Related Experiment Video
Updated: Feb 27, 2026

07:02
Monitoring Neuronal Survival via Longitudinal Fluorescence Microscopy
Published on: January 19, 2019
6.9K
Quantifying survival in patients with Proteus syndrome
Julie C Sapp1, Lian Hu2, Jean Zhao2
1National Human Genome Research Institute, Bethesda, Maryland, USA.
Summary
Proteus syndrome patients face a significant risk of premature death, with a 25% chance of mortality by age 22. This rare overgrowth disorder
Area of Science:
- Genetics
- Rare Diseases
- Clinical Research
Background:
- Proteus syndrome is a rare mosaic overgrowth disorder.
- Associated with severe complications and potentially reduced lifespan.
- Mortality data for rare diseases is crucial for evaluating interventions.
Purpose of the Study:
- To measure and estimate the survival rate in patients with Proteus syndrome.
- To quantify the risk of premature death in this population.
- To inform the development of targeted interventions and clinical trials.
Main Methods:
- Utilized clinical research records from a longitudinal natural history cohort.
- Included data from 64 patients at the National Institutes of Health.
- Employed Kaplan-Meier survival analysis to estimate survival probabilities.
Main Results:
- The median age of diagnosis was 19 months.
- A 25% probability of death was observed by 22 years of age.
- 10 out of 11 deaths occurred before age 22, with only one death thereafter.
Conclusions:
- Quantified the substantial risk of premature mortality in Proteus syndrome.
- Suggests the disease process may moderate after adolescence, with most deaths occurring before age 22.
- Highlights the need for mortality-reducing interventions to be focused on the pediatric age group.
Related Concept Videos
Actuarial Approach
336
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,...
336
Cancer Survival Analysis
805
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...
805
Kaplan-Meier Approach
662
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,...
662
Assumptions of Survival Analysis
466
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.
466
Survival Curves
788
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...
The Kaplan-Meier estimator is the most common method for constructing survival curves. This...
788
Comparing the Survival Analysis of Two or More Groups
674
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...
674

