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Abstract: SA-PO133

The Development of "Mayo MGRS" Score to Predict Likelihood of Monoclonal Gammopathy of Renal Significance in Patients With Monoclonal Gammopathy

Session Information

Category: Onconephrology

  • 1600 Onconephrology

Authors

  • Klomjit, Nattawat, University of Minnesota Medical School Twin Cities, Minneapolis, Minnesota, United States
  • Zand, Ladan, Mayo Clinic Minnesota, Rochester, Minnesota, United States
  • Fervenza, Fernando C., Mayo Clinic Minnesota, Rochester, Minnesota, United States
  • Sethi, Sanjeev, Mayo Clinic Minnesota, Rochester, Minnesota, United States
  • Leung, Nelson, Mayo Clinic Minnesota, Rochester, Minnesota, United States
Background

MGRS is found in only 3-4% of CKD with monoclonal gammopathy (MG). Therefore, there is a need for clinical tool to predict the likelihood of MGRS lesions in patients with MG.

Methods

The Mayo MGRS score was developed using all patients from 2013 to 2018 with biopsy proven MGRS and available MG and urine monoclonal protein. A multivariable logistic regression model between the predictors and MGRS status was fit. Using the odds ratios, score weights were derived for each variable and a MGRS risk score was computed for each patient. Internal validation of the risk score model was conducted by 10-fold cross-validation.

Results

Between 2013-2018, we included 130 patients in the cohort and 62 patients have MGRS. We found that urine protein (UP) ≥ 1.5 g/d (OR 4.01), hematuria (OR 4.88), affected/unaffected free light chain (FLC) ratio ≥ 4 (OR 10.87), and diabetes mellitus (DM) (OR 0.30) significantly predicted MGRS. Positive urine MG significantly predicted MGRS in univariate model but did not in multivariate model (OR 1.88). All significant parameters and positive urine MG were included in the scoring system due to clinical relevancy. We assigned the score to each parameter as follows: score 1 for UP ≥ 1.5 g/d, hematuria, positive urine MG; score 2 for affected/unaffected free light chain (FLC) ratio ≥ 4 (due to magnitude of the OR); score -1 for DM. Therefore, the score could range from -1 to 5. A univariate logistic regression model between the score and MGRS status showed a C-statistic of 0.831 (95% CI 0.758, 0.890) with predicted probability of MGRS increasing linearly from 0.026 (score -1) to 0.957 (score 5), figure 1. Ten-fold cross-validation has a median C-score of 0.73 (IQR 0.68, 0.75).

Conclusion

Mayo MGRS score is a useful tool to assist clinician in assessing the risk of having MGRS and decide who to biopsy. Future external validation study is needed.