Myocardial Injury after Noncardiac Surgery and Perioperative Atrial Fibrillation: From Evidence to Clinical Practice
Bibliographic record
Abstract
One in 60 patients who undergo major noncardiac surgery dies within 30 days following surgery. The most common cause is cardiac complications, of which myocardial injury after noncardiac surgery (MINS) and perioperative atrial fibrillation (POAF) are common, affecting about 18 and 11% of adults, respectively, after noncardiac surgery. Patients who suffer MINS are at a higher risk of death compared to patients without MINS. Similarly, patients who develop POAF are at a higher risk of stroke and death compared to patients who do not. Most patients who suffer MINS are asymptomatic, and its diagnosis is not possible without routine troponin monitoring. Observational studies support the use of statins and aspirin in the management of patients with MINS. The only randomized controlled trial to date that has specifically addressed the management of MINS was the MANAGE trial that demonstrated the efficacy and safety of intermediate dose dabigatran in this population. There are no specific prediction models for POAF and no randomised controlled trial evidence to guide the specific management of POAF. Management guidelines in the acute period follow the management of nonoperative atrial fibrillation. The role of long-term anticoagulation in this population is still uncertain and should be guided by a shared care decision model with the patient, and with consideration of the individual risk for stroke balanced against the risk of bleeding. In this review, we present a case-based approach to the detection, prognosis, and management of MINS and POAF based on the existing evidence.
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 | 0.006 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.009 | 0.011 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".