A Practical Approach to Predicting Surgical Site Infection Risk Among Patients Before Leaving the Operating Room.

Autor:	Woods MS; Global Chief Medical Officer, Caresyntax Corp, Boston, USA., Ekstrom V; Director, Clinical Affairs, Caresyntax Corp, Boston, USA., Darer JD; Medical and Innovation Director, Health Analytics LLC, Maryland, USA., Tonkel J; Senior Vice President, Client Engagement Clinical Transformation, Caresyntax Corp, Boston, USA., Twick I; Data Scientist, Caresyntx Corp, Boston, USA., Ramshaw B; Co-Founder and CEO, CQInsights PBC, Knoxville, USA., Nissan A; Department of General and Oncological Surgery - Surgery C, Chaim Sheba Medical Center, Tel Aviv, ISR., Assaf D; Department of General and Oncological Surgery - Surgery C, Chaim Sheba Medical Center, Tel Aviv, ISR.
Jazyk:	angličtina
Zdroj:	Cureus [Cureus] 2023 Jul 18; Vol. 15 (7), pp. e42085. Date of Electronic Publication: 2023 Jul 18 (Print Publication: 2023).
DOI:	10.7759/cureus.42085
Abstrakt:	A surgical site infection (SSI) prediction model that identifies at-risk patients before leaving the operating room can support efforts to improve patient safety. In this study, eight pre-operative and five perioperative patient- and procedure-specific characteristics were tested with two scoring algorithms: 1) count of positive factors (manual), and 2) logistic regression model (automated). Models were developed and validated using data from 3,440 general and oncologic surgical patients. In the automated algorithm, two pre-operative (procedure urgency, odds ratio [OR]: 1.7; and antibiotic administration >2 hours before incision, OR: 1.6) and three intraoperative risk factors (open surgery [OR: 3.7], high-risk procedure [OR: 3.5], and operative time OR: [2.6]) were associated with SSI risk. The manual score achieved an area under the curve (AUC) of 0.831 and the automated algorithm achieved AUC of 0.868. Open surgery had the greatest impact on prediction, followed by procedure risk, operative time, and procedure urgency. At 80% sensitivity, the manual and automated scores achieved a positive predictive value of 16.3% and 22.0%, respectively. Both the manual and automated SSI risk prediction algorithms accurately identified at-risk populations. Use of either model before the patient leaves the operating room can provide the clinical team with evidence-based guidance to consider proactive intervention to prevent SSIs. Competing Interests: The authors have declared financial relationships, which are detailed in the next section. (Copyright © 2023, Woods et al.)
Databáze:	MEDLINE
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