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Updated: Mar 16, 2026
In Vivo Mouse Model of Spinal Implant Infection
Published on: June 23, 2020
Risk factors for surgical site infection in elective routine degenerative lumbar surgeries
Istvan Klemencsics1, Aron Lazary2, Zsolt Szoverfi1
1National Center for Spinal Disorders, Buda Health Center, Budapest, Hungary; School of PhD Studies, Semmelweis University, Budapest, Hungary.
Background Context:
Surgical site infection (SSI) is one of the most serious complications of spine surgery. Its predisposing factors, especially in routine surgeries, are less reported. However, a number of patient- and procedure-related risk factors could be avoided or at least determined preoperatively. Moreover, the patient-specific risk for SSI could be estimated before the elective surgery.
Purpose:
The aim of the present study was to analyze the preoperatively determinable risk factors for SSI in patients who require elective routine surgery related to lumbar disc degeneration and to build a multivariable model for the individual risk prediction.
Study Design:
Analysis of prospectively collected standardized clinical data and the validation of the results on an independent prospective cohort were performed.
Patient Sample:
One thousand thirty (N=1,030) patients were included in the study. All subjects underwent primary lumbar single- or two-level decompression, microdiscectomy, or instrumented fusion.
Outcome Measures:
Occurrence of an SSI defined according to the current Centers for Disease Control and Prevention guidelines that required surgical or nonsurgical therapy.
Methods:
The effect of preoperative patient characteristics, comorbidities, disease history, and invasiveness of the elective surgery on the risk of SSI was determined in uni- and multivariate logistic regression models in the test cohort (N=723). The performance of the final multivariable regression model was assessed by measuring its discriminative ability (c-index) in receiver operating characteristic analysis. Performance of the multivariable risk estimation model was tested on the validation (N=307) cohort.
Results:
The prevalence of SSI was 3.5% and 3.9% in the test and in the validation cohorts, respectively. The final multivariable regression model predictive (p=.003) for SSI contained the patient's age, body mass index (BMI), and the presence of 5 comorbidities, such as diabetes, ischemic heart disease, arrhythmia, chronic liver disease, and autoimmune disease as risk factors. The c-index of the model was 0.71, showing good discriminative ability, and it was confirmed by the data of the independent validation cohort (c=0.72).
Conclusions:
Predisposing factors for SSI were older age, higher BMI, and the presence of certain comorbidities in the present study. The cumulative number of risk factors significantly associated with the increasing risk for an SSI (p<.0001). Our model needs further validation but it may be used for individual risk assessment and reduction in the future.
