Role of the Furosemide Stress Test in Renal Injury Prognosis

Update on persistent acute kidney injury in critical illnesses

Acute kidney injury (AKI) affects about half of patients admitted to the intensive care unit (ICU), and worsens their short- and long-term outcomes. Apparently self-limiting AKI episodes initiate a progression toward chronic kidney disease (CKD) through cellular and molecular mechanisms that are yet to be explained. In particular, persistent AKI, defined in 2016 by the Acute Dialysis Quality Initiative as an AKI which lasts more than 48 h from its onset, has been correlated with higher morbidity and mortality, and with a higher progression to acute kidney disease (AKD) and CKD than transient AKI (i.e. AKI with a reversal within 48 h). This classification has been also used in the setting of solid organ transplantation, demonstrating similar outcomes. Due to its incidence and poor prognosis and because prompt interventions seem to change its course, persistent AKI should be recognized early and followed-up also after its recovery. However, while AKI and CKD are well-described syndromes, persistent AKI and AKD are relatively new entities. The purpose of this review is to highlight the key phases of persistent AKI in ICU patients in terms of both clinical and mechanistic features in order to offer to clinicians and researchers an updated basis from which to start improving patients' care and direct future research.

Relationship Between Mean Arterial Pressure and Furosemide Stress Test Success Rates: A Retrospective Cohort Study

BACKGROUND

The furosemide stress test (FST) is a safe and easy assessment of renal tubular function. Other factors, such as mean arterial pressure (MAP), which may influence the success rates of the FST, have not been well defined.

OBJECTIVE

To evaluate the relationship between MAP and success rates of the FST in critically ill patients.

METHODS

Retrospective, single-center, institutional review board (IRB)-approved cohort study. Critically ill adult patients given at least one dose of intravenous (IV) furosemide (≥1-1.5 mg/kg) were included. Primary outcome was whether a MAP ≥ 75 mm Hg would equate to a higher FST success rate. Secondary outcome was the success rates of patient on one or more vasopressors.

RESULTS

Of 225 patients, 88 (39.1%) had a successful FST. In patients with a MAP ≥ 75 mm Hg, 60 out of 104 (57.7%) had a successful FST compared to 28 out of 121 (23.1%) of patients who had a MAP < 75 mm Hg (odds ratio [OR], 4.53, 95% CI, 2.55-8.74, P < 0.001). Patients on vasopressors at the time of the furosemide dose had lower rates of success compared to those not on vasoactive agents (30.4% versus 68.2%, p = 0.026). Limitations of this study include its retrospective design and reliance on documented urine output.

CONCLUSION AND RELEVANCE

Patients with a MAP ≥ 75 mm Hg were significantly more likely to have a successful FST compared to those with a MAP < 75 mm Hg. This represents the first report of factors that may influence FST success rates.

External validation of a deep-learning model to predict severe acute kidney injury based on urine output changes in critically ill patients

OBJECTIVES

The purpose of this study was to externally validate algorithms (previously developed and trained in two United States populations) aimed at early detection of severe oliguric AKI (stage 2/3 KDIGO) in intensive care units patients.

METHODS

The independent cohort was composed of 10'596 patients from the university hospital ICU of Amsterdam (the "AmsterdamUMC database") admitted to their intensive care units. In this cohort, we analysed the accuracy of algorithms based on logistic regression and deep learning methods. The accuracy of investigated algorithms had previously been tested with electronic intensive care unit (eICU) and MIMIC-III patients.

RESULTS

The deep learning model had an area under the ROC curve (AUC) of 0,907 (± 0,007SE) with a sensitivity and specificity of 80% and 89%, respectively, for identifying oliguric AKI episodes. Logistic regression models had an AUC of 0,877 (± 0,005SE) with a sensitivity and specificity of 80% and 81%, respectively. These results were comparable to those obtained in the two US populations upon which the algorithms were previously developed and trained.

CONCLUSION

External validation on the European sample confirmed the accuracy of the algorithms, previously investigated in the US population. The models show high accuracy in both the European and the American databases even though the two cohorts differ in a range of demographic and clinical characteristics, further underlining the validity and the generalizability of the two analytical approaches.

Machine learning model for predicting acute kidney injury progression in critically ill patients

Background

Acute kidney injury (AKI) is a serve and harmful syndrome in the intensive care unit. Comparing to the patients with AKI stage 1/2, the patients with AKI stage 3 have higher in-hospital mortality and risk of progression to chronic kidney disease. The purpose of this study is to develop a prediction model that predict whether patients with AKI stage 1/2 will progress to AKI stage 3.

Methods

Patients with AKI stage 1/2, when they were first diagnosed with AKI in the Medical Information Mart for Intensive Care, were included. We used the Logistic regression and machine learning extreme gradient boosting (XGBoost) to build two models which can predict patients who will progress to AKI stage 3. Established models were evaluated by cross-validation, receiver operating characteristic curve, and precision–recall curves.

Results

We included 25,711 patients, of whom 2130 (8.3%) progressed to AKI stage 3. Creatinine, multiple organ failure syndromes were the most important in AKI progression prediction. The XGBoost model has a better performance than the Logistic regression model on predicting AKI stage 3 progression. Thus, we build a software based on our data which can predict AKI progression in real time.

Conclusions

The XGboost model can better identify patients with AKI progression than Logistic regression model. Machine learning techniques may improve predictive modeling in medical research.

Fluid management, electrolytes imbalance and renal management in neonates with neonatal encephalopathy treated with hypothermia

Kidney dysfunction and acute kidney injury (AKI) frequently accompanies neonatal encephalopathy and contributes to neonatal morbidity and mortality. While there are currently no proven therapies for the treatment of AKI, understanding the pathophysiology along with early recognition and treatment of alterations in fluid, electrolyte and metabolic homeostasis that accompany AKI offer opportunity to reduce associated morbidity. Promising new tests and technologies, including urine and serum biomarkers and renal near-infrared spectroscopy offer opportunities to improve diagnosis and monitoring of neonates at risk for kidney injury. Furthermore, recent advances in neonatal kidney supportive therapies such as hemofiltration and hemodialysis may further improve outcomes in this population. This chapter provides an overview of disorders of fluid balance, electrolyte homeostasis and kidney function associated with neonatal encephalopathy and therapeutic hypothermia. Recommendations for fluid and electrolyte management based upon published literature and authors' opinions are provided.