Nomogram for thromboembolic events in primary membranous nephropathy associated with PLA2R antibody

Zihan Zhai1, Yanhong Guo1, Lu Yu1

  • 1Department of Nephropathy, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450052, Henan, People's Republic of China.

Scientific Reports
|April 15, 2025
PubMed

Insights

Patients with phospholipase A2 receptor (PLA2R) antibody-related primary membranous nephropathy face high thromboembolic risk. A new nomogram using age, PLA2R antibody levels, and urine protein accurately predicts this risk.

Area of Science:

  • Nephrology
  • Thrombosis
  • Predictive Modeling

Background:

  • Patients with nephrotic syndrome, especially primary membranous nephropathy (pMN), have an increased risk of thromboembolic events.
  • PhospholipasA2 receptor (PLA2R) antibody presence indicates active pMN and is crucial for risk stratification.
  • Accurate identification of high-risk patients is essential for clinical management, patient communication, and resource allocation.

Purpose of the Study:

  • To develop and validate a practical predictive model for thromboembolic events in patients with PLA2R antibody-related pMN.
  • To identify key clinical factors contributing to thromboembolic risk in this patient population.

Main Methods:

  • A cohort of 1384 patients with PLA2R antibody-related pMN was analyzed.
  • A predictive model (nomogram) was developed using logistic regression on a modeling group (969 patients) and validated externally on 415 patients.
  • Model performance was assessed using AUC, calibration curves, and decision curve analysis (DCAs).

Main Results:

  • The nomogram incorporated age, Anti-PLA2R antibody levels, and 24-hour urine protein quantification.
  • The model demonstrated good predictive performance with an AUC of 0.741.
  • Calibration curves and DCAs confirmed the nomogram's reliability and clinical utility in predicting thromboembolic events.

Conclusions:

  • A validated nomogram incorporating age, Anti-PLA2R antibody, and 24-hour urine protein can effectively predict thromboembolic risk in patients with PLA2R-related pMN.
  • This tool can aid clinicians in identifying at-risk individuals, guiding treatment decisions, and improving patient outcomes.

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