使用机器学习来识别风险因素,并建立一个临床预测模型来预测神经样硬化症并发症在异形膜性脏病的预测

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Yipeng Chen¹, Ying He², Guangqun Xing¹

¹Department of Nephropathy, Affiliated Hospital of Qingdao University, 266003 Qingdao, Shandong, China.

Discovery medicine

|August 8, 2023

概括

异形膜性病 (IMN) 患者面临更高的冠状动脉心脏病风险. 一个新的模型确定了年龄和脂质水平等关键风险因素,改善了心脏病IMN患者的诊断.

科学领域:

腎臟病學 (nephrology) 是一種醫學專業.
心脏病学心脏病学
医学诊断医学诊断医学诊断

背景情况:

异形膜性病 (IMN) 在临床上与冠状动脉心脏病 (CHD) 的风险增加有关.
了解IMN患者心血管疾病的特定风险因素对于早期干预和管理至关重要.
机理学方法为研究这些复杂的关系提供了一个新的视角.

研究的目的:

识别和分析导致冠心病 (CHD) 的风险因素,在被诊断为异常性膜性病 (IMN) 的患者中.
开发和验证IMN患者群体中CHD的预测性临床诊断模型.
利用机理学方法,更深入地了解IMN中的CHD病原性.

主要方法:

追溯分析了162名接受抗脂酶A2受体 (抗PLA2R) 测试的患者,其中73人被诊断患有心脏病.
使用曼 - 惠特尼U测试进行初始因子选和最小绝对收缩和选择操作员 (LASSO) 回归进行特征选择.
使用视觉统计方法评估模型性能,包括名图,C指数和临床决策曲线.

主要成果:

确定的主要危险因素包括年龄,淋巴细胞计数,HDL/LDL综合水平,血清肌素和抗血三 (p < 0.1).
拉索回归选择了14个临床特征,在训练组中达到0.90的曲线下面积 (AUC),在验证组中达到0.84.
开发的临床预测模型在培训和验证队列中都显示出高准确度,灵敏度和特异性.

结论:

一个有效的临床预测模型冠状动脉心脏病在异形膜性病患者已经成功建立.
该模型为增强IMN患者临床决策和风险分层提供了有价值的工具.
这些发现强调了综合评估IMN患者心血管风险的重要性.

关键词:

动脉样硬化 (AS) 的疾病异常病变的膜性脏病 (IMN)机器学习是机器学习.预测模型预测模型

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