在一次性膜性病中治疗失败的nomogram预测模型
Chanyu Geng1, Liming Huang1, Qiang Li2
1Department of Nephrology, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, China.
高的PLA2R抗体水平,炎症细胞透和C3沉积预测了初级膜性病 (PMN) 的治疗失败. 该模型有助于识别有风险的患者,以改善管理策略.
科学领域:
- 腎臟病學 (nephrology) 是一種醫學.
- 免疫学 免疫学 免疫学
- 内部医学 内部医学
背景情况:
- 初级膜性病 (PMN) 呈现出可变的临床过程.
- 免疫抑制疗法对于患有功能下降风险的PMN患者至关重要.
- 对于PMN治疗失败的预测模型很少.
研究的目的:
- 开发和验证一种预测模型,用于初级膜性病的治疗失败.
- 确定与PMN患者治疗失败相关的独立风险因素.
主要方法:
- 通过脏活检诊断的310名PMN患者的回顾性分析.
- 后勤回归分析以确定治疗失败的预测变量 (定义为12个月没有缓解).
- 模型性能使用C统计,校准图和决策曲线分析进行评估,并通过引导进行内部验证.
主要成果:
- 确定了PMN治疗失败的三个独立风险因素:PLA2R抗体标位 (OR=1.002),炎症细胞透 (OR=2.753) 和C3沉积在免疫光 (OR=0.217).
- 该预测模型实现了0.653的C统计值 (95%CI:0.590-0.717).
- 决策曲线分析显示,净收益在23%至77%之间.
结论:
- 在PMN中,PLA2R抗体水平,间歇性炎症和C3沉积是治疗失败的关键预测因素.
- 开发的预测模型显示了识别PMN患者治疗失败风险的潜力.
- 需要进一步改进,以提高模型的预测性能.
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