ArticleChinese medical journal2018
A Predictive Model for Estimation Risk of Proliferative Lupus Nephritis.
Article in Chinese medical journal, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Development of machine learning-based predictive models for evaluating risk in the contralateral ear among patients with unilateral sudden sensorineural hearing loss.Chinese medical journal · 2026Article
- Article
- Comprehensive analysis of predictive models for disease manifestations and case fatality in systemic lupus erythematosus.NPJ digital medicine · 2026Review
- Effects of remnant cholesterol on adverse renal outcomes in lupus nephritis.Lipids in health and disease · 2025Observational
- Development and validation of non-invasive prediction models for assessing kidney histopathological activity index in lupus nephritis.Clinical rheumatology · 2025Article
- Associations of the Total Cholesterol/high-density Lipoprotein Cholesterol Ratio with Outcomes in Lupus Nephritis.International journal of medical sciences · 2025Article
- Machine learning models predicts risk of proliferative lupus nephritis.Frontiers in immunology · 2024Article
- Clinicopathological characteristics and outcomes of lupus nephritis patients with scanty immune depositions in kidney biopsies.Journal of nephrology · 2023Article
- Revised ISN/RPS 2018 classification of lupus renal pathology predict clinical remission.International urology and nephrology · 2021Article
- Analysis of clinical and laboratory characteristics and pathology of lupus nephritis-based on 710 renal biopsies in China.Clinical rheumatology · 2020Article
- Toll-Like Receptor as a Potential Biomarker in Renal Diseases.International journal of molecular sciences · 2020Review
- Lupus nephritis: challenges and progress.Current opinion in rheumatology · 2019Review
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6 authors.
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No grant is acknowledged in the PubMed record.
Abstract
backgroundLupus nephritis (LN) is classified by renal biopsy into proliferative and nonproliferative forms, with distinct prognoses, but renal biopsy is not available for every LN patient. The present study aimed to establish an alternate tool by building a predictive model to evaluate the probability of proliferative LN.
methodsIn this retrospective cohort with biopsy-proven LN, 382 patients in development cohort, 193 in internal validation cohort, and 164 newly diagnosed patients in external validation cohort were selected. Logistic regression model was established, and the concordance statistics (C-statistics), Akaike information criterion (AIC), integrated discrimination improvement, Hosmer-Lemeshow test, and net reclassification improvement were calculated to evaluate the performance and validation of models.
resultsThe prevalence of proliferative LN was 77.7% in the whole cohort. A model, including age, gender, systolic blood pressure, hemoglobin, proteinuria, hematuria, and serum C3, performed well on good-of-fit and discrimination in the development chohort to predict the risk of proliferative LN (291 for AIC and 0.84 for C-statistics). In the internal and external validation cohorts, this model showed good capability for discrimination and calibration (0.84 and 0.82 for C-statistics, and 0.99 and 0.75 for P values, respectively).
conclusionThis study developed and validated a model including demographic and clinical indices to evaluate the probability of presenting proliferative LN to guide therapeutic decisions and outcomes.
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