Prospective model for predicting renal recovery in cardiac surgery patients with acute kidney injury requiring renal replacement therapy

Abstract

Aim: To develop a model for predicting renal recovery in cardiac surgery patients with acute kidney injury (AKI) requiring renal replacement therapy (RRT).Methods: Data 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.Results: Totally, 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. Conclusion:We developed and validated a model to predict the chance of renal recovery in cardiac surgery patients with AKI requiring RRT.

Year in Review 2021: Noteworthy Literature in Cardiothoracic Critical Care

“…Zarbock et al 13 Implementation of the KDIGO bundle in patients identified to be high risk for AKI by biomarkers is feasible in cardiac surgery patients and reduces the incidence of moderate to severe AKI in this population. Hu et al 16 A nomogram built on logistic regression provides a quantitative tool to predict individual likelihood of renal recovery after the initiation of CRRT in cardiac surgery patients. The tool uses presence or absence of mechanical ventilation and sepsis, baseline hypertension and eGFR, MAP, and BMI, all at the time of initiation, for risk stratification.…”

“…Hu et al set out to build a predictive model based on retrospective analysis of data from the Effect of the Intensity of Continuous Renal Replacement Therapy on patients with cardiac surgery-associated Acute kidney Injury (CRITERIA) study. 16 This single center investigation over 3 years was designed to compare survival between two dosages of hemofiltration in patients older than 18 years with AKI following cardiac surgery and deemed by the treatment team to require RRT, with those patients previously requiring RRT and those with a diagnosis of CKD prior to surgery excluded. Multivariable logistic regression analysis showed that mechanical ventilation, baseline eGFR, hypertension, sepsis, MAP, and BMI stratification were the best predictors of renal recovery.…”

Application of Machine Learning to Predict Acute Kidney Disease in Patients With Sepsis Associated Acute Kidney Injury

“…The AKD phase is a time window for potentially initiating key interventions to alter the natural history of kidney disease (7), and thus, the early identification of patients at high risk of developing AKD is important. Previous studies have shown that age, hypertension, diabetes mellitus, the history of CKD, the severity of AKI, and the use of mechanical ventilators were associated with the onset of AKD (11)(12)(13)(14)(15)(16)(17), however, machine learning methods have been seldom used to predict the occurrence of AKD. This study was aimed at using longitudinal data to predict the occurrence of AKD.…”