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Prediction tool for high risk of surgical site infection in spinal surgery

Published online by Cambridge University Press:  24 April 2020

Takanori Namba ,
Masaki Ueno ,
Gen Inoue ,
Takayuki Imura ,
Wataru Saito ,
Toshiyuki Nakazawa ,
Masayuki Miyagi ,
Eiki Shirasawa ,
Osamu Takahashi  and
Masashi Takaso
Takanori Namba
Affiliation:
Department of Orthopaedic Surgery, Sagamidai Hospital, Kanagawa, Japan
Masaki Ueno*
Affiliation:
Department of Orthopaedic Surgery, Machida Keisen Hospital, Tokyo, Japan
Gen Inoue
Affiliation:
Department of Orthopaedic Surgery, Kitasato University School of Medicine, Kanagawa, Japan
Takayuki Imura
Affiliation:
Department of Orthopaedic Surgery, Kitasato University School of Medicine, Kanagawa, Japan
Wataru Saito
Affiliation:
Department of Orthopaedic Surgery, Kitasato University School of Medicine, Kanagawa, Japan
Toshiyuki Nakazawa
Affiliation:
Department of Orthopaedic Surgery, Kitasato University School of Medicine, Kanagawa, Japan
Masayuki Miyagi
Affiliation:
Department of Orthopaedic Surgery, Kitasato University School of Medicine, Kanagawa, Japan
Eiki Shirasawa
Affiliation:
Department of Orthopaedic Surgery, Kitasato University School of Medicine, Kanagawa, Japan
Osamu Takahashi
Affiliation:
Department of General Internal Medicine, St. Luke’s International Hospital, Tokyo, Japan
Masashi Takaso
Affiliation:
Department of Orthopaedic Surgery, Kitasato University School of Medicine, Kanagawa, Japan
*
Author for correspondence: Masaki Ueno, E-mail: [email protected]
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Abstract

Objective:

The incidence of surgical site infection (SSI) is higher in spinal surgeries than in general orthopedic operations. In this study, we aimed to develop a scoring system with reduced health care costs for detecting spinal surgery patients at high risk for SSI.

Design:

Retrospective cohort study.

Patients:

In total, 824 patients who underwent spinal surgery at 2 university hospitals from September 2005 to May 2011.

Methods:

We reviewed the medical records of 824 patients, and we examined 19 risk factors to identify high-risk patients. After narrowing down the variables by univariate analysis, multiple logistic analysis was performed for factors with P values <.2, using SSI as a dependent variable. Only factors that showed P values <.05 were included in the final models, and each factor was scored based on the β coefficient values obtained. The clinical prediction rules were thereby prepared.

Results:

“Emergency operation,” “blood loss >400 mL,” “presence of diabetes,” “presence of skin disease,” and “total serum albumin value <3.2 g/dL” were detected by multivariable modeling and were incorporated into the risk scores. Applying these 5 independent predictive factors, we were able to predict the infection incidence after spinal surgery.

Conclusions:

Our present study could aid physicians in making decisions regarding prevention strategies in patients undergoing spinal surgery. Stratification of risks employing this scoring system will facilitate the identification of patients most likely to benefit from complex, time-consuming and expensive infection prevention strategies, thereby possibly reducing healthcare costs.

Type
Original Article
Information
Infection Control & Hospital Epidemiology , Volume 41 , Issue 7 , July 2020 , pp. 799 - 804
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Copyright
© 2020 by The Society for Healthcare Epidemiology of America. All rights reserved.

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