Derivation and independent validation of kidneyintelX.dkd: A prognostic test for the assessment of diabetic kidney disease progression.

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. RESULTS: In 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. CONCLUSIONS: Using 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.

Healthcare utilization and expenditures associated with hyperkalemia management: a retrospective study of Medicare Advantage patients.

AIMS: This study aimed to estimate the association of patiromer exposure vs. no potassium (K+) binder (NoKb) exposure with healthcare utilization and expenditures among a cohort of Medicare Advantage patients with hyperkalemia (HK). METHODS: Using Optum's Clinformatics Data Mart (study period 2016-2019), the authors assessed propensity score-matched patients (1:1) with a serum K+ concentration ≥5.0 mmol/L and an HK diagnosis that were exposed to patiromer or NoKb on baseline characteristics. The following outcomes were compared: (1) inpatient/emergency department (ED) encounters, (2) inpatient costs greater than or equal to mean Medicare Advantage inpatient cost (i.e. $14,900), and (3) the relative healthcare spending rate. Logistic regression and zero-inflated negative binomial regression were used to analyze the outcomes. RESULTS: The study cohort included 1,539 patiromer and NoKb matched pairs. Baseline characteristics were (patiromer/NoKb): age 74/75 years; female 42/40%; serum K+ 5.6/5.6 mmol/L; eGFR rate 36/36 mL/min/1.73 m2; low-income subsidy 42/41%, chronic kidney disease 96/96%; end-stage renal disease 12/12%; and congestive heart failure 37/36%. A total of 253 matched pairs (506 patients) remained uncensored and were analyzed at 6 months. Inpatient/ED encounters were observed for 25% (patiromer) and 37% (NoKb) (odds ratio [OR] 0.58, 95% confidence interval [CI]: 0.38-0.89). The relative odds of having inpatient costs ≥$14,900 were ∼50% less for patients exposed to patiromer vs. NoKb (OR [95% CI]: 0.47 [0.25-0.89]). The relative total healthcare spending rate (including inpatient, outpatient, ED, and pharmacy costs) was 19% less for patients exposed to patiromer vs. NoKb (spending rate ratio [95% CI]: 0.81 [0.67-0.98]). CONCLUSION AND LIMITATIONS: Among Medicare Advantage patients with HK, patiromer exposure (vs. NoKb) was associated with statistically significant reductions in the proportion with inpatient/ED encounters, inpatient costs ≥$14,900, and lower total healthcare spending. Further research, with larger sample size, is warranted to fully validate these findings.

Payer budget impact of an artificial intelligence in vitro diagnostic to modify diabetic kidney disease progression.

AIM: To evaluate the U.S. payer budget-impact of KidneyIntelX, an artificial intelligence-enabled in vitro diagnostic to predict kidney function decline in Type 2 Diabetic Kidney Disease (T2DKD) patients, stages 1-3b. MATERIALS AND METHODS: We developed an Excel-based model according to International Society of Pharmacoeconomics and Outcomes Research (ISPOR) good practices to assess U.S. payer budget impact associated with the use of the KidneyIntelX test to optimize therapy T2DKD patients compared to standard of care (SOC) (without KidneyIntelX). A hypothetical cohort of 100,000 stages 1-3b T2DKD patients was followed for 5 years. Peer-reviewed publications were used to identify model parameter estimates. KidneyIntelX costs incremental to SOC (without KidneyIntelX) included test cost, additional prescription medication use, specialist referrals and PCP office visits. Patients managed with KidneyIntelX experienced a 20% slowed progression rate compared to SOC (without KidneyIntelX) attributed to slowed DKD progression, delayed or prevented dialysis and transplants, and reduced dialysis crashes. Associated costs were compared to SOC (without KidneyIntelX). Sensitivity analyses were conducted by varying the definition of progression and the DKD progression rate associated with KidneyIntelX testing and related interventions. RESULTS: Projected undiscounted base case 5-year savings for 100,000 patients tested with KidneyIntelX were $1.052 billion, attributed mostly to slowed progression through DKD stages. The breakeven point for the health plan adopting KidneyIntelX is expected to occur prior to year 2 after adoption. Sensitivity analysis based on the assessment of the most conservative definition of progression and a 5% reduction in progression rate attributed to KidneyIntelX, resulted in a projected 5-year savings of $145 million associated with KidneyIntelX. LIMITATIONS AND CONCLUSIONS: Limitations included reliance on literature-based parameter estimates, including effect size of delayed progression supported by the literature. Incorporating KidneyIntelX in contemporary care of early-stage T2DKD patients is projected to result in substantial savings to payers.

Post-Acute Kidney Injury Proteinuria and Subsequent Kidney Disease Progression: The Assessment, Serial Evaluation, and Subsequent Sequelae in Acute Kidney Injury (ASSESS-AKI) Study.

