Novel Approach to Establishing an Aldosterone: Renin Ratio Cutoff for Primary Aldosteronism
Bibliographic record
Abstract
Direct renin concentration is replacing plasma renin activity in many laboratories for the investigation of primary aldosteronism, which may have a significant impact on the resulting aldosterone:renin ratios. We sought to develop a population-based approach to establishing an aldosterone:renin ratio cutoff when transitioning between assays. A population-based study was performed in Calgary, Alberta, Canada of 4301 individuals who received testing from January 2012 to November 2015. In 2014, direct renin concentration replaced plasma renin activity in routine testing. We described the prevalence of primary aldosteronism in our population before the change and, using the assumption of disease prevalence stability, determined the corresponding ratio cutoffs after the introduction of the new assay. During the initial portion of the study (using plasma renin activity), 4.9% of those screened were classified as highly probable cases, whereas 10.4% were considered probable and 28.9% possible using locally validated cutoffs. Aldosterone:renin ratio cutoffs were then determined for the direct renin concentration assay. A highly probable case of primary aldosteronism corresponded to a cutoff of >100 pmol L −1 mIU −1 L −1 with hypokalemia. A probable case corresponded to a cutoff of >100 and a possible case to >35 pmol L −1 mIU −1 L −1 . In contrast, cutoffs derived using a conversion factor resulted in significantly higher cutoffs and the potential for missed cases. In conclusion, using large population data, historically consistent aldosterone:renin ratio cutoffs can be established when transitioning between assays. Population-derived cutoffs may be more appropriate for clinical use and less likely to result in false-negative classification than those obtained from conventional direct method comparisons.
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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 | 0.008 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".