Comparison of Approaches for Prediction of Renal Replacement Therapy-Free Survival in Patients with Acute Kidney Injury
| Autor: | Girish N. Nadkarni, Ishan Paranjpe, Pattharawin Pattharanitima, Aine Duffy, Akhil Vaid, Suraj K. Jaladanki, Ross O'Hagan, Lili Chan, Steven G. Coca, Kinsuk Chauhan, Tielman Van Vleck, Kumardeep Chaudhary, Javier A. Neyra, Benjamin S. Glicksberg |
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| Rok vydání: | 2021 |
| Předmět: |
Male
medicine.medical_specialty Critical Care medicine.medical_treatment Logistic regression Machine Learning Interquartile range Internal medicine Intensive care medicine Humans Renal replacement therapy Simplified Acute Physiology Score Kidney transplantation Aged Receiver operating characteristic business.industry Age Factors Acute kidney injury Hematology General Medicine Acute Kidney Injury Middle Aged Prognosis medicine.disease Renal Replacement Therapy Logistic Models Nephrology Cardiology Female business |
| Zdroj: | Blood Purification. 50:621-627 |
| ISSN: | 1421-9735 0253-5068 |
| Popis: | Background/Aims: Acute kidney injury (AKI) in critically ill patients is common, and continuous renal replacement therapy (CRRT) is a preferred mode of renal replacement therapy (RRT) in hemodynamically unstable patients. Prediction of clinical outcomes in patients on CRRT is challenging. We utilized several approaches to predict RRT-free survival (RRTFS) in critically ill patients with AKI requiring CRRT. Methods: We used the Medical Information Mart for Intensive Care (MIMIC-III) database to identify patients ≥18 years old with AKI on CRRT, after excluding patients who had ESRD on chronic dialysis, and kidney transplantation. We defined RRTFS as patients who were discharged alive and did not require RRT ≥7 days prior to hospital discharge. We utilized all available biomedical data up to CRRT initiation. We evaluated 7 approaches, including logistic regression (LR), random forest (RF), support vector machine (SVM), adaptive boosting (AdaBoost), extreme gradient boosting (XGBoost), multilayer perceptron (MLP), and MLP with long short-term memory (MLP + LSTM). We evaluated model performance by using area under the receiver operating characteristic (AUROC) curves. Results: Out of 684 patients with AKI on CRRT, 205 (30%) patients had RRTFS. The median age of patients was 63 years and their median Simplified Acute Physiology Score (SAPS) II was 67 (interquartile range 52–84). The MLP + LSTM showed the highest AUROC (95% CI) of 0.70 (0.67–0.73), followed by MLP 0.59 (0.54–0.64), LR 0.57 (0.52–0.62), SVM 0.51 (0.46–0.56), AdaBoost 0.51 (0.46–0.55), RF 0.44 (0.39–0.48), and XGBoost 0.43 (CI 0.38–0.47). Conclusions: A MLP + LSTM model outperformed other approaches for predicting RRTFS. Performance could be further improved by incorporating other data types. |
| Databáze: | OpenAIRE |
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