开发和验证用于预测严重流感的诺米克图
Mingzhen Zhao1, Bo Zhang1, Mingjun Yan1
1Pulmonary and Critical Care Medicine, Affiliated Hospital of Chengde Medical University, Chengde, Hebei, China.
Immunity, inflammation and disease
|September 28, 2024
概括
早期预测严重的流感是减少死亡率的关键. 使用骨髓氧化酶 (MPO) 和球蛋白 (HP) 水平以及疾病持续时间的新模型可以准确地识别高风险患者.
科学领域:
- 医学研究 医学研究
- 生物统计学 生物统计学
- 基因组学就是基因组学.
背景情况:
- 流感是一种严重的急性呼吸道疾病,具有重大公共卫生影响.
- 早期识别患有严重流感风险的患者可以降低死亡率.
研究的目的:
- 使用临床和分子数据开发和验证严重流感的预测模型.
- 评估特定生物标志物和临床因素与流感严重程度之间的关联.
主要方法:
- 从基因表达综合 (GEO) 数据库中分析了146名流感患者的数据.
- 使用R软件进行变量选择,包括最小绝对收缩和选择运算符 (LASSO) 和多变量后勤回归.
- 开发了一种用于严重流感预测的诺米克图,使用C指数,AUC,DCA和校准曲线进行验证.
主要成果:
- 在32.20%的患者中观察到严重的流感,并且与年龄和疾病持续时间相关.
- 多变量逻辑回归确定了骨髓氧化酶 (MPO) 水平,球蛋白 (HP) 水平和疾病持续时间作为重要预测因素.
- 开发的诺米图表实现了C指数和AUC的0.904.
结论:
- 纳米图包括MPO,HP水平和疾病持续时间,可以有效预测早期的严重流感.
- 该模型可以帮助指导重型流感病例的预防和治疗策略.
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