Outpatient kidney recovery after acute kidney injury requiring dialysis: a systematic review protocol

Current Fluid Management Practice in Critically Ill Adults on Continuous Renal Replacement Therapy: A Binational, Observational Study

INTRODUCTION

In critically ill patients undergoing continuous renal replacement therapy (CRRT), a positive fluid balance (FB) is associated with adverse outcomes. However, current FB management practices in CRRT patients are poorly understood. We aimed to study FB and its components in British and Australian CRRT patients to inform future trials.

METHODS

We obtained detailed electronic health record data on all fluid-related variables during CRRT and hourly FB for the first 7 days of treatment.

RESULTS

We studied 1,616 patients from three tertiary intensive care units (ICUs) in two countries. After the start of CRRT, the mean cumulative FB became negative at 31 h and remained negative over 7 days to a mean nadir of -4.1 L (95% confidence interval (CI) of -4.6 to -3.5). The net ultrafiltration (NUF) rate was the dominant fluid variable (-67.7 mL/h; standard deviation (SD): 75.7); however, residual urine output (-34.7 mL/h; SD: 54.5), crystalloid administration (48.1 mL/h; SD: 44.6), and nutritional input (36.4 mL/h; SD: 29.7) significantly contributed to FB. Patients with a positive FB after 72 h of CRRT were more severely ill, required high-dose vasopressors, and had high lactate concentrations (5.0 mmol/L; interquartile range: 2.3-10.5). A positive FB was independently associated with increased hospital mortality (odds ratio: 1.70; 95% CI; p = 0.004).

CONCLUSION

In the study ICUs, most CRRT patients achieved a predominantly NUF-dependent negative FB. Patients with a positive FB at 72 h had greater illness severity and haemodynamic instability. Achieving equipoise for conducting trials that target a negative early FB in such patients may be difficult.

Ensemble Machine Learning for Predicting 90-Day Outcomes and Analyzing Risk Factors in Acute Kidney Injury Requiring Dialysis

Purpose

Our objectives were to (1) employ ensemble machine learning algorithms utilizing real-world clinical data to predict 90-day prognosis, including dialysis dependence and mortality, following the first hospitalized dialysis and (2) identify the significant factors associated with overall outcomes.

Patients and Methods

We identified hospitalized patients with Acute kidney injury requiring dialysis (AKI-D) from a dataset of the Taipei Medical University Clinical Research Database (TMUCRD) from January 2008 to December 2020. The extracted data comprise demographics, comorbidities, medications, and laboratory parameters. Ensemble machine learning models were developed utilizing real-world clinical data through the Google Cloud Platform.

Results

The Study Analyzed 1080 Patients in the Dialysis-Dependent Module, Out of Which 616 Received Regular Dialysis After 90 Days. Our Ensemble Model, Consisting of 25 Feedforward Neural Network Models, Demonstrated the Best Performance with an Auroc of 0.846. We Identified the Baseline Creatinine Value, Assessed at Least 90 Days Before the Initial Dialysis, as the Most Crucial Factor. We selected 2358 patients, 984 of whom were deceased after 90 days, for the survival module. The ensemble model, comprising 15 feedforward neural network models and 10 gradient-boosted decision tree models, achieved superior performance with an AUROC of 0.865. The pre-dialysis creatinine value, tested within 90 days prior to the initial dialysis, was identified as the most significant factor.

Conclusion

Ensemble machine learning models outperform logistic regression models in predicting outcomes of AKI-D, compared to existing literature. Our study, which includes a large sample size from three different hospitals, supports the significance of the creatinine value tested before the first hospitalized dialysis in determining overall prognosis. Healthcare providers could benefit from utilizing our validated prediction model to improve clinical decision-making and enhance patient care for the high-risk population.

The potential for artificial intelligence to predict clinical outcomes in patients who have acquired acute kidney injury during the perioperative period

Acute kidney injury (AKI) is a common medical problem in hospitalised patients worldwide that may result in negative physiological, social and economic consequences. Amongst patients admitted to ICU with AKI, over 40% have had either elective or emergency surgery prior to admission. Predicting outcomes after AKI is difficult and the decision on whom to initiate RRT with a goal of renal recovery or predict a long-term survival benefit still poses a challenge for acute care physicians. With the increasing use of electronic healthcare records, artificial intelligence may allow postoperative AKI prognostication and aid clinical management. Patients will benefit if the data can be readily accessed andregulatory, ethical and human factors challenges can be overcome.