ArticleAnnals of medicine2023
Development and validation of a practical machine learning model to predict sepsis after liver transplantation.
Article in Annals of medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 4 of them syntheses that pooled it.
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19 citing papers in PubMed, 4 syntheses or guidelines pooled it, 42 citations in OpenAlex.
- Predictive ability of hypotension prediction index and machine learning methods in intraoperative hypotension: a systematic review and meta-analysis.Journal of translational medicine · 2024Pooled it
- Early detection of sepsis using machine learning algorithms: a systematic review and network meta-analysis.Frontiers in medicine · 2024Pooled it
- Diagnostic performance of machine-learning algorithms for sepsis prediction: An updated meta-analysis.Technology and health care : official journal of the European Society for Engineering and Medicine · 2024Pooled it
- Prediction of Complications and Prognostication in Perioperative Medicine: A Systematic Review and PROBAST Assessment of Machine Learning Tools.Anesthesiology · 2024Pooled it
- Artificial intelligence and machine learning in transplantation surgery care pathway.World journal of transplantation · 2026Review
- Inhibition of red blood cell-derived small extracellular vesicles release by calpeptin attenuates neutrophil inflammation.Scientific reports · 2026Article
- Artificial Intelligence in Post-Liver Transplantation: A Scoping Review of Comparative Model Performance.Journal of clinical medicine · 2026Review
- A reinforcement learning-guided interpretable method for postoperative sepsis prediction with Hilbert-Schmidt Independence Criterion.Frontiers in big data · 2026Article
- Identification of a postoperative survival scoring index for adult liver transplantation.Annals of medicine · 2025Article
- Evaluation of model performance in predicting sepsis after intestinal obstruction surgery: a multicenter retrospective study.Annals of medicine · 2025Article
- Machine learning in solid organ transplantation: Charting the evolving landscape.World journal of transplantation · 2025Article
- Perspectives and Tools in Liver Graft Assessment: A Transformative Era in Liver Transplantation.Biomedicines · 2025Review
- Comparison of time-to-event machine learning models in predicting biliary complication and mortality rate in liver transplant patients.Scientific reports · 2025Article
- A Supervised Explainable Machine Learning Model for Perioperative Neurocognitive Disorder in Liver-Transplantation Patients and External Validation on the Medical Information Mart for Intensive Care IV Database: Retrospective Study.Journal of medical Internet research · 2025Article
- Utilizing radiomics for differential diagnosis of inverted papilloma and chronic rhinosinusitis with polyps based on unenhanced CT scans.Scientific reports · 2024Article
- Review
- The transformative potential of artificial intelligence in solid organ transplantation.Frontiers in transplantation · 2024Review
- The use of probabilistic graphical models in pediatric sepsis: a feasibility and scoping review.Translational pediatrics · 2023Article
- Managing a Prospective Liver Transplant Recipient on the Waiting List.Journal of clinical and experimental hepatologyReview
Corrections and comments
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Authors and funding
10 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundPostoperative sepsis is one of the main causes of mortality after liver transplantation (LT). Our study aimed to develop and validate a predictive model for postoperative sepsis within 7 d in LT recipients using machine learning (ML) technology.
methodsData of 786 patients received LT from January 2015 to January 2020 was retrospectively extracted from the big data platform of Third Affiliated Hospital of Sun Yat-sen University. Seven ML models were developed to predict postoperative sepsis. The area under the receiver-operating curve (AUC), sensitivity, specificity, accuracy, and f1-score were evaluated as the model performances. The model with the best performance was validated in an independent dataset involving 118 adult LT cases from February 2020 to April 2021. The postoperative sepsis-associated outcomes were also explored in the study.
resultsAfter excluding 109 patients according to the exclusion criteria, 677 patients underwent LT were finally included in the analysis. Among them, 216 (31.9%) were diagnosed with sepsis after LT, which were related to more perioperative complications, increased postoperative hospital stay and mortality after LT (all
conclusionsOur study enrolled eight pre- and intra-operative variables to develop an RF-based predictive model of post-LT sepsis to assist clinical decision-making procedure.
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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.