NEW YORK, July 1, 2020 /PRNewswire/ -- Renalytix AI
plc (LSE: RENX), an artificial intelligence-enabled in
vitro diagnostics company, focused on optimizing clinical
management of kidney disease to drive improved patient
outcomes and lower healthcare costs, today announced that results
of a clinical validation study have undergone peer-review and have
been accepted for publication in the American Society of Nephrology
Journal, Kidney360.
The published manuscript titled, "Validation of a
machine-learning-derived prognostic test (KidneyIntelX) integrating
biomarkers and EHR data to predict longitudinal-kidney
outcomes," is available through the Early Access format of the
American Society of Nephrology journal, Kidney360:
https://kidney360.asnjournals.org/content/early/2020/06/29/KID.0002252020
The study provides details of the primary analysis and numerous
sub-analyses which demonstrate robust performance of the
KidneyIntelX test in the two clinical contexts. These
validation results complement the multi-center validation study in
patients with prevalent diabetic kidney disease previously reported
(https://www.medrxiv.org/content/10.1101/2020.06.01.20119552v3).
These findings were reported in part previously in BioXriv, the
preprint server for biology, operated by Coldspring Harbor
Laboratory (bioRxiv 587774; doi:
https://doi.org/10.1101/587774),
The primary objective of this validation study was to
demonstrate if the KidneyIntelX artificial
intelligence-enabled algorithm was able to predict which patients
are at highest risk of adverse kidney outcomes with more accuracy
than the existing standard of care. The optimized
KidneyIntelX assay, combining sTNFR1, sTNFR2 and KIM-1
together with clinical data from electronic health records,
achieved a PPV of 62%in the top 15% highest risk of the T2D
population vs. 46% as classified by the clinical model (p<0.01
for comparison). Likewise, in the Apolipoprotein L1 high-risk
(APOL1) genotype cohort, the PPV of KidneyIntelX was 62% in
the top 15% highest risk of APOL1-HR population vs. PPV of
39%, as classified by the clinical model, (p<0.01 for
comparison). The study included 871 patients with Type 2 diabetes
and 498 patients of African Ancestry with APOL1 high-risk
genotypes (i.e., one copy of the genetic risk variant on both
chromosomes).
Better risk stratification tools are needed to facilitate the
application of novel treatments for DKD and CKD in patients with
relatively preserved kidney function. Earlier identification of
high-risk patients should allow for the improved ability to slow
progressive decline in kidney function before patients reach late
stages of CKD and need a kidney transplant or dialysis.
About Kidney Disease
Kidney disease is now recognized as a public health epidemic
affecting over 850 million people globally. The Centers for Disease
Control and Prevention ("CDC") estimates that 15% of US adults, or
37 million people, currently have CKD. Further, the CDC reports
that 9 out of 10 adults with CKD do not know they have it and 1 out
of 2 people with very low kidney function who are not on dialysis
do not know they have CKD*. Kidney disease is referred to as a
"silent killer" because it often has no symptoms and can go
undetected until a very advanced stage. Each year kidney disease
results in more deaths than breast or prostate cancer. Every day,
13 patients in the United States
die while waiting for a kidney transplant.
*
https://www.cdc.gov/kidneydisease/publications-resources/2019-national-facts.html
About RenalytixAI
RenalytixAI is a developer of artificial intelligence-enabled
clinical in vitro diagnostic solutions for kidney disease, one of
the most common and costly chronic medical conditions globally.
RenalytixAI's products are being designed to make significant
improvements in kidney disease diagnosis, transplant management,
clinical care, patient stratification for drug clinical trials, and
drug target discovery. For more information, visit
www.renalytixai.com.
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