Diabetic Kidney Disease (DKD) is a severe complication of diabetes that leads to kidney failure in about 5-15% of cases. The kidneyIntelX.dkd is an FDA approved prognostic blood test for assessing the risk of DKD progression, enabling a more personalized approach to treatment and care for those individuals based on risk. The test uses a non-linear random forest algorithm which combines novel prognostic biomarkers Tumor Necrosis Factor Receptors 1 and 2 (TNFR1, TNFR-2) and Kidney Injury Molecule (KIM-1) with readily available clinical laboratory values of Blood Urea Nitrogen (BUN), Hemoglobin A1c (HbA1c) and urinary Albumin Creatinine ratio (UACR). The test reports a low, moderate, or high risk for either a sustained 40% decline in eGFR or kidney failure over 5 years. Measurement of novel biomarkers is performed using an analytically validated assay on received test samples with results algorithmically combined with the aforementioned standard laboratory values obtained from the patient’s health record within the past 12 months. In this study, we sought to characterize the risk and impact of imprecision in the measurement of the inputs, including those taken from patient records, on the accuracy of final kidneyintelX.dkd result. Simulation study assessed the cumulative measurement imprecision of six (6) inputs on the random forest algorithm outputs. The kidneyintelX.dkd test provides a predicted probability of the kidney event and categorizes patients as low, moderate and high based on predetermined cut-offs. Imprecision for KIM-1, TNFR-1 and TNFR-2 was established from precision studies conducted in accordance with CLSI and FDA guidelines. For HbA1c, BUN and UACR, the %CV studied was based on data derived from College of American Pathologists (CAP) and American Proficiency Institute (API) survey data, published literature on observed measurement variability and data published in decision summaries on the FDA website on cleared diagnostic devices and methods routinely used in clinical practice. Subjects (n=675) included in the clinical validation study submitted to FDA and included in product literature were assessed in these analyses. Assuming a normal distribution of measurement variability, input feature levels were randomly varied (n=100 per subject) across the range of X-Y to X+Y where X was the actual input feature level reported for subjects and Y was the value after applying the expected range of variation. The mean intra-individual %CV of predicted probabilities from the kidneyintelX.dkd test in 100 simulation experiments in each subject was 3.3%. In total, 65,700 simulation experiments were performed with just 3.9% resulting in a recategorization of the kidneyintelX.dkd risk level result (e.g. from high to moderate risk). Across the three risk categories, the mean %CV and the % of experiments resulting in risk level reclassification were as follows: Low: 1.2% and 2.3%, Moderate: 6.3% and 5.7%, and High 4.6% and 6.2%. The cumulative level of variability in the kidneyintelX.dkd test result is significantly less than that observed in the individual inputs, thereby providing a comprehensive, accurate, reliable, and robust assessment of risk for progressive decline in kidney function or kidney failure.

Surgical pathology and the practice of general medicine are often described as having legitimate components of art as well as science. That perception is perhaps less relevant in the current information age with the adoption of genomic medicine, in large part due to the growth in disease-specific causal knowledge. As a surgical pathologist, gestalt and intuition are words you may not use to describe how you arrive at a specific patient's diagnosis, but it is certainly understood that with experience, in particular with a deep immersion in a subspecialty, comes an understanding of histopathology that cannot easily be transmitted or formalized. This is not in any way to delegitimize pathology or relegate it to the unscientific, rather to illustrate a strength of the human brain. For well over 100 years using a microscope, pathologists have been applying this strength to group and separate disease entities into diagnoses and their subtypes. The brain seems to be good at pattern recognition, filtering noise, and dealing with variability, and from a histopathology perspective, this has complemented the medical need quite well for over a century. Although the concept of tumor differentiation and its relationship to clinical outcome dates back to the early days of histopathology until fairly recently, limited treatment options diminished the necessity for more precise and standardized grading. The following chapter explores how the growing fields of computational pathology, machine learning, and deep learning are coming together to address the need for more precise and standardized grading. Starting with tissue preparation and image acquisition, the discussion roughly follows the steps involved in developing an image analysis–based multivariate grading system with attention drawn in each subsection to inherent obstacles, published state-of-the-art techniques, and the authors' own experience with solutions.

