Synapse
⌘+K
Synapse
PulseExploreClubsResearchersJournals
Instagram
HomeClubsExplore
January 1, 2010Health and Quality of Life OutcomesOpen Access

A mapping model incorporating age, gender, disease stage, and Seattle Angina Questionnaire scales explained 48% of the variance in the EQ-5D index, but prediction accuracy was poor for index values below 0.4.

View Full Paper
Ask AI
Bookmark
Share

Why the study?

Can demographic and clinical outcome variables, including the Seattle Angina Questionnaire (SAQ), be used to accurately predict the EQ-5D index in patients with coronary heart disease?

Population

4,742 patient records from 5 studies of patients with coronary heart disease, ranging from early disease…

Comparison

Mapping model using demographic variables… vs Observed EQ-5D index values.

Design

Other

Key result

A mapping model incorporating age, gender, disease stage, and Seattle Angina Questionnaire scales explained 48% of the variance in the EQ-5D index, but prediction accuracy was poor for index values below 0.4.

Authors

KGKimberley GoldsmithMDMatthew DyerMBMartin Buxton

Discussion

Loading...

Member takes

Overview

Mapping from SAQ may support CHD cost-effectiveness analyses but not in severe states; leaves open refined algorithms for low utilities.

Study Design

Type

Observational (n=4,742)

Multicenter

Yes

Structured PICO

Can demographic and clinical outcome variables, including the Seattle Angina Questionnaire (SAQ), be used to accurately predict the EQ-5D index in patients with coronary heart disease?

P
Population
4,742 patient records from 5 UK studies of cardiac interventions were used to map demographic and clinical variables to the EQ-5D index.
E
Exposure
Mapping model using demographic variables (age, gender), disease stage proxy, and Seattle Angina Questionnaire (SAQ) scales (Exertional Capacity, Disease Perception, Anginal Frequency).
C
Comparator
Observed EQ-5D index values.
O
Outcome
Prediction accuracy of the EQ-5D index (measured by root mean square error [RMSE], mean absolute error [MAE], and adjusted R²).patient reported

Main Result

Effect estimate: Adjusted R2 0.48, RMSE 0.170

Mapping the EQ-5D index from demographic and clinical variables in cardiac patients is feasible and explains nearly half of the variance, but prediction accuracy is poor for patients in severe health states.

Limitations

  • Only three out of the five studies had all of the necessary covariates.
  • The UK algorithm for calculating the EQ-5D index was used, so models may not be applicable to cardiac patients from other countries.
  • Did not explicitly account for the correlation between baseline and treatment measurements on individuals.
  • Prediction for values of the EQ-5D index below 0.4 was not accurate.
  • Sparse data for patients with low EQ-5D index values (<= 0.4).
  • Possible missing important predictors of HRQoL, such as social isolation or mental state.

Cite This Study

Goldsmith et al. (2010) conducted an observational in Coronary heart disease (n=4,742). Demographic and clinical outcome variables (SAQ scales, disease stage) was evaluated on Prediction of EQ-5D index (Model 11) (Adjusted R2 0.48, RMSE 0.170). A mapping model incorporating age, gender, disease stage, and Seattle Angina Questionnaire scales explained 48% of the variance in the EQ-5D index, but prediction accuracy was poor for index values below 0.4.

synapsesocial.com/papers/6a1eec464eb1f4a9a3c8e109https://doi.org/10.1186/1477-7525-8-54
View Full Paper
Ask AI
Bookmark
Share