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智能方法预测在患有深腔的患者进行排水手术后进入重症监护室:一项多中心回顾性研究
Han Lei1, Yu Lin2, Weixiong Chen3
1Otorhinolaryngology Hospital, The First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, China.
Head & neck
|March 15, 2024
概括
预测在DNSA患者中需要重症监护的需求至关重要. 这项研究开发了准确的名图和XGBoost模型,以指导DNSA患者的术后重症监护室入院决策.
科学领域:
- 耳鼻喉科 耳鼻喉科 耳鼻喉科
- 手术重症监护手术重症监护
- 医疗信息学 医疗信息学
背景情况:
- 深部位 (DNSA) 是一种严重的感染,患病率和死亡率高.
- 及时和适当的重症监护对于管理DNSA患者至关重要.
- 需要预测工具来优化术后护理和资源配置.
研究的目的:
- 开发和验证用于识别需要术后重症监护的DNSA患者的预测模型.
- 为了帮助临床决策,关于DNSA患者的ICU入院.
- 潜在地改善患者的治疗结果,并简化重症监护资源的利用.
主要方法:
- 对332名接受排水手术的DNSA患者 (2015-2020年) 的回顾性分析.
- 使用多变量逻辑回归和XGBoost算法开发预测模型.
- 使用曲线下的面积 (AUC) 评估模型性能.
主要成果:
- 成功开发了两个预测模型:一个名ogram和一个XGBoost模型.
- 诺米图实现了0.911.1的AUC.
- XGBoost模型显示出更高的预测准确度,AUC为0.935.
结论:
- 开发的诺姆图和XGBoost模型可以有效地预测DNSA患者的术后重症监护需求.
- 这些模型为管理DNSA的临床决策支持提供了有价值的工具.
- 这些模型的实施可能会改善患者的预后,并优化重症监护资源的分配.
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