Predicting Thromboembolic Risk in PLA2R-Related Primary Membranous Nephropathy: A Practical Model for Clinical Use

Patients with nephrotic syndrome carry a well known risk of venous and arterial thromboembolism. Among these patients, those with primary membranous nephropathy represent a group with especially high risk. The presence of anti-PLA2R antibody signals active disease and is linked to the likelihood of complications, including thromboembolic events. Because these events increase morbidity, influence treatment plans, and raise overall medical costs, clinicians need a practical way to identify patients who are most vulnerable.

This study developed and validated a prediction model that estimates the probability of thromboembolism in patients with PLA2R-related primary membranous nephropathy. The model is presented as a nomogram that incorporates age, anti-PLA2R antibody level, and 24-hour urine protein.

Background

Primary membranous nephropathy is an autoimmune glomerular disease and carries the highest incidence of thromboembolic complications among glomerular diseases. The 2021 KDIGO guidelines highlight the sharp increase in venous thromboembolism when serum albumin falls below 20 g/L and the elevated arterial thrombosis risk when albumin is below 30 g/L. Data from China note that more than one-third of patients with pMN experience venous thromboembolism.

Most patients with pMN have circulating or glomerular anti-PLA2R antibody. Antibody titers track with disease activity and progression. Several studies suggest that higher titers correlate with increased thromboembolic risk, even in patients who do not yet have full nephrotic syndrome. Evidence remains limited, so a quantitative model based on a large patient cohort is valuable for everyday clinical decision making.

Methods

Study population

This retrospective study included 1384 adults diagnosed with PLA2R antibody-related primary membranous nephropathy at the First Affiliated Hospital of Zhengzhou University between January 2015 and July 2023. All patients were untreated at the time of data collection.

To allow temporal validation, the data set was split chronologically.

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  Model group: 969 patients enrolled before August 2020

  
  
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  External validation group: 415 patients enrolled after that date

  
  

All patients met the following criteria:

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  Positive PLA2R staining on kidney biopsy or positive serum anti-PLA2R antibody

  
  
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  Age 18 or older

  
  
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  No identifiable secondary cause of membranous nephropathy

  
  

Exclusion criteria removed patients with confounding glomerular diseases, pregnancy, missing PLA2R data, missing imaging for thrombosis screening, or exposure to major external thrombotic risks such as recent surgery or antiphospholipid syndrome.

Data collection

Collected variables included demographics, thromboembolic history, PLA2R antibody levels, 24-hour urine protein, serum albumin, and renal function. All patients underwent vascular ultrasound at diagnosis. Additional imaging such as head CT, MRI, or CTPA was performed only when symptoms suggested stroke or pulmonary embolism.

Statistical approach

The modeling group was divided into thromboembolic and non-thromboembolic subgroups. Logistic regression identified independent risk factors. These variables were incorporated into a nomogram. Model performance was measured using:

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  Area under the ROC curve (AUC)

  
  
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  Calibration curves

  
  
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  Decision curve analysis

  
  
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  Hosmer–Lemeshow goodness of fit test

  
  

Results

Baseline characteristics

Of the 1384 patients, 557 were women and 827 were men. In the model group, the mean age was 51 years. Median PLA2R antibody level was 58.9 RU/mL, and median 24-hour urine protein was 4.86 g. Serum albumin and creatinine values reflected typical nephrotic profiles.

The modeling group included 126 patients with confirmed thromboembolic events, a rate of 13 percent. Significant differences existed between the thromboembolism and non-thromboembolism subgroups.

Independent risk factors

Multivariate logistic regression identified three variables that independently predicted thromboembolic events:

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  Age

  
  
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  Anti-PLA2R antibody level

  
  
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  24-hour urine protein

  
  

Model performance

The resulting nomogram showed solid discrimination with an AUC of 0.741 (95 percent CI 0.695 to 0.788, P < 0.001). Calibration curves showed strong agreement between predicted and observed outcomes. Decision curve analysis indicated net clinical benefit across a wide range of threshold probabilities. Performance in the external validation set closely matched the modeling cohort, confirming generalizability.

Clinical Implications

The nomogram provides a simple and quantitative way to assess thromboembolic risk in patients with PLA2R-related primary membranous nephropathy. It relies on variables that clinicians already measure in routine care. This tool can support:

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  More informed conversations with patients about complications

  
  
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  Decisions on prophylaxis in those with high predicted risk

  
  
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  Closer monitoring during periods of elevated disease activity

  
  
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  Evaluation of treatment response and cost implications

  
  

Because thromboembolic events strongly influence morbidity and mortality in pMN, a validated model adds practical value in both hospital and outpatient settings.

Conclusion

This study presents a predictive nomogram based on age, anti-PLA2R antibody level, and 24-hour urine protein. The model demonstrates strong discrimination, reliable calibration, and meaningful clinical utility. It offers a practical way to identify patients with PLA2R-related primary membranous nephropathy who face heightened thromboembolic risk and supports more precise, patient-centered management.

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