Primary Aldosteronism in Chronic Kidney Disease: Blood Pressure Control and Kidney and Cardiovascular Outcomes After Surgical Versus Medical Management
Bibliographic record
Abstract
BACKGROUND: Diagnosis and treatment of primary aldosteronism (PA) in chronic kidney disease (CKD) may be deferred due to limited evidence supporting safety and efficacy of treatment. Our goal was to assess clinical outcomes in patients with PA and CKD who received surgical or medical management. METHODS: We conducted a multicenter, retrospective cohort study of patients with PA and CKD who underwent adrenal vein sampling from 2009-2019. We characterized clinical outcomes and evaluated differences by surgical versus medical management. Primary outcomes were systolic blood pressure and number of antihypertensive medications. Secondary outcomes were diastolic blood pressure, serum potassium, estimated glomerular filtration rate (eGFR), and kidney and cardiovascular events. Analyses were adjusted for age, sex, race, cardiovascular disease, diabetes, and eGFR. RESULTS: , and 49% were on potassium supplementation. At 5 years, mean blood pressure decreased from 149±22/85±14 to 131±28/78±16 mm Hg and mean number of antihypertensive medications decreased from 4.0±1.5 to 2.4±1.4. Adrenalectomy, compared to medical management, was associated with similar systolic blood pressure (-0.90 mm Hg [95% CI, -6.99 to 5.07]) but fewer medications (1.7 [95% CI, -2.24 to -1.10]), and no difference in potassium levels or kidney or cardiovascular outcomes. CONCLUSIONS: Patients with PA and CKD are likely to benefit from either surgical adrenalectomy or medical management. Detection and treatment of PA may help to reduce blood pressure and medication burden in patients with CKD.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".