Development and External Validation of an Online Clinical Prediction Model for Augmented Renal Clearance in Adult Mixed Critically Ill Patients: The Augmented Renal Clearance Predictor. Issue 12 (December 2020)

• Record Type: Journal Article
• Authors: Gijsen, Matthias; Huang, Chao-yuan; Flechet, Marine; Van Daele, Ruth; Declercq, Peter; Debaveye, Yves; Meersseman, Philippe; Meyfroidt, Geert; Wauters, Joost; Spriet, Isabel
• Is Part Of: Critical care medicine. Volume 48:Issue 12(2020)
• Publication Date: 2020-12
• Subjects: augmented renal clearance -- creatinine clearance -- critically ill -- external validation -- intensive care unit -- prediction model; Critical care medicine -- Periodicals; Soins intensifs -- Périodiques; 616.028
• DOI: 10.1097/CCM.0000000000004667
• Languages: English
• ISSNs: 0090-3493

Abstract

Abstract : Objectives: Augmented renal clearance might lead to subtherapeutic plasma levels of drugs with predominant renal clearance. Early identification of augmented renal clearance remains challenging for the ICU physician. We developed and validated our augmented renal clearance predictor, a clinical prediction model for augmented renal clearance on the next day during ICU stay, and made it available via an online calculator. We compared its predictive performance with that of two existing models for augmented renal clearance. Design: Multicenter retrospective registry-based cohort study. Setting: Three Belgian tertiary care academic hospitals. Patients: Adult medical, surgical, and cardiac surgery ICU patients. Interventions: None. Measurements and Main Results: Development of the prediction model was based on clinical information available during ICU stay. Out of 33, 258 ICU days, we found augmented renal clearance on 19.6% of all ICU days in the development cohort. We retained six clinical variables in our augmented renal clearance predictor: day from ICU admission, age, sex, serum creatinine, trauma, and cardiac surgery. We assessed performance by measuring discrimination, calibration, and net benefit. We externally validated the final model in a single-center population ( n = 10, 259 ICU days). External validation confirmed good performance with an area under the curve of 0.88 (95% CI 0.87–0.88) and a sensitivity and specificity of 84.1 (95% CI 82.5–85.7) and 76.3 (95% CI 75.4–77.2) at the default threshold probability of 0.2, respectively. Conclusions: Augmented renal clearance on the next day can be predicted with good performance during ICU stay, using routinely collected clinical information that is readily available at bedside. Our augmented renal clearance predictor is available at www.arcpredictor.com . Abstract : Supplemental Digital Content is available in the text.