A Novel Patient-Specific Model for Predicting Severe Oliguria; Development and Comparison With Kidney Disease: Improving Global Outcomes Acute Kidney Injury Classification. Issue 1 (January 2020)

• Record Type: Journal Article
• Authors: Howitt, Samuel H.; Oakley, Jordan; Caiado, Camila; Goldstein, Michael; Malagon, Ignacio; McCollum, Charles; Grant, Stuart W.
• Is Part Of: Critical care medicine. Volume 48:Issue 1(2020)
• Publication Date: 2020-01
• Subjects: acute kidney injury -- critical care -- urine; Critical care medicine -- Periodicals; Soins intensifs -- Périodiques; 616.028
• DOI: 10.1097/CCM.0000000000004074
• Languages: English
• ISSNs: 0090-3493

Abstract

Abstract : 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. Abstract : Supplemental Digital Content is available in the text.