Current clinical guidelines recommend gene expression profiling to guide treatment in early-stage breast cancer. PreciseBreast (PDxBR) is a digital prognostic tool that integrates artificial intelligence (AI)-derived features from hematoxylin and eosin (H E) slides with clinicopathologic data to predict recurrence risk. This study externally validated PDxBR in an independent cohort and compared its performance to other risk models. We retrospectively analyzed PDxBR in a cohort of 739 patients with early-stage hormone receptor-positive, HER2-negative breast cancer (median follow-up of 8.8 years). For each case, one H E-stained slide was digitized and analyzed to generate recurrence risk scores using the full PDxBR model, as well as image-only and clinical-only variants. A subset of patients who underwent MammaPrint testing was also evaluated. Model performance was assessed by AUC/C-index, hazard ratios, sensitivity, specificity, and negative and positive predictive values (NPV and PPV, respectively). PDxBR showed prognostic accuracy in this external cohort (AUC/C-index 0.71, 95

PDxBr is a digital test, which generates an artificial -intelligent tumor grade and phenotype utilizing morphometric features derived from H and E images of invasive breast cancer to predict outcome. Analytical validation of the image analysis platform including robust accuracy of cell type identification and tissue architecture composition, combined with test reproducibility and reliability, is a critical requirement for approval and clinical adoption. Background: PreciseDx Breast (PDxBr) is a digital test that predicts early -stage breast cancer recurrence within 6years of diagnosis. Materials and Methods: Using hematoxylin and eosin -stained whole slide images of invasive breast cancer (IBC) and artificial intelligence -enabled morphology feature array, microanatomic features are generated. Morphometr ic attr ibutes in combination with patient's age, tumor size, stage, and lymph node status predict disease free survival using a proprietary algorithm. Here, analytical validation of the automated annotation process and extracted histologic digital features of the PDxBr test, including impact of methodologic variability on the composite risk score is presented. Studies of precision, repeatability, reproducibility and interference were performed on morphology feature array -derived features. The final risk score was assessed over 20 -days with 2 -operators, 2-runs/day, and 2 -replicates across 8 -patients, allowing for calculation of within -run repeatability, between -run and within -laboratory reproducibility. Results: Analytical validation of features derived from whole slide images demonstrated a high degree of precision for tumor segmentation (0.98, 0.98), lymphocyte detection (0.91, 0.93), and mitotic figures (0.85, 0.84). Correlation of variation of the assay risk score for both reproducibility and repeatability were less than 2%, and interference from variation in hematoxylin and eosin staining or tumor thickness was not observed demonstrating assay robustness across standard histopathology preparations. Conclusion: In summary, the analytical validation of the digital IBC risk assessment test demonstrated a strong performance across all features in the model and complimented the clinical validation of the assay previously shown to accurately predict recurrence within 6 -years in early -stage invasive breast cancer patients.

