Prediction Model and Decision Analysis for Early Recognition of SDNS/FRNS in Children.

PURPOSE

This study identified factors that identification of progression-predicting utility from steroid-sensitive nephrotic syndrome(SSNS) to steroid-dependent or frequently relapsing nephrotic syndrome (SDNS/FRNS) in patients and developed a corresponding predictive model.

PATIENTS AND METHODS

This retrospective study analyzed clinical data from 756 patients aged 1 to 18 years, diagnosed with SSNS, who received treatment at the Department of Nephrology, Children's Hospital of Chongqing Medical University, between November 2007 and May 2023. We developed a shrinkage and selection operator (LASSO) - logistic regression model, which was visualized using a nomogram. The model's performance, validity, and clinical utility were evaluated through receiver operating characteristic curve analysis, confusion matrix, calibration plot, and decision curve analysis.

RESULTS

The platelet-to-lymphocyte ratio (PLR) was identified as an independent risk factor for progression, with an odds ratio (OR) of 1.01 (95% confidence interval (CI) = 1.01-1.01, p = 0.009). Additionally, other significant factors included the time for urinary protein turned negative (OR = 1.17, 95% CI = 1.12-1.23, p < 0.001), estimated glomerular filtration rate(eGFR) (OR = 0.99, 95% CI = 0.98-0.99, p < 0.001), low-density lipoprotein (OR = 0.90, 95% CI = 0.83-0.97, p = 0.006), thrombin time (OR = 1.22, 95% CI = 1.07-1.39, p = 0.003), and neutrophil absolute counts (OR = 1.10, 95% CI = 1.02-1.18, p = 0.009). The model's performance was assessed through internal validation, yielding an area under the curve of 0.78 (0.73-0.82) for the training set and 0.81 (0.75-0.87) for the validation set.

CONCLUSION

PLR, eGFR, the time for urinary protein turned negative, low-density lipoprotein, thrombin time, and neutrophil absolute counts may be effective predictors for identifying SSNS patients at risk of progressing to SDNS/FRNS. These findings offer valuable insights for early detection and support the use of precision medicine strategies in managing SDNS/FRNS.