MO340: Acute Kidney Disease in the Outpatient Setting: From Big Data Phenotyping to Biomarker Validation Using the NGAL and DNLITE-IVD103 Tests
Authors: Kuo Chin-Chi , Yeh Hung-Chieh , Chiang Hsiu-Yin , Tseng Tzu-Ling 
Published: May 2022

Abstract

BACKGROUND AND AIMS
Acute kidney injury (AKI) and acute kidney disease (AKD), a typical inpatient disease spectrum, pose an exceptional burden on the current healthcare system worldwide. By contrast, developing operative diagnostic criteria for AKD in the outpatient setting (AKDOPT) remains an unattained goal due to the data silos built around the disconnected healthcare systems. In 2017, Taiwan launched the National Cloud-based Health Information Exchange Platform and established the interoperability standard that makes the automated monitoring of change of serum creatinine (S-Cre) in the outpatient setting a possible practice. This study aims to verify our previously proposed AKDOPT diagnostic algorithms using both the conventional neutrophil gelatinase-associated lipocalin (NGAL) and DNlite-IVD103 tests, detecting a novel biomarker, a post-translational modified (PTM) fragment of Fetuin-A [1, 2].

METHOD
The Big Data Center of China Medical University Hospital (CMUH) has launched the Acute Kidney Injury Detection System (AKIDS) in the outpatient setting since December 2017 based on our AKDOPT diagnostic algorithms (Fig. 1). In November 2020, we conducted a pragmatic randomized trial to evaluate the clinical effectiveness of AKIDS in reducing the risk of AKDOPT progression to dialysis. Among patients who participated in this trial, both urine and blood were obtained at the enrollment and the urine samples were sent for the quantification of NGAL and the unique PTM-Fetuin-A fragment, an ELISA test developed by Bio Preventive Medicine (BPM). The risk of S-Cre doubling was estimated by Firth's logistic regression.

RESULTS
A total of 56 patients who were captured by the AKIDS and with the last estimated glomerular filtration rate (eGFR) below 45 mL/min/1.73 m2 recruited in this trial with a median age of 73 years (IQR: 66.2–82.5) and a baseline eGFR of 36.4 (IQR: 21.0–46.3) mL/min/1.7 3 m2. During the follow-up, there were six participants reached the S-Cre double status. The median levels of NGAL and IVD103 were 1360 (IQR: 299–1827) ng/mg and 74.7 (IQR: 37.3–236) ng/mg, respectively, among patients experiencing the endpoint, and they were 187 (81.4–1152) ng/mg and 15.9 (6.16–67.3) ng/mg for patients free of the primary outcome. The odds ratios of the S-Cre doubling for each log unit increase in NGAL and IVD103 were 2.26 (95% confidence interval 0.73–6.98; p-value .16) and 3.48 (0.78–15.6; p-value .10), respectively. In the dose-response plot, we found only IVD103 showed a positive linear relationship with the risk of S-Cre doubling despite the two biomarkers showed comparable area under the receiver operating characteristic curve for predicting poor kidney outcome (NGAL 0.697 versus IVD103 0.723).

CONCLUSION
This is the first study to phenotypically validate the previously unrecognized phenotype, AKDOPT, by the AKIDS developed under the National Health Insurance (NHI) information technology infrastructure and molecularly validate its prognostic role using kidney injury biomarkers, NGAL and a novel eGFR decline prediction biomarker, a unique PTM-fragment of Fetuin-A (IVD103). The linear association between IVD103 and the risk of S-Cre doubling supports its prognostic value in the risk assessment of AKDOPT.