Surgical endoscopy
October 21, 2026 · via Europe PMC
An updated analysis of discharge opioid-prescribing patterns across surgical specialties in the USA.
Filardi KFXC, Saeidishahri S, Wu Y, Bond DS, Tishler DS, Fillingim RB, Serrano OK, Papasavas PK.
AI summary — for doctors, generated from the abstract only
Three in four US surgical patients still leave hospital with an opioid prescription, and rates vary widely by specialty and procedure.
What was studied
Cross-sectional analysis of 757,548 procedures in the 2024 ACS-NSQIP database, describing opioid receipt at discharge and daily dose in morphine milligram equivalents.
Key findings
- 74.7% of patients received an opioid prescription at discharge.
- Median doses were 30-45 MME/day and 4.1% received a refill.
- Adjusted prescribing probability ranged from 54.1% in cardiac surgery to 85.3% in thoracic surgery.
- Several common procedures (e.g. knee and hip arthroplasty, spine fusion) exceeded 83%.
Clinical relevance
Gives current benchmarks surgeons can compare against to reduce unwarranted variation in postoperative opioids.
Who should care
Limitations
Descriptive data from participating NSQIP hospitals with no information on appropriateness, pain control or outcomes such as misuse.
Compare your own discharge prescribing with these benchmarks and consider tailoring or limiting opioids where procedures are low-pain.
This is an AI-generated summary of the published abstract, not a clinical recommendation. It may not reflect the full text or subsequent updates — verify against the original source before acting on it.
Abstract
Background
Opioid overdose still accounts for substantial deaths and hospitalizations in the United States. Opioids continue to be commonly prescribed for postoperative pain. Although variation in postoperative opioid prescribing has been previously described, contemporary national data using newly available discharge-prescribing variables may provide useful benchmarks across surgical specialties and procedures. We sought to describe current patterns of opioid prescribing at discharge across surgical specialties and high-volume procedures in the United States.
Methods
This cross-sectional analysis used the 2024 American College of Surgeons National Surgical Quality Improvement Program (ACS-NSQIP) database and included adults undergoing selected procedures in the United States. The primary outcomes were the receipt of an opioid prescription at discharge and quantify the daily discharge opioid dose, expressed as morphine milligram equivalents per day (MME/day).
Results
Among 757,548 selected surgical procedures (mean [SD] age of patients, 56.35 [16.97] years; 441,799 [58.32%] female patients), 565,959 patients (74.71%) received an opioid prescription at discharge. Median daily opioid doses ranged from 30 to 45 MME/day across surgical specialties, and 4.1% of patients received a refill. Adjusted probabilities of receiving an opioid prescription at discharge varied by specialty: from 54.1% (95% CI 52.0%-55.9%) in cardiac surgery and 60.0% (95% CI 59.2%-60.7%) in vascular surgery to 84.3% (95% CI 84.1-84.5%) in orthopedics and 85.3% (95% CI, 84.7-85.9%) in thoracic surgery. Laparoscopic hernia repair, knee arthroplasty, hip arthroplasty, spine fusion, and laminectomy/corpectomy each had an adjusted probability of opioid prescription at discharge that exceeded 83%.
Conclusions and relevance
This study provides an updated descriptive benchmark of postoperative opioid-prescribing patterns across surgical specialties and selected high-volume procedures. This evidence offers a foundation for strategic actions to reduce variation and improve patient care.
DOI
10.1007/s00464-026-13518-6
PMID
42907591
Research metadata (titles, abstracts, authors, publication dates) is sourced from Europe PMC, which aggregates content from PubMed/MEDLINE, PubMed Central, and other providers under the U.S. National Library of Medicine (NLM/NCBI). NLM does not claim copyright on abstracts; the original journal publishers or authors may hold rights to the full articles. AI-generated summaries are derived only from the abstract, never the full text, and are always labeled as AI-generated.
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