Ultrafiltration in Japanese critically ill patients with acute kidney injury on renal replacement therapy

AbstractA recent worldwide survey indicates an international diversity in net ultrafiltration (UFNET) practices for the treatment of fluid overload in critically ill patients with acute kidney injury (AKI) requiring renal replacement therapy (RRT). The sub-analysis of the survey has demonstrated that maximum doses of furosemide used before determination of diuretic resistance are lower in Japan than those prescribed worldwide and UFNET is lower but is initiated earlier. In contrast, the interval during which practitioners evaluate fluid balance is longer. The characterization of RRT in critically ill patients in Japan should unveil more appropriate approaches to the successful treatment of AKI.

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Safety of Argatroban in critically ill patients with acute kidney injury and renal replacement therapy after cardiac surgery

The Thoracic and Cardiovascular Surgeon ◽

10.1055/s-0030-1269141 ◽

2011 ◽

Vol 59 (S 01) ◽

Author(s):

M Klingele ◽

A Lerner-Gräber ◽

D Fliser ◽

HJ Schäfers ◽

H Groesdonk

Keyword(s):

Acute Kidney Injury ◽

Cardiac Surgery ◽

Renal Replacement Therapy ◽

Replacement Therapy ◽

Critically Ill ◽

Critically Ill Patients ◽

Kidney Injury

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Renal replacement therapy in critically ill patients with acute kidney injury: 2020 nephrologist’s perspective

Nefrología (English Edition) ◽

10.1016/j.nefroe.2021.05.003 ◽

2021 ◽

Author(s):

María Valdenebro ◽

Leyre Martín-Rodríguez ◽

Blanca Tarragón ◽

Paula Sánchez-Briales ◽

Jose Portolés

Keyword(s):

Acute Kidney Injury ◽

Renal Replacement Therapy ◽

Replacement Therapy ◽

Critically Ill ◽

Critically Ill Patients ◽

Kidney Injury

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

Blood Purification ◽

10.1159/000513700 ◽

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.

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