Integrating machine learning for advanced analysis of bioelectrical impedance parameters in children with nephrotic syndrome: phase angle, impedance ratio, and cell membrane capacitance

Background Nephrotic syndrome (NS) in children, entailing kidney-related protein leakage and peripheral oedema, remains difficult to assess. Bioelectrical impedance analysis (BIA) provides several body composition measures, and integration of machine learning (ML) may improve clinical care. We tested an ML model to identify NS in children, compared with healthy children. Methods This was a cross-sectional study, conducted on children with active NS in the acute phase (aNS group) included from the Department of Paediatrics and Adolescent Medicine, Aarhus University Hospital, Denmark. Anonymized MF-BIA data from frequences between 5-1000 kHz were added to the JustAddDataBio (JADBio)®, a web-based ML platform for analysing potential biomarkers to diagnose. Results Eight children with aNS and 38 age-matched healthy children were included. The ML software employed a ridge logistic regression with the penalty hyperparameter lambda = 0.001, with a selected threshold of 0.81 by JADBio, and the area under the curve (AUC) was 0.84 [95% confidence interval (CI): 0.72;0.94] as the best model. The software selected the following features: height, age, resistance at 50 kHz, impedance at 50 kHz, the characteristic frequency, phase angle at 50 kHz and sex. The model had a statistically significant true positive classification of a healthy child of 0.92 (92%) [CI: 0.88;0.96], and a specificity of 0.22 (22%) [CI: 0.08;0.36]. Conclusion Applying an ML-supported evaluation of BIA improved diagnostics. A low specificity limits the clinical application. To obtain a more acceptable model, a larger population of patients and the inclusion of more biomarkers may be needed.