Tokita et al- conducted a prospective data collection study which enrolled 1686 patients in a large metro health system over 16 months to assess the impact of the KidneyIntelX test result on clinical decision-making and outcomes. The median age was 68 years, 52% were female, 26% self-identified as Black, and 94% had hypertension. Determination of a new referral to a specialty consult service (i.e., nephrology, endocrinology, nutrition), any new prescriptions, or modification to any existing prescription medication for ACEi/ARB, SLGT2i, or GLP-1 agonists was based on a 6-month pre-baseline to 6-month post-test assessment. Limitations included patient compliance with filling prescriptions were not available. 53% of all KidneyIntelX high risk patients had a follow-up within a month while standard of care for follow-up is every 12 months.1 The authors found that 53% of all KidneyIntelX high-risk patients had a follow-up visit within 1 month and 57% had action taken (medication change or referral) within 3 months compared to 13% and 35%, respectively, for low-risk individuals. Traditionally, the standard-of-care for follow-up visit frequency is every 12 months. Thus, these results reflect a needed change in management for high-risk patients regarding visit frequency and any action taken. When evaluating new or modified prescriptions for antihypertensive at 6-months, both ACEi and ARBs achieved a greater than 20% change in the high-risk group (ACEi, OR = 1.36; 95% CI: 0.77-2.30; ARBs, OR = 1.65; 95% CI: 1.01-2.63). Early evidence suggests that the introduction of the SGLT2i lowered HbA1c levels most notably in the high-risk category (median 8.2% HbA1c at 6 months pre KidneyIntelX vs 7.45% post-test. In conclusion, the authors found that patients with early-stage DKD who were identified as high-risk via the KidneyIntelX score received earlier follow-up visits, necessary change in medications or specialist referral compared to those who were identified as low- or intermediate-risk patients. Specifically, high-risk patients were more likely to be referred to a nephrologist and by 6 months, these patients had a significant increase in anti-hypertension medications compared to those of intermediate- and low-risk who were more likely to receive standard of care.
Nadkarni et al- The authors conducted a post hoc analysis, assessing the association of KidneyIntelX at baseline with the time-to-event composite end point of 57% decline in eGFR or adjudicated ESKD, HHF, or death. The authors studied 1278 participants in the CANagliflozin Cardiovascular Assessment Study (CANVAS) trial as they hypothesized that KidneyIntelX would also risk stratify patients with prevalent DKD for a clinically relevant kidney outcome, HHF, and all-cause mortality. KidneyIntelX was evaluated in the subgroup of the CANVAS population that met the criteria for prevalent DKD (eGFR ≥30–59.9 ml/min per 1.73 m2 [G3a and G3b] or those with an eGFR ≥60 ml/min per 1.73 m2 with a urine albumin-creatinine ratio [uACR] ≥30 mg/g) at the time of enrollment with existing bio banked blood samples. Measurements were obtained of soluble TNF receptors (sTNFR) 1 and 2, and kidney injury molecule-1 (KIM-1) via proprietary assays, and calculated KidneyIntelX scores using the existing validated algorithm. Among the 1278 CANVAS participants in this post hoc analysis, the mean age was 64 years, 32% were women, the mean baseline eGFR was 65 ml/min per 1.73 m2, the median uACR was 56 mg/g, 498 (40%) had an eGFR< 60 ml/min per 1.73 m2, and 209 (16%) had heart failure at baseline. During a mean of 5.6 years follow-up, 282 (22%) experienced the composite outcome, 41 (3%) developed a 57% decline in eGFR or ESKD, 78 (6%) were hospitalized for heart failure, and 209 (16%) died. The risk for the composite event was reduced by 22%–24% across all risk strata in participants randomized to canagliflozin versus placebo, with absolute risk reductions of 11% in the high-risk stratum, 6% in the intermediate-risk stratum, and 4% in the low-risk stratum (P<0.01 for high versus low risk). Although KidneyIntelX has been validated for an outcome of