ArticleFrontiers in medicine2022
Development and validation of outcome prediction models for acute kidney injury patients undergoing continuous renal replacement therapy.
Article in Frontiers in medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Construction and Validation of a Nomogram Based on Preoperative Inflammatory Indicators for Predicting Acute Kidney Injury After Pancreaticoduodenectomy.Journal of inflammation research · 2026Article
- Systematic Review and Meta-Analysis of Machine Learning Models for Acute Kidney Injury Risk Classification.Journal of the American Society of Nephrology : JASN · 2025Article
- Review
- Development and validation of a predictive model for continuous renal replacement therapy in sepsis patients using the MIMIC-IV database.Scientific reports · 2025Article
- Review
- Construction and evaluation of a mortality prediction model for patients with acute kidney injury undergoing continuous renal replacement therapy based on machine learning algorithms.Annals of medicine · 2024Article
- Cystatin C and Kidney Function Recovery in Patients Requiring Continuous KRT for Acute Kidney Injury.Clinical journal of the American Society of Nephrology : CJASN · 2024Article
- Risk prediction models for successful discontinuation in acute kidney injury undergoing continuous renal replacement therapy.iScience · 2024Article
- Continuous Renal Replacement Therapy: Application of Kidney Disease Improving Global Outcomes Guidelines for Treatment Prescription in Dogs With Acute Kidney Injury and Outcome Prediction.Journal of veterinary internal medicineArticle
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Object: This study aimed to develop and validate a set of practical predictive tools that reliably estimate the 28-day prognosis of acute kidney injury patients undergoing continuous renal replacement therapy. Methods: The clinical data of acute kidney injury patients undergoing continuous renal replacement therapy were extracted from the Medical Information Mart for Intensive Care IV database with structured query language and used as the development cohort. An all-subset regression was used for the model screening. Predictive models were constructed Results: Clinical prediction models were developed with clinical data from 1,148 patients and validated with data from 121 patients. The predictive model based on seven predictors (age, vasopressor use, red cell volume distribution width, lactate, white blood cell count, platelet count, and phosphate) exhibited good predictive performance, as indicated by a C-index of 0.812 in the development cohort, 0.811 in the internal validation cohort and 0.768 in the external validation cohort. Conclusions: The model reliably predicted the 28-day prognosis of acute kidney injury patients undergoing continuous renal replacement therapy. The predictive items are readily available, and the web-based prognostic calculator (https://libo220284.shinyapps.io/DynNomapp/) can be used as an adjunctive tool to support the management of patients.
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