Machine learning-based prediction of circuit clotting during pediatric continuous kidney replacement therapy sessions.
Background: Continuous kidney replacement therapy (CKRT) is commonly used for managing acute kidney injury (AKI) in critically ill pediatric patients. However, unexpected circuit clotting remains a frequent complication, resulting in therapy interruptions, blood loss, and increased clinical workload...
| Publicado en: | Pediatric Nephrology Vol. 40; no. 12; pp. 3795 - 3803 |
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| Autores principales: | , , , , , , |
| Formato: | pictorial research tables/charts Journal Article |
| Publicado: |
Springer Nature
Dec2025
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=188853876&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 188853876 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 0931041X EF1 jtl: Pediatric Nephrology issn: 0931041X maglogo: N pubinfo: dt: Dec2025 vid: 40 iid: 12 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 188853876 187110580 188853876 188853876 10.1007/s00467-025-06910-2 188853876 ppf: 3795 ppct: 8 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Machine learning-based prediction of circuit clotting during pediatric continuous kidney replacement therapy sessions. aug: au: Buccione, Emanuele Passaro, Davide Tardella, Luca Maffeo, Marina Tedesco, Brigida Colosimo, Denise Ricci, Zaccaria affil: Health Local Authority3 of Pescara, Pescara, Italy sug: subj: Continuous Renal Replacement Therapy Machine Learning Utilization Blood Coagulation Prediction Models Evaluation Pediatrics Human Male Female Retrospective Design Descriptive Statistics Logistic Regression Random Forest Neural Networks (Computer) Data Analysis Software Intensive Care Units, Pediatric Kidney Failure, Acute Hospitals Italy Male Female ab: Background: Continuous kidney replacement therapy (CKRT) is commonly used for managing acute kidney injury (AKI) in critically ill pediatric patients. However, unexpected circuit clotting remains a frequent complication, resulting in therapy interruptions, blood loss, and increased clinical workload. Timely prediction of clotting could enhance circuit management and patient outcomes. Methods: We retrospectively analyzed de-identified data from 23 pediatric patients undergoing 101 CKRT sessions at a tertiary PICU between 2012 and 2017. Time-series data were collected from CKRT machines and patient records, including demographic, clinical, and treatment-related variables. A machine learning (ML) classification model was developed to predict clotting events 60 min before occurrence. The dataset was preprocessed and split into training (70%) and validation (30%) sets, preserving class balance. Feature selection was performed using LightGBM, and model performance was evaluated using the Extra Trees classifier with cross-validation. Results: Of 101 CKRT sessions, 59 ended due to clotting. After data cleaning and exclusion of sessions shorter than 60 min, 88 sessions and over 218,000 data points were analyzed. The final model achieved an AUROC of 0.99 in the training set and performed well in validation, predicting clotting events 60 min in advance in 148 instances. The most important predictive features included effluent volume, treatment duration, fluid removal, and dialysate flow. Conclusion: This study demonstrates that ML can effectively predict circuit clotting during pediatric CKRT, offering a valuable tool for early clinical intervention. Integration of predictive algorithms into clinical workflows may help reduce complications, optimize treatment continuity, and improve outcomes for critically ill children. pubtype: Academic Journal doctype: pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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