Association Between Urine Output and Mortality in Critically Ill Patients

2021 ◽  
Vol Publish Ahead of Print ◽  
Author(s):  
Aaron J. Heffernan ◽  
Stephanie Judge ◽  
Stephen M. Petrie ◽  
Rakshitha Godahewa ◽  
Christoph Bergmeir ◽  
...  
Keyword(s):  
Critically Ill ◽  
Critically Ill Patients ◽  
Urine Output

scholarly journals Incidence, timing and outcome of AKI in critically ill patients varies with the definition used and the addition of urine output criteria

2017 ◽  
Vol 18 (1) ◽  
Author(s):  
J. Koeze ◽  
F. Keus ◽  
W. Dieperink ◽  
I. C. C. van der Horst ◽  
J. G. Zijlstra ◽  
...  
Keyword(s):  
Critically Ill ◽  
Critically Ill Patients ◽  
Urine Output

scholarly journals Defining urine output criterion for acute kidney injury in critically ill patients

2010 ◽  
Vol 26 (2) ◽  
pp. 509-515 ◽  
Author(s):  
E. Macedo ◽  
R. Malhotra ◽  
R. Claure-Del Granado ◽  
P. Fedullo ◽  
R. L. Mehta
Keyword(s):  
Acute Kidney Injury ◽  
Critically Ill ◽  
Critically Ill Patients ◽  
Urine Output ◽  
Kidney Injury ◽  
Urine Output Criterion ◽  
Output Criterion

scholarly journals A comparison of RIFLE with and without urine output criteria for acute kidney injury in critically ill patients

Critical Care ◽  
2012 ◽  
Vol 16 (5) ◽  
pp. R200 ◽  
Author(s):  
Kama A Wlodzimirow ◽  
Ameen Abu-Hanna ◽  
Mathilde Slabbekoorn ◽  
Robert AFM Chamuleau ◽  
Marcus J Schultz ◽  
...  
Keyword(s):  
Acute Kidney Injury ◽  
Critically Ill ◽  
Critically Ill Patients ◽  
Urine Output ◽  
Kidney Injury

scholarly journals Validating a Lower Urine Output Criteria in Predicting Death in Critically Ill Patients

2020 ◽  
Vol 16 (1) ◽  
Author(s):  
Azrina Md Ralib ◽  
Mohd Basri Mat Nor
Keyword(s):  
Acute Kidney Injury ◽  
Critically Ill ◽  
Critically Ill Patients ◽  
Urine Output ◽  
Moving Average ◽  
Secondary Analysis ◽  
Kidney Injury ◽  
Optimal Prediction ◽  
Output Data ◽  
The Ideal

Introduction: Urine output provides a rapid estimate for kidney function, and its use has been incorporated in the diagnosis of acute kidney injury. However, not many studies had validated its use compared to the plasma creatinine. It has been showed that the ideal urine output threshold for prediction of death or the need for dialysis was 0.3 ml/kg/h. We aim to assess this threshold in our local ICU population. Methods: This was a secondary analysis of an observational study done in critically ill patients. Hourly urine output data was collected, and a moving average of 6-hourly urine output was calculated over the first 48 hours of ICU admission. AKIuo was defined if urine output ≤ 0.5 ml/kg/h, and UO0.3 was defined as urine output ≤ 0.3 ml/kg/h. Results: 143 patients were recruited into the study, of these, 87 (61%) had AKIuo, and 52 (36%) had UO0.3. The AUC of AKIuo in predicting death was 0.62 (0.51 to 0.72), and UO0.3 was 0.66 (0.55 to 0.77). There was lower survival in patients with AKIuo and UO0.3 compared to those without (p=0.01, and 0.001, respectively). However, only UO0.3 but not AKIuo independently predicted death (HR 2.44 (1.15 to 5.18). Conclusions: A threshold of 6 hourly urine output of 0.3 ml/kg/h but not 0.5 ml/kg/h independently predictive of death. This support previous finding of a lower threshold of urine output criteria for optimal prediction.

Abstract 16730: Computerized Prediction of Avoidable Serum Potassium Testing in Critically Ill Cardiac Patients

Circulation ◽  
2020 ◽  
Vol 142 (Suppl_3) ◽  
Author(s):  
Francesca Sperotto ◽  
Bhaven B Patel ◽  
Mathieu Molina ◽  
Satoshi Kimura ◽  
Marlon Delgado ◽  
...  
Keyword(s):  
Machine Learning ◽  
Critically Ill ◽  
Serum Potassium ◽  
Critically Ill Patients ◽  
Urine Output ◽  
Pediatric Patients ◽  
Cardiac Patients ◽  
Machine Learning Techniques ◽  
Median Percentage ◽  
K Value

Introduction: Electrolytes are frequently monitored among critically ill patients, especially among cardiac patients due to higher risk of arrhythmias. However, a percentage of these blood draws may potentially be saved. To date, no studies have investigated strategies to safely reduce the density of electrolyte monitoring in critically ill patients. Hypothesis: We hypothesized that machine learning models can identify potentially avoidable blood draws for serum potassium (K) among pediatric patients following cardiac surgery. Methods: We retrospectively reviewed data of all patients admitted to the CICU at Boston Children’s Hospital during 2010-2018, having a length of stay ≥4 days and ≥2 recorded serum K measurements. We collected variables related to K homeostasis, including serum chemistry, hourly K intake, diuretics, and urine output. Using established machine learning techniques (Random Forest classifiers and hyperparameters) we created models predicting whether a patient’s K would be normal or abnormal based on the most recent K level, medications administered, urine output, and markers of renal function. We developed multiple models based on different age categories and temporal proximity of the most recent K measurement. We assessed the predictive performance of the models using an independent test set. Results: Of the 7,269 admissions (6,196 patients) included, 95,674 serum K was measured on average of 1 (IQR 0-1) time per day. 96% of patients received at least one dose of IV diuretic and 83% received a form of K supplementation. Our models predicted a normal K value with a median positive predictive value of 0.90. A median percentage of 2.1% measurements (mean 2.5%, IQR 1.3%-3.7%) were incorrectly predicted as normal when they were abnormal. A median percentage of 0.0% (IQR 0.0%-0.4%) were incorrectly predicted as normal while being critically low or high. A median of 27.2% (IQR 7.8%-32.4%) of samples were correctly predicted to be normal and could have been potentially avoided. Conclusions: Machine-learning methods can be used to accurately predict avoidable blood tests for serum K in critically ill pediatric patients. A median of 27.2% of samples could have been saved, with decreased costs and risk of infection or anemia.

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