ArticleBMC nephrology2020
Establishment of a novel nomogram for the clinically diagnostic prediction of minimal change disease, -a common cause of nephrotic syndrome.
Article in BMC nephrology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.
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Who cites it
4 citing papers in PubMed, 1 synthesis or guideline pooled it, 5 citations in OpenAlex.
- Systematic Review of the Efficacy and Safety of Traditional Chinese Medicine Granules Associated with Hormone When Treating Primary Nephrotic Syndrome in Children.Contrast media & molecular imaging · 2022Pooled it
- An exploratory analysis of disulfidptosis-related gene signatures in minimal change disease identifies metabolic and immune associations.Frontiers in cell and developmental biology · 2026Article
- A nomogram to predict the risk of relapse in patients with minimal change disease: a retrospective cohort study.Renal failure · 2024Article
- Machine learning-based diagnostic prediction of minimal change disease: model development study.Scientific reports · 2024Article
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Authors and funding
9 authors at 2 institutions in 2 countries.
Funding
Abstract
backgroundMinimal change disease (MCD) is one of the major causes of nephrotic syndrome (NS). A confirmed MCD diagnosis mainly depends on renal biopsy at present, which is an invasive procedure with many potential risks. The overall incidence of complications caused by renal biopsy procedures has been reported as approximately 11 and 6.6% outside and within China, respectively. Unfortunately, there is currently no noninvasive procedure or practical classification method for distinguishing MCD from other primary glomerular diseases available.
methodA total of 1009 adult patients who underwent renal biopsy between January 2017 and November 2019 were enrolled in this study. Twenty-five parameters extracted from patient demographics, clinical manifestations, and laboratory test results were statistically analysed. LASSO regression analysis was further performed on these parameters. The parameters with the highest area under the curve (AUC) were selected and used to establish a logistic diagnostic prediction model.
resultsOf the 25 parameters, 14 parameters were significantly different (P < 0.05). MCD patients were mostly younger (36 (22, 55) vs. 41 (28.75, 53)) and male (59% vs. 52%) and had lower levels of diastolic blood pressure (DBP) (79 (71, 85.5) vs. 80 (74, 89)) and IgG (5.42 (3.17, 6.36) vs. 9.38 (6.79, 12.02)) and higher levels of IgM (1.44 (0.96, 1.88) vs. 1.03 (0.71, 1.45)) and IgE (160 (46.7, 982) vs. 47.3 (19, 126)) than those in the non-MCD group. Using the LASSO model, we established a classifier for adults based on four parameters: DBP and the serum levels of IgG, IgM, IgE. We were able to clinically classify adult patients with NS into MCD and non-MCD using this model. The validation accuracy of the logistic regression model was 0.88. A nomogram based on these four classifiers was developed for clinical use that could predict the probability of MCD in adult patients with NS.
conclusionsA LASSO model can be used to distinguish MCD from other primary glomerular diseases in adult patients with NS. Combining the model and the nomogram potentially provides a novel and valuable approach for nephrologists to diagnose MCD, avoiding the complications caused by renal biopsy.
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