ArticleJournal of advanced nursing2025
A Predictive Model of Pressure Injury in Children Undergoing Living Donor Liver Transplantation Based on Machine Learning Algorithm.
Article in Journal of advanced nursing, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Study on the construction and verification of intraoperative pressure injury risk prediction model for children undergoing cardiac surgery.BMC pediatrics · 2026Article
- Artificial Intelligence in Post-Liver Transplantation: A Scoping Review of Comparative Model Performance.Journal of clinical medicine · 2026Review
- Development and Validation of a Nomogram for Predicting Intravenous Amiodarone-Induced Phlebitis: A Prospective Cohort Study.Health science reports · 2025Article
- Effects of different nursing interventions for intraoperative acquired pressure injuries: a network meta-analysis.BMC nursing · 2025Article
- Cognitive frailty in maintenance hemodialysis: a scoping review.International urology and nephrology · 2025Article
- Risk Prediction of Liver Injury in Pediatric Tuberculosis Treatment: Development of an Automated Machine Learning Model.Drug design, development and therapy · 2025Article
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Authors and funding
7 authors.
Funding
Abstract
aimsThe aim of our study was to formulate and validate a prediction model using machine learning algorithms to forecast the risk of pressure injuries (PIs) in children undergoing living donor liver transplantation (LDLT).
designA retrospective cohort study.
methodsThe research was carried out at China's largest paediatric liver transplantation centre. A total of 438 children who underwent LDLT between June 2021 and December 2022 constituted the study cohort. The dataset was partitioned randomly into 70% for training datasets (306 cases) and 30% for testing datasets (132 cases). Utilising four machine learning algorithms-Decision Tree, Random Forest, Gradient Boosting Decision Tree and eXtreme Gradient Boosting-we identified risk factors and constructed predictive models.
resultsOut of 438 children, 42 developed PIs, yielding an incidence rate of 9.6%. Notably, 94% of these cases were categorised as Stage 1, and 54% were localised on the occiput. Upon evaluating the four prediction models, the Decision Tree model emerged as the most effective. The primary contributors to pressure injury in the Decision Tree model were identified as operation time, intraoperative corticosteroid administration, preoperative skin protection measures and preoperative skin conditions. A visualisation elucidating the logical inference process for the 10 variables within the Decision Tree model was presented. Ultimately, based on the Decision Tree model, a predictive system was developed.
conclusionMachine learning algorithms facilitate the identification of crucial factors, enabling the creation of an effective Decision Tree model to forecast pressure injury development in children undergoing LDLT. IMPACT: With this predictive model at their disposal, nurses can assess the pressure injury risk level in children more intuitively. Subsequently, they can implement tailored preventive strategies to mitigate the occurrence of PIs. PATIENT OR PUBLIC CONTRIBUTION: Paediatric patients contributed electronic health records datasets.
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