DKD progression, the results from this subsequent post hoc analysis from CANVAS demonstrated that KidneyIntelX robustly stratified patients for a composite end point consisting of clinically relevant outcomes. In conclusion, the authors found that KidneyIntelX, a composite risk score trained and validated for a kidney-specific outcome, provided risk stratification for a triple composite end point that included not only the kidney-specific outcome of progression, but also clinically relevant outcomes of hospitalizations for heart failure and all-cause mortality, even after adjusting for several other risk factors for these outcomes. 2
Lam et al- The authors measured soluble tumor necrosis factor receptor (TNFR)-1, soluble TNFR-2, and kidney injury molecule 1 on banked samples from 1325 CANagliflozin cardioVascular Assessment Study (CANVAS) trial participants with baseline DKD (estimated glomerular filtration rate [eGFR] 30–59 mL/min/1.73 m2 or urine albumin-to-creatinine ratio [UACR] ≥30 mg/g) and generated KidneyIntelX risk scores at baseline and years 1, 3, and 6. The mean age of the full study population was 64 years, where 32% were female, the mean eGFR was 65 mL/min/1.73 m2, and the median UACR was 56 mg/g. Overall, stratified by the baseline KidneyIntelX score and adjusted for the treatment arm, each 10% reduction in KidneyIntelX risk was associated with a 20% lower risk of experiencing the composite kidney outcome (adjusted odds ratio per 10% reduction of 0.80 [95% CI: 0.77, 0.83]; p < 0.001). In conclusion, the authors found KidneyIntelX successfully risk-stratified a large multinational external cohort for risk of progression of DKD, with larger differences in the eGFR slope for canagliflozin versus placebo in those with higher versus lower baseline KidneyIntelX scores.3 The authors found the effects of the SGLT2i canagliflozin on the chronic eGFR slope were numerically greater in magnitude in participants who scored as high risk by KidneyIntelX at enrollment. Second, canagliflozin decreased KidneyIntelX risk scores over time compared to an increase in the placebo, and this improvement in prognosis was maintained over the follow-up period.
Chauhan et al- studied 1369 patients that were selected from a biobank at an institutional review board–approved biorepository that includes consented access to the patients’ EHR from NYC. The authors selected two cohorts from the biobank: (1) T2D, enrollment eGFR 45–90 ml/min, and ≥3 years of follow-up data (n=871); and (2) APOL1-HR with African ancestry, enrollment eGFR >30 ml/min and ≥3 years of follow-up data (n=498). The authors measured plasma tumor necrosis factor receptors (TNFR) 1 and 2 and kidney injury molecule-1 (KIM-1) and used random forest algorithms to integrate biomarker and EHR data to generate a risk score for a composite outcome: RKFD (eGFR decline of ≥5 ml/min per year), or 40% sustained eGFR decline, or kidney failure. Performance was compared to a validated clinical model and thresholds applied to assess the utility of the prognostic test (KidneyIntelX) to accurately stratify patients into risk categories. The positive predictive values for KidneyIntelX were 62% and 62% versus 46% and 39% for the clinical models (P<0.01) in high-risk (top 15%) stratum for T2D and APOL1-HR, respectively. The negative predictive values for KidneyIntelX were 92% in T2D and 96% for APOL1-HR versus 85% and 93% for the clinical model, respectively (P=0.76 and 0.93, respectively), in low-risk stratum (bottom 50%). 4
Liu et al- completed a database literature search to capture studies evaluating the associations between single or multiple kidney biomarkers and any of the following CKD outcomes: incident CKD, CKD progression, or incident ESKD (e.g., initiation of chronic hemodialysis, peritoneal dialysis requirement, or transplant). 129 studies were included in the meta-analysis for the most frequently studied plasma biomarkers (TNFR1, FGF23, TNFR2, KIM-1, suPAR, and others) and urine biomarkers (KIM-1, NGAL, and others). The authors found that studies of preclinical biomarkers for CKD outcomes have considerable heterogeneity across study cohorts and designs, limiting comparisons of prognostic performance across studies. Plasma TNFR1, FGF23, TNFR2, KIM-1, and suPAR were among the most frequently investigated in the setting of CKD outcomes. 5
