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

Authors :
Feng Wang
Jiayi Xu
Fumei Wang
Xu Yang
Yang Xia
Hongli Zhou
Na Yi
Congcong Jiao
Xuesong Su
Beiru Zhang
Hua Zhou
Yanqiu Wang
Source :
BMC Medical Informatics and Decision Making, Vol 24, Iss 1, Pp 1-11 (2024)
Publication Year :
2024
Publisher :
BMC, 2024.

Abstract

Abstract 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.

Details

Language :
English
ISSN :
14726947
Volume :
24
Issue :
1
Database :
Directory of Open Access Journals
Journal :
BMC Medical Informatics and Decision Making
Publication Type :
Academic Journal
Accession number :
edsdoj.bdff299f128a447aa50433a8e80886d5
Document Type :
article
Full Text :
https://doi.org/10.1186/s12911-024-02568-2