Analysis of risk factors for the prognosis of proliferative IgA nephropathy and construction of a prediction model
ObjectiveTo explore the factors affecting the prognosis of proliferative IgA nephropathy， and to construct a disease prediction model.MethodsClinical data of patients diagnosed with proliferative IgA nephropathy in the Department of Nephrology， the First Affiliated Hospital of University of Science...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | zho |
| Published: |
Editorial Department of Journal of Clinical Nephrology
2025-07-01
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| Series: | Linchuang shenzangbing zazhi |
| Subjects: | |
| Online Access: | http://www.lcszb.com/thesisDetails#10.3969/j.issn.1671-2390.2025.07.001 |
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| Summary: | ObjectiveTo explore the factors affecting the prognosis of proliferative IgA nephropathy， and to construct a disease prediction model.MethodsClinical data of patients diagnosed with proliferative IgA nephropathy in the Department of Nephrology， the First Affiliated Hospital of University of Science and Technology of China （Anhui Provincial Hospital） from January 2020 to December 2022 were retrospectively analyzed. All patients were followed up for one year， and the prognosis was determined according to the occurrence of joint endpoint events. The least absolute shrinkage and selection operator （LASSO） regression was used to screen the risk factors for joint endpoint events in patients with proliferative IgA nephropathy. The adjustment parameter λ in LASSO regression was verified by 10-fold cross-validation method. The variables with the regression coefficient not equal to 0 corresponding to the λ value with the smallest deviation were included in the multivariate Logistic regression model. Risk factor prediction model for joint endpoint events in patients with proliferative IgA nephropathy was constructed. Based on Bootstrap method， the model was verified with 1000 repeated samples， and validated by the receiver operating characteristic （ROC） curve， and the area under the curve （AUC）. Calibration curves were drawn to evaluate the stability of the model.ResultsAccording to the inclusion and exclusion criteria， 215 patients were included in the study， of which 52 had endpoint events and 163 had no endpoint events. Univariate analysis indicated ten indicators with statistically significant differences. LASSO regression was used to reduce dimensionality， and 6 optimal modeling indicators were suggested. Logistic regression analysis was performed on them， and finally 3 indicators were obtained： 24-hour urinary protein quantification， hemoglobin and blood uric acid. The risk factor model was established： Logit（<italic>P</italic>）=-1.017＋0.198×24-hour urinary protein quantity （mg） ‒ 0.089×hemoglobin （g/L）＋0.435×blood uric acid （μmol/L）. The calibration curve indicated that the prediction probability of the model was highly consistent with the actual probability， with a C-index of 0.915. The ROC curve indicated that the AUC was 0.904 （95%<italic>CI</italic>：0.874-0.936）， and the model had good prediction ability.ConclusionA prediction model for the prognosis of proliferative IgA nephropathy is established based on LASSO-Logistic， which is helpful to judge the prognosis of the disease in clinical practice and has good application value. |
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| ISSN: | 1671-2390 |