Peritoneal dialysis in the critically ill

Author(s):  
Jeffrey C. Sirota ◽  
Isaac Teitelbaum

Peritoneal dialysis, the first modality of renal replacement therapy used in patients with acute kidney injury, has now largely been supplanted by haemofiltration and haemodialysis. However, as acute kidney injury becomes more common and the need for renal replacement therapy increases, the technical advantages of peritoneal dialysis have made it an increasingly attractive option in acute settings, particularly in resource-deprived areas where haemodialysis is not available. Peritoneal modality can offer distinct advantages over haemodialytic techniques in patients with certain concomitant conditions. A variety of infectious, mechanical, pulmonary, and metabolic complications are possible with peritoneal dialysis, but the incidence of these is low in the acute setting. While not yet studied in robust comparative trials against the various haemodialytic modalities, there is some emerging evidence that peritoneal dialysis can provide adequate renal replacement therapy in acute settings, and acute peritoneal dialysis should be considered when haemodialysis is not available or its attendant complications are undesired.

2015 ◽  
Vol 8 (1) ◽  
pp. 41-44
Author(s):  
Mayoor V Prabhu ◽  
Subhramanyam S.V ◽  
Sinoj Antony ◽  
Nayak K.S

Peritoneal Dialysis (PD) has been an underutilized modality in the treatment of Acute Kidney Injury (AKI). Concerns regarding clearance, fluid removal, infection, complications of therapy, and the hypercatabolic state of AKI has led to PD falling into disrepute. Recent studies have challenged this notion of ineffectiveness. The lower cost, and simplicity of the procedure makes it a particularly attractive option for the developing world which may lack even basic HD facilities, and patients continue to die for want of Renal Replacement Therapy (RRT). We present a review of the available literature about PD in the AKI setting with special reference to the developing world, including the procedure, costs, and effectiveness of the treatment. We also describe the procedure in detail to help ‘hand hold’ physicians interested in performing this lifesaving procedure.


2021 ◽  
pp. 1-7
Author(s):  
Pattharawin Pattharanitima ◽  
Akhil Vaid ◽  
Suraj K. Jaladanki ◽  
Ishan Paranjpe ◽  
Ross O’Hagan ◽  
...  

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.


2009 ◽  
Vol 24 (1) ◽  
pp. 129-140 ◽  
Author(s):  
Sean M. Bagshaw ◽  
Shigehiko Uchino ◽  
Rinaldo Bellomo ◽  
Hiroshi Morimatsu ◽  
Stanislao Morgera ◽  
...  

Sign in / Sign up

Export Citation Format

Share Document