NPJ primary care respiratory medicine · 2014
Prediction of COPD-specific health-related quality of life in primary care COPD patients: a prospective cohort study.
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Siebeling Lara, Musoro Jammbe Z, Geskus Ronald B, Zoller Marco, Muggensturm Patrick, Frei Anja, Puhan Milo A, ter Riet Gerben
Аннотация
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 FEV1 or other commonly used predictors add little to the prediction of HRQL.
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 FEV1 or other commonly used predictors add little to the prediction of HRQL.