Is Routine Screening with Serum and Urine Protein Electrophoresis in Evaluation of CKD Justified?
Bibliographic record
Abstract
Background: Current guidelines for the evaluation of chronic kidney disease (CKD) in Ontario, Canada do not include routine screening with serum and urine protein electrophoresis (SPEP and UPEP). Previous studies suggest that M-spike is more common in CKD compared to the general population (1,2). This study aims to examine the use and cost of screening SPEP and UPEP in the evaluation of CKD pts. Methods: This is a retrospective study of 149 sequential incident pts referred to a teaching General Nephrology clinic for evaluation of CKD between Jan and Nov, 2018. The SPEP and UPEP testing frequency, and proportion with M-spike were obtained by chart review, along with the routinely performed clinical, blood and urine tests, imaging, as well as reports of any Hematology consultation, renal and bone marrow biopsies performed. Results: Screening SPEP and UPEP tests were done in 104 (70 %) pts, mean age 72.2 yrs, 42 (40 %) female, 52 (50 %) DM, and Caucasian majority. M-spike was present in 11 pts (10.6 %, 96 % CI 5.4 - 18.8 %), 2 IgG-κ, 5 IgG-λ, 1 IgA-λ, 2 LC-κ, and 1 LC-λ. Eight had Hematology consultation, 6 had bone marrow biopsy, and 3 had renal biopsy. Diagnoses were 7 MGUS, 2 myeloma (MM), 1 amyloid (AL), and 1 both MM + AL. There were no differences in clinical, demographic, CBC, serum calcium, urine albumin to creatinine ratio, urinalysis, or renal imaging among pts with and without M-spike but sample size did not allow multivariate analysis. Conclusions: In this study, the prevalence of M-spike in CKD is higher than has been reported in the literature in the general population (5.4 - 18.8 % vs. 3 - 4 %). The cost for testing and interpretation fees for SPEP and UPEP are CDN $ 25.53 and $ 32.99 respectively, with detection costs of $ 553.28 per M-spike and $ 1,521.25 per myeloma or amyloid. These costs are consistent with previous studies controlled for inflation (1,2). Routine screening with SPEP and UPEP in the evaluation of CKD may be useful and cost-effective. Larger prospective studies are needed to identify subgroups with higher likelihood of M-spike to target testing. 1. Al-Hwiesh et al. J Am Soc Nephrol 2003 14:295A. 2. Chew et al. Am J Kidney Dis. 1999 Jul;34(1):135-9.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".