嗜酸性粒细胞慢性鼻炎伴鼻息肉术后复发的风险因素:建立预测模型。

IF 1.7 4区 医学 Q3 MEDICINE, RESEARCH & EXPERIMENTAL
American journal of translational research Pub Date : 2024-10-15 eCollection Date: 2024-01-01 DOI:10.62347/UJWU7059
Li Lin, Bi Deng, Chenghong Guo, Changshu Zhuo, Lan Luo, Bangshu Zhao, Xiaozhu Zheng, Jianhua Xu
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引用次数: 0

摘要

目的确定嗜酸性粒细胞慢性鼻炎伴鼻息肉(ECRSwNP)患者术后复发的风险因素,并建立一个识别高复发风险患者的提名图预测模型:一项涉及 200 名 ECRSwNP 患者的队列研究采用单变量和多变量逻辑回归分析法对复发预测因素的临床数据进行了分析。利用接收者操作特征曲线(ROC)建立并验证了一个提名图模型。平均绝对误差(MAE)计算评估了模型的预测准确性:随访六个月时,39 名患者(19.5%)复发。术前组织嗜酸性粒细胞百分比、嗜酸性粒细胞阳离子蛋白(ECP)、血清特异性免疫球蛋白 E(IgE)、白细胞介素-5(IL-5)和术后鼻腔环境等因素被确定为复发的风险因素。包含这些因素的提名图显示出很高的预测准确性(AUC = 0.989,MAE = 0.026):本研究强调了个体化风险评估对控制 ECRSwNP 复发的重要意义。所开发的提名图为临床预后提供了强有力的工具,有助于制定个性化治疗策略并改善患者预后。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Risk factors for postoperative recurrence in eosinophilic chronic rhinosinusitis with nasal polyps: development of a prediction model.

Objective: To identify risk factors for postoperative recurrence in patients with eosinophilic chronic rhinosinusitis with nasal polyps (ECRSwNP) and develop a nomogram prediction model for identifying patients at high risk of recurrence.

Methods: A cohort study involving 200 ECRSwNP patients analyzed clinical data for recurrence predictors using univariate and multivariate logistic regression analyses. A nomogram model was developed and validated using Receiver Operating Characteristic (ROC) curves. Mean absolute error (MAE) calculations evaluated the model's predictive accuracy.

Results: At six-month follow-up, 39 patients (19.5%) experienced recurrence. Factors such as preoperative tissue eosinophil percentage, eosinophil cationic protein (ECP), serum-specific immunoglobulin E (IgE), interleukin-5 (IL-5), and postoperative nasal environment were identified as risk factors for recurrence. The nomogram incorporating these factors demonstrated high predictive accuracy (AUC = 0.989, MAE = 0.026).

Conclusion: This study underscores the significance of individualized risk assessment in managing ECRSwNP recurrence. The developed nomogram provides a robust tool for clinical prognostication, aiding personalized treatment strategies and improving patient outcomes.

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American journal of translational research
American journal of translational research ONCOLOGY-MEDICINE, RESEARCH & EXPERIMENTAL
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