A Novel Patient-Specific Model for Predicting Severe Oliguria; Development and Comparison With Kidney Disease: Improving Global Outcomes Acute Kidney Injury Classification
SourceCritical Care Medicine, 48, 1, (2020), pp. e18-e25
Article / Letter to editor
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Critical Care Medicine
SubjectRadboudumc 18: Healthcare improvement science RIHS: Radboud Institute for Health Sciences
OBJECTIVES: 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. DESIGN: This was a retrospective cohort study utilizing prospectively collected data. SETTING: A cardiac ICU in the United Kingdom. PATIENTS: Patients undergoing cardiac surgery between January 2013 and November 2017. INTERVENTIONS: None. 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. CONCLUSIONS: 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.
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