Machine learning-based prediction model for volume responsiveness in critically ill patients with oliguric acute kidney injury

Abstract

Abstract Background and objectives: Fluid balance in acute kidney injury (AKI) patients can have adverse consequences if it is too high or too low, so rational fluid management is needed according to the patient’s volume status. This study aimed to develop a prediction model that can effectively identify volume-responsive (VR) and volume-unresponsive (VU) AKI patients. Methods We selected AKI patients from the US-based critical care database (Medical Information Mart for Intensive Care, MIMIC-IV2.2) who had urine output <0.5 ml/kg/h in the first 6 h after ICU admission and fluid intake >5 l in the next 6 h. Patients who received diuretics and renal replacement therapy on day 1 were excluded. We developed three predictive models, based on either machine learning Gradient Boosting Machine (GBM), random forest or logistic regression, to predict urine output >0.65 ml/kg/h in the 18 h following the initial 6 h of oliguria assessment, we divided the whole sample into training and testing sets by a ratio of 3:1，after training and optimizing the model, ranked the importance of features and evaluated the stability and accuracy of the model. Main results We analyzed 6295 patients, of whom 1438 (22.8%) experienced volume responsiveness and exhibited increased urine output after receiving more than 5 liters of fluid. Urinary creatinine, blood urea nitrogen (BUN), blood glucose and age were identified as important predictive factors for volume responsiveness. The Random Forest model performed the best, followed by the GBM model.The machine learning GBM outperformed the traditional logistic regression model in distinguishing between the volume responsive (VR) and volume unresponsive (VU) groups (AU-ROC, 0.874; 95% CI, 0.867 to 0.874 vs. 0.789; 95% CI, 0.779 to 0.789, respectively). Conclusions The Random Forest and GBM model, compared to the traditional logistic regression model, demonstrated a better ability to differentiate patients who did not exhibit a response in urine output to fluid intake. This finding suggests that machine learning techniques have the potential to improve the development and validation of predictive models in critical care research. Based on the feature importance ranking, creatinine, bun, age, glucose, and bicarbonate were identified as highly important features in the model could predicted VR in AKI patients.