (n = 5465) and a validation set (n = 2341). The clinical characteristics of 7806 patients with TIVADs were analysed. Model 1 was developed using both least absolute shrinkage and selection operator (LASSO) regression and bidirectional stepwise logistic regression, whereas Model 2 was developed using only bidirectional stepwise logistic regression. Two models were compared using the Akaike information criterion (AIC), decision curve analysis (DCA), clinical impact curves (CICs), area under the curve (AUC) of receiver operating characteristic curves (ROCs) and calibration curves, and the better model was chosen as the final model.

resultsModel 1 included body mass index (BMI), sex, outpatient or inpatient status, tunnel length, catheter-related thrombosis (CRT), primary catheter dislodgement and age. Model 2 included sex, catheter diameter, CRT, age, outpatient or inpatient status, catheter length, primary catheter dislodgement, BMI and tumour type. Both models demonstrated clinical usefulness according to DCA and CICs. Model 2 was chosen as the final model because of its superior AIC value and calibration performance.

conclusionsWe developed and validated a model for predicting TIVAD-related infections. We recommend a 5F catheter and prophylactic antibiotics for patients with a Model 2 infection risk score above 0.026. We also suggest appropriately increasing the tunnel length, using a smaller catheter, and strictly adhering to heparin flush protocols to prevent thrombosis.

Indexed as

Artificial IntelligenceCatheterization, Central VenousCatheter-Related InfectionsCatheters, IndwellingCentral Venous CathetersAgedFemaleHumansMaleMiddle AgedRetrospective StudiesRisk AssessmentRisk FactorsInfectionModelPredictionTotally implantable venous access devices

Identifiers

What OpenQuestion holds

Textmetadata
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.