Derivation and internal–external validation of clinical prediction model for postoperative clinically important hypotension in patients undergoing noncardiac surgery: an international prospective cohort study
Bibliographic record
Abstract
Background: Intraoperative and postoperative hypotension are associated with myocardial injury/infarction, stroke, acute kidney injury, and death. Because of its prolonged duration, postoperative hypotension contributes more to the risk of organ injury compared with intraoperative hypotension. A prediction model for clinically important postoperative hypotension after noncardiac surgery is needed to guide clinicians. Methods: We performed a secondary analysis of the Vascular Events in Noncardiac Surgery Patients Cohort Evaluation (VISION) study. Patients aged ≥45 yr who had inpatient noncardiac surgery across 28 centres in 14 countries were included. In 14 of the centres selected at random (derivation cohort), we evaluated 49 variables using logistic regression to develop a model to predict postoperative clinically important hypotension, defined as a systolic blood pressure ≤90 mm Hg, that resulted in clinical intervention. The postoperative period was defined from the Post-Anesthesia Care Unit to hospital discharge. We then evaluated its calibration and discrimination in the other 14 centres (validation cohort). Results: Among 40 004 patients in VISION, 20 442 (51.1%) were included in the derivation cohort, and 19 562 (48.9%) patients were included in the validation cohort. The incidence of clinically important postoperative hypotension in the entire cohort was 12.4% (4959 patients). A 41-variable model predicted the risk of clinically important postoperative hypotension (bias-corrected C-statistic: 0.73, C-statistic in validation cohort: 0.72). A simplified prediction model also predicted clinically important hypotension (bias-corrected C-statistic: 0.68) based on four information items. Conclusions: Postoperative clinically important hypotension may be estimated before surgery using our primary model and a simple four-element model. Clinical trial registration: NCT00512109.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".