CHAN, LILI;
NADKARNI, GIRISH N.;
FLEMING, FERGUS;
SALEM, FADI E.;
MURPHY, BARBARA;
DONOVAN, MICHAEL J.;
COCA, STEVEN;
DAMRAUER, SCOTT M.
499-P: Clinical Validation of an AI-Enabled Prognostic Test (KidneyintelX) to Accurately Predict Rapid Kidney Function Decline in Patients with Type 2 Diabetic Kidney Disease
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Medientyp:
E-Artikel
Titel:
499-P: Clinical Validation of an AI-Enabled Prognostic Test (KidneyintelX) to Accurately Predict Rapid Kidney Function Decline in Patients with Type 2 Diabetic Kidney Disease
Beteiligte:
CHAN, LILI;
NADKARNI, GIRISH N.;
FLEMING, FERGUS;
SALEM, FADI E.;
MURPHY, BARBARA;
DONOVAN, MICHAEL J.;
COCA, STEVEN;
DAMRAUER, SCOTT M.
Beschreibung:
<jats:p>The ability to predict rapid kidney function decline (RKFD) in patients with early stages of type 2 diabetic kidney disease (T2DKD) can improve long-term health outcomes through earlier intervention. Our objective was to develop a robust risk score for patients with early stages of T2DKD. We obtained EHR and plasma from patients with T2DKD (Kidney Disease Improving Global Outcomes [KDIGO] G1-G2, A2-A3 or G3a-G3b, A1-A3) from the Mount Sinai BioMe Biobank and the Penn Medicine Biobank and measured soluble tumor necrosis factor receptor (sTNFR1, sTNFR2) and kidney injury molecule-1 (KIM-1) via a validated quantitative electrochemiluminescent assay. A machine learning model was trained using the EHR/biomarker data to predict RKFD, defined as eGFR decline of ≥5 ml/min/year or ≥40% sustained decline in eGFR or kidney failure within 5 years, and performance was compared to a clinical model and KDIGO risk-matrix. A total of 1146 subjects with a mean age of 63, baseline eGFR 54 ml/min, and median UACR 61 mg/g were randomly divided into 60% training (n=686) and 40% validation (n=460) sets. The median follow-up was 4.3 years and 241 (21%) experienced RKFD. The AUC of KidneyIntelX for RFKD was 0.85 (95% CI 0.84-0.86) in training and 0.77 (95% CI 0.76-0.79) in the validation set vs. AUC of 0.67 (95% CI 0.64-0.70) for a clinical model. Using pre-specified cutoffs from the training set, KidneyIntelX stratified 47%, 37% and 16% of the validation set into low-, intermediate- and high-risk strata, with a PPV of 62% (vs. 41% for KDIGO) in the high-risk group (p &lt;0.001), a net reclassification improvement for events of 41% (p &lt;0.05), and a NPV of 91% in the low-risk group (vs. 85% for KDIGO). KidneyIntelX, a machine learning model combining biomarkers and EHR data, improved prediction of RKFD over standard clinical metrics in patients with T2DKD. Future studies will prospectively evaluate KidneyIntelX in clinical decision-making and impact.</jats:p>
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<jats:title>Disclosure</jats:title>
<jats:p>L. Chan: None. G.N. Nadkarni: Advisory Panel; Self; Renalytix AI. Consultant; Self; AstraZeneca, Reata, Renalytix AI. Stock/Shareholder; Self; Renalytix AI. Other Relationship; Self; Renalytix AI. F. Fleming: Board Member; Self; Renalytix AI plc. F.E. Salem: None. B. Murphy: Board Member; Self; RenalytixAI. M.J. Donovan: Consultant; Self; Renalytix AI plc. S. Coca: Advisory Panel; Self; Renalytix AI plc. Consultant; Self; Bayer Healthcare Pharmaceuticals Inc., CHF Solutions, Relypsa, Inc., Takeda Pharmaceutical Company Limited. Stock/Shareholder; Self; Renalytix AI plc. S.M. Damrauer: Consultant; Spouse/Partner; Rhythm Pharmaceuticals. Research Support; Self; CytoVas LLC, Renalytix AI plc.</jats:p>
</jats:sec>
<jats:sec>
<jats:title>Funding</jats:title>
<jats:p>Renalytix AI plc</jats:p>
</jats:sec>