Persistent Opioid Use After Cardiac Implantable Electronic Device Procedures
Bibliographic record
Abstract
Background: Prescription opioids are a major contributor to the ongoing epidemic of persistent opioid use (POU). The incidence of POU among opioid-naïve patients after cardiac implantable electronic device (CIED) procedures is unknown. Methods: This retrospective cohort study used data from a national administrative claims database from 2004 to 2018 of patients undergoing CIED procedures. Adult patients were included if they were opioid-naïve during the 180-day period before the procedure and did not undergo another procedure with anesthesia in the next 180 days. POU was defined by filling an additional opioid prescription >30 days after the CIED procedure. Results: Of the 143 400 patients who met the inclusion criteria, 15 316 (11%) filled an opioid prescription within 14 days of surgery. Among these patients, POU occurred in 1901 (12.4%) patients 30 to 180 days after surgery. The likelihood of developing POU was increased for patients who had a history of drug abuse (odds ratio, 1.52; P =0.005), preoperative muscle relaxant (odds ratio, 1.52; P <0.001) or benzodiazepine (odds ratio, 1.23; P =0.001) use, or opioid use in the previous 5 years (OR, 1.76; P <0.0001). POU did not differ after subcutaneous implantable cardioverter defibrillator or other CIED procedures (11.1 versus 12.4%; P =0.5). In a sensitivity analysis excluding high-risk patients who were discharged to a facility or who had a history of drug abuse or previous opioid, benzodiazepine, or muscle relaxant use, 8.9% of the remaining cohort had POU. Patients prescribed >135 mg of oral morphine equivalents had a significantly increased risk of POU. Conclusions: POU is common after CIED procedures, and 12% of patients continued to use opioids >30 days after surgery. Higher initially prescribed oral morphine equivalent doses were associated with developing POU.
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 | 0.000 | 0.000 |
| 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".