Furosemide stress test as a predictive marker of acute kidney injury progression or renal replacement therapy: a systemic review and meta-analysis

Chen, Jia‐Jin; Chang, Chih‐Hsiang; Huang, Yuejiao; Kuo, George

doi:10.1186/s13054-020-02912-8

Cited by 44 publications

(23 citation statements)

References 48 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, the methods for measuring most biomarkers are expensive and might not be suitable in daily practice. The furosemide stress test, a cheap and readily available functional test, has shown promise in AKI progression prediction [10][11][12]. Neutrophilto-lymphocyte ratio (NLR), which can be readily measured in daily clinical practice, is calculated using the complete blood count.…”

Section: Introductionmentioning

confidence: 99%

Neutrophil-to-lymphocyte ratio is a marker for acute kidney injury progression and mortality in critically ill populations: a population-based, multi-institutional study

et al. 2021

Self Cite

View full text Add to dashboard Cite

Backgrounds Neutrophil-to-lymphocyte ratio (NLR), a surrogate marker of systemic response to physiological stress, is used for prognosis prediction in many diseases. However, the usefulness of this marker for predicting acute kidney injury (AKI) progression is unclear. Methods This retrospective study was based on the Chang Gung Research Database. Patients admitted to the intensive care unit with a diagnosis of stage 1 or 2 AKI were identified. The primary outcome was a composite of progression to stage 3 AKI, requirement of renal replacement therapy, or 14-day in-hospital mortality. The association between NLR and the primary outcome was examined using a logistic regression model and multivariable analysis. The nonlinearity and cutoff points of this relationship were determined using a restricted cubic spline model. Results A total of 10,441 patients were enrolled. NLR level at the time of stage 1-2 AKI diagnosis was a marker of adverse outcomes. After adjustment for confounders, NLR was independently associated with the composite outcome of AKI progression, renal replacement therapy, or mortality. The restricted cubic spline model revealed a J-shaped curve, with the lowest odds ratio for an NLR between 7 and 38. Subgroup analysis revealed linear and J-shaped relationships between NLR and the primary outcome in patients admitted to the intensive care unit for medical reasons and for cardiovascular surgery, respectively. Conclusions NLR is an independent marker of AKI progression and in-hospital mortality. Because it is readily available in daily practice, it might be used for risk stratification in the AKI population.

show abstract

Section: Introductionmentioning

confidence: 99%

Neutrophil-to-lymphocyte ratio is a marker for acute kidney injury progression and mortality in critically ill populations: a population-based, multi-institutional study

et al. 2021

Self Cite

View full text Add to dashboard Cite

show abstract

“…Therefore, predicting AKI progression is always one of research highlights. FST, a method for predicting AKI progression, had desirable prediction ability, which of AU-ROC was 0.88 [ 6 ]. Our model and previous FST studies have comparable prediction ability (AU-ROC 0.926 vs. 0.88).…”

Section: Discussionmentioning

confidence: 99%

“…Currently, few methods were developed to predict AKI stage 1/2 to AKI stage 3. Furosemide stress test (FST) was considered as a robust predictive approach to identify who will progress to AKI stage 3 [ 6 ]. However, the clinical application has been hampered for several reasons such as lacking of high quality RCT [ 5 ], not stable for patients with unstable hemodynamics [ 7 ], no standardization of dosage and time [ 8 ] and ambiguous effect of other factors such as fluid balance and diuretic on the outcome [ 9 ].…”

Section: Introductionmentioning

confidence: 99%

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

Wei

Zhang

Feng

et al. 2022

BMC Med Inform Decis Mak

View full text Add to dashboard Cite

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.

show abstract

“…Furosemide stress test 15 , 16 was well studied to predict the progression of AKI and requirement for RRT. However, patients with frank hypovolemia may not tolerate intravenous furosemide and the efficacy was not well validated for patients with late chronic kidney disease, 16 whereas direct measurement of creatinine clearance theoretically do not pose these limitations. Furthermore, the furosemide stress test cannot be used for continuous monitoring due to its intrinsic limitation for repeated testing.…”

Section: Discussionmentioning

confidence: 99%

Direct Measurement of Creatinine Clearance over a Short Interval in Intensive Care Settings

Law¹,

Lo²,

Yeoh³

2021

Indian Journal of Critical Care Medicine

View full text Add to dashboard Cite

A bstract Background The definition of acute kidney injury (AKI), based on serum creatinine and urine output, bears significant limitations in intensive care units (ICUs). Serum creatinine has significant lag-time as it needs to be accumulated and stabilized at a new level whereas urine output is affected by diuresis, antidiuresis, and antinatriuresis. Direct measurement of creatinine clearance (CrCl direct = urine creatinine × urine flow rate/serum creatinine) over a short interval (3–6 hours) was explored to identify patients with AKI. Materials and methods We reanalyzed a previous published dataset. We included 11 patients who had serial daily urine collections over 0 to 3 days of stay in ICU and baseline (day -1) serum creatinine levels. Result The ratio of CrCl direct on day 0 to baseline creatinine clearance predicted the progression of AKI over the subsequent 1 to 3 days of ICU stay [area under receiver operating characteristic curve = 0.933 and 95% confidence interval (CI) = 0.780–1.000]. Discussion CrCl direct over a short interval may be an alternative marker of kidney function. Future studies may explore its use to identify patients with AKI who may benefit from early renal replacement therapy. How to cite this article Law LSC, Lo EAG, Yeoh SF. Direct Measurement of Creatinine Clearance over a Short Interval in Intensive Care Settings. Indian J Crit Care Med 2021;25(7):800–802.

show abstract

Furosemide stress test as a predictive marker of acute kidney injury progression or renal replacement therapy: a systemic review and meta-analysis

Cited by 44 publications

References 48 publications

Neutrophil-to-lymphocyte ratio is a marker for acute kidney injury progression and mortality in critically ill populations: a population-based, multi-institutional study

Neutrophil-to-lymphocyte ratio is a marker for acute kidney injury progression and mortality in critically ill populations: a population-based, multi-institutional study

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

Direct Measurement of Creatinine Clearance over a Short Interval in Intensive Care Settings

Contact Info

Product

Resources

About