Screening Urinalysis in Detection of Chronic Kidney Disease in Children

Symptoms for early diagnosis of chronic kidney disease in children - a machine learning-based score

The objective of this study was to reveal the signs and symptoms for the classification of pediatric patients at risk of CKD using decision trees and extreme gradient boost models for predicting outcomes. A case-control study was carried out involving children with 376 chronic kidney disease (cases) and a control group of healthy children (n = 376). A family member responsible for the children answered a questionnaire with variables potentially associated with the disease. Decision tree and extreme gradient boost models were developed to test signs and symptoms for the classification of children. As a result, the decision tree model revealed 6 variables associated with CKD, whereas twelve variables that distinguish CKD from healthy children were found in the "XGBoost". The accuracy of the "XGBoost" model (ROC AUC = 0.939, 95%CI: 0.911 to 0.977) was the highest, while the decision tree model was a little lower (ROC AUC = 0.896, 95%CI: 0.850 to 0.942). The cross-validation of results showed that the accuracy of the evaluation database model was like that of the training.

CONCLUSION

In conclusion, a dozen symptoms that are easy to be clinically verified emerged as risk indicators for chronic kidney disease. This information can contribute to increasing awareness of the diagnosis, mainly in primary care settings. Therefore, healthcare professionals can select patients for more detailed investigation, which will reduce the chance of wasting time and improve early disease detection.

WHAT IS KNOWN

• Late diagnosis of chronic kidney disease in children is common, increasing morbidity. • Mass screening of the whole population is not cost-effective.

WHAT IS NEW

• With two machine-learning methods, this study revealed 12 symptoms to aid early CKD diagnosis. • These symptoms are easily obtainable and can be useful mainly in primary care settings.

Dipping at home: is it better, easier, and more convenient? A feasibility and acceptability study of a novel home urinalysis using a smartphone application

BACKGROUND

Monitoring proteinuria in patients with kidney disease is of crucial importance given its implications for long-term disease progression and clinical management. Leveraging digital health technology to provide a clinical grade urinalysis result from home holds the potential to greatly enhance the clinical experience and workflows for patients, caregivers, and providers. The goal of this study was to evaluate the acceptability and feasibility of a home-based urinalysis kit using a smartphone application.

METHODS

This is a prospective cohort study of children and young adults (5-21 years of age) at a single pediatric center. The study received ethical board approval. Families performed a home urine test using the Healthy.io smartphone app. The app was compared with standard of care of either home dipstick monitoring or urinalysis performed in clinic or a local laboratory. Patient satisfaction was compared between the new app and current practice.

RESULTS

A total of 103 children, 63 (61%) male and median age 10.9 years (inter-quartile range 7.8-14.2), were enrolled. Primary diagnosis included 47 (46%) glomerular disease, 48 (47%) non-glomerular kidney disease, and 8 (8%) kidney transplant recipients. One hundred and one (98%) patients reported being satisfied with the smartphone app compared to 41 (40%) patients who were satisfied with the current practice for urine protein monitoring (p < 0.0001). Positive themes identified included ease of use, convenience, and immediacy and accuracy of results.

CONCLUSIONS

The Healthy.io home urine testing app received very high rates of satisfaction among patients and caregivers compared to current practice and holds great potential to enhance patient-centered care. A higher resolution version of the Graphical abstract is available as Supplementary information.

Functional Prediction of Chronic Kidney Disease Susceptibility Gene PRKAG2 by Comprehensively Bioinformatics Analysis

The genetic predisposition to chronic kidney disease (CKD) has been widely evaluated especially using the genome-wide association studies, which highlighted some novel genetic susceptibility variants in many genes, and estimated glomerular filtration rate to diagnose and stage CKD. Of these variants, rs7805747 in PRKAG2 was identified to be significantly associated with both serum creatinine and CKD with genome wide significance level. Until now, the potential mechanism by which rs7805747 affects CKD risk is still unclear. Here, we performed a functional analysis of rs7805747 variant using multiple bioinformatics software and databases. Using RegulomeDB and HaploReg (version 4.1), rs7805747 was predicated to locate in enhancer histone marks (Liver, Duodenum Mucosa, Fetal Intestine Large, Fetal Intestine Small, and Right Ventricle tissues). Using GWAS analysis in PhenoScanner, we showed that rs7805747 is not only associated with CKD, but also is significantly associated with other diseases or phenotypes. Using metabolite analysis in PhenoScanner, rs7805747 is identified to be significantly associated with not only the serum creatinine, but also with other 16 metabolites. Using eQTL analysis in PhenoScanner, rs7805747 is identified to be significantly associated with gene expression in multiple human tissues and multiple genes including PRKAG2. The gene expression analysis of PRKAG2 using 53 tissues from GTEx RNA-Seq of 8555 samples (570 donors) in GTEx showed that PRKAG2 had the highest median expression in Heart-Atrial Appendage. Using the gene expression profiles in human CKD, we further identified different expression of PRKAG2 gene in CKD cases compared with control samples. In summary, our findings provide new insight into the underlying susceptibility of PRKAG2 gene to CKD.