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Prediction of COPD-specific health-related quality of life in primary care COPD patients: a prospective cohort study.

Siebeling L, Musoro JZ, Geskus RB, Zoller M, Muggensturm P, Frei A, Puhan MA, ter Riet G - NPJ Prim Care Respir Med (2014)

Bottom Line: 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.

View Article: PubMed Central - PubMed

Affiliation: Department of General Practice, Academic Medical Centre, University of Amsterdam, Amsterdam, The Netherlands.

ABSTRACT

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.

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Related in: MedlinePlus

Nomogram for CRQ dyspnoea outcome at 6 months. FEV1, forced expiratory volume in 1 s; FT, feeling thermometer; CRQ, chronic respiratory questionnaire.
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fig3: Nomogram for CRQ dyspnoea outcome at 6 months. FEV1, forced expiratory volume in 1 s; FT, feeling thermometer; CRQ, chronic respiratory questionnaire.

Mentions: We simplified the potential application of the prediction models by creating nomograms. For reasons of readability and practicality, some predictors that were initially retained by the lasso procedure (see below) were dropped from the nomograms as they turned out to be weak predictors. We defined ‘weak’ as an effect of <5 on the upper points scale (see nomogram in Figure 3), which corresponds to a change of <0.1 on the HRQL outcome scale, clearly less than the minimal important difference of 0.5.29


Prediction of COPD-specific health-related quality of life in primary care COPD patients: a prospective cohort study.

Siebeling L, Musoro JZ, Geskus RB, Zoller M, Muggensturm P, Frei A, Puhan MA, ter Riet G - NPJ Prim Care Respir Med (2014)

Nomogram for CRQ dyspnoea outcome at 6 months. FEV1, forced expiratory volume in 1 s; FT, feeling thermometer; CRQ, chronic respiratory questionnaire.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4373411&req=5

fig3: Nomogram for CRQ dyspnoea outcome at 6 months. FEV1, forced expiratory volume in 1 s; FT, feeling thermometer; CRQ, chronic respiratory questionnaire.
Mentions: We simplified the potential application of the prediction models by creating nomograms. For reasons of readability and practicality, some predictors that were initially retained by the lasso procedure (see below) were dropped from the nomograms as they turned out to be weak predictors. We defined ‘weak’ as an effect of <5 on the upper points scale (see nomogram in Figure 3), which corresponds to a change of <0.1 on the HRQL outcome scale, clearly less than the minimal important difference of 0.5.29

Bottom Line: 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.

View Article: PubMed Central - PubMed

Affiliation: Department of General Practice, Academic Medical Centre, University of Amsterdam, Amsterdam, The Netherlands.

ABSTRACT

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.

Show MeSH
Related in: MedlinePlus