前列腺穿刺活检术术后感染的危险因素分析及预测模型建立

Analysis of risk factors and establishment of a prediction model for infection after prostate biopsy

摘要

目的 分析前列腺穿刺活检术（PB）患者术后发生感染的危险因素，建立列线图预测模型并进行验证。方法 收集2023年1月-2024年7月于山东第一医科大学附属省立医院泌尿外科行超声引导下PB的523例患者的临床资料。根据术后是否出现感染将患者分为感染组和未感染组。采用单因素及多因素二元logistic回归分析筛选影响PB术后感染的独立危险因素，并构建列线图预测模型。采用受试者工作特征（ROC）曲线、校准曲线和决策曲线分析（DCA）评估与验证该列线图模型的预测效能。结果 523例行PB的患者中有54例（10.3%）发生术后感染。单因素和多因素logistic回归分析显示，＞65岁（OR=3.535，P=0.003）、糖尿病（OR=5.693，P＜0.001）、低蛋白血症（OR=8.936，P＜0.001）、术前尿路感染（OR=6.153，P＜0.001）、穿刺针数＞12针（OR=4.347，P＜0.001）、经直肠穿刺（OR=3.701，P＜0.001）是PB术后感染的独立危险因素。基于多因素logistic回归分析结果构建PB术后感染的风险预测列线图模型，该模型ROC曲线下面积（AUC）为0.894，校准曲线和DCA均表明该模型具有较高的预测精度和临床决策效率。结论 ＞65岁、有糖尿病、低蛋白血症、术前尿路感染、穿刺针数＞12针、经直肠穿刺是PB术后感染的独立危险因素。基于上述因素构建的列线图预测模型有助于筛选出PB术后感染的高危患者，从而制定个体化治疗方案，降低PB术后感染的发生率。

Abstract

Objective To analyze the risk factors leading to infection after prostate biopsy, establish a nomogram prediction model and verify it. Methods Clinical data of 523 patients who underwent ultrasound-guided prostate biopsy at our hospital during Jan. 2023 and Jul. 2024 were retrospectively analyzed. Patients were divided into an infection group and a non-infection group. Independent risk factors for infection after prostate biopsy were identified with univariate and multivariate binary logistic regression analyses, and a nomogram prediction model was constructed, which was validated with receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis (DCA). Results Infection occurred in 54 cases (10.3%). Univariate and multivariate logistic regression analyses showed that age >65 years (OR=3.535, P=0.003), diabetes (OR=5.693, P<0.001), hypoproteinemia (OR=8.936, P<0.001), preoperative urinary tract infection (OR=6.153, P<0.001), puncture needles >12 (OR=4.347, P<0.001), and transrectal puncture (OR=3.701, P<0.001) were independent risk factors for infection. Based on the multivariate logistic analysis results, a risk prediction nomogram model was constructed, with an area under the ROC curve (AUC) of 0.894. The calibration curve and DCA both indicated that the model had high predictive accuracy and clinical decision-making efficiency. Conclusion Age >65 years, diabetes, hypoproteinemia, preoperative urinary tract infection, puncture needles >12, and transrectal puncture are independent risk factors for infection after prostate biopsy. The nomogram prediction model based on these factors helps identify high-risk patients, thereby enabling individualized treatment plans to reduce the incidence of infection.

关键词

前列腺癌 / 前列腺穿刺活检术 / 术后感染 / 列线图预测模型

Key words

prostate cancer / prostatic biopsy / postoperative infection / nomogram prediction model

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基金资助

山东省自然科学基金项目(ZR2021MH283)