scholarly journals Reduced serum albumin as a risk factor for poor prognosis in critically ill patients receiving renal replacement therapy

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Lang Jing Zheng ◽  
Weiming Jiang ◽  
Lingling Pan ◽  
Jingye Pan
Keyword(s):  
Renal Replacement Therapy ◽  
Replacement Therapy ◽  
Critically Ill ◽  
Critically Ill Patients ◽  
Poor Prognosis ◽  
Kidney Injury ◽  
Smooth Curve ◽  
Body Protein ◽  
Primary Body ◽  
The Impact

Abstract Background Albumin is the primary body protein, which can predict the poor prognosis of several critical diseases. However, there are a few scientific studies on the relationship between albumin and the prognosis of dialysis patients. This study aims to explore the impact of hypoalbuminemia on the prognosis of critically ill patients with acute kidney injury (AKI) receiving continuous renal replacement therapy (CRRT). Methods This was a secondary study. Clinical, biochemical, and 28-day and 90-day mortality rates for critical patients with AKI who received CRRT between 2009 and 2016 were searched from the database to determine the effect of hypoalbuminemia on poor outcomes by univariate, multivariate, smooth curve fitting, and subgroup analysis. Results A total of 837 participants were enrolled in this study. Multivariate Cox proportional hazard regression analysis showed that hypoalbuminemia was associated with both 28-day and 90-day mortality risks after full adjustment for confounding variables, with an adjusted hazard ratio (95% confidence interval) of 0.63 (0.50–0.80) and 0.63 (0.51–0.78), respectively for each 1 g/dL increase of albumin. Stratified analysis showed that hypoalbuminemia was not associated with poor prognosis in oliguria. Conclusion Hypoalbuminemia is associated with poor prognosis in critically ill AKI patients with CRRT; therefore, measuring albumin may be helpful for predicting the prognosis. However, in those with oliguria, this conclusion is not valid.

scholarly journals Impact of Renal Replacement Therapy on Mortality in Critically Ill Patients—The Nephrologist’s View within an Interdisciplinary Intensive Care Team

2021 ◽  
Vol 10 (15) ◽  
pp. 3379
Author(s):  
Matthias Klingele ◽  
Lea Baerens
Keyword(s):  
Intensive Care ◽  
Renal Replacement Therapy ◽  
Replacement Therapy ◽  
Critically Ill ◽  
Critically Ill Patients ◽  
Positive Impact ◽  
Kidney Injury ◽  
Care Team ◽  
Critically Ill Patient ◽  
Late Start

Acute kidney injury (AKI) is a common complication in critically ill patients with an incidence of up to 50% in intensive care patients. The mortality of patients with AKI requiring dialysis in the intensive care unit is up to 50%, especially in the context of sepsis. Different approaches have been undertaken to reduce this high mortality by changing modalities and techniques of renal replacement therapy: an early versus a late start of dialysis, high versus low dialysate flows, intermittent versus continuous dialysis, anticoagulation with citrate or heparin, the use of adsorber or special filters in case of sepsis. Although in smaller studies some of these approaches seemed to have a positive impact on the reduction of mortality, in larger studies these effects could not been reproduced. This raises the question of whether there exists any impact of renal replacement therapy on mortality in critically ill patients—beyond an undeniable impact on uremia, hyperkalemia and/or hypervolemia. Indeed, this is one of the essential challenges of a nephrologist within an interdisciplinary intensive care team: according to the individual situation of a critically ill patient the main indication of dialysis has to be identified and all parameters of dialysis have to be individually chosen with respect to the patient’s situation and targeting the main dialysis indication. Such an interdisciplinary and individual approach would probably be able to reduce mortality in critically ill patients with dialysis requiring AKI.

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
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