TY - JOUR
T1 - Prediction of COPD-specific health-related quality of life in primary care COPD patients
T2 - A prospective cohort study
AU - Siebeling, Lara
AU - Musoro, Jammbe Z.
AU - Geskus, Ronald B.
AU - Zoller, Marco
AU - Muggensturm, Patrick
AU - Frei, Anja
AU - Puhan, Milo A.
AU - Ter Riet, Gerben
PY - 2014/8/28
Y1 - 2014/8/28
N2 - Background:Health-related quality of life (HRQL) is an important patient-reported outcome for chronic obstructive pulmonary disease (COPD).Aim:We developed models predicting chronic respiratory questionnaire (CRQ) dyspnoea, fatigue, emotional function, mastery and overall HRQL at 6 and 24 months using predictors easily available in primary care.Methods:We used the "least absolute shrinkage and selection operator" (lasso) method to build the models and assessed their predictive performance. Results were displayed using nomograms.Results:For each domain-specific CRQ outcome, the corresponding score at baseline was the best predictor. Depending on the domain, these predictions could be improved by adding one to six other predictors, such as the other domain-specific CRQ scores, health status and depression score. To predict overall HRQL, fatigue and dyspnoea scores were the best predictors. Predicted and observed values were on average the same, indicating good calibration. Explained variance ranged from 0.23 to 0.58, indicating good discrimination.Conclusions:To predict COPD-specific HRQL in primary care COPD patients, previous HRQL was the best predictor in our models. Asking patients explicitly about dyspnoea, fatigue, depression and how they cope with COPD provides additional important information about future HRQL whereas FEV 1 or other commonly used predictors add little to the prediction of HRQL.
AB - Background:Health-related quality of life (HRQL) is an important patient-reported outcome for chronic obstructive pulmonary disease (COPD).Aim:We developed models predicting chronic respiratory questionnaire (CRQ) dyspnoea, fatigue, emotional function, mastery and overall HRQL at 6 and 24 months using predictors easily available in primary care.Methods:We used the "least absolute shrinkage and selection operator" (lasso) method to build the models and assessed their predictive performance. Results were displayed using nomograms.Results:For each domain-specific CRQ outcome, the corresponding score at baseline was the best predictor. Depending on the domain, these predictions could be improved by adding one to six other predictors, such as the other domain-specific CRQ scores, health status and depression score. To predict overall HRQL, fatigue and dyspnoea scores were the best predictors. Predicted and observed values were on average the same, indicating good calibration. Explained variance ranged from 0.23 to 0.58, indicating good discrimination.Conclusions:To predict COPD-specific HRQL in primary care COPD patients, previous HRQL was the best predictor in our models. Asking patients explicitly about dyspnoea, fatigue, depression and how they cope with COPD provides additional important information about future HRQL whereas FEV 1 or other commonly used predictors add little to the prediction of HRQL.
UR - http://www.scopus.com/inward/record.url?scp=84907361203&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84907361203&partnerID=8YFLogxK
U2 - 10.1038/npjpcrm.2014.60
DO - 10.1038/npjpcrm.2014.60
M3 - Article
C2 - 25164146
AN - SCOPUS:84907361203
SN - 2055-1010
VL - 24
JO - npj Primary Care Respiratory Medicine
JF - npj Primary Care Respiratory Medicine
M1 - 14060
ER -