ArticleScientific reports2021
Machine learning model to predict hypotension after starting continuous renal replacement therapy.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers, 1 of them a synthesis that pooled it.
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Who cites it
21 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Prediction of intradialytic hypotension by machine learning: A systematic review.Journal of nephrology · 2025Pooled it
- From Intelligent Operating Room to Smart ICU: Digital Continuity, AI-Supported Decision-Making and CRRT as a Model of Pharmacokinetic Personalization in Critical Care.Healthcare (Basel, Switzerland) · 2026Review
- An interpretable XGboost algorithm for predicting 30-day mortality in acute pancreatitis using routine biomarkers.BMC medical research methodology · 2026Article
- Intradialytic hypotension and hemodynamic phenotypes in children following continuous renal replacement therapy initiation.Pediatric research · 2026Observational
- Machine learning model for predicting hypotension following continuous renal replacement therapy initiation in end-stage kidney disease patients: a SHAP-interpretable approach.Frontiers in medicine · 2026Article
- Enhancing outcomes in pediatric populations with continuous renal replacement therapy: Current multidisciplinary strategies and innovations.Journal of intensive medicine · 2025Review
- A machine learning model to predict intradialytic hypotension in pediatric continuous kidney replacement therapy.Pediatric nephrology (Berlin, Germany) · 2025Article
- Observational
- Predictors of intradialytic hypotension in critically ill patients undergoing kidney replacement therapy: a systematic review.Intensive care medicine experimental · 2024Review
- Development and external validation of temporal fusion transformer models for continuous intraoperative blood pressure forecasting.EClinicalMedicine · 2024Article
- Machine learning in risk prediction of continuous renal replacement therapy after coronary artery bypass grafting surgery in patients.Clinical and experimental nephrology · 2024Article
- Personalized Medicine Transformed: ChatGPT's Contribution to Continuous Renal Replacement Therapy Alarm Management in Intensive Care Units.Journal of personalized medicine · 2024Article
- Digital health and acute kidney injury: consensus report of the 27th Acute Disease Quality Initiative workgroup.Nature reviews. Nephrology · 2023Review
- Systemic lupus erythematosus with high disease activity identification based on machine learning.Inflammation research : official journal of the European Histamine Research Society ... [et al.] · 2023Article
- Expert consensus on blood pressure management in critically ill patients.Journal of intensive medicine · 2023Article
- Predicting dry weight change in Hemodialysis patients using machine learning.BMC nephrology · 2023Article
- Machine learning-based prediction of intraoperative hypoxemia for pediatric patients.PloS one · 2023Article
- Can Artificial Intelligence Assist in Delivering Continuous Renal Replacement Therapy?Advances in chronic kidney disease · 2022Review
- Machine Learning for Acute Kidney Injury Prediction in the Intensive Care Unit.Advances in chronic kidney disease · 2022Review
- Explainable Machine Learning-Based Risk Prediction Model for In-Hospital Mortality after Continuous Renal Replacement Therapy Initiation.Diagnostics (Basel, Switzerland) · 2022Article
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8 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Hypotension after starting continuous renal replacement therapy (CRRT) is associated with worse outcomes compared with normotension, but it is difficult to predict because several factors have interactive and complex effects on the risk. The present study applied machine learning algorithms to develop models to predict hypotension after initiating CRRT. Among 2349 adult patients who started CRRT due to acute kidney injury, 70% and 30% were randomly assigned into the training and testing sets, respectively. Hypotension was defined as a reduction in mean arterial pressure (MAP) ≥ 20 mmHg from the initial value within 6 h. The area under the receiver operating characteristic curves (AUROCs) in machine learning models, such as support vector machine (SVM), deep neural network (DNN), light gradient boosting machine (LGBM), and extreme gradient boosting machine (XGB) were compared with those in disease-severity scores such as the Sequential Organ Failure Assessment and Acute Physiology and Chronic Health Evaluation II. The XGB model showed the highest AUROC (0.828 [0.796-0.861]), and the DNN and LGBM models followed with AUROCs of 0.822 (0.789-0.856) and 0.813 (0.780-0.847), respectively; all machine learning AUROC values were higher than those obtained from disease-severity scores (AUROCs < 0.6). Although other definitions of hypotension were used such as a reduction of MAP ≥ 30 mmHg or a reduction occurring within 1 h, the AUROCs of machine learning models were higher than those of disease-severity scores. Machine learning models successfully predict hypotension after starting CRRT and can serve as the basis of systems to predict hypotension before starting CRRT.
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