Prognostic analysis of orthostatic intolerance using survival model in children

Yawen Li1, Hongxia Li2, Xueying Li1

  • 1School of Economics and Management, Tsinghua University, Beijing 100084, China.

Chinese Medical Journal
|November 11, 2014
PubMed

Insights

Symptom score significantly impacts treatment effectiveness for pediatric orthostatic intolerance (OI). Higher scores indicate longer recovery times, highlighting its importance in managing children with OI.

Area of Science:

  • Pediatric Medicine
  • Cardiology
  • Neurology

Background:

  • Orthostatic intolerance (OI) is a prevalent condition in children, affecting their physical and mental well-being.
  • Understanding factors influencing OI prognosis is crucial for effective pediatric care.

Purpose of the Study:

  • To investigate factors affecting the prognosis of pediatric orthostatic intolerance.
  • To identify key indicators for improved treatment outcomes in children with OI.

Main Methods:

  • A cohort of 170 children (ages 6-17) diagnosed with OI was analyzed.
  • Univariate and COX proportional hazards regression models were used to assess prognostic factors.
  • Age, symptom score, duration, disease subtype, and treatment were evaluated.

Main Results:

  • Symptom score at diagnosis significantly correlated with the time to symptom improvement after treatment (P < 0.05).
  • Children with a lower initial symptom score (1) demonstrated higher symptom-free survival rates compared to those with higher scores (≥2).
  • Other factors like age, duration, and disease subtype did not show a significant impact on prognosis in this cohort.

Conclusions:

  • Symptom score is a critical predictor of treatment response in pediatric OI.
  • A lower symptom score at diagnosis is associated with a better prognosis and faster symptom improvement.
Abstract

Related Concept Videos

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...
729
Cancer Survival Analysis01:21

Cancer Survival Analysis

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...
857
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.
503
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...
1.0K
Actuarial Approach01:20

Actuarial Approach

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,...
387
Parametric Survival Analysis: Weibull and Exponential Methods01:14

Parametric Survival Analysis: Weibull and Exponential Methods

Parametric survival analysis models survival data by assuming a specific probability distribution for the time until an event occurs. The Weibull and exponential distributions are two of the most commonly used methods in this context, due to their versatility and relatively straightforward application.
Weibull Distribution
The Weibull distribution is a flexible model used in parametric survival analysis. It can handle both increasing and decreasing hazard rates, depending on its shape parameter...
1.3K