使用泰国全国横截面研究,对未经控制的2型糖尿病进行预后诺摩图
Teeraboon Lertwanichwattana1, Picha Suwannahitatorn2, Mathirut Mungthin2
1Department of Military and Community Medicine, Phramongkutklao College of Medicine, Bangkok, Thailand.
PloS one
|April 10, 2024
概括
在泰国,一种新的名图有效地识别了2型糖尿病 (T2DM) 和高HbA1c水平的患者. 该工具有助于预测糖尿病并发症,改善资源有限的环境中的护理.
科学领域:
- 内分泌学 在内分泌学.
- 公共卫生 公共卫生
- 医疗信息学 医疗信息学
背景情况:
- 在泰国社区医院中,不受控制的2型糖尿病 (T2DM) 构成了重大挑战,因有限的血红蛋白A1c (HbA1c) 监测而加剧.
- 使用患者数据的预后名录可以识别患糖尿病并发症高风险的个体.
研究的目的:
- 开发和验证一种有效的预后名谱,用于预测泰国人口中不受控制的T2DM.
- 为资源有限的医疗保健机构提供风险分层的实用工具.
主要方法:
- 针对T2DM患者,从2018年 (开发) 和2015年 (验证) 开始使用了连续的全国性横截面研究.
- 定义的不受控制的T2DM为HbA1c ≥9%. 采用多变量回归分析来构建名图.
- 使用接收机操作特征曲线,Hosmer-Lemeshow测试和决策曲线分析 (DCA) 评估的诺姆图表性能.
主要成果:
- 在2018年,38,568名参与者中,有24%的人患有不受控制的T2DM (HbA1c ≥9%).
- 开发的名图集包括人口统计数据,药物,并发症史和实验室结果,达到0.77.7的C统计值.
- 适合性和DCA的良好性证实了nomogram在T2DM管理中的临床应用的强烈一致性.
结论:
- 预后诺基图是简单的,准确的,并有价值的增强糖尿病护理.
- 这种工具对泰国等资源有限的国家在管理T2DM方面特别有益.
- 纳米图有助于识别高风险患者,可能减少糖尿病并发症的负担.
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