Identification of Major Adverse Kidney Events Within the Electronic Health Record
Matthew W Semler, Todd W Rice, Andrew D Shaw, Edward D Siew, Wesley H Self, Avinash B Kumar, Daniel W Byrne, Jesse M Ehrenfeld, Jonathan P Wanderer, Matthew W Semler, Todd W Rice, Andrew D Shaw, Edward D Siew, Wesley H Self, Avinash B Kumar, Daniel W Byrne, Jesse M Ehrenfeld, Jonathan P Wanderer
Abstract
Acute kidney injury is common among critically ill adults and is associated with increased mortality and morbidity. The Major Adverse Kidney Events by 30 days (MAKE30) composite of death, new renal replacement therapy, or persistent renal dysfunction is recommended as a patient-centered outcome for pragmatic trials involving acute kidney injury. Accurate electronic detection of the MAKE30 endpoint using data within the electronic health record (EHR) could facilitate the use of the EHR in large-scale kidney injury research. In an observational study using prospectively collected data from 200 admissions to a single medical intensive care unit, we tested the performance of electronically-extracted data in identifying the MAKE30 composite compared to the reference standard of two-physician manual chart review. The incidence of MAKE30 on manual-review was 16 %, which included 8.5 % for in-hospital mortality, 3.5 % for new renal replacement therapy, and 8.5 % for persistent renal dysfunction. There was strong agreement between the electronic and manual assessment of MAKE30 (98.5 % agreement [95 % CI 96.5-100.0 %]; kappa 0.95 [95 % CI 0.87-1.00]; P < 0.001), with only three patients misclassified by electronic assessment. Performance of the electronic MAKE30 assessment was similar among patients with and without CKD and with and without a measured serum creatinine in the 12 months prior to hospital admission. In summary, accurately identifying the MAKE30 composite outcome using EHR data collected as a part of routine care appears feasible.
Keywords: Acute kidney injury; Electronic health record; Intensive care unit; Major adverse kidney events.
Conflict of interest statement
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Source of Funding and Conflicts of Interest
Biostatistical support was provided by Vanderbilt Institute for Clinical and Translational Research grant support (UL1 TR000445 from NCATS/NIH). M.W.S. was supported by a National Heart, Lung, and Blood Institute (NHLBI) T32 award (HL087738 09). E.W.S. received support from the Vanderbilt Center for Kidney Disease (VCKD) and the VA Health Services Research and Development Service (HSR&D IIR-13-073). W.H.S was supported in part by K23GM110469 from the National Institute of General Medical Sciences. J.P.W received support from by the Foundation for Anesthesia Education and Research (FAER, Schaumburg, IL, USA) and Anesthesia Quality Institute (AQI, Schaumburg, IL, USA)’s Health Service Research Mentored Research Training Grant (HSR-MRTG). The funding institutions had no role in (1) conception, design, or conduct of the study, (2) collection, management, analysis, interpretation, or presentation of the data, or (3) preparation, review, or approval of the manuscript. All authors have completed and submitted the ICMJE Form for Disclosure of Potential Conflicts of Interest. The authors declare no potential conflicts of interest. T.W.R. reported serving on an advisory board for Avisa Pharma, LLC and as a DSMB member for GlaxoSmithKline PLC. W.H.S. reported serving on advisory boards for BioFire Diagnostics, Inc and Venaxis, Inc.
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Source: PubMed