ArticleAsia-Pacific journal of oncology nursing2024
Machine learning risk prediction model for bloodstream infections related to totally implantable venous access ports in patients with cancer.
Article in Asia-Pacific journal of oncology nursing, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.
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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.
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Who cites it
6 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Effect of various interventions on relieving non-coring needle puncture-related pain in patients with totally implantable venous access port: a network meta-analysis of randomized control trials.Frontiers in surgery · 2026Pooled it
- Comparative predictive performance of three machine learning algorithms for acute radiation enteritis risk among patients with cervical cancer undergoing radiotherapy: A prospective cohort study.Asia-Pacific journal of oncology nursing · 2026Article
- Development and internal-external validation of a risk prediction model for acute pain after HAIC for patients with liver cancer using logistic regression and XGBoost algorithm.Asia-Pacific journal of oncology nursing · 2026Article
- Analysis of current status and risk factors of catheter infection in hemodialysis patients in Gansu Province, China.Scientific reports · 2026Article
- Development of a risk prediction model for catheter-related bloodstream infection in hemodialysis patients based on identified risk factors.American journal of translational research · 2026Article
- Effects of totally implantable venous access ports on complications and quality of life in gastrointestinal cancer chemotherapy.World journal of gastrointestinal surgery · 2025Article
Corrections and comments
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Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Objective: This study aimed to develop and validate a machine learning-based risk prediction model for catheter-related bloodstream infection (CRBSI) following implantation of totally implantable venous access ports (TIVAPs) in patients. Methods: A retrospective cohort study design was employed, utilizing the R software package mlr3. Various algorithms including logistic regression, naive Bayes, K nearest neighbor, classification tree, and random forest were applied. Addressing class imbalance, benchmarks were used, and model performance was assessed using the area under the curve (AUC). The final model, chosen for its superior performance, was interpreted using variable importance scores. Additionally, a nomogram was developed to calculate individualized risk probabilities, enhancing clinical utility. Results: The study involved 755 patients across both development and validation cohorts, with a TIVAP-CRBSI rate of 14.17%. The random forest model demonstrated the highest discrimination ability, achieving a validated AUC of 0.94, which was consistent in the validation cohort. Conclusions: This study successfully developed a robust predictive model for TIVAP-CRBSI risk post-implantation. Implementation of this model may aid healthcare providers in making informed decisions, thereby potentially improving patient outcomes.
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Registered trials
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.