Introduction/Objective: The KidneyIntelX is a multiplex, bioprognostic, immunoassay consisting of 3 plasma biomarkers and clinical variables that uses machine learning to predict a patient’s risk for a progressive decline in kidney function over 5 years. We report the 1-year pre- and post-test clinical impact on care management, eGFR slope, and A1C along with engagement of population health clinical pharmacists and patient coordinators to promote a program of sustainable kidney, metabolic, and cardiac health. Methods: The KidneyIntelX in vitro prognostic test was previously validated for patients with type 2 diabetes and diabetic kidney disease (DKD) to predict kidney function decline within 5 years was introduced into the RWE study (NCT04802395) across the Health System as part of a population health chronic disease management program from [November 2020 to April 2023]. Pre- and post-test patients with a minimum of 12 months of follow-up post KidneyIntelX were assessed across all aspects of the program. Results: A total of 5348 patients with DKD had a KidneyIntelX assay. The median age was 68 years old, 52% were female, 27% self-identified as Black, and 89% had hypertension. The median baseline eGFR was 62 ml/min/1.73 m 2 , urine albumin-creatinine ratio was 54 mg/g, and A1C was 7.3%. The KidneyIntelX risk level was low in 49%, intermediate in 40%, and high in 11% of cases. New prescriptions for SGLT2i, GLP-1 RA, or referral to a specialist were noted in 19%, 33%, and 43% among low-, intermediate-, and high-risk patients, respectively. The median A1C decreased from 8.2% pre-test to 7.5% post-test in the high-risk group ( P < .001). UACR levels in the intermediate-risk patients with albuminuria were reduced by 20%, and in a subgroup treated with new scripts for SGLT2i, UACR levels were lowered by approximately 50%. The median eGFR slope improved from −7.08 ml/min/1.73 m 2 /year to −4.27 ml/min/1.73 m 2 /year in high-risk patients ( P = .0003), −2.65 to −1.04 in intermediate risk, and −3.26 ml/min/1.73 m 2 /year to +0.45 ml/min/1.73 m 2 /year in patients with low-risk ( P < .001). Conclusions: Deployment and risk stratification by KidneyIntelX was associated with an escalation in action taken to optimize cardio-kidney-metabolic health including medications and specialist referrals. Glycemic control and kidney function trajectories improved post-KidneyIntelX testing, with the greatest improvements observed in those scored as high-risk.

1569 Background: Traditional invasive breast cancer (IBC) grading, although useful, remains limited due to diagnostic subjectivity and absence of phenotypic diversity including the recently observed importance of tumor epithelial – stromal interactions and lymphocyte content-distribution. We developed and validated a clinical grade digital test (PreciseDx Breast, PDxBr) which combines image-derived Artificial Intelligent (AI)-grading features and clinical data (i.e. age, stage, tumor size, LN status) to predict recurrence in early-stage IBC and sought to understand performance in a MammaPrint cohort with outcome data. Methods: A MammaPrint cohort with median 6-year follow-up was identified at the Laboratory of Pathology, Dordrecht, the Netherlands (NTH). H&E stained images (digitized at Philips, Eindhoven, NTH) with clinical data (from the pathology and Dutch cancer registries) including demographics, pathology results, MammaPrint risk classification, treatment type and recurrence events were obtained. Performance of the PDxBr validated model (AI-grade + clinical) on the MammaPrint cohort was evaluated using the AUC/concordance index, along with NPV, PPV, Hazards ratio (HR), sensitivity, and specificity. Results: 250 patients, median age 57 years, majority stage 1-IIIa, 100% HR+ve, Her2-ve, 84% LN-ve, 67% grade 2 and 66% MammaPrint low risk. There were 15 events (6%: 7 deaths, 3 second primaries,4 metastases and 1 local regional). PDxBR model classified 134 patients as high risk and 116 as low with 10 of 15 (67%) events identified as high risk. The NPV for PDxBr was 96% with a HR 1.63, Se 70%, Sp 46% vs. MammaPrint with an NPV of 94%, HR of 0.96, Se 40%, Sp 66%; identifying only 5 of the 15 events (33%) as high risk while missing 10. Of note, the AI- grade/imaging model (without clinical features) yielded an NPV of 95% with a HR of 1.46, Se 70%, Sp 42% and correctly identified 10 of 15 events. Conclusions: The results from this observational study suggest that triaging patients with the PDxBr test could potentially be adjunctive to the management decision process of patients with early-stage IBC including the use and subsequent interpretation of genomic tests such as MammaPrint. Additional studies are underway to confirm these initial findings.

