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A SCORING SYSTEM TO PREDICT THE RISK OF SURGICAL SITE INFECTIONS AFTER SPINAL SURGERY

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NIAID Data Ecosystem2026-03-13 收录
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https://figshare.com/articles/dataset/A_SCORING_SYSTEM_TO_PREDICT_THE_RISK_OF_SURGICAL_SITE_INFECTIONS_AFTER_SPINAL_SURGERY/19906441
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ABSTRACT Objective This retrospective clinical study was carried out to generate and cross-validate a scoring system for the identification of patients at risk of SSIs after spinal surgery. Methods A retrospective study was conducted, which included patients who underwent spinal surgery. The potential variables for SSIs were extracted from the database, including preoperative, intraoperative and postoperative risk factors for univariate and multivariate regression analyses. Results A total of 2347 patients were included in this retrospective clinical study. Postoperative SSIs were observed in 53 patients (2.2%). The multivariate logistic regression analysis revealed the following risk factors for SSIs after spinal surgery: diabetes mellitus ( P =0.029), body mass index ( P =0.008), low serum calcium concentration ( P =0.012), low pre- and postoperative albumin ( P =0.023, P =0.037), more than three operated segments ( P =0.008), operation time of more than 180 minutes ( P =0.019), estimated blood loss ( P =0.011), low postoperative hemoglobin ( P =0.017) and prolonged drainage time ( P =0.025). Each of these factors contributed 1 point to the risk score. The predicted rates of incidence for the low-, intermediate-, high-, and extremely high-risk categories in the validation set were 1.4%, 12%, 41.6%, and 66.6%, respectively. Conclusions Our scoring system allows for easy and validated risk stratification of SSIs after spinal surgery. Level of evidence III; Cross-sectional Observational Study.
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2021-09-01
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