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

Abstract

Background and objectives The 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. Methods Two 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. Result Renal 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. Conclusion The predictive nomogram model provides useful prognostic tool for renal function recovery in PMN patients with AKI.

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

“…Previous researches had shown that age and serum uric acid were associated with a poor prognosis [25,27] , but these associations have not been found in this study. There were several reasons for these inconsistencies.…”

“…As a quantitative tool for risk and bene t assessment, clinical prediction model can provide more intuitive and rational information for doctors, patients and medical policy makers. In recent years, a number of nomograms with IMN had been established [24][25][26][27] , which were used to predict progression and relapse of patients with IMN, and to distinguish malignancy-associated membranous nephropathy from IMN.…”

“…In Liu's investigation endpoint was de ned as a reduction of eGFR [24] , but the endpoint event was no remission of proteinuria in this study. Moreover, differences in follow-up time [25][26][27] may also lead to inconsistency, these factors may lead to different independent risk factors. The results of the retrospective study indicated that course ≥ 6 months, UTP, D-Dimer, and sPLA2R-Ab were signi cant independent predictors of renal outcome in IMN patients.…”

A preliminary nomogram model for predicting relapse of patients with primary membranous nephropathy

“…When PLA2R Abs re-emergences or increases, it indicates that the patient may have clinically relapsed, rather than predicting relapse in advance to take intervention measures to prevent relapse. In recent years, nomogram prediction models have been used to predict the probability of outcome events by combining multiple factors to comprehensively determine the prognosis of diseases [ 9 , 10 ]. In this study, we aimed to identify the predictors of relapse in PMN patients, and construct a nomogram model identifying patients with high relapse risk early and guiding management to decrease relapse risk of PMN.…”

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

“…The nomogram is a useful and accessible tool for physicians to predict the disease progression, to plan for individualized treatment, and to decide the interval for follow-up [15,16]. Nomograms have been previously developed for IMN [17][18][19][20], but most of the nomograms lack of external validity [17,19], and no dynamic online nomogram related to IMN prognosis is found at present to our knowledge. Machine learning has recently been used to produce a prediction model for practice.…”