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Renalytix AI PLC KidneyIntelX study accepted for publication

01/07/2020 7:00am

RNS Non-Regulatory


TIDMRENX

Renalytix AI PLC

01 July 2020

Renalytix AI plc

("RenalytixAI", the "Company")

KidneyIntelX validation study accepted for publication in American Society of Nephrology Journal Kidney360

NEW YORK, July 1, 2020 - 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 , announces results of a clinical validation study have undergone peer-review and have been accepted for publication in the American Society of Nephrology Journal, Kidney360.

Highlights:

-- KidneyIntelX identifies patients at the highest risk of progressive kidney function decline in two distinct cohorts and clinical settings with more accuracy than existing methods

-- The KidneyIntelX(TM) algorithm accurately predicted rapid kidney function decline (RKFD) and/or 40% sustained decline in kidney function, or kidney failure in patients with Type 2 diabetes and of African Ancestry with high-risk APOL1 genotypes

-- The positive predictive value (PPV) of KidneyIntelX(TM) for progressive kidney function both patient groups exceeded 60% and the negative predictive value exceeded 90%

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 optimised 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.

For further information, please contact:

 
  Renalytix AI plc                                                         www.renalytixai.com 
  James McCullough, CEO                                                        Via Walbrook PR 
 
  Stifel (Nominated Adviser & Broker)                                       Tel: 020 7710 7600 
  Alex Price / Nicholas Moore 
 
  N+1 Singer (Joint Broker)                                                 Tel: 020 7496 3000 
  Aubrey Powell / George Tzimas (Corporate 
   Finance) 
  Tom Salvesen (Corporate Broking) 
 
  Walbrook PR Limited                           Tel: 020 7933 8780 or renalytix@walbrookpr.com 
  Paul McManus / Lianne Cawthorne                               Mob: 07980 541 893 / 07584 391 
                                                                                           303 
 
 

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 .

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 chronic kidney disease (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 kills more people than breast and 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

This information is provided by RNS, the news service of the London Stock Exchange. RNS is approved by the Financial Conduct Authority to act as a Primary Information Provider in the United Kingdom. Terms and conditions relating to the use and distribution of this information may apply. For further information, please contact rns@lseg.com or visit www.rns.com.

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