从临床参数预测狼性炎的组织学活性的一种名录图
Cui Gao1, Xueyan Bian2, Longlong Wu1
1Department of Nephrology, Fourth Affiliated Hospital, Zhejiang University School of Medicine, Yiwu, Zhejiang, China.
概括
这项研究开发了预测模型,以识别具有高组织学活性 (AI) 的狼性炎 (LN) 患者. 这些工具有助于临床医生评估结局和复发风险,改善患者管理.
科学领域:
- 腎臟病學 (nephrology) 是一種醫學專業.
- 免疫学 免疫学 免疫学
- 临床预测建模临床预测建模
背景情况:
- 病:改善全球结果 (KDIGO) 2021年指南强调了狼性炎 (LN) 管理中的组织学活性指数 (AI).
- 在LN患者中,高的AI与较差的结局和增加的复发率相关.
- 对人工智能的准确预测对于有效的NL患者护理至关重要.
研究的目的:
- 构建和验证用于识别狼性炎 (LN) 患者高组织学活性 (AI>2) 的预测模型.
- 开发一种定量工具,以帮助临床医生评估LN疾病活性.
- 为了改善狼性炎患者的临床决策.
主要方法:
- 开发了两种预测模型 (逻辑回归和LASSO),使用了337名LN患者的基线实验室数据.
- 在单独的测试队列中使用5倍交叉验证验证模型.
- 基于LASSO模型构建了一个名ogram和一个webtool,用于个人AI预测.
主要成果:
- 在LN中发现了高AI的关键预测因子:平均动脉压升高,EGFR降低,C3低,尿红细胞增加和阳性抗dsDNA抗体.
- 这两种模型在测试队列中都表现出强大的区分能力 (物流的AUC为0.855,LASSO为0.896).
- 创建了一个用户友好的网络工具,用于预测LN患者高AI的概率.
结论:
- 开发的名图和网络工具作为有价值的辅助工具,用于区分具有高组织活性的LN患者.
- 这些工具支持医生做出明智的临床决策,以管理狼性炎.
- 预测AI有助于分层患者进行风险评估和向治疗策略.
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