Prediction of outcomes after acute kidney injury in hospitalised patients: protocol for a systematic review. Issue 12 (22nd December 2020)

• Record Type: Journal Article
• Authors: Arora, Tanima; Martin, Melissa; Grimshaw, Alyssa; Mansour, Sherry; Wilson, Francis P
• Is Part Of: BMJ open. Volume 10:Issue 12(2020)
• Publication Date: 2020-12-22
• Subjects: adult nephrology -- dialysis -- acute renal failure; Medicine -- Research -- Periodicals; 610.72
• DOI: 10.1136/bmjopen-2020-042035
• Languages: English
• ISSNs: 2044-6055

Abstract

Abstract : Introduction: Acute kidney injury (AKI) is common and is associated with negative long-term outcomes. Given the heterogeneity of the syndrome, the ability to predict outcomes of AKI may be beneficial towards effectively using resources and personalising AKI care. This systematic review will identify, describe and assess current models in the literature for the prediction of outcomes in hospitalised patients with AKI. Methods and analysis: Relevant literature from a comprehensive search across six databases will be imported into Covidence. Abstract screening and full-text review will be conducted independently by two team members, and any conflicts will be resolved by a third member. Studies to be included are cohort studies and randomised controlled trials with at least 100 subjects, adult hospitalised patients, with AKI. Only those studies evaluating multivariable predictive models reporting a statistical measure of accuracy (area under the receiver operating curve or C-statistic) and predicting resolution of AKI, progression of AKI, subsequent dialysis and mortality will be included. Data extraction will be performed independently by two team members, with a third reviewer available to resolve conflicts. Results will be reported using Preferred Reporting Items for Systematic Reviews and Meta-Analysis guidelines. Risk of bias will be assessed using Prediction model Risk Of Bias ASsessment Tool. Ethics and dissemination: We are committed to open dissemination of our results through the registration of our systematic review on PROSPERO and future publication. We hope that our review provides a platform for future work in realm of using artificial intelligence to predict outcomes of common diseases. PROSPERO registration number: CRD42019137274.