Publication details for Camila CaiadoHowitt, Samuel H., Oakley, Jordan, Caiado, Camila, Goldstein, Michael, Malagon, Ignacio, McCollum, Charles & Grant, Stuart W. (2020). A Novel Patient-Specific Model for Predicting Severe Oliguria; Development and Comparison With Kidney Disease. Critical Care Medicine 48(1): e18-e25.
- Publication type: Journal Article
- ISSN/ISBN: 0090-3493, 1530-0293
- DOI: 10.1097/CCM.0000000000004074
- Further publication details on publisher web site
- Durham Research Online (DRO) - may include full text
Author(s) from Durham
The Kidney Disease: Improving Global Outcomes urine output criteria for acute kidney injury lack specificity for identifying patients at risk of adverse renal outcomes. The objective was to develop a model that analyses hourly urine output values in real time to identify those at risk of developing severe oliguria.
This was a retrospective cohort study utilizing prospectively collected data.
A cardiac ICU in the United Kingdom.
Patients undergoing cardiac surgery between January 2013 and November 2017.
Measurement and Main Results:
Patients were randomly assigned to development (n = 981) and validation (n = 2,389) datasets. A patient-specific, dynamic Bayesian model was developed to predict future urine output on an hourly basis. Model discrimination and calibration for predicting severe oliguria (< 0.3 mL/kg/hr for 6 hr) occurring within the next 12 hours were tested in the validation dataset at multiple time points. Patients with a high risk of severe oliguria (p > 0.8) were identified and their outcomes were compared with those for low-risk patients and for patients who met the Kidney Disease: Improving Global Outcomes urine output criterion for acute kidney injury. Model discrimination was excellent at all time points (area under the curve > 0.9 for all). Calibration of the model’s predictions was also excellent. After adjustment using multivariable logistic regression, patients in the high-risk group were more likely to require renal replacement therapy (odds ratio, 10.4; 95% CI, 5.9–18.1), suffer prolonged hospital stay (odds ratio, 4.4; 95% CI, 3.0–6.4), and die in hospital (odds ratio, 6.4; 95% CI, 2.8–14.0) (p < 0.001 for all). Outcomes for those identified as high risk by the model were significantly worse than for patients who met the Kidney Disease: Improving Global Outcomes urine output criterion.
This novel, patient-specific model identifies patients at increased risk of severe oliguria. Classification according to model predictions outperformed the Kidney Disease: Improving Global Outcomes urine output criterion. As the new model identifies patients at risk before severe oliguria develops it could potentially facilitate intervention to improve patient outcomes.