scholarly journals SAPS 3 scores at the start of renal replacement therapy predict mortality in critically ill patients with acute kidney injury

2010 ◽  
Vol 77 (1) ◽  
pp. 51-56 ◽  
Author(s):  
Elizabeth Maccariello ◽  
Carla Valente ◽  
Lina Nogueira ◽  
Helio Bonomo ◽  
Marcia Ismael ◽  
...  
Keyword(s):  
Acute Kidney Injury ◽  
Renal Replacement Therapy ◽  
Replacement Therapy ◽  
Critically Ill ◽  
Critically Ill Patients ◽  
Kidney Injury ◽  
Saps 3

scholarly journals Comparison of Approaches for Prediction of Renal Replacement Therapy-Free Survival in Patients with Acute Kidney Injury

2021 ◽  
pp. 1-7
Author(s):  
Pattharawin Pattharanitima ◽  
Akhil Vaid ◽  
Suraj K. Jaladanki ◽  
Ishan Paranjpe ◽  
Ross O’Hagan ◽  
...  
Keyword(s):  
Acute Kidney Injury ◽  
Renal Replacement Therapy ◽  
Replacement Therapy ◽  
Critically Ill ◽  
Critically Ill Patients ◽  
Kidney Injury ◽  
Support Vector ◽  
Adaptive Boosting ◽  
Free Survival ◽  
Extreme Gradient Boosting

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.

Timing of renal replacement therapy and clinical outcomes in critically ill patients with severe acute kidney injury

2009 ◽  
Vol 24 (1) ◽  
pp. 129-140 ◽  
Author(s):  
Sean M. Bagshaw ◽  
Shigehiko Uchino ◽  
Rinaldo Bellomo ◽  
Hiroshi Morimatsu ◽  
Stanislao Morgera ◽  
...  
Keyword(s):  
Acute Kidney Injury ◽  
Renal Replacement Therapy ◽  
Replacement Therapy ◽  
Clinical Outcomes ◽  
Critically Ill ◽  
Critically Ill Patients ◽  
Kidney Injury

scholarly journals Effect of Early vs Delayed Initiation of Renal Replacement Therapy on Mortality in Critically Ill Patients With Acute Kidney Injury

JAMA ◽  
2016 ◽  
Vol 315 (20) ◽  
pp. 2190 ◽  
Author(s):  
Alexander Zarbock ◽  
John A. Kellum ◽  
Christoph Schmidt ◽  
Hugo Van Aken ◽  
Carola Wempe ◽  
...  
Keyword(s):  
Acute Kidney Injury ◽  
Renal Replacement Therapy ◽  
Replacement Therapy ◽  
Critically Ill ◽  
Critically Ill Patients ◽  
Kidney Injury ◽  
Delayed Initiation
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