Development and validation of a clinical prediction model for diagnosing nephrotic syndrome in adults: a retrospective diagnostic prediction study
- 1Department of Dermatology, The First Affiliated Hospital of Xinjiang Medical University, Urumqi, Xinjiang, China
Abstract
Background: The high incidence of nephrotic syndrome (NS) in adults imposes a heavy burden on families and public health care systems, highlighting the significance of early and accurate diagnosis.
Objectives: To develop and internally validate a predictive model and scoring system for diagnosing NS in adults.
Methods: This retrospective diagnostic prediction study enrolled 8,950 hospitalized adults with a preliminary diagnosis of NS and evidence of proteinuria from the First Affiliated Hospital of Xinjiang Medical University. The dataset was randomly divided into a training set and an internal validation set at a 7:3 ratio. Missing data were imputed separately within each set. LASSO regression was used for variable selection in the training set. Variables with univariate P < 0.05 were entered into a multivariable logistic model using bidirectional stepwise selection. Model discrimination, calibration, and clinical utility were evaluated. Internal validation was performed using the bootstrap-impute-predict framework with 500 bootstrap samples. A diagnostic scoring system and risk stratification were subsequently developed.
Results: LASSO regression retained 10 key variables, and the final multivariable logistic model included 9 variables: age, hypertension, hyperlipidemia, edema, 24-hour urinary protein excretion, urine protein qualitative result, serum albumin, cystatin C, and D-dimer. The likelihood ratio tests were statistically significant in both the training and internal validation sets (both P < 0.001). The apparent AUC was 0.89 (95% CI: 0.89–0.90) in the training set, and the Harrell-corrected AUC remained 0.89 after 500 bootstrap resamples. The AUC was 0.90 (95% CI: 0.89–0.91) in the internal validation set. Calibration curves indicated good agreement, and decision curve analysis demonstrated clinical net benefit. Sensitivity analyses further supported the robustness of the model. The score-based system effectively distinguished patients with different risks of NS, and the observed proportion of patients with NS increased progressively with higher scores.
Conclusions: In hospitalized adults with suspected NS and evidence of proteinuria, this model and scoring system may support early diagnostic assessment and risk stratification, pending external validation.
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BibTeX
@article{dong2026development,
title = {{Development and validation of a clinical prediction model for diagnosing nephrotic syndrome in adults: a retrospective diagnostic prediction study}},
author = {Xiaoyang Dong and Jing Nie and Yi Yan},
journal = {Frontiers in Medicine},
year = {2026},
month = oct,
doi = {10.3389/fmed.2026.1825840},
url = {https://doi.org/10.3389/fmed.2026.1825840},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Development and validation of a clinical prediction model for diagnosing nephrotic syndrome in adults: a retrospective diagnostic prediction study
AU - Xiaoyang Dong
AU - Jing Nie
AU - Yi Yan
PY - 2026
DA - 2026/10/21
JO - Frontiers in Medicine
DO - 10.3389/fmed.2026.1825840
UR - https://doi.org/10.3389/fmed.2026.1825840
AB - Background: The high incidence of nephrotic syndrome (NS) in adults imposes a heavy burden on families and public health care systems, highlighting the significance of early and accurate diagnosis. Objectives: To develop and internally validate a predictive model and scoring system for diagnosing NS in adults. Methods: This retrospective diagnostic prediction study enrolled 8,950 hospitalized adults with a preliminary diagnosis of NS and evidence of proteinuria from the First Affiliated Hospital of Xinjiang Medical University. The dataset was randomly divided into a training set and an internal validation set at a 7:3 ratio. Missing data were imputed separately within each set. LASSO regression was used for variable selection in the training set. Variables with univariate P < 0.05 were entered into a multivariable logistic model using bidirectional stepwise selection. Model discrimination, calibration, and clinical utility were evaluated. Internal validation was performed using the bootstrap-impute-predict framework with 500 bootstrap samples. A diagnostic scoring system and risk stratification were subsequently developed. Results: LASSO regression retained 10 key variables, and the final multivariable logistic model included 9 variables: age, hypertension, hyperlipidemia, edema, 24-hour urinary protein excretion, urine protein qualitative result, serum albumin, cystatin C, and D-dimer. The likelihood ratio tests were statistically significant in both the training and internal validation sets (both P < 0.001). The apparent AUC was 0.89 (95% CI: 0.89–0.90) in the training set, and the Harrell-corrected AUC remained 0.89 after 500 bootstrap resamples. The AUC was 0.90 (95% CI: 0.89–0.91) in the internal validation set. Calibration curves indicated good agreement, and decision curve analysis demonstrated clinical net benefit. Sensitivity analyses further supported the robustness of the model. The score-based system effectively distinguished patients with different risks of NS, and the observed proportion of patients with NS increased progressively with higher scores. Conclusions: In hospitalized adults with suspected NS and evidence of proteinuria, this model and scoring system may support early diagnostic assessment and risk stratification, pending external validation.
SN - 2296-858X
ER -