Development and validation of a discrimination model between primary PLA2R-negative membranous nephropathy and

Feng Wu1, Yiding Zhang1, Wen Cui1

  • 1Department of Nephrology, The First Affiliated Hospital, Zhengzhou University, No. 1, East Jianshe Road, Zhengzhou, 450052, Henan, People's Republic of China.

Scientific Reports
|September 11, 2021
PubMed

Insights

A new model using age, albumin, urea, HDL, C3, and RBC count can help differentiate between membranous nephropathy (MN) and minimal change disease (MCD) in patients without a biopsy. This noninvasive tool aids in diagnosis when M-type phospholipase A2 receptor (PLA2R) testing is inconclusive.

Area of Science:

  • Nephrology
  • Immunology

Background:

  • Membranous nephropathy (MN) and minimal change disease (MCD) are leading causes of nephrotic syndrome (NS) with overlapping clinical presentations.
  • Distinguishing between MN and MCD is crucial as they require different therapeutic approaches and have varied prognoses.
  • While M-type phospholipase A2 receptor (PLA2R) is a marker for MN, its sensitivity is limited (approx. 70%), necessitating alternative diagnostic methods for PLA2R-negative cases.

Purpose of the Study:

  • To develop and validate a noninvasive diagnostic model to differentiate between PLA2R-negative idiopathic MN and MCD.
  • To identify key clinical and laboratory parameters that can effectively distinguish these two conditions without the need for renal biopsy.

Main Methods:

  • A cohort of 949 patients with pathologically confirmed idiopathic MN (805) or MCD (144) was analyzed.
  • A logistic regression model was developed using data from 200 PLA2R-negative MN and 144 MCD patients, incorporating basic information and laboratory results.
  • The model's performance was evaluated using area under the curve (AUC) in training and testing groups, and further validated on the entire patient cohort.

Main Results:

  • A novel discrimination model incorporating age, albumin, urea, high-density lipoprotein (HDL) levels, C3 levels, and red blood cell (RBC) count was established.
  • The model demonstrated strong discriminatory power, with an AUC of 0.904 in the training group and 0.886 in the test group.
  • The model also showed good discrimination when applied to the entire cohort of 949 patients, highlighting its robustness.

Conclusions:

  • The developed model offers a promising noninvasive tool for differentiating PLA2R-negative MN from MCD.
  • This approach can aid clinicians in making timely and accurate diagnoses, potentially avoiding invasive procedures like kidney biopsies.
  • The model's inclusion of readily available clinical and laboratory parameters makes it a practical diagnostic aid in nephrotic syndrome management.

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