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预测女性患有慢性骨盆疼痛的风险因素和名录构造:一个横截面人口研究

Mingyue Zhu¹, Fei Huang², Jingyun Xu^3,1

¹School of Medicine, Southeast University, Nanjing, 210009, Jiangsu, China.

|August 19, 2024

概括

这项研究确定了女性慢性骨盆疼痛 (CPP) 的关键风险因素,包括体重,经期失调和骨盆炎症疾病史. 一个新的名图有效地预测了CPP风险,有助于早期识别和管理这种情况.

科学领域:

妇科妇科妇科妇科
妇女健康妇女健康
疼痛管理疼痛管理

背景情况:

慢性盆腔疼痛 (CPP) 显著影响女性的健康和医疗保健系统.
目前对CPP风险因素和预测模型的研究是有限的.
了解CPP病因学对于有效的诊断和治疗至关重要.

研究的目的:

确定与女性慢性骨盆疼痛相关的独立风险因素.
开发和验证用于识别高风险CPP妇女的预测性名录.
为慢性骨盆疼痛的临床风险评估提供一个实用的工具.

主要方法:

一项前性研究,涉及问卷调查和骨盆地表面电肌图.
使用多变量逻辑回归来构建CPP的预测模型.
使用区分,校准和决策曲线分析对名ogram的内部和外部验证.

主要成果:

在1108名女性中,有169名女性 (15.3%) 被诊断患有CPP.
确定的独立预测因素包括体重,经期障碍,性功能障碍,尿失禁,骨盆炎症病史和电肌图学值.
在培训和验证组中,开发的名图表显示出强大的预测性能,AUC为0.85,在培训和验证组中均为0.85.

结论:

体重,更年障碍,性功能障碍,尿失禁,盆腔炎症病史和特定的电肌图值是CPP的重要预测因素.
这项研究成功开发了一种易于使用的名图,用于预测女性慢性骨盆疼痛.
这种工具可以帮助临床医生识别和管理高风险患CPP的妇女.

关键词:

经期不良症经期不良症经期不良症疼痛过敏过敏对疼痛的过敏预测模型的预测模型.表面电力学图 (surface electromyography) 是一种表面电力学图.

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