Rule-Mining for the Early Prediction of Chronic Kidney Disease Based on Metabolomics and Multi-Source Data

<div><p>¹H Nuclear Magnetic Resonance (NMR)-based metabolic profiling is very promising for the diagnostic of the stages of chronic kidney disease (CKD). Because of the high dimension of NMR spectra datasets and the complex mixture of metabolites in biological samples, the identification of discriminant biomarkers of a disease is challenging. None of the widely used chemometric methods in NMR metabolomics performs a local exhaustive exploration of the data. We developed a descriptive and easily understandable approach that searches for discriminant local phenomena using an original exhaustive rule-mining algorithm in order to predict two groups of patients: 1) patients having low to mild CKD stages with no renal failure and 2) patients having moderate to established CKD stages with renal failure. Our predictive algorithm explores the m-dimensional variable space to capture the local overdensities of the two groups of patients under the form of easily interpretable rules. Afterwards, a L2-penalized logistic regression on the discriminant rules was used to build predictive models of the CKD stages. We explored a complex multi-source dataset that included the clinical, demographic, clinical chemistry, renal pathology and urine metabolomic data of a cohort of 110 patients. Given this multi-source dataset and the complex nature of metabolomic data, we analyzed 1- and 2-dimensional rules in order to integrate the information carried by the interactions between the variables. The results indicated that our local algorithm is a valuable analytical method for the precise characterization of multivariate CKD stage profiles and as efficient as the classical global model using chi2 variable section with an approximately 70% of good classification level. The resulting predictive models predominantly identify urinary metabolites (such as 3-hydroxyisovalerate, carnitine, citrate, dimethylsulfone, creatinine and N-methylnicotinamide) as relevant variables indicating that CKD significantly affects the urinary metabolome. In addition, the simple knowledge of the concentration of urinary metabolites classifies the CKD stage of the patients correctly.</p></div

Variables’ frequency over the 100 multi-source local 1D models.

<p>Each horizontal segment corresponds to a 1D rule characterized by its variable condition: the variable’s name and the set of covered values or bins (n.b., ranges of bucket’s bins could be interpreted as relative concentrations). The color scale reflects the frequency of the variables’ values covered by the rules. As the two non-bucket variables were discretized according to the clinical relevance for the urinary retinol binding protein concentration (URBP) and the variable distribution for the Age, we indicated with a red line the corresponding upper bin of these two variables. The more robust the rule, the darker it will be. (A) shows the rules corresponding to the subgroup of patients with an eGFR < 60 ml/min/1.73m² and conversely, (B) shows the rules corresponding to the subgroup of patients with an eGFR ≥ 60 ml/min/1.73m².</p

Buckets’ frequency over the 100 metabolomic local 1D models.

<p>Each horizontal segment corresponds to a 1D rule characterized by its bucket’s name, covered values ranges (i.e., buckets bins which could be interpreted as relative concentration) and frequency (color scale). The more robust the rule is, the darker it will be. (A) shows the rules corresponding to the subgroup of patients with an eGFR <60 ml/min/1.73m² and conversely, (B) shows the rules corresponding to the subgroup of patients with an eGFR ≥ 60 ml/min/1.73m².</p

Evaluation of the global, local 1D and local 1&2D models.

<p>Evaluation of the global, local 1D and local 1&2D models.</p

Frequency of the cluster of buckets present in at least 50% of the local 1&2D models.

<p>This figure shows the frequency heatmap of the interactions (color scale) of each bucket pair of the cluster over the 100 local 1&2D models. The more robust the interaction is, the darker it will be. The interactions corresponding to the subgroup of patients with an eGFR < 60 ml/min/1.73m² are displayed in the lower triangle and conversely, the interactions corresponding to the subgroup of patients with an eGFR ≥ 60 ml/min/1.73m² are displayed in the upper triangle. The limit of the two triangles is represented by the red dashed-line.</p

Frequency of the bucket interaction over the 100 multi-source local 1&2D models.

