The occurrence of intra‐operative hypotension varies between hospitals: observational analysis of more than 147,000 anaesthesia
Bibliographic record
Abstract
BACKGROUND: Hypotension, a common intra-operative incident, bears an important potential for morbidity. It is most often manageable and sometimes preventable, which renders its study important. Therefore, we aimed at examining hospital variations in the occurrence of intra-operative hypotension and its predictors. As secondary endpoints, we determined to what extent hypotension relates to the risk of post-operative incidents and death. METHODS: We used the Anaesthesia Databank Switzerland, built on routinely and prospectively collected data on all anaesthesias in 21 hospitals. The three outcomes were assessed using multi-level logistic regression models. RESULTS: Among 147,573 anaesthesias, hypotension ranged from 0.6% to 5.2% in participating hospitals, and from 0.3% up to 12% in different surgical specialties. Most (73.4%) were minor single events. Age, ASA status, combined general and regional anaesthesia techniques, duration of surgery and hospitalization were significantly associated with hypotension. Although significantly associated, the emergency status of the surgery had a weaker effect. Hospitals' odds ratios for hypotension varied between 0.12 and 2.50 (P < or = 0.001), even after adjusting for patient and anaesthesia factors, and for type of surgery. At least one post-operative incident occurred in 9.7% of the procedures, including 0.03% deaths. Intra-operative hypotension was associated with a higher risk of post-operative incidents and death. CONCLUSION: Wide variations remain in the occurrence of hypotension among hospitals after adjustment for risk factors. Although differential reporting from hospitals may exist, variations in anaesthesia techniques and blood pressure maintenance may also have contributed. Intra-operative hypotension is associated with morbidities and sometimes death, and constant vigilance must thus be advocated.
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 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".