开发适应性临床基于网络的预测工具,用于慢性病儿童的替代疗法
Derek K Ng1, Matthew B Matheson1, George J Schwartz2
1Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA.
Kidney international
|June 30, 2023
概括
临床医生现在可以更好地预测慢性病 (CKD) 的儿童何时需要置换疗法 (KRT). 一个新的预测工具使用常见的临床数据来估计这个时间表,帮助治疗规划.
科学领域:
- 腎臟病學 (nephrology) 是一種醫學.
- 儿科医学 儿科医学
- 生物统计学 生物统计学
- 医疗信息学 医疗信息学
背景情况:
- 准确预测到置换疗法 (KRT) 的时间对于管理儿科慢性病 (CKD) 至关重要.
- 现有的儿科CKD预测模型可能缺乏全面的验证或临床应用的方便性.
研究的目的:
- 开发和验证一个预测工具,用于估计患有CKD的儿童的KRT时间.
- 基于开发的预测模型,为临床使用设计一个用户友好的在线计算器.
主要方法:
- 利用统计学学习,特别是随机生存森林,在CKiD研究中的890名儿童的数据上.
- 评估了172个潜在预测因素,包括社会人口统计学,脏/心血管健康和治疗使用,以及纵向变化.
- 开发了一个基本模型和一个使用最佳子集选择的丰富模型,并对欧洲儿科CKD队列进行进一步验证.
主要成果:
- 一个丰富的预测模型被确定,结合了诊断,eGFR,蛋白尿,血压,eGFR变化,贫血,白蛋白,化物和碳酸.
- 模型在基本模型的交叉验证和外部验证方面表现良好.
- 成功开发了一个用户友好的在线临床预测工具.
结论:
- 通过使用广泛的数据和先进的统计方法,开发了一种强大的临床预测工具,用于儿科CKD的KRT时间.
- 该工具显示出有希望的内部和外部验证结果,为临床医生提供了宝贵的资源.
- 建议对丰富模型进行进一步的外部验证,以确认其可通用性.
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