A nomogram model based on SII, AFR, and NLR to predict infectious complications of laparoscopic hysterectomy for cervical cancer

Abstract

Background: This study aimed to investigate the potential risk factors associated with postoperative infectious complications following laparoscopic hysterectomy for cervical cancer and to develop a prediction model based on these factors. Methods: This study enrolled patients who underwent selective laparoscopic hysterectomy for cervical cancer between 2019 and 2024. A multivariate regression analysis was performed to identify independent risk factors associated with postoperative infectious complications. A nomogram prediction model was subsequently constructed and evaluated using R software. Results: Out of 301 patients were enrolled and 38 patients (12.6%) experienced infectious complications within one month postoperatively. Six variables were independent risk factors for postoperative infectious complications: age ≥ 60 (OR: 3.06, 95% confidence interval (CI): 1.06–8.79, P = 0.038), body mass index (BMI) ≥ 24.0 (OR: 3.70, 95%CI: 1.4–9.26, P = 0.005), diabetes (OR: 2.91, 95% CI: 1.10–7.73, P = 0.032), systemic immune-inflammation index (SII) ≥ 830 (OR: 6.95, 95% CI: 2.53–19.07, P < 0.001), albumin-to-fibrinogen ratio (AFR) < 9.25 (OR: 4.94, 95% CI: 2.02–12.07, P < 0.001), and neutrophil-to-lymphocyte ratio (NLR) ≥ 3.45 (OR: 7.53, 95% CI: 3.04–18.62, P < 0.001). Receiver operator characteristic (ROC) curve analysis indicated an area under the curve (AUC) of this nomogram model of 0.928, a sensitivity of 81.0%, and a specificity of 92.1%. Conclusions: The nomogram model, incorporating age, BMI, diabetes, SII, AFR, and NLR, demonstrated strong predictive capabilities for postoperative infectious complications following laparoscopic hysterectomy for cervical cancer.