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Georgia Quality Improvement Programs Multi-Institutional Collection of Postoperative Opioid Data Using ACS-NSQIP Abstraction.
Codner, Jesse A; Falconer, Elissa A; Ashley, Dennis W; Sweeney, John F; Saeed, Muhammad I; Langer, Jason M; Shaffer, Virginia O; Finley, Charles R; Solomon, Gina; Sharma, Jyotirmay.
Affiliation
  • Codner JA; Department of Surgery, 12239Emory University School of Medicine, Atlanta, GA, USA.
  • Falconer EA; Department of Surgery, 12239Emory University School of Medicine, Atlanta, GA, USA.
  • Ashley DW; Department of Surgery, 5223Navicent Health Medical Center, Macon, GA, USA.
  • Sweeney JF; Department of Surgery, 12239Emory University School of Medicine, Atlanta, GA, USA.
  • Saeed MI; Department of Surgery, 1421Augusta University School of Medicine, Augusta, GA, USA.
  • Langer JM; Department of Surgery, 232321Phoebe Putney Memorial, Albany, GA, USA.
  • Shaffer VO; Department of Surgery, 12239Emory University School of Medicine, Atlanta, GA, USA.
  • Finley CR; Department of Surgery, 12239Emory University School of Medicine, Atlanta, GA, USA.
  • Solomon G; Department of Surgery, Georgia Quality Improvement Program, Atlanta, GA, USA.
  • Sharma J; Department of Surgery, 12239Emory University School of Medicine, Atlanta, GA, USA.
Am Surg ; 88(7): 1510-1516, 2022 Jul.
Article in En | MEDLINE | ID: mdl-35333645
ABSTRACT

BACKGROUND:

Excessive postoperative opioid prescribing contributes to opioid misuse throughout the US. The Georgia Quality Improvement Program (GQIP) is a collaboration of ACS-NSQIP participating hospitals. GQIP aimed to develop a multi-institutional opioid data collection platform as well as understand our current opioid-sparing strategy (OSS) usage and postoperative opioid prescribing patterns.

METHODS:

This study was initiated 7/2019, when 4 custom NSQIP variables were developed to capture OSS usage and postoperative opioid oral morphine equivalents (OMEs). After pilot collection, our discharge opioid variable required optimization for adequate data capture and was expanded from a free text option to 4 drop-down selection variables. Data collection then continued from 2/2020-5/2021. Logistic regression was used to determine associations with OSS usage. Average OMEs were calculated for common general surgery procedures and compared to national guidelines.

RESULTS:

After variable optimization, the percentage where a total discharge prescription OME could be calculated increased from 26% to 70% (P < .001). The study included 820 patients over 10 operations. There was a significant variation in OSS usage between GQIP centers. Laparoscopic cases had higher odds of OSS use (1.92 (1.38-2.66)) while OSS use had lower odds in black patients on univariate analysis (.69 (.51-.94)). On average 7 out of the 10 cases had higher OMEs prescribed compared to national guidelines recommendations.

CONCLUSION:

Developing a multi-institutional opioid data collection platform through ACS-NSQIP is feasible. Preselected drop-down boxes outperform free text variables. GQIP future quality improvement targets include variation in OSS use and opioid overprescribing.
Subject(s)

Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Analgesics, Opioid / Opioid-Related Disorders Type of study: Observational_studies / Prognostic_studies / Risk_factors_studies Limits: Humans Country/Region as subject: America do norte Language: En Journal: Am Surg Year: 2022 Type: Article Affiliation country: United States

Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Analgesics, Opioid / Opioid-Related Disorders Type of study: Observational_studies / Prognostic_studies / Risk_factors_studies Limits: Humans Country/Region as subject: America do norte Language: En Journal: Am Surg Year: 2022 Type: Article Affiliation country: United States