在现实世界中的急性高血压疾病的预测建模:整合临床预测和数据驱动的方法
Ilaria Fucile1, Filomena Liccardi2, Maria Virginia Manzi1
1Hypertension Research Center, Department of Advanced Biomedical Science, Federico II University, 80131 Naples, Italy.
Diagnostics (Basel, Switzerland)
|August 28, 2025
概括
高血I水平,年龄较大,血压较高可能表明急诊室患者出现高血压紧急情况. 区分高血压和高血压紧急情况对于有效治疗至关重要.
科学领域:
- 心脏病学
- 紧急医疗
- 高血压研究
背景情况:
- 急性高血压疾病,包括高血压紧急情况和紧急情况,是急诊室访问的常见原因.
- 由于临床治疗和预后不同,区分高血压紧急情况和紧急情况至关重要.
研究的目的:
- 确定诊断急诊的关键预测因子,以区分高血压紧急情况和高血压紧急情况.
- 评估后勤回归和机器学习模型在预测高血压紧急情况中的实用性.
主要方法:
- 对261名急性高血压疾病患者 (SBP ≥180 mmHg或DBP ≥110 mmHg) 的回顾性分析.
- 应用后勤回归,弹性网和随机森林模型来识别高血压紧急情况的预测因素.
- 根据急性高血压引起的器官损伤的存在来对患者进行分类.
主要成果:
- 提升的 I 型血红素水平独立预测高血压紧急情况 (OR: 2. 82; p < 0. 001).
- 机器学习模型确定了Troponin I,年龄和SBP作为主要预测因素,随机森林显示高性能 (AUC: 0. 93).
- 对于诊断高血压紧急情况,确定了0. 12 ng/ mL的最佳托罗邦素I值 (AUC: 0. 66).
结论:
- 高血I水平,年龄较大,SBP较高是急诊室高血压紧急情况的重要早期指标.
- 这些发现有助于及时区分高血压紧急情况和紧急情况,指导适当的临床治疗.
- 机器学习方法在提高高血压紧急情况的预测准确性方面具有前景.
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