Early identification of acute kidney injury progression in critically ill patients with sepsis: interpretable machine learning approach
Source: PubMed Central Open Access, NCBI / U.S. National Library of Medicine
ABSTRACT Background Acute kidney injury (AKI) is a common and severe complication of sepsis and is often associated with a poor prognosis. However, there is still a lack of an effective prediction model for early identification of AKI progression in critical septic patients, defined as AKI stage 1 or 2 to stage 3 within 7 days after diagnosis of sepsis-associated AKI (SA-AKI). abs1 Methods We extracted the clinical data of patients with SA-AKI from the Medical Information Mart for Intensive Care (MIMIC) datasets, eICU Collaborative Research Database (eICU-CRD) and Salzburg Intensive Care database (SICdb), with the MIMIC-IV (version 3.1) database used for training and internal validation, the MIMIC-III Clinical Database CareVue subset used as temporal validation, and the eICU-CRD and SICdb used as external validation. Lasso regression and recursive feature elimination were used for feature selection. Six machine learning (ML) algorithms, including k-nearest neighbors, logistic regression, naïve Bayes, random forest (RF), support vector machine and decision tree, were utilized to establish the prediction model. Model performance was assessed using receiver operating characteristic curves, calibration curves and decision curve analysis. SHapley Additive exPlanations (SHAP) method was used for the interpretation of the models. abs2 Results The MIMIC-IV, MIMIC-III subset, eICU-CRD and SICdb included 9193, 2178, 10 332 and 1701 patients with SA-AKI. Twelve variables were selected f
Abstract
ABSTRACT Background Acute kidney injury (AKI) is a common and severe complication of sepsis and is often associated with a poor prognosis. However, there is still a lack of an effective prediction model for early identification of AKI progression in critical septic patients, defined as AKI stage 1 or 2 to stage 3 within 7 days after diagnosis of sepsis-associated AKI (SA-AKI). abs1 Methods We extracted the clinical data of patients with SA-AKI from the Medical Information Mart for Intensive Care (MIMIC) datasets, eICU Collaborative Research Database (eICU-CRD) and Salzburg Intensive Care database (SICdb), with the MIMIC-IV (version 3.1) database used for training and internal validation, the MIMIC-III Clinical Database CareVue subset used as temporal validation, and the eICU-CRD and SICdb used as external validation. Lasso regression and recursive feature elimination were used for feature selection. Six machine learning (ML) algorithms, including k-nearest neighbors, logistic regression, naïve Bayes, random forest (RF), support vector machine and decision tree, were utilized to establish the prediction model. Model performance was assessed using receiver operating characteristic curves, calibration curves and decision curve analysis. SHapley Additive exPlanations (SHAP) method was used for the interpretation of the models. abs2 Results The MIMIC-IV, MIMIC-III subset, eICU-CRD and SICdb included 9193, 2178, 10 332 and 1701 patients with SA-AKI. Twelve variables were selected for model construction, including weight, liver disease, mechanical ventilation, systolic blood pressure, hemoglobin, glucose, blood urea nitrogen, creatinine, chloride, anion gap and urine output. An RF model achieved the best performance in both internal, temporal and external validation (area under the curve is 0.779, 0.758 and 0.713, respectively). A user-friendly platform was built to early predict SA-AKI progression for clinician use. abs3 Conclusion ML could be a useful tool for predicting AKI progression in septic patients. We developed an RF model to predict the risk of SA-AKI progression, which may provide a reference for early identification and prompt intervention of high-risk group. abs4 Graphical Abstract Graphical Abstract For image description, please refer to the figure legend and surrounding text. http://www.w3.org/1999/xlink float portrait sfag216gra.jpg float ga1 portrait graphical