A Predictive Model for Wound Sepsis in Oncologic Surgery of the Head and Neck
- 1 February 1987
- journal article
- research article
- Published by Wiley in Otolaryngology -- Head and Neck Surgery
- Vol. 96 (2) , 165-171
- https://doi.org/10.1177/019459988709600209
Abstract
A prospective analysis of patients undergoing surgical resection of squamous cell carcinoma of the upper aerodigestive tract was performed in order to identify the patients at risk of postoperative wound infection and to develop a model predictive of wound infection. Fifty-nine patients who underwent extirpative clean-contaminated procedures—all of whom received cefazolin as the sole chemoprophylactic agent, were studied over a 1-year period. Twenty-three variables were recorded for each patient in the study. The overall rate of wound infection was 25.4%. Univariate analysis indicated that three variables were significantly related to the likelihood of postoperative wound infection. These included tumor stage (P = 0.0180), nodal stage (P = 0.0062), and duration of surgery (P = 0.0151). The Biomedical Computer Program (BMDP), a logistic regression program specifically designed for a binary dependent variable (infection vs. no infection) based on independent variables that may be continuous or categorical, was used in development of a model predictive of wound infection. T-stage, N-stage, and the presence of concomitant disease made up the combination of factors found to be most predictive of infection in our study population. Considering “success” to be the development of infection if the probability was 75% or higher, and the absence of infection if the probability was less than 25%, the multiple regression analysis model demonstrated a predictive success rate of 74.6%. Our results indicate that the risk of infection in patients undergoing clean-contaminated oncologic surgery of the head and neck is greatest for patients who have advanced disease that requires prolonged surgery in the presence of concomitant diseases. The correlates and predictive model generated by this study can be used as assurance that patients at risk for postoperative infection have been appropriately randomized in future prospective antibiotic trials planned at this institution.Keywords
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