相关实验视频
Updated: Jul 2, 2025

05:37
An R-Based Landscape Validation of a Competing Risk Model
Published on: September 16, 2022
2.1K
一个模型的开发和内部验证,用于预测MAFLD患者的整体存活率:一个队列研究
Caterina Bonfiglio1, Angelo Campanella1, Rossella Donghia1
1National Institute of Gastroenterology-IRCCS 'S de Bellis', 70013 Castellana Grotte, BA, Italy.
Journal of clinical medicine
|February 24, 2024
概括
一个新的模型预测了代谢功能障碍相关的脂肪肝疾病 (MAFLD) 患者的死亡风险. 这种经过验证的工具有助于评估患有这种疾病的个体的预后,改善患者的护理.
科学领域:
- 肝病学 肝病学是一种肝病学.
- 代谢疾病 代谢疾病
- 预测模型的预测建模
背景情况:
- 代谢功能障碍相关的脂肪肝疾病 (MAFLD) 是一个最近定义的疾病,取代了非酒精性肝硬化症 (NAFLD).
- 准确的预后对于管理MAFLD患者至关重要.
- 现有的模型可能无法完全捕捉MAFLD人群中的死亡风险.
研究的目的:
- 开发和内部验证一种预后模型,用于预测MAFLD患者的死亡率.
- 为了确定关键的独立预后因素与死亡相关的这个队列.
- 为估计MAFLD患者死亡概率提供一个工具.
主要方法:
- 为死亡风险开发一个启动式的多变量考克斯模型.
- 使用队列的单独子集对预后模型的内部验证.
- 使用LASSO Cox程序进行独立的预后因素选择.
主要成果:
- 最终的模型包括九个独立的预后因素:性别,年龄,血糖,总胆固醇,胺转酶,SBP,DBP,ALP和寡妇.
- 该模型表现出强大的区分能力,R2D为0.6845 (开发) 和0.6930 (验证),Harrell的C为0.8422 (开发) 和0.8465 (验证).
- 所有选择的预后因素都显示出与死亡风险的统计学上显著关联 (p < 0.05,除了ALP).
结论:
- 为估计MAFLD患者的死亡率,已经开发出了一种强大且经过验证的预后模型.
- 该模型具有令人满意的预测能力,为临床使用提供了有价值的工具.
- 该模型可以帮助医疗保健提供者评估被诊断患有MAFLD的个人的预后.
相关概念视频
Kaplan-Meier Approach
138
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,...
138
Cancer Survival Analysis
346
Cancer survival analysis focuses on quantifying and interpreting the time from a key starting point, such as diagnosis or the initiation of treatment, to a specific endpoint, such as remission or death. This analysis provides critical insights into treatment effectiveness and factors that influence patient outcomes, helping to shape clinical decisions and guide prognostic evaluations. A cornerstone of oncology research, survival analysis tackles the challenges of skewed, non-normally...
346
Assumptions of Survival Analysis
127
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.
127
Comparing the Survival Analysis of Two or More Groups
186
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...
186
Actuarial Approach
78
The actuarial approach, a statistical method originally developed for life insurance risk assessment, is widely used to calculate survival rates in clinical and population studies. This method accounts for participants lost to follow-up or those who die from causes unrelated to the study, ensuring a more accurate representation of survival probabilities.
Consider the example of a high-risk surgical procedure with significant early-stage mortality. A two-year clinical study is conducted,...
Consider the example of a high-risk surgical procedure with significant early-stage mortality. A two-year clinical study is conducted,...
78

