Effect of Delayed Parathyroidectomy on Risk of Future Cardiovascular and Nephrolithiasis Interventions in Adults With Primary Hyperparathyroidism
Bibliographic record
Abstract
OBJECTIVE: To determine whether the timing of parathyroid surgery impacts the risk of renal stone retreatment and cardiovascular interventions. BACKGROUND: Long-term, untreated primary hyperparathyroidism (pHPT) is associated with significant morbidity, including nephrolithiasis and cardiovascular disease. METHODS: We conducted a population-based cohort study of New York and California state-wide data from 2000 to 2020. Adult patients who underwent renal stone treatment and were subsequently diagnosed with pHPT and underwent parathyroidectomy (PTX) were included. Patients were excluded if PTX was before the index stone procedure, they underwent second stone treatment within 6 months, with stage V chronic kidney disease, with secondary or tertiary hyperparathyroidism, with prior kidney transplant or hemodialysis, or with prior cancer diagnosis. The rate of renal stone retreatment and cardiovascular interventions after PTX in patients with pHPT with nephrolithiasis who underwent parathyroid surgery at ≤2 years and >2 years after the index stone procedure was measured. RESULTS: We identified 2093 patients who underwent first-time stone treatment and subsequent PTX. The median time to PTX was 560 days (interquartile range: 187-1477), and follow-up was 7.4 years (interquartile range: 4.5-13.1). Delaying PTX for more than 2 years increased the risk of renal stone retreatment by 59% (HR=1.59; P <0.001), increased the risk of experiencing coronary disease or associated interventions by 118% (HR=2.18; P =0.01), and increased the risk of experiencing an overall cardiovascular event by 52% (HR=1.52; P <0.01). CONCLUSIONS: In symptomatic pHPT, delaying PTX significantly increases the risk of requiring future stone retreatment and cardiac/vascular surgical interventions. This highlights the importance of early surgical referral and multidisciplinary approaches to optimize outcomes and resource utilization in pHPT.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".