Prevalence of and risk factors for persistent postoperative nonanginal pain after cardiac surgery: a 2-year prospective multicentre study
Bibliographic record
Abstract
BACKGROUND: Persistent postoperative pain continues to be an underrecognized complication. We examined the prevalence of and risk factors for this type of pain after cardiac surgery. METHODS: We enrolled patients scheduled for coronary artery bypass grafting or valve replacement, or both, from Feb. 8, 2005, to Sept. 1, 2009. Validated measures were used to assess (a) preoperative anxiety and depression, tendency to catastrophize in the face of pain, health-related quality of life and presence of persistent pain; (b) pain intensity and interference in the first postoperative week; and (c) presence and intensity of persistent postoperative pain at 3, 6, 12 and 24 months after surgery. The primary outcome was the presence of persistent postoperative pain during 24 months of follow-up. RESULTS: A total of 1247 patients completed the preoperative assessment. Follow-up retention rates at 3 and 24 months were 84% and 78%, respectively. The prevalence of persistent postoperative pain decreased significantly over time, from 40.1% at 3 months to 22.1% at 6 months, 16.5% at 12 months and 9.5% at 24 months; the pain was rated as moderate to severe in 3.6% at 24 months. Acute postoperative pain predicted both the presence and severity of persistent postoperative pain. The more intense the pain during the first week after surgery and the more it interfered with functioning, the more likely the patients were to report persistent postoperative pain. Pre-existing persistent pain and increased preoperative anxiety also predicted the presence of persistent postoperative pain. INTERPRETATION: Persistent postoperative pain of nonanginal origin after cardiac surgery affected a substantial proportion of the study population. Future research is needed to determine whether interventions to modify certain risk factors, such as preoperative anxiety and the severity of pain before and immediately after surgery, may help to minimize or prevent persistent postoperative pain.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".