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A Novel Patient-Specific Model for Predicting Severe Oliguria; Development and Comparison With Kidney Disease

Improving Global Outcomes Acute Kidney Injury Classification

Howitt, Samuel H. MBChB, FRCA1,2; Oakley, Jordan MSc1,3; Caiado, Camila PhD3; Goldstein, Michael PhD3; Malagon, Ignacio PhD, FRCA, FFICM1,2; McCollum, Charles MD, FRCS1; Grant, Stuart W. PhD, MRCS1

doi: 10.1097/CCM.0000000000004074
Online Clinical Investigation: PDF Only
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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.

1Division of Cardiovascular Sciences, University of Manchester, ERC, Manchester University Hospitals Foundation Trust, Manchester, United Kingdom.

2Department of Cardiothoracic Anaesthesia and Critical Care, Wythenshawe Hospital, Manchester University Hospitals Foundation Trust, Manchester, United Kingdom.

3Department of Mathematical Sciences, Durham University, Durham, United Kingdom.

This study was undertaken at Wythenshawe Hospital, Manchester University Hospital Foundation Trust, Manchester, United Kingdom.

Supplemental digital content is available for this article. Direct URL citations appear in the printed text and are provided in the HTML and PDF versions of this article on the journal’s website (http://journals.lww.com/ccmjournal).

A grant from the British Heart Foundation (grant number PG/16/80/32411) was used to support this research.

Drs. Howitt’s, Oakley’s, Caiado's, Goldstein's, Malagon's, McCollum’s, and Grant’s institutions received funding from the British Health Foundation. Dr. Grant received funding from Rinicare. Dr. Malagon disclosed that he does not have any potential conflicts of interest.

For information regarding this article, E-mail: samuel.howitt@manchester.ac.uk

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