The Value of Peripheral Blood Cell Ratios in Primary Membranous Nephropathy: A Single Center Retrospective Study

Zhang, Aihua; Dai, Guang-Xia; Zhang, Qidong; Huang, Hongdong; Liu, Wenhu

doi:10.2147/jir.s404591

Cited by 4 publications

(1 citation statement)

References 36 publications

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“…We also found that patients with a long course of disease had a poor response to treatment, which may be due to the persistence of chronic inflammation, resulting in increased deposition of immune complexes on the outside of the glomerular basement membrane, massive formation of basement membrane “spike” and thickening of the basement membrane, thereby aggravating renal injury and leading to poor prognosis [ 35 , 36 ]. Moreover, studies had shown that immune-inflammation index and monocyte-lymphocyte ratio were reliable markers which might be used to predict prognosis for IMN patients [ 37 , 38 ].…”

Section: Discussionmentioning

confidence: 99%

A dynamic online nomogram for predicting renal outcomes of idiopathic membranous nephropathy

Wang,

Xu,

Wang

et al. 2024

BMC Med Inform Decis Mak

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Background Because 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. Methods From 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. Results A 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. Conclusions The 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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Section: Discussionmentioning

confidence: 99%

A dynamic online nomogram for predicting renal outcomes of idiopathic membranous nephropathy

Wang,

Xu,

Wang

et al. 2024

BMC Med Inform Decis Mak

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A dynamic online nomogram for predicting renal outcomes of idiopathic membranous nephropathy

Wang,

Xu,

Wang

et al. 2023

Preprint

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Background Idiopathic membranous nephropathy (IMN) is the most common cause of nephrotic syndrome in nondiabetic adults. The natural course of IMN is variable, 30% of patients may progress to end-stage renal disease in 10 years. Hence there is an increasing need to develop a dynamic online nomogram for predicting the prognosis of IMN. Methods All the data were obtained from the newly diagnosed IMN patients enrolled in 3 hospitals in Liaoning Province. The nomogram prognostic model was developed by independent risk factors of multivariate logistic regression. The prognostic performance was evaluated using the ROC, calibration and decision curves. Results A total of 130 patients were in the training cohort and 102 patients in the validation cohort. Course ≥ 6 months (OR, 0.225; 95% confidence interval (CI) 0.081, 0.628; P = .004), UTP (OR, 1.140; 95% CI 1.029, 1.262; P = .012), D-Dimer (OR, 1.001; 95% CI 1.000, 1.002; P = .009), and sPLA2R-Ab (OR, 1.005; 95% CI 1.001, 1.008; P = .006) were independently associated with the IMN progression. The nomogram model showed good calibration with a concordance index (C-index) of 0.835 in the training cohort and 0.874 in the validation cohort, with excellent calibration ability and clinical utility. Conclusions We developed a dynamic online nomogram model that can be used to predict the risk of progression in IMN, showing good discrimination and calibration ability.

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Prediction Model and Decision Analysis for Early Recognition of SDNS/FRNS in Children

Yin,

Lin,

Gan

et al. 2024

JIR

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Purpose This study identified factors that identification of progression-predicting utility from steroid-sensitive nephrotic syndrome(SSNS) to steroid-dependent or frequently relapsing nephrotic syndrome (SDNS/FRNS) in patients and developed a corresponding predictive model. Patients and Methods This retrospective study analyzed clinical data from 756 patients aged 1 to 18 years, diagnosed with SSNS, who received treatment at the Department of Nephrology, Children’s Hospital of Chongqing Medical University, between November 2007 and May 2023. We developed a shrinkage and selection operator (LASSO) - logistic regression model, which was visualized using a nomogram. The model’s performance, validity, and clinical utility were evaluated through receiver operating characteristic curve analysis, confusion matrix, calibration plot, and decision curve analysis. Results The platelet-to-lymphocyte ratio (PLR) was identified as an independent risk factor for progression, with an odds ratio (OR) of 1.01 (95% confidence interval (CI) = 1.01–1.01, p = 0.009). Additionally, other significant factors included the time for urinary protein turned negative (OR = 1.17, 95% CI = 1.12–1.23, p < 0.001), estimated glomerular filtration rate(eGFR) (OR = 0.99, 95% CI = 0.98–0.99, p < 0.001), low-density lipoprotein (OR = 0.90, 95% CI = 0.83–0.97, p = 0.006), thrombin time (OR = 1.22, 95% CI = 1.07–1.39, p = 0.003), and neutrophil absolute counts (OR = 1.10, 95% CI = 1.02–1.18, p = 0.009). The model’s performance was assessed through internal validation, yielding an area under the curve of 0.78 (0.73–0.82) for the training set and 0.81 (0.75–0.87) for the validation set. Conclusion PLR, eGFR, the time for urinary protein turned negative, low-density lipoprotein, thrombin time, and neutrophil absolute counts may be effective predictors for identifying SSNS patients at risk of progressing to SDNS/FRNS. These findings offer valuable insights for early detection and support the use of precision medicine strategies in managing SDNS/FRNS.

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The Value of Peripheral Blood Cell Ratios in Primary Membranous Nephropathy: A Single Center Retrospective Study

Cited by 4 publications

References 36 publications

A dynamic online nomogram for predicting renal outcomes of idiopathic membranous nephropathy

A dynamic online nomogram for predicting renal outcomes of idiopathic membranous nephropathy

A dynamic online nomogram for predicting renal outcomes of idiopathic membranous nephropathy

Prediction Model and Decision Analysis for Early Recognition of SDNS/FRNS in Children

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