Relationship between Intraoperative Mean Arterial Pressure and Clinical Outcomes after Noncardiac Surgery
Bibliographic record
Abstract
BACKGROUND: Intraoperative hypotension may contribute to postoperative acute kidney injury (AKI) and myocardial injury, but what blood pressures are unsafe is unclear. The authors evaluated the association between the intraoperative mean arterial pressure (MAP) and the risk of AKI and myocardial injury. METHODS: The authors obtained perioperative data for 33,330 noncardiac surgeries at the Cleveland Clinic, Ohio. The authors evaluated the association between intraoperative MAP from less than 55 to 75 mmHg and postoperative AKI and myocardial injury to determine the threshold of MAP where risk is increased. The authors then evaluated the association between the duration below this threshold and their outcomes adjusting for potential confounding variables. RESULTS: AKI and myocardial injury developed in 2,478 (7.4%) and 770 (2.3%) surgeries, respectively. The MAP threshold where the risk for both outcomes increased was less than 55 mmHg. Compared with never developing a MAP less than 55 mmHg, those with a MAP less than 55 mmHg for 1-5, 6-10, 11-20, and more than 20 min had graded increases in their risk of the two outcomes (AKI: 1.18 [95% CI, 1.06-1.31], 1.19 [1.03-1.39], 1.32 [1.11-1.56], and 1.51 [1.24-1.84], respectively; myocardial injury 1.30 [1.06-1.5], 1.47 [1.13-1.93], 1.79 [1.33-2.39], and 1.82 [1.31-2.55], respectively]. CONCLUSIONS: Even short durations of an intraoperative MAP less than 55 mmHg are associated with AKI and myocardial injury. Randomized trials are required to determine whether outcomes improve with interventions that maintain an intraoperative MAP of at least 55 mmHg.
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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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".