Based on Logistic regression analysis, the prediction model of joint detection factors for postoperative incision infection in spinal surgery was established

Abstract

Abstract OBJECTIVES A retrospective study was conducted on patients who underwent spinal implant surgery in our hospital from June 2019 to June 2022. The predictive model of postoperative incision infection combined detection factor was constructed by Logistic regression analysis and other statistical methods, and receiver operating characteristic curve (ROC) was drawn to analyze the application value of the model. METHODS A total of 888 patients who underwent spinal surgery in our hospital from June 2019 to June 2022 were included. The patients' age, hypertension, diabetes and other disease history were retrospectively analyzed. Patients who underwent blood cell analysis and C-reactive protein serological detection 3 days after surgery were screened out, and relevant clinical data were collected. The independent risk factors of SSI were screened out by Lasso regression and Logistics regression analysis, and the prediction model of SSI joint detection factors was established according to the independent risk factors, and the application value of the model was analyzed by receiver operating characteristic curve (ROC) and calibration curve. RESULTS A total of 16 risk factors of 888 patients were analyzed by Lasoo regression model. Gender (regression coefficient: -0.241), age (regression coefficient: 0.0382), hypertension (regression coefficient: -0.826), diabetes (regression coefficient: 1.953), smoking history (regression coefficient: 0.692) 5 related predictors. Logistic regression analysis of Lasoo analysis results showed that age: (OR= 1.024,95%CI: 0.984-1.169), smoking history: (OR=1.512,95%CI :0.416-4.513), diabetes: (OR=5.898,95%CI: 2.075-16.240); CRP: (OR= 1.029,95%CI: 1.020-1.039) four independent risk factors. Combined factor prediction Normogram was established according to age, diabetes, smoking history and C-reactive protein value 3 days after operation. The C index of the Normograph model based on the above predictive factors was 0.9, and the AUC value was 0.900. The calibration curve shows that the predicted results are in good agreement with the observed results。 CONCLUSION The combined prediction model based on age, diabetes, smoking history and C-reactive protein 3 days after surgery has potential clinical application value for surgical site infection after spinal surgery.