ArticleClinical and experimental nephrology2024
Prediction model for treatment response of primary membranous nephropathy with nephrotic syndrome.
Article in Clinical and experimental nephrology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
objectiveTo investigate the predictors and establish a nomogram model for the prediction of the response to treatment in primary membranous nephropathy (PMN) with nephrotic syndrome (NS).
methodsThe clinical, laboratory, pathological and follow-up data of patients with biopsy-proven membranous nephropathy at the Affiliated Hospital of Qingdao University were collected. A total of 373 patients were randomly assigned into development group (n = 262) and validation group (n = 111). Logistic regression analysis was performed in the development group to determine the predictors of treatment response. A nomogram model was established based on the multivariate logistic regression analysis and validated in the validation group. The C-index and calibration plots were used for the evaluation of the discrimination and calibration performance, respectively.
resultsSerum albumin levels (OR = 1.151, 95% CI 1.078-1.229, P < 0.001) and glomerular C3 deposition (OR = 0.407, 95% CI 0.213-0.775, P = 0.004) were identified as independent predictive factors for treatment response in PMN with NS, then a nomogram was established combining the above indicators and treatment regimen. The C-indices of this model were 0.718 (95% CI 0.654-0.782) and 0.789 (95% CI 0.705-0.873) in the development and validation groups, respectively. The calibration plots showed that the predicted probabilities of the model were consistent with the actual probabilities (P > 0.05), which indicated favorable performance of this model in predicting the treatment response probability.
conclusionsSerum albumin levels and glomerular C3 deposition were predictors for treatment response of PMN with NS. A novel nomogram model with good discrimination and calibration was constructed to predict treatment response probability at an early stage.
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