ArticleBMC medical informatics and decision making2024
A dynamic online nomogram for predicting renal outcomes of idiopathic membranous nephropathy.
Article in BMC medical informatics and decision making, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
6 citing papers in PubMed.
- Prognostic Stratification in Primary Glomerulonephritis: Integrating Histology, Biomarkers, and Risk Prediction Models.Life (Basel, Switzerland) · 2026Review
- Artificial intelligence in membranous nephropathy: transforming clinical management toward precision medicine.Frontiers in medicine · 2026Review
- Predicting membranous nephropathy remission: a nomogram based on early dynamic biomarkers.Frontiers in medicine · 2026Article
- Development and External Validation of a Nomogram for Individualized Risk Prediction of Hyperprolactinemia in Chronic Kidney Disease: A Retrospective Multicenter Study.International journal of general medicine · 2026Article
- Telitacicept monotherapy for refractory idiopathic membranous nephropathy: a case report and literature review.Frontiers in medicine · 2025Article
- Spontaneous remission in primary membranous nephropathy: mechanisms, predictive factors, and implications for personalized management.Frontiers in immunology · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
12 authors.
Funding
Abstract
backgroundBecause spontaneous remission is common in IMN, and there are adverse effects of immunosuppressive therapy, it is important to assess the risk of progressive loss of renal function before deciding whether and when to initiate immunosuppressive therapy. Therefore, this study aimed to establish a risk prediction model to predict patient prognosis and treatment response to help clinicians evaluate patient prognosis and decide on the best treatment regimen.
methodsFrom September 2019 to December 2020, a total of 232 newly diagnosed IMN patients from three hospitals in Liaoning Province were enrolled. Logistic regression analysis selected the risk factors affecting the prognosis, and a dynamic online nomogram prognostic model was constructed based on extreme gradient boost, random forest, logistic regression machine learning algorithms. Receiver operating characteristic and calibration curves and decision curve analysis were utilized to assess the performance and clinical utility of the developed model.
resultsA total of 130 patients were in the training cohort and 102 patients in the validation cohort. Logistic regression analysis identified four risk factors: course ≥ 6 months, UTP, D-dimer and sPLA2R-Ab. The random forest algorithm showed the best performance with the highest AUROC (0.869). The nomogram had excellent discrimination ability, calibration ability and clinical practicability in both the training cohort and the validation cohort.
conclusionsThe dynamic online nomogram model can effectively assess the prognosis and treatment response of IMN patients. This will help clinicians assess the patient's prognosis more accurately, communicate with the patient in advance, and jointly select the most appropriate treatment plan.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.