Background To create a nomogram prediction model for the efficacy of endoscopic nasal septoplasty, and the likelihood of patient benefiting from the operation. Methods A retrospective analysis of 155 patients with nasal septum deviation (NSD) was performed to develop a predictive model for the efficacy of endoscopic nasal septoplasty. Quality of life (QoL) data was collected before and after surgery using Sinonasal Outcome Test-22 (SNOT-22) scores to evaluate the surgical outcome. An effective surgical outcome was defined as a SNOT-22 score change ≥ 9 points after surgery. Multivariate logistic regression analysis was then used to establish a predictive model for the NSD treatment. The predictive quality and clinical utility of the predictive model were assessed by C-index, calibration plots, and decision curve analysis. Results The identified risk factors for inclusion in the predictive model were included. The model had a good predictive power, with a AUC of 0.920 in the training group and a C index of 0.911 in the overall sample. Decision curve analysis revealed that the prediction model had a good clinical applicability. Conclusions Our prediction model is efficient in predicting the efficacy of endoscopic surgery for NSD through evaluation of factors including: history of nasal surgery, preoperative SNOT-22 score, sinusitis, middle turbinate plasty, BMI, smoking, follow-up time, seasonal allergies, and advanced age. Therefore, it can be cost-effective for individualized preoperative assessment.

中文翻译：

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基于 SNOT-22 评分的内窥镜鼻中隔成形术预测模型：一项回顾性研究

背景 为内窥镜鼻中隔成形术的疗效以及患者从手术中受益的可能性创建列线图预测模型。方法对155例鼻中隔偏曲（NSD）患者进行回顾性分析，建立内镜下鼻中隔成形术疗效预测模型。使用鼻腔鼻腔结果测试 22 (SNOT-22) 评分在手术前后收集生活质量 (QoL) 数据以评估手术结果。有效的手术结果定义为手术后 SNOT-22 评分变化≥ 9 分。然后使用多变量逻辑回归分析建立NSD治疗的预测模型。通过 C 指数、校准图和决策曲线分析评估预测模型的预测质量和临床效用。结果 包括在预测模型中的已识别风险因素。该模型具有良好的预测能力，训练组的 AUC 为 0.920，整体样本的 C 指数为 0.911。决策曲线分析表明，该预测模型具有良好的临床适用性。结论 我们的预测模型通过评估因素包括：鼻部手术史、术前 SNOT-22 评分、鼻窦炎、中鼻甲成形术、BMI、吸烟、随访时间、季节性过敏、和高龄。因此，个体化的术前评估具有成本效益。911 在整体样本中。决策曲线分析表明，该预测模型具有良好的临床适用性。结论 我们的预测模型通过评估因素包括：鼻部手术史、术前 SNOT-22 评分、鼻窦炎、中鼻甲成形术、BMI、吸烟、随访时间、季节性过敏、和高龄。因此，个体化的术前评估具有成本效益。911 在整体样本中。决策曲线分析表明，该预测模型具有良好的临床适用性。结论 我们的预测模型通过评估因素包括：鼻部手术史、术前 SNOT-22 评分、鼻窦炎、中鼻甲成形术、BMI、吸烟、随访时间、季节性过敏、和高龄。因此，个体化的术前评估具有成本效益。吸烟、随访时间、季节性过敏和高龄。因此，个体化的术前评估具有成本效益。吸烟、随访时间、季节性过敏和高龄。因此，个体化的术前评估具有成本效益。