目的 探讨肛周脓肿手术患者预后不良的影响因素,并构建风险量化评价模型。方法 回顾性选取2022-07至2024-03在联勤保障部队960医院行手术治疗的145例肛周脓肿患者,术后均随访6个月。根据预后情况将患者分为预后良好组(n=93)与预后不良组(n=52)。比较两组患者临床资料,采用多因素Logistic回归分析法筛选肛周脓肿手术患者预后不良的影响因素。基于多因素回归分析结果通过R软件和rms程序包构建风险量化诺莫图(Nomogram)评价模型,采用Bootstrap法行内部验证,绘制校准曲线和受试者工作特征(ROC)曲线评估模型的预测效能。结果 多因素Logistic分析结果显示,体质量指数(BMI)≥28 kg/m²、糖尿病、深部脓肿、马蹄形脓肿、单纯切开引流手术方式均为肛周脓肿手术患者预后不良的危险因素(P＜0.05);基于上述多因素分析结果构建肛周脓肿手术患者预后不良的风险量化Nomogram评价模型,经验证,其内部一致性指数(C-index)为0.884(95%CI:0.836~0.931),校准曲线显示校正曲线与理想曲线一致性良好,ROC曲线显示模型预测患者术后预后不良的灵敏度、特异度、曲线下面积分别为88.46%、89.25%、0.905(95%CI:0.851~0.959)。结论 BMI≥28 kg/m²、糖尿病、深部脓肿、马蹄形脓肿、单纯切开引流手术方式均为肛周脓肿手术患者预后不良的危险因素,基于此构建的预后不良风险量化Nomogram评价模型能较好地指导临床早期筛查预后不良高风险患者,及时制定相应干预对策改善预后。

Objective To explore the influencing factors for poor prognosis in patients after perianal abscess surgery, and construct a quantitative risk evaluation model. Methods A total of 145 patients with perianal abscess who underwent surgical treatment in the 960th Hospital of PLA Joint Logistics Support Force from July 2022 to March 2024 were retrospectively selected and followed up for 6 months. The patients were divided into a good prognosis group (n=93) and a poor prognosis group (n=52). The clinical data of the two groups were compared, and the influencing factors for poor prognosis in patients after perianal abscess surgery was screened using multiple logistic regression analysis. Based on the results of multiple regression analysis, a Nomogram evaluation model was constructed using R software and rms package. Bootstrap method was used for internal validation, and calibration curves and receiver operating characteristic (ROC) curves were drawn to evaluate the predictive efficiency of the model. Results Multivariate Logistic analysis showed that body mass index (BMI) ≥ 28 kg/m², diabetes, deep abscess, horseshoe abscess and simple incision and drainage surgery were risk factors for the poor prognosis (P<0.05). Based on the results of multifactor analysis, a Nomogram evaluation model was constructed to evaluate the risk of poor prognosis in patients with perianal abscess surgery. After verification, its internal consistency index (C-index) was 0.884 (95% CI: 0.836~0.931), the calibration curve showed that calibration curve had good consistency with the ideal curve, and the ROC curve showed that the sensitivity, specificity and area under the curve of the model for predicting poor prognosis were 88.46%, 89.25% and 0.905 (95% CI: 0.851~0.959) respectively. Conclusions BMI ≥ 28 kg/m², diabetes, deep abscess, horseshoe abscess, and simple incision and drainage are all risk factors for poor prognosis in patients with perianal abscess, and the quantitative risk Nomogram evaluation model based on this analysis can effectively guide the early screening of high-risk patients with poor prognosis and timely formulate corresponding intervention strategies to improve prognosis.