The ADA DKD Prevention Model was developed to define key concepts for DKD prevention and treatment. We sought to describe and quantify specific outcome measures outlined in the ADA model following deployment of a system-wide interdisciplinary approach that centered around KidneyIntelX, a prognostic test that utilizes combines prognostic biomarkers and clinical variables to generate patient-specific risk assessment for kidney function decline. We assessed the changes in various process measures, including clinical assessments, referrals, and changes in short- and intermediate-term metrics. 6258 patients were tested with KidneyIntelX across the Mount Sinai Health System between 2021 and 2023. The median age was 67 years, with 51% females and 25% identified as African American. Baseline eGFR was 65 ml/min/1.73 m2, UACR 56 mg/g, and HbA1c 7.2%. Among tested patients scoring intermediate (40%) or high (10%) risk, UACR testing increased by 25%, NSAID prescriptions decreased by 11%, and use of SGLT2i and GLP1-RAs increased by 14-37%. Hypertension control and control of HbA1c improved by 12 to 27%, and albuminuria decreased by 19%. In conclusion, risk assessment in patients with stages G1-3b DKD resulted in improvements in several process measures including UACR testing, guideline-recommended therapies, less nephrotoxin usage, and better control of hypertension, glycemia, and albuminuria. Disclosure J. Tokita: Research Support; Renalytix. T. McNicholas: None. G.N. Nadkarni: Stock/Shareholder; Renalytix. S. Coca: Stock/Shareholder; Renalytix. Consultant; Bayer Inc., Takeda Pharmaceutical Company Limited, 3ive Labs, Vifor Pharma Management Ltd., Boehringer-Ingelheim. M.J. Donovan: Employee; Renalytix. Funding Renalytix

Background: Invasive Breast cancer (IBC) has surpassed lung cancer as the leading diagnosed cancer worldwide, representing some 15.5% of all cancer deaths. There remains an outstanding need to improve the current standard of care at diagnosis including a reproducible and quantitative assessment of histologic grade and biological phenotype. We previously validated a digital laboratory developed test to predict breast cancer recurrence (BCR) using the surgical resection specimen. We now present the same approach for the diagnostic biopsy, providing a risk of recurrence earlier in the treatment planning and decision process.

AimsUse of gene expression signatures to predict adjuvant chemotherapy benefit in women with early-stage breast cancer is increasing. However, high cost, limited access, and eligibility for these tests results in the adoption of less precise assessment approaches. This study evaluates the cost impact of PreciseDx Breast (PDxBr), an AI-augmented histopathology platform that assesses the 6-year risk of recurrence in early-stage invasive breast cancer patients to help improve informed use of adjuvant chemotherapy.Materials and methodsA decision-tree Markov model was developed to compare the costs of treatment guided by standard of care (SOC) risk assessment (i.e. clinical diagnostic workup with or without Oncotype DX) versus PDxBr with SOC in a hypothetical cohort of U.S. women with early-stage invasive breast cancer. A commercial payer perspective compares costs of testing, adjuvant therapy, recurrence, adverse events, surveillance, and end-of-life care.ResultsPDxBr use in prognostic evaluation resulted in savings of $4 million (M) in year one compared to current SOC in 1 M females members. Over 6-years, savings increased to $12.5 M. The per-treated patient costs in year one amounted to $19.5 thousand (K) for SOC and $16.9K for PDxBr.LimitationsFor simplicity, recurrence was not specified. We performed scenario analyses to account for variations in rates for local, regional, and distant recurrence. Second, a recurrent patient incurs the total cost of treated recurrence in the first year and goes back to remission or death. Third, CDK4/6i treatment is only incorporated in the recurrence costs but not in the first line of treatment for early-stage breast cancer due to limited data.ConclusionsSensitivity analyses demonstrated robust overall savings to changes in all variables in the model. The use of PDxBr to assess breast cancer recurrence risk has the potential to fill gaps in care and reduce costs when gene expression signatures are not available.