Importance: Among patients who had acute kidney injury (AKI) during hospitalization, there is a need to improve risk prediction such that those at highest risk for subsequent loss of kidney function are identified for appropriate follow-up. Objective: To evaluate the association of post-AKI proteinuria with increased risk of future loss of renal function. Design, Setting, and Participants: The Assessment, Serial Evaluation, and Subsequent Sequelae in Acute Kidney Injury (ASSESS-AKI) Study was a multicenter prospective cohort study including 4 clinical centers in North America included 1538 patients enrolled 3 months after hospital discharge between December 2009 and February 2015. Exposures: Urine albumin-to-creatinine ratio (ACR) quantified 3 months after hospital discharge. Main Outcomes and Measures: Kidney disease progression defined as halving of estimated glomerular filtration rate (eGFR) or end-stage renal disease. Results: Of the 1538 participants, 769 (50%) had AKI durring hospitalization. The baseline study visit took place at a mean (SD) 91 (23) days after discharge. The mean (SD) age was 65 (13) years; the median eGFR was 68 mL/min/1.73 m2; and the median urine ACR was 15 mg/g. Overall, 547 (37%) study participants were women and 195 (13%) were black. After a median follow-up of 4.7 years, 138 (9%) participants had kidney disease progression. Higher post-AKI urine ACR level was associated with increased risk of kidney disease progression (hazard ratio [HR], 1.53 for each doubling; 95% CI, 1.45-1.62), and urine ACR measurement was a strong discriminator for future kidney disease progression (C statistic, 0.82). The performance of urine ACR was stronger in patients who had had AKI than in those who had not (C statistic, 0.70). A comprehensive model of clinical risk factors (eGFR, blood pressure, and demographics) including ACR provided better discrimination for predicting kidney disease progression after hospital discharge among those who had had AKI (C statistic, 0.85) vs those who had not (C statistic, 0.76). In the entire matched cohort, after taking into account urine ACR, eGFR, demographics, and traditional chronic kidney risk factors determined 3 months after discharge, AKI (HR, 1.46; 95% CI, 0.51-4.13 for AKI vs non-AKI) or severity of AKI (HR, 1.54; 95% CI, 0.50-4.72 for AKI stage 1 vs non-AKI; HR, 0.56; 95% CI, 0.07-4.84 for AKI stage 2 vs non-AKI; HR, 2.24; 95% CI, 0.33-15.29 for AKI stage 3 vs non-AKI) was not independently associated with more rapid kidney disease progression. Conclusions and Relevance: Proteinuria level is a valuable risk-stratification tool in the post-AKI period. These results suggest there should be more widespread and routine quantification of proteinuria after hospitalized AKI.

Evaluating biomarkers for prognostic enrichment of clinical trials.

BACKGROUND/AIMS: A potential use of biomarkers is to assist in prognostic enrichment of clinical trials, where only patients at relatively higher risk for an outcome of interest are eligible for the trial. We investigated methods for evaluating biomarkers for prognostic enrichment. METHODS: We identified five key considerations when considering a biomarker and a screening threshold for prognostic enrichment: (1) clinical trial sample size, (2) calendar time to enroll the trial, (3) total patient screening costs and the total per-patient trial costs, (4) generalizability of trial results, and (5) ethical evaluation of trial eligibility criteria. Items (1)-(3) are amenable to quantitative analysis. We developed the Biomarker Prognostic Enrichment Tool for evaluating biomarkers for prognostic enrichment at varying levels of screening stringency. RESULTS: We demonstrate that both modestly prognostic and strongly prognostic biomarkers can improve trial metrics using Biomarker Prognostic Enrichment Tool. Biomarker Prognostic Enrichment Tool is available as a webtool at http://prognosticenrichment.com and as a package for the R statistical computing platform. CONCLUSION: In some clinical settings, even biomarkers with modest prognostic performance can be useful for prognostic enrichment. In addition to the quantitative analysis provided by Biomarker Prognostic Enrichment Tool, investigators must consider the generalizability of trial results and evaluate the ethics of trial eligibility criteria.

Traditional urinary biomarkers in the assessment of hospital-acquired AKI.

Traditional biomarkers, such as urine chemistries and urine microscopic elements, are used in the diagnosis and care of patients with AKI. Urine chemistries, such as fractional excretion of sodium and fractional excretion of urea, are useful for differentiating prerenal AKI from acute tubular necrosis only in select patients. Urine microscopy using a quantitative evaluation of the urine sediment for renal tubular epithelial cells, renal tubular epithelial cell casts, and granular casts has recently been shown to differentiate prerenal AKI from acute tubular necrosis and also provide prognostic information. Urine microscopy has also been noted to compare favorably with new urine biomarkers for diagnosis and prognosis of AKI. Thus, current information on urine diagnostics suggests that urine chemistries have a limited role in differential diagnosis of AKI, whereas urine microscopy and new urine biomarkers may be used together to differentiate prerenal AKI from acute tubular necrosis and predict such outcomes as worsened AKI, acute dialysis, and death.

The assessment, serial evaluation, and subsequent sequelae of acute kidney injury (ASSESS-AKI) study: design and methods.

BACKGROUND: The incidence of acute kidney injury (AKI) has been increasing over time and is associated with a high risk of short-term death. Previous studies on hospital-acquired AKI have important methodological limitations, especially their retrospective study designs and limited ability to control for potential confounding factors. METHODS: The Assessment, Serial Evaluation, and Subsequent Sequelae of Acute Kidney Injury (ASSESS-AKI) Study was established to examine how a hospitalized episode of AKI independently affects the risk of chronic kidney disease development and progression, cardiovascular events, death, and other important patient-centered outcomes. This prospective study will enroll a cohort of 1100 adult participants with a broad range of AKI and matched hospitalized participants without AKI at three Clinical Research Centers, as well as 100 children undergoing cardiac surgery at three Clinical Research Centers. Participants will be followed for up to four years, and will undergo serial evaluation during the index hospitalization, at three months post-hospitalization, and at annual clinic visits, with telephone interviews occurring during the intervening six-month intervals. Biospecimens will be collected at each visit, along with information on lifestyle behaviors, quality of life and functional status, cognitive function, receipt of therapies, interim renal and cardiovascular events, electrocardiography and urinalysis. CONCLUSIONS: ASSESS-AKI will characterize the short-term and long-term natural history of AKI, evaluate the incremental utility of novel blood and urine biomarkers to refine the diagnosis and prognosis of AKI, and identify a subset of high-risk patients who could be targeted for future clinical trials to improve outcomes after AKI.