在成人细菌脑膜炎中开发和验证30天死亡率的预测模型:一个回顾性队列研究
Jun Zhou1,2, Jicheng Xing3, Xiangjun Cheng1,2
1Department of Laboratory Medicine, The First Affiliated Hospital With Nanjing Medical University, Nanjing, China.
Frontiers in medicine
|December 8, 2025
概括
一种新的脑膜图准确地预测了成人细菌脑膜炎患者的死亡率. 该工具使用年龄,神经复发症,多药耐药性感染,脑脊液中性粒细胞和C反应蛋白来识别高风险个体以获得更好的治疗.
科学领域:
- 传染性疾病 传染性疾病
- 临床医学 临床医学
- 流行病学 流行病学
背景情况:
- 细菌性脑膜炎仍然是死亡的重要原因,尽管抗菌药物治疗的进展.
- 早期识别高风险患者是临床实践中持续存在的挑战.
- 开发有效的风险分层工具对于改善患者治疗结果至关重要.
研究的目的:
- 开发和验证一种预测模型,用于识别患有细菌性脑膜炎的高风险成年患者.
- 将随时可用的临床和实验室参数纳入一个用户友好的工具.
- 帮助临床医生及时做出明智的治疗决策.
主要方法:
- 对277名成人细菌性脑膜炎患者 (2016-2024) 的回顾性分析.
- 患者被分为训练 (n=194) 和验证 (n=83) 队列.
- 最小绝对收缩和选择运算符 (LASSO) 和多变量回归被用来构建一个预测名录,通过AUC和决策曲线分析评估性能.
主要成果:
- 整体30天死亡率为29.2%.
- 死亡率的独立预测因素包括年龄,神经并发症,多抗药性 (MDR) 感染,脑脊髓液中性粒细胞百分比和血清C反应蛋白.
- 命名图表显示出良好的区别,AUC为0.851 (培训) 和0.814 (验证).
结论:
- 一个包含可访问的临床和实验室数据的验证预测模型为成人细菌脑膜炎提供了准确的风险分层.
- 这种名图可以帮助临床医生识别高风险患者,这些患者可能受益于加强监测和治疗.
- 该工具已证实在广泛的概率值范围内具有临床实用性.
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