<p>This figure shows the frequency heatmap (color scale) of the interactions of each multi-source variable pair over the 100 local 1&2D models. The more robust the interaction, the darker it will be. The interactions corresponding to the subgroup of patients with an eGFR < 60 ml/min/1.73m² are displayed in the lower triangle and conversely, the interactions corresponding to the subgroup of patients with an eGFR ≥ 60 ml/min/1.73m² are displayed in the upper triangle. The limit of the two triangles is represented by the red dashed-line.</p

Baseline characteristics of the study cohort (n = 110).

<p>Baseline characteristics of the study cohort (n = 110).</p

Buckets’ frequency over the 100 metabolomic global models.

<p>This figure shows the frequency (y axis) of each bucket selected over the 100 global models built on the metabolomic data (x axis). The red horizontal line corresponds to a frequency threshold of 50. Buckets with a frequency higher than this threshold are labeled with their corresponding metabolites.</p

Rule-mining algorithm.

<p>(A) shows the representation of a rule in the multi-dimensional variable space (left), projected on each variable (middle) and as binary variable spanning over all the subjects (right). (B) shows the selection of the candidate rules proceeding in the following two steps: thresholding on two rule quality measures (left) and a minimization procedure to remove redundant rules (right).</p

Cost-effectiveness analysis of elbasvir-grazoprevir regimen for treating hepatitis C virus genotype 1 infection in stage 4-5 chronic kidney disease patients in France

<div><p>Objective</p><p>To assess the cost-effectiveness of the elbasvir/grazoprevir (EBR/GZR) regimen in patients with genotype 1 chronic hepatitis C virus (HCV) infection with severe and end-stage renal disease compared to no treatment.</p><p>Design</p><p>This study uses a health economic model to estimate the cost-effectiveness of treating previously untreated and treatment experienced chronic hepatitis C patients who have severe and end stage renal disease with the elbasvir-grazoprevir regimen versus no treatment in the French context. The lifetime homogeneous markovian model comprises of forty combined health states including hepatitis C virus and chronic kidney disease. The model parameters were from a multicentre randomized controlled trial, ANRS CO22 HEPATHER French cohort and literature. 1000 Monte Carlo simulations of patient health states for each treatment strategy are used for probabilistic sensitivity analysis and 95% confidence intervals calculations. The results were expressed in cost per quality-adjusted life year (QALY) gained.</p><p>Patients</p><p>The mean age of patients in the HEPATHER French cohort was 59.6 years and 56% of them were men. 22.3% of patients had a F0 fibrosis stage (no fibrosis), 24.1% a F1 stage (portal fibrosis without septa), 7.1% a F2 stage (portal fibrosis with few septa), 21.4% a F3 stage (numerous septa without fibrosis) and 25% a F4 fibrosis stage (compensated cirrhosis). Among these HCV genotype 1 patients, 30% had severe renal impairment stage 4, 33% had a severe renal insufficiency stage 5 and 37% had terminal severe renal impairment stage 5 treated by dialysis.</p><p>Intervention</p><p>Fixed-dose combination of direct-acting antiviral agents elbasvir and grazoprevir compared to no-treatment.</p><p>Results</p><p>EBR/GZR increased the number of life years (6.3 years) compared to no treatment (5.1 years) on a lifetime horizon. The total number of QALYs was higher for the new treatment because of better utility on health conditions (6.2 versus 3.7 QALYs). The incremental cost-utility ratio (ICUR) was of €15,212 per QALY gained for the base case analysis.</p><p>Conclusions</p><p>This cost-utility model is an innovative approach that simultaneously looks at the disease evolution of chronic hepatitis C and chronic kidney disease. EBR/GZR without interferon and ribavirin, produced the greatest benefit in terms of life expectancy and quality-adjusted life years (QALY) in treatment-naïve or experienced patients with chronic hepatitis C genotype 1 and stage 4–5 chronic kidney disease including dialysis patients. Based on shape of the acceptability curve, EBR/GZR can be considered cost-effective at a willingness to pay of €20,000 /QALY for patients with renal insufficiency with severe and end-stage renal disease compared to no treatment.</p></div