Department of Infectology, Shaoxing Maternity and ChildHealth Care Hospital, Shaoxing, China.
Department of Anesthesiology, Shaoxing Maternity and ChildHealth Care Hospital, Shaoxing, China.
Sci Rep. 2024 Sep 4;14(1):20538. doi: 10.1038/s41598-024-71592-z.
This study aimed to develop a predictive tool for surgical site infections (SSI) following hysterectomy and propose strategies for their prevention and control. We conducted a retrospective analysis at a tertiary maternity and child specialist hospital in Zhejiang Province, focusing on patients who underwent hysterectomy between January 2018 and December 2023 for gynecological malignancies or benign reproductive system diseases resistant to medical treatment. Risk factors associated with surgical site infections (SSI) following hysterectomy were identified using LASSO regression analysis on data from 2018 to 2022 as the training set. Independent risk factors were then used to develop a nomogram. The model was validated using data from 2023 as the validation set. Model performance was assessed using the area under the receiver operating characteristic curve (ROC), while calibration curves were employed to gauge model accuracy. Furthermore, clinical utility was evaluated through clinical decision curve analysis (DCA) and clinical impact curve analysis (CIC), providing insights into the practical application of the nomogram. Multivariate analysis identified six independent risk factors associated with SSI development after hysterectomy: BMI ≥ 24 kg/m (OR: 2.58; 95% CI 1.14-6.19; P < 0.05), hypoproteinaemia diagnosis (OR: 4.99; 95% CI 1.95-13.02; P < 0.05), postoperative antibiotic use for ≥ 3 days (OR: 49.53; 95% CI 9.73-91.01; P < 0.05), history of previous abdominal surgery (OR: 7.46; 95% CI 2.93-20.01; P < 0.05), hospital stay ≥ 10 days (OR: 9.67; 95% CI 2.06-76.46; P < 0.05), and malignant pathological type (OR: 4.62; 95% CI 1.78-12.76; P < 0.05). A nomogram model was constructed using these variables. ROC and calibration curves demonstrated good model calibration and discrimination in both training and validation sets. Analysis with DCA and CIC confirmed the clinical utility of the nomogram. Personalized nomogram mapping for SSI after hysterectomy enables early identification of high-risk patients, facilitating timely interventions to reduce SSI incidence post-surgery.
本研究旨在开发一种预测子宫切除术后手术部位感染（SSI）的工具，并提出预防和控制 SSI 的策略。我们在浙江省一家三级妇产专科医院进行了回顾性分析，研究对象为 2018 年 1 月至 2023 年 12 月期间因妇科恶性肿瘤或对药物治疗有抵抗力的良性生殖系统疾病而行子宫切除术的患者。使用 2018 年至 2022 年的数据进行 LASSO 回归分析，确定与子宫切除术后手术部位感染（SSI）相关的危险因素作为训练集。然后使用独立的危险因素来开发一个列线图。使用 2023 年的数据作为验证集来验证该模型。使用受试者工作特征曲线（ROC）下的面积来评估模型性能，同时使用校准曲线来评估模型的准确性。此外，通过临床决策曲线分析（DCA）和临床影响曲线分析（CIC）评估临床实用性，为列线图的实际应用提供了思路。多变量分析确定了与子宫切除术后 SSI 发生相关的六个独立危险因素：BMI≥24kg/m（OR：2.58；95%CI 1.14-6.19；P<0.05）、低蛋白血症诊断（OR：4.99；95%CI 1.95-13.02；P<0.05）、术后抗生素使用≥3 天（OR：49.53；95%CI 9.73-91.01；P<0.05）、既往腹部手术史（OR：7.46；95%CI 2.93-20.01；P<0.05）、住院时间≥10 天（OR：9.67；95%CI 2.06-76.46；P<0.05）和恶性病理类型（OR：4.62；95%CI 1.78-12.76；P<0.05）。使用这些变量构建了列线图模型。ROC 和校准曲线表明，在训练集和验证集上均具有良好的模型校准和区分能力。通过 DCA 和 CIC 分析，确认了列线图的临床实用性。个性化列线图映射有助于早期识别高风险患者，以便在手术后及时干预以降低 SSI 发生率。
