Predicting chronic renal insufficiency in idiopathic membranous glomerulonephritis (original) (raw)

1992, Kidney International

Predicting chronic renal insufficiency in idiopathic membranous gbmerulonephritis. We developed an approach in quantifying the risk of developing chronic renal insufficiency (CR1) based on a cohort of 184 patients with idiopathic membranous glomerulonephritis (IMGN), prospectively followed by the Toronto Glomerulonephritis Registry between 1974 and 1988. After a mean follow-up period of 5.8 years, 26% of patients developed CR1 (defined as persistent reduction of creatinine clearance (Car) 60 mI/min/! .73 m2 for 12 months). We found that when compared to the baseline probability of the unselected patients, the severity of proteinuria at kidney biopsy added only marginally to the prediction of CR1. We introduced a special test condition: persistent proteinuria (PP) (that is, duration of proteinuria, g/day, above different cut-off levels). We examined the positive predictive value (PPV) and sensitivity (SEN) of 15 arbitrarily chosen levels of PP (that is, proteinuria 4, 6 or 8 glday persisting for 9, 12, 18 or 24 months) to select levels with optimal predictive characteristics. We found that PP 8 g/day for six months was a simple and useful predictor of CR1 with a PPV and SEN of 66%. To further improve our prediction, we tested the following parameters: age, sex, initial Sr and proteinuria, serum albumin, hypertension, rate of change of Cr over time, and therapy (steroids immunosuppressive drugs) in a multivariate analysis. Proteinuria, initial C, and rate of change of C. were most important in predicting CR1. Fifteen models were then developed by including each patient's Cr at the start of PP and its rate of change during the time period selected. Two models based on PP 4 g/day for 18 months, or 6 g/day for 9 months significantly improved the PPV's for CR1 from those based on the same levels of PP alone. Using these test conditions, we can improve the prediction of CR1 from a baseline probability of 26% in unselected patients to a range of 55 to 86% in the "high-risk" patients (with SEN > 60%). Application of these predictive strategies in IMGN will be useful in managing the individual patients and in selecting patients for clinical trials by limiting the exposure of potentially toxic therapy to the "high-risk" patients.