Postoperative Copeptin Concentration Predicts Diabetes Insipidus After Pituitary Surgery
Bibliographic record
Abstract
CONTEXT: Copeptin is a stable surrogate marker of vasopressin release; the peptides are stoichiometrically secreted from the neurohypophysis due to elevated plasma osmolality or nonosmotic stress. We hypothesized that following stress from pituitary surgery, patients with neurohypophyseal damage and eventual diabetes insipidus (DI) would not exhibit the expected pronounced copeptin elevation. OBJECTIVE: The objective was to evaluate copeptin's accuracy to predict DI following pituitary surgery. DESIGN: This was a prospective multicenter observational cohort study. SETTING: Three Swiss or Canadian referral centers were used. PATIENTS: Consecutive pituitary surgery patients were included. MEASUREMENTS: Copeptin was measured postoperatively daily until discharge. Logistic regression models and diagnostic performance measures were calculated to assess relationships of postoperative copeptin levels and DI. RESULTS: Of 205 patients, 50 (24.4%) developed postoperative DI. Post-surgically, median [25th-75th percentile] copeptin levels were significantly lower in patients developing DI vs those not showing this complication: 2.9 [1.9-7.9] pmol/L vs 10.8 [5.2-30.4] pmol/L; P < .001. Logistic regression analysis revealed strong association between postoperative copeptin concentrations and DI even after considering known predisposing factors for DI: adjusted odds ratio (95% confidence interval) 1.41 (1.16-1.73). DI was seen in 22/27 patients with copeptin <2.5 pmol/L (positive predictive value, 81%; specificity, 97%), but only 1/40 with copeptin >30 pmol/L (negative predictive value, 95%; sensitivity, 94%) on postoperative day 1. LIMITATIONS: Lack of standardized DI diagnostic criteria; postoperative blood samples for copeptin obtained during everyday care vs at fixed time points. CONCLUSIONS: In patients undergoing pituitary procedures, low copeptin levels despite surgical stress reflect postoperative DI, whereas high levels virtually exclude it. Copeptin therefore may become a novel tool for early goal-directed management of postoperative DI.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".