Journal of Inflammation Research (Dec 2024)

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

  • Yin H,
  • Lin X,
  • Gan C,
  • Xiao H,
  • Jiang Y,
  • Zhou X,
  • Yang Q,
  • Jiang W,
  • Wang M,
  • Yang H,
  • Zhang G,
  • Chan H,
  • Li Q

Journal volume & issue
Vol. Volume 17
pp. 10585 – 10598

Abstract

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Hui Yin,* Xiao Lin,* Chun Gan, Han Xiao, Yaru Jiang, Xindi Zhou, Qing Yang, Wei Jiang, Mo Wang, Haiping Yang, Gaofu Zhang, Han Chan, Qiu Li Department 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*These authors contributed equally to this workCorrespondence: Han Chan; Qiu Li, Chongqing Medical University, Chongqing, People’s Republic of China, Email [email protected]; [email protected]: 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.Keywords: PLR, LASSO, SSNS, SDNS/FRNS, nomogram, prediction

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