ArticleChinese medical journal2019
A non-invasive diagnostic model of immunoglobulin A nephropathy and serological markers for evaluating disease severity.
Article in Chinese medical journal, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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7 citing papers in PubMed, 9 citations in OpenAlex.
- Development of a novel combined nomogram model integrating deep learning radiomics to diagnose IgA nephropathy clinically.Renal failure · 2023Trial
- Establishing a differential diagnosis model between primary membranous nephropathy and non-primary membranous nephropathy by machine learning algorithms.Renal failure · 2024Article
- Development and Validation of a Machine Learning-Based Prognostic Model for IgA Nephropathy with Chronic Kidney Disease Stage 3 or 4.Kidney diseases (Basel, Switzerland) · 2024Article
- Machine learning-based diagnostic prediction of IgA nephropathy: model development and validation study.Scientific reports · 2024Article
- Prognostic factors affecting long-term outcomes in patients with concurrent IgA nephropathy and membranous nephropathy.Heliyon · 2024Article
- A noninvasive artificial neural network model to predict IgA nephropathy risk in Chinese population.Scientific reports · 2022Article
- Correlations of Serological Markers with Development of Systemic Involvement in Adult Immunoglobulin A Vasculitis: A Retrospective Study of 259 Patients in Central China.Current medical science · 2021Article
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8 authors at 3 institutions in 1 country.
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Abstract
backgroundImmunoglobulin A nephropathy (IgAN) is the most common pathological type of glomerular disease. Kidney biopsy, the gold standard for IgAN diagnosis, has not been routinely applied in hospitals worldwide due to its invasion nature. Thus, we aim to establish a non-invasive diagnostic model and determine markers to evaluate disease severity by analyzing the serological parameters and pathological stages of patients with IgAN.
methodsA total of 272 biopsy-diagnosed IgAN inpatients and 518 non-IgA nephropathy inpatients from the Department of Nephrology of Chinese People's Liberation Army General Hospital were recruited for this study. Routine blood examination, blood coagulation testing, immunoglobulin-complement testing, and clinical biochemistry testing were conducted and pathological stages were analyzed according to Lee grading system. The serological parameters and pathological stages were analyzed. The receiver operating characteristic (ROC) analysis was performed to estimate the diagnostic value of the clinical factors. Logistic regression was used to establish the diagnostic model.
resultsThere were 15 significantly different serological parameters between the IgAN and non-IgAN groups (all P < 0.05). The ROC analysis was performed to measure the diagnostic value for IgAN of these parameters and the results showed that the area under the ROC curve (AUC) of total protein (TP), total cholesterol (TC), fibrinogen (FIB), D-dimer (D2), immunoglobulin A (IgA), and immunoglobulin G (IgG) were more than 0.70. The AUC of the "TC + FIB + D2 + IgA + age" combination was 0.86, with a sensitivity of 85.98% and a specificity of 73.85%. Pathological grades of I, II, III, IV, and V accounted for 2.21%, 17.65%, 62.50%, 11.76%, and 5.88%, respectively, with grade III being the most prevalent. The levels of urea nitrogen (UN) (13.57 ± 5.95 vs. 6.06 ± 3.63, 5.92 ± 2.97, 5.41 ± 1.73, and 8.41 ± 3.72 mmol/L, respectively) and creatinine (Cr) (292.19 ± 162.21 vs. 80.42 ± 24.75, 103.79 ± 72.72, 96.41 ± 33.79, and 163.04 ± 47.51 μmol/L, respectively) were significantly higher in grade V than in the other grades, and the levels of TP (64.45 ± 7.56, 67.16 ± 6.94, 63.22 ± 8.56, and 61.41 ± 10.86 vs. 37.47 ± 5.6 mg/d, respectively), direct bilirubin (DB) (2.34 ± 1.23, 2.58 ± 1.40, 1.91 ± 0.97, and 1.81 ± 1.44 vs. 0.74 ± 0.57 μmol/L, respectively), and IgA (310.35 ± 103.78, 318.48 ± 107.54, 292.58 ± 81.85, and 323.29 ± 181.67 vs. 227.17 ± 68.12 g/L, respectively) were significantly increased in grades II-V compared with grade I (all P < 0.05).
conclusionsThe established diagnostic model that combined multiple factors (TC, FIB, D2, IgA, and age) might be used for IgAN non-invasive diagnosis. TP, DB, IgA, Cr, and UN have the potential to be used to evaluate IgAN disease severity.
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