节的EBM:将基于事件的疾病进展模型对同时发生的事件进行概括
Parker Cs1, Oxtoby Np1, Young Al1
1UCL Hawkes Institute, Department of Computer Science, UCL, London, UK.
NeuroImage
|March 21, 2025
概括
以节事件为基础的模型 (P-EBM) 通过允许每个阶段多个生物标志物变化来改善疾病阶段. 这种新模型准确地分阶段阿尔茨海默病患者,并揭示了潜在的疾病过程.
科学领域:
- 生物统计学 生物统计学
- 神经退行性疾病研究
- 计算生物学是一种计算生物学.
背景情况:
- 基于事件的模型 (EBM) 从生物标志物数据推断出疾病阶段,但将阶段限制在生物标志物的数量上.
- 这种限制限制了EBM能够识别简单的分期系统和潜在的疾病驱动因素的能力.
研究的目的:
- 引入基于事件的节模式 (P-EBM),以克服EBM的局限性.
- 概括EBM,允许每个疾病阶段出现多个生物标志物异常 ("同时发生事件").
- 评估P-EBM在疾病分期和潜伏过程发现方面的表现.
主要方法:
- 开发了以事件为基础的节模式 (P-EBM),将EBM概括起来.
- 评估P-EBM使用模拟数据重建事件顺序.
- 应用P-EBM到阿尔茨海默病神经成像计划 (ADNI) 间歇性AD数据 (12个生物标志物).
主要成果:
- 在模拟数据中,P-EBM成功地重建了事件顺序.
- 在ADNI数据中,P-EBM从12个生物标志物中确定了7个阶段,表现优于EBM.
- 推断的同时发生的事件 (例如,CSF tau和p-tau181) 与已知的AD病理学一致.
- P-EBM阶段强烈预测了临床诊断和未来的转化.
- 为了准确的患者分期,P-EBM所需的生物标志物比EBM少.
结论:
- 通过P-EBM,可以通过数据驱动发现更简单的疾病分期系统.
- 该模型可以突出显示新的潜在疾病过程.
- P-EBM提供了改善神经退行性疾病患者病期的实用策略.
更多相关视频
12:21A Mouse Model for the Transition of Streptococcus pneumoniae from Colonizer to Pathogen upon Viral Co-Infection Recapitulates Age-Exacerbated Illness
Published on: September 28, 2022
2.3K
04:57Establishing a Competing Risk Regression Nomogram Model for Survival Data
Published on: October 23, 2020
10.1K
相关概念视频
01:18Steps in Outbreak Investigation
101
In the ever-evolving field of public health, statistical analysis serves as a cornerstone for understanding and managing disease outbreaks. By leveraging various statistical tools, health professionals can predict potential outbreaks, analyze ongoing situations, and devise effective responses to mitigate impact. For that to happen, there are a few possible stages of the analysis:
101
02:07Tumor Progression
6.2K
Tumor progression is a phenomenon where the pre-formed tumor acquires successive mutations to become clinically more aggressive and malignant. In the 1950s, Foulds first described the stepwise progression of cancer cells through successive stages.
Colon cancer is one of the best-documented examples of tumor progression. Early mutation in the APC gene in colon cells causes a small growth on the colon wall called a polyp. With time, this polyp grows into a benign, pre-cancerous tumor. Further...
Colon cancer is one of the best-documented examples of tumor progression. Early mutation in the APC gene in colon cells causes a small growth on the colon wall called a polyp. With time, this polyp grows into a benign, pre-cancerous tumor. Further...
6.2K
01:18Introduction To Survival Analysis
147
Survival analysis is a statistical method used to study time-to-event data, where the "event" might represent outcomes like death, disease relapse, system failure, or recovery. A unique feature of survival data is censoring, which occurs when the event of interest has not been observed for some individuals during the study period. This requires specialized techniques to handle incomplete data effectively.
The primary goal of survival analysis is to estimate survival time—the time...
The primary goal of survival analysis is to estimate survival time—the time...
147
01:15Assumptions of Survival Analysis
77
Survival models analyze the time until one or more events occur, such as death in biological organisms or failure in mechanical systems. These models are widely used across fields like medicine, biology, engineering, and public health to study time-to-event phenomena. To ensure accurate results, survival analysis relies on key assumptions and careful study design.
77
01:20Comparing the Survival Analysis of Two or More Groups
110
Survival analysis is a cornerstone of medical research, used to evaluate the time until an event of interest occurs, such as death, disease recurrence, or recovery. Unlike standard statistical methods, survival analysis is particularly adept at handling censored data—instances where the event has not occurred for some participants by the end of the study or remains unobserved. To address these unique challenges, specialized techniques like the Kaplan-Meier estimator, log-rank test, and...
110
01:24Kaplan-Meier Approach
71
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,...
71