Title |
Predictors of In-hospital Postoperative Opioid Overdose After Major Elective Operations
|
---|---|
Published in |
Annals of Surgery, April 2017
|
DOI | 10.1097/sla.0000000000001945 |
Pubmed ID | |
Authors |
Christy E. Cauley, Geoffrey Anderson, Alex B. Haynes, Mariano Menendez, Brian T. Bateman, Karim Ladha |
Abstract |
The aim of this study was to describe national trends and outcomes of in-hospital postoperative opioid overdose (OD) and identify predictors of postoperative OD. In 2000, the Joint Commission recommended making pain the 5th vital sign, increasing the focus on postoperative pain control. However, the benefits of pain management must be weighed against the potentially lethal risk of opioid OD. This is a retrospective multi-institutional cohort study of patients undergoing 1 of 6 major elective inpatient operation from 2002 to 2011 using the Nationwide Inpatient Sample, an approximately 20% representative sample of all United States hospital admissions. Patients with postoperative OD were identified using ICD-9 codes for poisoning from opioids or adverse effects from opioids. Multivariate logistic regression was used to identify independent predictors. Among 11,317,958 patients, 9458 (0.1%) had a postoperative OD; this frequency doubled over the study period from 0.6 to 1.1 overdoses per 1000 cases. Patients with postoperative OD died more frequently during their hospitalization (1.7% vs 0.4%, P < 0.001). Substance abuse history was the strongest predictor of OD (odds ratio = 14.8; 95% confidence interval: 12.7-17.2). Gender, age, income, geographic location, operation type, and certain comorbid diseases also predicted OD (P < 0.05). Hospital variables, including teaching status, size, and urban/rural location, did not predict postoperative OD. Postoperative OD is a rare, but potentially lethal complication, with increasing incidence. Postoperative monitoring and treatment safety interventions should be thoughtfully employed to target high-risk patients and avoid this potentially fatal complication. |
Twitter Demographics
Geographical breakdown
Country | Count | As % |
---|---|---|
United States | 25 | 37% |
United Kingdom | 6 | 9% |
Spain | 4 | 6% |
Canada | 3 | 4% |
Germany | 2 | 3% |
Ireland | 1 | 1% |
Australia | 1 | 1% |
India | 1 | 1% |
Italy | 1 | 1% |
Other | 2 | 3% |
Unknown | 21 | 31% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Members of the public | 33 | 49% |
Practitioners (doctors, other healthcare professionals) | 18 | 27% |
Scientists | 13 | 19% |
Science communicators (journalists, bloggers, editors) | 3 | 4% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
Unknown | 79 | 100% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Other | 13 | 16% |
Researcher | 10 | 13% |
Student > Bachelor | 7 | 9% |
Professor > Associate Professor | 6 | 8% |
Student > Doctoral Student | 6 | 8% |
Other | 21 | 27% |
Unknown | 16 | 20% |
Readers by discipline | Count | As % |
---|---|---|
Medicine and Dentistry | 35 | 44% |
Pharmacology, Toxicology and Pharmaceutical Science | 6 | 8% |
Nursing and Health Professions | 6 | 8% |
Unspecified | 2 | 3% |
Psychology | 2 | 3% |
Other | 6 | 8% |
Unknown | 22 | 28% |