发生性高血压的预后风险模型:PRISMA系统审查和元分析
Filip Emil Schjerven1, Frank Lindseth1, Ingelin Steinsland2
1Department of Computer Science, Norwegian University of Science and Technology, Trondheim, Norway.
PloS one
|March 11, 2024
概括
这次审查发现,预测高血压风险的研究质量不佳,尽管机器学习模型表现比传统模型更好. 对于可靠的高血压风险评估,需要更严格的验证和报告.
科学领域:
- 心血管流行病学心血管流行病学
- 生物统计学 生物统计学
- 医疗信息学 医疗信息学
背景情况:
- 偶发性高血压是一个重大的公共卫生挑战.
- 准确的预后风险预测模型对于早期干预和预防策略至关重要.
- 现有的模型需要对其性能和质量进行全面评估.
结论:
- 对高血压风险预测模型的研究的整体质量很差.
- 虽然机器学习模型显示出希望,但高异质性需要谨慎的解释和进一步的研究.
- 建议遵守报告准则,强有力的外部验证和模型可用性,以改善高血压风险评估.
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