Prediction model of renal function recovery for primary membranous nephropathy with acute kidney injury View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2022-07-13

AUTHORS

Tianxin Chen, Ying Zhou, Jianfen Zhu, Xinxin Chen, Jingye Pan

ABSTRACT

Background and objectivesThe clinical and pathological impact factors for renal function recovery in acute kidney injury (AKI) on the progression of renal function in primary membranous nephropathy (PMN) with AKI patients have not yet been reported, we sought to investigate the factors that may influence renal function recovery and develop a nomogram model for predicting renal function recovery in PMN with AKI patients.MethodsTwo PMN with AKI cohorts from the Nephrology Department, the First Affiliated Hospital of Wenzhou Medical University during 2012–2018 and 2019–2020 were included, i.e., a derivation cohort during 2012–2018 and a validation cohort during 2019–2020. Clinical characteristics and renal pathological features were obtained. The outcome measurement was the recovery of renal function within 12 months. Lasso regression was used for clinical and pathological features selection. Prediction model was built and nomogram was plotted. Model evaluations including calibration curves were performed.ResultRenal function recovery was found in 72 of 124 (58.1%) patients and 41 of 72 (56.9%) patients in the derivation and validation cohorts, respectively. The prognostic nomogram model included determinants of sex, age, the comorbidity of hypertensive nephropathy, the stage of glomerular basement membrane and diuretic treatment with a reasonable concordance index of 0.773 (95%CI,0.716–0.830) in the derivation cohort and 0.773 (95%CI, 0.693–0.853) in the validation cohort. Diuretic use was a significant impact factor with decrease of renal function recovery in PMN with AKI patients.ConclusionThe predictive nomogram model provides useful prognostic tool for renal function recovery in PMN patients with AKI. More... »

PAGES

247

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URI

http://scigraph.springernature.com/pub.10.1186/s12882-022-02882-9

DOI

http://dx.doi.org/10.1186/s12882-022-02882-9

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https://app.dimensions.ai/details/publication/pub.1149448339

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/35831820


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