Background: Genomic testing such as OncotypeDx remains an important component of the treatmentdecision process for many breast cancer (BC) patients. Evidence from Sparano et al. JCO 2021;39:557-564 demonstrated the importance of combining clinical features such as tumor grade, size and age with the 21-gene recurrence score (i.e., RSClin). Given the challenges associated with reliability of BC grading as a prognostic feature, we sought to develop a broadly accessible AI-digital test (PDxBr) which included a BC AI-grade combined with clinical features (i.e., age, tumor size, stage and lymph node status) to predict recurrence risk in an Oncotype categorized cohort. Methods: We evaluated performance of the PDxBr test along with the AI-grade, clinical feature (i.e., age, size, tumor stage and LN status) and histology grade models in a subgroup analysis from a retrospective longitudinal clinical development validation study utilizing samples from breast cancer (BC) patients in the Mount Sinai Health Care System (NYC, NY) from 2004-2016. Eligible participants were ≥23 years old with infiltrating ductal or mixed ductal and lobular carcinoma of the breast (IDC) and all with an Oncotype RS, and a median 6-year follow-up. All participants had H&E slides or paraffin blocks (for slide generation) from the resected BC specimen. H&E slides were digitized (40X magnification) using a Philips UltraFast Digital slide scanner (Netherlands) and a single whole slide image (WSI) was selected for model development. The AUC/C-index was used to demonstrate performance. Results: 599 patients with Oncotype RS results were interrogated: 57% white, mean age 57, mean tumor size 1.3cm, 100% T1/2 and HR+ve, 55% grade 2, 26% grade 3 and 18% grade 1; 95% pN0 with 36 events (6%). 21 (60%) of events were local-regional recurrences. Of note, there were 55% histologic Grade 2 cases in this population. Combining Oncotype RS with assorted sub-models including histologic grade, clinical features, AI-grade, or PDxBr model in a SVRc analysis demonstrated incremental improvement in the C-index for predicting BC recurrence (Table 1). Conclusionss: Both PDxBr test and AI-grade when combined with Oncotype were superior to Oncotype alone, or Oncotype with grade or clinical features suggesting that the incorporation of an improved BC grade with Oncotype RS enhances overall risk discrimination. PDxBr is the first digital BC test combining automated AI-BC prognostic grade with clinical-pathologic features to predict risk of early-stage BC recurrence. Additional validation studies are underway to confirm these results. AUC comparison of Oncotype alone and then combined with histology grade, clinical data (age, stage, tumor size and LN pos), AI-grade, and the PDxBr model. Citation Format: Gerardo Fernandez, Marcel Prastawa, Richard Scott, Bahram Marami, Nina Shpalensky, Abishek Madduri, Krystal Cascetta, Mary Sawyer, Monica S. Chan, Giovanni Koll, Rebecca E. DeAngel, Alexander Shtabsky, Aaron Feliz, Thomas Hansen, Brandon Veremis, Carlos Cordon-Cardo, Jack Zeineh, Michael Donovan. A novel AI-digital Test with an automated approach for grading and phenotyping breast cancer enriches recurrence score risk prediction in an Oncotype evaluated cohort. [abstract]. In: Proceedings of the 2022 San Antonio Breast Cancer Symposium; 2022 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2023;83(5 Suppl):Abstract nr P3-05-21.

Supplemental Figure 3. Selected BTM geneset enrichments (FDR < 0,25) of SD (patient 002) and PD patients (004 and 008) at tumor site. Gene names of enriched genesets are publicly available online in supplemental data for original paper23.

Supplemental Table 1. Quantitative Immunohistochemistry (IHC). In the patient 002 with clinical benefit (stable disease, right two columns), IHC analysis of tumor showed increased CD4 (60x), CD8 (10x), PD1 (20x) and PDL1 (3x). In patients with disease (001, 004, 005, 008), quantitative IHC showed unchanged or decreased levels of CD4, CD8, PD-1, and PDL-1 over treatment periods. Individual antigens were quantified using the CRI Inform software (Perkin Elmer) which applies user-directed antigen thresholds to generate percentages normalized to the total tumor area.