Connolly et al- this study was based on Clinical Laboratory Standards Institute (CLSI) guidelines, analytical performance studies of sensitivity, precision, and linearity were performed on three biomarkers assayed in multiplexed format: kidney injury molecule-1 (KIM-1), soluble tumor necrosis factor receptor-1 (sTNFR-1) and soluble tumor necrosis factor receptor-2 (sTNFR-2). Analytical variability across twenty (20) experiments across multiple days, operators, and reagent lots was assessed to examine the impact on the reproducibility of the composite risk score. The sensitivity, reproducibility, and linearity of the assay for the simultaneous measurements of KIM-1, sTNFR-1 and sTNFR-2 in human plasma are integral to assuring robust and consistent results for each analyte. Additionally, demonstrating reproducibility of the risk score and disease risk categorization is key to confirming that inherent variation does not impact reported clinical results of the test. The authors found that the assays for KIM-1, sTNFR-1 and sTNFR-2 demonstrated acceptable sensitivity. The authors found that the set of analytical validation studies demonstrated robust analytical performance across all three biomarkers contributing to the KidneyIntelX risk score, meeting or exceeding specifications established during characterization studies. 6
Chan et al- This study sought to develop/validate a machine-learned, prognostic risk score (KidneyIntelX™) combining electronic health records (EHR) and biomarkers. The authors performed an observational cohort study of patients with prevalent DKD/banked plasma from two EHR-linked biobanks. The study has 1146 patients, the median age was 63 years, 51% were female, the baseline eGFR was 54 ml min-1 [1.73 m]-2, the urine albumin to creatinine ratio (uACR) was 6.9 mg/mmol. The authors found KidneyIntelX scores correctly classified more cases into the appropriate risk strata (NRI event = 55% in the derivation set and 41% in the validation set, p < 0.05; ESM Table 5) than the KDIGO risk strata did. NRI non-event was −8.2% in the derivation set and − 7.9% in the validation set.7
Datar et al- This study was a qualitative analysis based on 30–45-min interviews with 16 primary care physicians (PCP) treating Type 2 diabetic (T2D) patients. The interviews found testing for kidney disease was not consistently top of mind, with 56% reportedly performing kidney function testing in their T2D patients. PCPs most frequently reported using estimated glomerular filtration rate (eGFR) alone to monitor and stage DKD; only 25% PCPs reported testing for albuminuria. The authors felt this study showcased the important unmet needs in T2D DKD testing, staging, and stratification in the PCP setting that limit effective patient care. 8
Datar et al- This study was a prospective web-based survey administered among 401 PCPs in the United States to assess the decision-making impact of an artificial intelligence–enabled prognostic test, KidneyIntelX, in the management of DKD by primary care physicians (PCPs). The survey included hypothetical patient profiles with 6 attributes: albuminuria, eGFR, age, blood pressure (BP), hemoglobin A1c (HbA1c), and KidneyIntelX result. For each patient, PCPs were asked to indicate whether they would prescribe a sodium-glucose cotransporter-2 (SGLT2) inhibitor, increase angiotensin receptor blocker (ARB) dose, and/or refer to a nephrologist. The authors found the relative importance of the top 2 attributes for each decision were HbA1c (52%) and KidneyIntelX result (23%) for prescribing SGLT2 inhibitors, BP (62%) and KidneyIntelX result (13%) for increasing ARB dose, and eGFR (42%) and KidneyIntelX result (27%) for nephrologist referral. The authors concluded KidneyIntelX test had greater relative importance than albuminuria and eGFR to PCPs in making treatment decisions and was second only to eGFR for nephrologist referrals.9
