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Prediction Model and Decision Analysis for Early Recognition of SDNS/FRNS in Children
Hui Yin1, Xiao Lin1, Chun Gan1
1Department of Nephrology, Children's Hospital of Chongqing Medical University, National Clinical Research Center for Child Health and Disorders, Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing Key Laboratory of Pediatric Metabolism and Inflammatory Diseases, Chongqing, People's Republic of China.
Insights
This study identified key predictors like platelet-to-lymphocyte ratio (PLR) to forecast steroid-sensitive nephrotic syndrome (SSNS) progression. The findings aid early detection and personalized treatment for steroid-dependent or frequently relapsing nephrotic syndrome (SDNS/FRNS).
Area of Science:
- Nephrology
- Pediatrics
- Clinical Medicine
Background:
- Steroid-sensitive nephrotic syndrome (SSNS) can progress to steroid-dependent or frequently relapsing nephrotic syndrome (SDNS/FRNS).
- Identifying predictive factors for SSNS progression is crucial for timely intervention and management.
Purpose of the Study:
- To identify factors predicting the progression of SSNS to SDNS/FRNS in pediatric patients.
- To develop and validate a predictive model for SSNS progression.
Main Methods:
- Retrospective analysis of clinical data from 756 pediatric patients with SSNS.
- Development of a LASSO-logistic regression model visualized with a nomogram.
- Model performance evaluated using ROC curve analysis, confusion matrix, calibration plot, and decision curve analysis.
Main Results:
- Platelet-to-lymphocyte ratio (PLR), time for urinary protein to turn negative, eGFR, LDL, thrombin time, and neutrophil counts were significant predictors.
- The predictive model demonstrated good performance with an AUC of 0.78 for the training set and 0.81 for the validation set.
Conclusions:
- PLR, eGFR, urinary protein negativity time, LDL, thrombin time, and neutrophil counts can effectively predict SSNS progression to SDNS/FRNS.
- These predictors support early detection and precision medicine strategies for managing SDNS/FRNS.
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.
