Development of a risk score for earlier diagnosis 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.

Access to treatment for chronic kidney disease by children and adolescents in Brazil

BACKGROUND

This study aimed to identify main factors associated with child and adolescent access to chronic kidney disease (CKD) treatment in Brazil.

METHODS

Multi-center cross-sectional study conducted in eight pediatric nephrology centers across all Brazilian geographic regions. Information was collected on characteristics associated with referral and treatment of patients with CKD. The following outcomes were analyzed as follows: (1) age at first consultation, and (2) time elapsed between referral and treatment at the specialized service.

RESULTS

Three hundred thirty-five children were assessed. Variables associated with age at first consultation were as follows: CAKUT (HR=1.7; 95%CI 1.3-2.2, p<0.01); private health plan (HR=1.54; 95%CI 1.06-2.23, p=0.02); modified Medical Outcomes Study Social Support Survey mMOS-SS score (HR=1.02; 95%CI 1.00-1.03, p=0.024); maternal age (HR=0.96; 95%CI 0.95-0.97, p<0.01); and number of siblings in the household (HR=0.86; 95%CI 0.79-0.83, p<0.01). Significant variables associated with time elapsed between referral and treatment at the specialized service were as follows: each additional occupant sharing the household (HR=0.94; 95%CI:0.89-0.99, p=0.02), residing in the Northeast (HR=0.81; 95%CI:0.67-0.98, p=0.03) and having someone to take them to the physician (HR=1.36; 95%CI 1.07-1.74, p=0.01). The median time interval between patient referral and treatment by the service was 11 days (IQR 10-31).

CONCLUSION

There are potentially modifiable factors hampering access of children with CKD to specialized treatment. The importance of the role of social support for the two outcomes should serve as an alert for health managers and professionals to consider this aspect throughout all steps of the care process of children with CKD.