Prevalence of isolated non-albumin proteinuria in the US population tested for both, urine total protein and urine albumin: An unexpected discovery.

BACKGROUND: Isolated non-albumin proteinuria (NAP) is a condition when urine total protein concentrations are elevated without elevation of urine albumin. The prevalence of NAP in the US population tested for both, urine total protein and albumin was assessed in this study. METHODS: The database of a US nationwide laboratory network was queried for test results when random urine albumin was ordered together with urine total protein and also when timed 24-hour urine albumin was ordered together with urine total protein. The total prevalence of NAP in the US population tested for both, urine total protein and albumin was calculated for patient groups having normal and low-normal urine albumin (random and timed) with elevated and severely increased urine total protein (random and timed). Also, the prevalence of NAP was calculated for patients with normal urine albumin to assess the probability of missing proteinuria if only urine albumin is measured. RESULTS: The prevalence of NAP in the random samples group was 10.1% (15.2% for females and 4.7% for males). Among patients with normal random albumin, there were 20.0% (27.3% of females and 10.7% of males) patients with NAP. The prevalence of NAP in the timed samples group was 24.6% (29.8% for females and 18.5% for males). Among patients with normal timed urine albumin, there were 36.2% (40.0% of females and 30.8% of males) patients with NAP. There was a strong positive association with female gender and NAP in most patients groups. CONCLUSIONS: Testing for only urine (micro)albumin can miss up to 40% of females and 30.8% of males with gross proteinuria.

Reference intervals data mining: no longer a probability paper method.

OBJECTIVES: To describe the application of a data-mining statistical algorithm for calculation of clinical laboratory tests reference intervals. METHODS: Reference intervals for eight different analytes and different age and sex groups (a total of 11 separate reference intervals) for tests that are unlikely to be ordered during routine screening of disease-free populations were calculated using the modified algorithm for data mining of test results stored in the laboratory database and compared with published peer-reviewed studies that used direct sampling. The selection of analytes was based on the predefined criteria that include comparability of analytical methods with a statistically significant number of observations. RESULTS: Of the 11 calculated reference intervals, having upper and lower limits for each, 21 of 22 reference interval limits were not statistically different from the reference studies. CONCLUSIONS: The presented statistical algorithm is shown to be an accurate and practical tool for reference interval calculations.