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相关概念视频

Regression Toward the Mean01:52

Regression Toward the Mean

Regression toward the mean (“RTM”) is a phenomenon in which extremely high or low values—for example, and individual’s blood pressure at a particular moment—appear closer to a group’s average upon remeasuring. Although this statistical peculiarity is the result of random error and chance, it has been problematic across various medical, scientific, financial and psychological applications. In particular, RTM, if not taken into account, can interfere when researchers try to extrapolate results...
Cluster Sampling Method01:20

Cluster Sampling Method

Appropriate sampling methods ensure that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a cluster sample, divide the population into clusters (groups) and then randomly select some of the clusters. All the members from these clusters are in the cluster sample. For example, if you randomly sample four departments from your...
Data Collection by Observations01:08

Data Collection by Observations

Data collection refers to a systematic way of obtaining, observing, measuring, and analyzing accurate information. Observational studies are one of the most widely used methods of data collection. It involves collecting data by observing the behavior and physical characteristics of a sample without making any modifications to the sample.
An astronomer viewing the motion and brightness of stars in the sky and recording the data is an example of observational data collection. A botanist recording...
Sampling Plans01:23

Sampling Plans

Sampling is a crucial step in analytical chemistry, allowing researchers to collect representative data from a large population. Common sampling methods include random, judgmental, systematic, stratified, and cluster sampling.
Random sampling is a method where each member of the population has an equal chance of being selected for the sample. It involves selecting individuals randomly, often using random number generators or lottery-type methods. For example, when analyzing the properties of a...
Analysis of Population Pharmacokinetic Data01:12

Analysis of Population Pharmacokinetic Data

Analysis of population pharmacokinetic data involves studying the behavior of drugs within diverse populations to understand their pharmacokinetic parameters. Traditional pharmacokinetic methods typically involve collecting samples from a few individuals and estimating these parameters. While these methods are commonly used, they have limitations in capturing the variability in drug response among individuals or heterogeneous populations. Population pharmacokinetics is employed to address these...
Statistical Methods for Analyzing Epidemiological Data01:25

Statistical Methods for Analyzing Epidemiological Data

Epidemiological data primarily involves information on specific populations' occurrence, distribution, and determinants of health and diseases. This data is crucial for understanding disease patterns and impacts, aiding public health decision-making and disease prevention strategies. The analysis of epidemiological data employs various statistical methods to interpret health-related data effectively. Here are some commonly used methods:

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相关实验视频

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Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
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一项以人口为基础的研究,通过使用无监督机器学习方法,探索表型集群和中风的临床结果.

Ralph K Akyea1, George Ntaios2, Evangelos Kontopantelis3,4

  • 1PRISM Research Group, Centre for Academic Primary Care, School of Medicine, University of Nottingham, Nottingham, United Kingdom.

PLOS digital health
|September 13, 2023
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概括

这项研究确定了中风后四种不同的患者表型,揭示了复发性中风和心血管死亡的不同风险. 这些发现表明,个性化护理策略可以改善中风幸存者的结果.

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科学领域:

  • 心脏病学 心脏病学
  • 神经学 神经学
  • 数据科学数据科学数据科学

背景情况:

  • 脑卒中患者表现出不同的临床,人口和生化特征.
  • 这种异质性影响心血管疾病 (CVD) 发病率和死亡率.
  • 目前的护理分层可能无法完全解决患者中风后的个体风险.

研究的目的:

  • 用一种新的方法将发生中风的患者分为不同的表型集群.
  • 评估这些表型中复发性中风和其他主要心血管结果的差异性风险.
  • 探索改善患者护理分层的机会.

主要方法:

  • 利用了48114名发生中风的成年患者的相关英国临床数据 (初级保健,住院,死亡记录).
  • 应用数据驱动的集群分析 (卡米拉算法) 来识别患者的表型.
  • 采用Cox比例危险回归来估计不良结果的风险,包括冠心病,复发性中风,心力衰竭和死亡率.

主要成果:

  • 确定了四种不同的中风患者表型.
  • 与集群1相比,集群2,3和4显示复合复发性中风和心血管疾病相关死亡率的风险明显更高 (HRs1.07-1.44).
  • 对于复发性中风和全因死亡率观察到类似的风险趋势,但对于所有个体心血管结局都不一致.

结论:

  • 证明了异质性中风患者成功分层成四种同质的表型.
  • 这些表型对复发性中风和主要心血管结果的风险存在差异.
  • 这些发现支持重新审视中风护理分层,以提高患者的治疗结果.