Predictive modeling for identifying infection risk following spinal surgery: Optimizing patient management

Wang,Ruiyu; Xiao,Jie; Gao,Qi; Xu,Guangxin; Ni,Tingting; Zou,Jingcheng; Wang,Tingting; Luo,Ge; Cheng,Zhenzhen; Wang,Ying; Tao,Xinchen; Sun,Dawei; Yao,Yuanyuan; Yan,Min

doi:10.3892/etm.2024.12569

Predictive modeling for identifying infection risk following spinal surgery: Optimizing patient management

Authors:
- Ruiyu Wang
- Jie Xiao
- Qi Gao
- Guangxin Xu
- Tingting Ni
- Jingcheng Zou
- Tingting Wang
- Ge Luo
- Zhenzhen Cheng
- Ying Wang
- Xinchen Tao
- Dawei Sun
- Yuanyuan Yao
- Min Yan
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Affiliations: Department of Anesthesiology, Weifang Medical University, Weifang, Shandong 261041, P.R. China, Department of Anesthesiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang 330100, P.R. China, Department of Anesthesiology, Xuzhou Medical University, Xuzhou, Jiangsu 221004, P.R. China
Published online on: May 13, 2024 https://doi.org/10.3892/etm.2024.12569
Article Number: 281
Copyright: © Wang et al. This is an open access article distributed under the terms of Creative Commons Attribution License.

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Abstract

Infection is known to occur in a substantial proportion of patients following spinal surgery and predictive modeling may provide a useful means for identifying those at higher risk of complications and poor prognosis, which could help optimize pre‑ and postoperative management strategies. The outcome measure of the present study was to investigate the occurrence of all‑cause infection during hospitalization following scoliosis surgery. To meet this aim, the present study retrospectively analyzed 370 patients who underwent surgery at the Second Affiliated Hospital, Zhejiang University School of Medicine (Hangzhou, China) between January 2016 and October 2022, and patients who either experienced or did not experience all‑cause infection while in hospital were compared in terms of their clinicodemographic characteristics, surgical variables and laboratory test results. Logistic regression was subsequently applied to data from a subset of patients in order to build a model to predict infection, which was validated using another subset of patients. All‑cause, in‑hospital postoperative infections were found to have occurred in 66/370 patients (17.8%). The following variables were included in a predictive model: Sex, American Society of Anesthesiologists (ASA) classification, body mass index (BMI), diabetes mellitus, hypertension, preoperative levels of white blood cells and preoperative C‑reactive protein (CRP) and duration of surgery. The model exhibited an area under the curve of 0.776 against the internal validation set. In conclusion, dynamic nomograms based on sex, ASA classification, BMI, diabetes mellitus, hypertension, preoperative levels of white blood cells and CRP and duration of surgery may have the potential to be a clinically useful predictor of all‑cause infection following scoliosis. The predictive model constructed in the present study may potentially facilitate the real‑time visualization of risk factors associated with all‑cause infection following surgical procedures.

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