Background Patient outcomes were assessed based on a pre-biopsy ExoDx Prostate (EPI) score at 2.5 years of the 5-year follow-up of ongoing prostate biopsy Decision Impact Trial of the ExoDx Prostate (IntelliScore). Methods Prospective, blinded, randomized, multisite clinical utility study was conducted from June 2017 to May 2018 (NCT03235687). Urine samples were collected from 1049 men (≥50 years old) with a PSA 2–10 ng/mL being considered for a prostate biopsy. Patients were randomized to EPI vs. standard of care (SOC). All had an EPI test, but only EPI arm received results during biopsy decision process. Clinical outcomes, time to biopsy and pathology were assessed among low (<15.6) or high (≥15.6) EPI scores. Results At 2.5 years, 833 patients had follow-up data. In the EPI arm, biopsy rates remained lower for low-risk EPI scores than high-risk EPI scores (44.6% vs 79.0%, p < 0.001), whereas biopsy rates were identical in SOC arm regardless of EPI score (59.6% vs 58.8%, p = 0.99). Also in the EPI arm, the average time from EPI testing to first biopsy was longer for low-risk EPI scores compared to high-risk EPI scores (216 vs. 69 days; p < 0.001). Similarly, the time to first biopsy was longer with EPI low-risk scores in EPI arm compared to EPI low-risk scores in SOC arm (216 vs 80 days; p < 0.001). At 2.5 years, patients with low-risk EPI scores from both arms had less HGPC than high-risk EPI score patients (7.9% vs 26.8%, p < 0.001) and the EPI arm found 21.8% more HGPC than the SOC arm. Conclusions This follow-up analysis captures subsequent biopsy outcomes and demonstrates that men receiving EPI low-risk scores (<15.6) significantly defer the time to first biopsy and remain at a very low pathologic risk by 2.5-years after the initial study. The EPI test risk stratification identified low-risk patients that were not found with the SOC.

Supplemental Figure 2. Hierarchical clustering of total gene expression of PBMC and tumor RNA samples. Only genes commonly expressed in PBMC and tumor samples are shown. Of note, up-regulation of specific genes at tumor site may come from tumor rather immune cells. On x-axis, each column represents patient at specific treatment time point, as numbered below. For example, 002- C1W1 is patient 002 at time point Cycle 1 week1. 1 - 002_Screen 12 - 004_Screen 2 - 002_C1W1 13 - 004_C1W2 3 - 002_C1W3 14 - 008_Screen 4 - 002_C1W7 15 - 008_C1W1 5 - 002_C1W10 16 - 008_C1W3 6 - 002_C2W11 17 - 008_C1W7 7 - 002_C2W12 18 - 008_C1W10 8 - 002_C2W13 9 - 002_C2W17 10 - 002_C2W20 11 - 002_C2W26

PDF - 475K, Radiological-progression analysis according to gene expression levels of markers differentially expressed in treated vs non-treated patients.

