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相关概念视频

Pathophysiology of Heart Failure01:17

Pathophysiology of Heart Failure

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Heart failure (HF) is a progressive syndrome involving ventricles that leads to inadequate cardiac output. It can be classified based on location and output or ejection fraction. Ejection fraction (EF) is an essential measurement in the diagnosis and surveillance of HF. Reduced EF corresponds to systolic heart failure (HFrEF). However, HF with preserved ejection fraction (HFpEF) is becoming increasingly prevalent. Also known as diastolic HF, this form of HF is related to aging. The...
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Kaplan-Meier Approach01:24

Kaplan-Meier Approach

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The Kaplan-Meier estimator is a non-parametric method used to estimate the survival function from time-to-event data. In medical research, it is frequently employed to measure the proportion of patients surviving for a certain period after treatment. This estimator is fundamental in analyzing time-to-event data, making it indispensable in clinical trials, epidemiological studies, and reliability engineering. By estimating survival probabilities, researchers can evaluate treatment effectiveness,...
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相关实验视频

Updated: Jun 22, 2025

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
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用机器学习模型预测心力衰竭或没有心力衰竭的患者的死亡率.

Se Yong Jang1,2, Jin Joo Park1,3, Eric Adler1

  • 1Department of Cardiology, University of California, San Diego, California, USA.

JACC. Advances
|June 28, 2024
PubMed
概括

马克尔-HF模型准确地预测心力衰竭 (HF) 患者和没有HF患者的1年死亡率,在各种患者群体中展示了广泛的适用性.

关键词:
标记器-HF 的时间.心脏衰竭是因为心脏衰竭.死亡率 死亡率风险评分风险评分是什么意思

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Author Spotlight: Unveiling Prognostic Indicators in Heart Failure - The Role of Phase Angle and Bioelectrical Impedance Analysis
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Lumped-Parameter and Finite Element Modeling of Heart Failure with Preserved Ejection Fraction
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相关实验视频

Last Updated: Jun 22, 2025

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Lumped-Parameter and Finite Element Modeling of Heart Failure with Preserved Ejection Fraction
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Lumped-Parameter and Finite Element Modeling of Heart Failure with Preserved Ejection Fraction

Published on: February 13, 2021

6.4K

科学领域:

  • 心脏病学 心脏病学
  • 医疗信息学 医疗信息学
  • 公共卫生 公共卫生

背景情况:

  • 现有的风险预测模型通常针对特定条件,限制了它们在一般患者群体中的使用.
  • 马克尔-HF模型最初是为心力衰竭 (HF) 患者开发的.

研究的目的:

  • 评估MARKER-HF模型在预测1年死亡率方面的能力.
  • 评估其在一个大型的社区医院注册表中的表现,包括患有和没有HF的患者.

主要方法:

  • 对41,749名连续接受心声扫描的患者的分析.
  • 包括患有 (n=4,640) 和没有HF的患者 (n=37,109).
  • 基于心血管疾病,急性冠状动脉综合征,心房动,COPD,CKD,糖尿病,高血压和恶性瘤的非HF患者的亚组分析.

主要成果:

  • 标志性HF显示强大的预测性表现为1年死亡率在两个HF (AUC=0.729) 和非HF患者 (AUC=0.770) 的HF.
  • 在各种子组中观察到一致的准确性,包括那些患有心血管疾病和常见并发症的人.
  • 患有恶性瘤的患者在类似的MARKER-HF得分下表现出更高的死亡率.

结论:

  • 马克尔-HF模型有效预测心力衰竭患者的死亡率.
  • 它的预测能力扩展到没有心力衰竭的患者,包括患有各种其他疾病的患者.
  • 马克尔-HF为广泛的患者提供了一种多功能工具,用于对广泛的患者进行死亡风险评估.