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| PEI Honglei,ZHANG Chang,GUO Yafeng.Development of a prediction model of surgical site infection after posterior lumbar interbody fusion in patients based on random forest algorithm[J].Chinese Journal of Spine and Spinal Cord,2024,(2):177-185. |
| Development of a prediction model of surgical site infection after posterior lumbar interbody fusion in patients based on random forest algorithm |
| Received:May 17, 2023 Revised:September 07, 2023 |
| English Keywords:Lumbar posterior interbody fusion SSI Risk factors Predictive model Random forest |
| Fund:河北省卫生健康委医学科学课题（课题号：20221371） |
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| English Abstract: |
| 【Abstract】 Objectives: To analyze the risk factors related to infection after posterior lumbar interbody fusion(PLIF) by random forest algorithm and develop a prediction model, providing a certain reference for clinical prevention of surgical site infection(SSI) after PLIF. Methods: A retrospective study was conducted on the masked data of patients hospitalized for PLIF in the spinal surgery department of some third-level grade A hospitals in Beijing municipality and Hebei Province from June 2019 to June 2021 provided by Beijing Zhongwei Cloud Medical Data Analysis and Application Technology Research Institute through data processing and analysis. The classification data were analyzed and compared between SSI group and non-SSI group to obtain variables that significantly impacted the postoperative infection. SPSS Modeler 20 system was used as the tool for model development, and random forest algorithm was applied to analyze, obtaining the patient characteristics of postoperative infection, namely the infection model. Results: A total of 8,764 patients were included in study, and 373 patients were diagnosed with SSI, with an incidence rate of 4.4%(95%CI: 2.2% to 6.5%). After statistical analysis, six variables, including obesity, ASA Ⅲ and above, prolonged operative time, chronic heart disease, diabetes and renal dysfunction, were independently associated with SSI. Classification with a random forest model yielded a high accuracy of 90.6%. The characteristics of patients prone to infection after PLIF(two models of infection) was: [(BMI=1) and (SD=1) and (ASA=1) and (RI=1)] or [(BMI=0) and (SD=1) and (DM=1) and (RI=1)]. Conclusions: The random forest algorithm applied in this study could obtain an average accuracy of 90.6%, and two infection models were obtained as: (1)Patients with obesity, renal insufficiency, ASA grade Ⅲ or above, and operative time≥3h; (2)Patients who are not obese, but with diabetes, renal insufficiency, and the operative time≥3h. |
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