Trial reportNephrology (Carlton, Vic.)2021
Prospective model for predicting renal recovery in cardiac surgery patients with acute kidney injury requiring renal replacement therapy.
Trial report in Nephrology (Carlton, Vic.), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
4 citing papers in PubMed, 6 citations in OpenAlex.
- Prospective model for predicting renal recovery in cardiac surgery patients with acute kidney injury requiring renal replacement therapy.Nephrology (Carlton, Vic.) · 2021Trial
- Construction and evaluation of prediction model for renal function recovery in acute kidney injury patients undergoing continuous renal replacement therapy based on machine learning algorithms.Annals of medicine · 2025Article
- Development and validation of a nomogram model for central venous access device-related thrombosis in hospitalized children.European journal of pediatrics · 2023Article
- Application of Machine Learning to Predict Acute Kidney Disease in Patients With Sepsis Associated Acute Kidney Injury.Frontiers in medicine · 2021Article
Corrections and comments
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Authors and funding
13 authors at 2 institutions in 1 country.
Funding
Abstract
aimTo develop a model for predicting renal recovery in cardiac surgery patients with acute kidney injury (AKI) requiring renal replacement therapy (RRT).
methodsData from a prospective randomized controlled trial, conducted in a tertiary hospital to compare the survival effect of two dosages of hemofiltration for continuous RRT in cardiac surgery patients between 20 March 2012 and 9 August 2015, were used to develop the model. The outcome was renal recovery defined as alive and dialysis-free 90 days after RRT initiation. Multivariate logistic regression with a stepwise backward selection of variables based on Akaike Information Criterion was applied to develop the model, which was internally validated using bootstrapping. Model discrimination, calibration and clinical value were assessed using the concordance index (C-Index), calibration plots and decision curve analysis, respectively.
resultsTotally, 211 patients with AKI requiring RRT (66.8% male) with median age of 57 years were included. The incidence of renal recovery was 33.2% (n = 70). The model included six variables: body mass index stratification, baseline estimated glomerular filtration rate, hypertension, sepsis, mean arterial pressure and mechanical ventilation. The C-Index for this model was 0.807 (95% CI, 0.744-0.870). After correction by the bootstrap, the C-Index was 0.780 (95% CI, 0.720-0.845). The calibration plots indicated good consistency between actual observations and model prediction of renal recovery. Decision curve analysis demonstrated the model was clinical usefulness.
conclusionWe developed and validated a model to predict the chance of renal recovery in cardiac surgery patients with AKI requiring RRT.
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Registered trials
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.