Aims To develop and validate an updated version of KidneyIntelX (kidneyintelX.dkd) to stratify patients for risk of progression of diabetic kidney disease (DKD) stages 1 to 3, to simplify the test for clinical adoption and support an application to the US Food and Drug Administration regulatory pathway.Methods We used plasma biomarkers and clinical data from the Penn Medicine Biobank (PMBB) for training, and independent cohorts (BioMe and CANVAS) for validation. The primary outcome was progressive decline in kidney function (PDKF), defined by a =40% sustained decline in estimated glomerular filtration rate or end-stage kidney disease within 5 years of follow-up.ResultsIn 573 PMBB participants with DKD, 15.4% experienced PDKF over a median of 3.7 years. We trained a random forest model using biomarkers and clinical variables. Among 657 BioMe participants and 1197 CANVAS participants, 11.7% and 7.5%, respectively, experienced PDKF. Based on training cut-offs, 57%, 35% and 8% of BioMe participants, and 56%, 38% and 6% of CANVAS participants were classified as having low-, moderate- and high-risk levels, respectively. The cumulative incidence at these risk levels was 5.9%, 21.2% and 66.9% in BioMe and 6.7%, 13.1% and 59.6% in CANVAS. After clinical risk factor adjustment, the adjusted hazard ratios were 7.7 (95% confidence interval [CI] 3.0-19.6) and 3.7 (95% CI 2.0-6.8) in BioMe, and 5.4 (95% CI 2.5-11.9) and 2.3 (95% CI 1.4-3.9) in CANVAS, for high- versus low-risk and moderate- versus low-risk levels, respectively.ConclusionsUsing two independent cohorts and a clinical trial population, we validated an updated KidneyIntelX test (named kidneyintelX.dkd), which significantly enhanced risk stratification in patients with DKD for PDKF, independently from known risk factors for progression.

PDF - 72K, Genes expression values by qRT-PCR in treated patients vs non-treated patients; TLDA: TaqMan Low Density Arrays; FDR: False Discovery Rate.

We have previously shown that plasma TNFR1, TNFR2, and KIM-1 combined with clinical variables provides effective risk stratification for kidney outcomes in individuals with diabetic kidney disease (DKD) stages 1-3. With the recommended use of a race-free eGFR and a refinement of our progression endpoint, we sought to develop and independently validate a version 2.0 of KidneyIntelX assay in a previously untested, independent, contemporary cohort of subjects from BioMe Biobank (Mount Sinai Health System, NYC, NY). The primary outcome was a composite of ≥40% sustained decline in eGFR or kidney failure (sustained eGFR<15 ml/min/1.73 m2) within 5 years from enrollment. In a training cohort of 573 patients with DKD from the Penn Medicine Biobank (PMBB), 15.4% experienced the progressive decline in kidney function over a median of 3.1 years. The following features were selected in a training model utilizing optimized random forests to predict the kidney outcome: TNFR1, TNFR2, KIM-1, baseline UACR, HbA1c, and blood urea nitrogen. The external validation cohort (BioMe, n=657) had an 11.7% event rate over a median of 3.8 years. Based on risk categorization cutoffs derived from training, 57%, 35% and 8.3% of participants were classified as low, intermediate and high-risk, respectively. The cumulative event incidence probability in the low, intermediate, and high-risk groups were 5.9% (95% CI: 3.6-9.4%), 21.2% (95% CI: 15.6-28.3%) and 66.9% (95% CI:, 49.3-83.5%). The unadjusted HRs for high vs. low and intermediate vs. low were 18.4 (95% CI: 9.4 -34.6) and 4.2 (95% CI: 2.4-7.4), respectively. After adjustment for age, sex, race, baseline eGFR, uACR, SBP, and HbA1c, the adjusted HRs were 7.7 (95% CI: 3.0-19.6) and 3.7 (95% CI: 2.0-6.8), respectively. In summary, we developed and independently validated a version 2.0 KidneyIntelX with previously established biomarkers combined with selected clinical variables and demonstrated robust prediction of progressive decline in kidney function, independent of common risk factors. Disclosure G.N.Nadkarni: Advisory Panel; Renalytix, Consultant; Renalytix, Other Relationship; Renalytix, Speaker's Bureau; Daiichi Sankyo, GlaxoSmithKline plc., Menarini Group, Stock/Shareholder; Renalytix. F.Fleming: Employee; Renalytix. M.J.Donovan: Employee; Renalytix. S.Coca: Advisory Panel; Renalytix, Bayer Inc., Boehringer Ingelheim International GmbH, Consultant; Renalytix, Nuwellis, 3ive Labs, Reprieve Cardiovascular, Vifor Pharma Management Ltd., Stock/Shareholder; Renalytix. Funding Renalytix