相关实验视频

Updated: Feb 13, 2026

Fetal Mouse Cardiovascular Imaging Using a High-frequency Ultrasound 30/45MHZ System
07:34

Fetal Mouse Cardiovascular Imaging Using a High-frequency Ultrasound 30/45MHZ System

Published on: May 5, 2018

12.4K

在丹麦的Fontan手术后的30年生存期

Benjamin Kelly1, Maren Ravndal2, Anne Kathrine Møller Nielsen2

  • 1Department of Cardiothoracic Surgery, Copenhagen University Hospital, Blegdamsvej 9, 2100, Copenhagen, Denmark. benjaminkelly@clin.au.dk.

Pediatric cardiology
|May 22, 2025
PubMed
概括

在Fontan完成后,心脏病患者的30年生存率为87%. 近年来,结果有所改善,特别是对于那些没有低可塑性左心综合征 (HLHS) 的人来说,尽管随着年龄的增长,患病率会增加.

关键词:
这里是Fontan Fontan.缺血性左心综合征是什么意思长期生存率 长期生存率一个单心室单心室.

更多相关视频

Survivable Stereotaxic Surgery in Rodents00:08

Survivable Stereotaxic Surgery in Rodents

Published on: October 6, 2008

70.2K
Operation of a Benchtop Bioreactor12:54

Operation of a Benchtop Bioreactor

Published on: September 12, 2013

92.2K

相关概念视频

Survival Curves01:18

Survival Curves

Survival curves are graphical representations that depict the survival experience of a population over time, offering an intuitive way to track the proportion of individuals who remain event-free at each time point. These curves are widely used in fields such as medicine, public health, and reliability engineering to visualize and compare survival probabilities across different groups or conditions.
The Kaplan-Meier estimator is the most common method for constructing survival curves. This...
732
Survival Tree01:19

Survival Tree

Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
 Building a Survival Tree
Constructing a...
436
Introduction To Survival Analysis01:18

Introduction To Survival Analysis

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...
831
Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

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...
617
Truncation in Survival Analysis01:09

Truncation in Survival Analysis

Truncation in survival analysis refers to the exclusion of individuals or events from the dataset based on specific criteria related to the time of the event. This exclusion can happen in two primary forms: left truncation and right truncation.
Left truncation occurs when individuals who experienced the event of interest before a certain time are not included in the study. This is often due to a "delayed entry" into the study where only those who survive until a certain entry point are...
635
Assumptions of Survival Analysis01:15

Assumptions of Survival Analysis

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.
433