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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

Calibration curve for the dyspnoea model at 6 months. x axis, predicted CRQ score; y axis, observed CRQ score (scores range from 1 (worst) to 7 (best)); (──) diagonal, x=y, perfect prediction; (- - -) regression line, note that predicted score > observed score up to a predicted score of 4.5, and predicted score < observed score for values above 4.5; (······)±0.5 (minimal important difference); (■) deciles, note that all deciles remained within the 0.5 range, meaning that the average per decile is within the limits of the minimal important difference; grey numbers, all predicted values per decile. Note that CRQ dyspnoea scores are relatively high, which is expected in this primary care cohort.
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fig2: Calibration curve for the dyspnoea model at 6 months. x axis, predicted CRQ score; y axis, observed CRQ score (scores range from 1 (worst) to 7 (best)); (──) diagonal, x=y, perfect prediction; (- - -) regression line, note that predicted score > observed score up to a predicted score of 4.5, and predicted score < observed score for values above 4.5; (······)±0.5 (minimal important difference); (■) deciles, note that all deciles remained within the 0.5 range, meaning that the average per decile is within the limits of the minimal important difference; grey numbers, all predicted values per decile. Note that CRQ dyspnoea scores are relatively high, which is expected in this primary care cohort.

Mentions: Figure 2 visually displays calibration for the dyspnoea outcome. The other models were similar (see Supplementary Appendix Figures 1 and 2). For at least 90% of the deciles, the predicted CRQ values did not differ from those observed by >0.5, the minimal important difference.29 Note that CRQ dyspnoea scores were relatively high, which is to be expected in this primary care cohort.


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)

Calibration curve for the dyspnoea model at 6 months. x axis, predicted CRQ score; y axis, observed CRQ score (scores range from 1 (worst) to 7 (best)); (──) diagonal, x=y, perfect prediction; (- - -) regression line, note that predicted score > observed score up to a predicted score of 4.5, and predicted score < observed score for values above 4.5; (······)±0.5 (minimal important difference); (■) deciles, note that all deciles remained within the 0.5 range, meaning that the average per decile is within the limits of the minimal important difference; grey numbers, all predicted values per decile. Note that CRQ dyspnoea scores are relatively high, which is expected in this primary care cohort.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

fig2: Calibration curve for the dyspnoea model at 6 months. x axis, predicted CRQ score; y axis, observed CRQ score (scores range from 1 (worst) to 7 (best)); (──) diagonal, x=y, perfect prediction; (- - -) regression line, note that predicted score > observed score up to a predicted score of 4.5, and predicted score < observed score for values above 4.5; (······)±0.5 (minimal important difference); (■) deciles, note that all deciles remained within the 0.5 range, meaning that the average per decile is within the limits of the minimal important difference; grey numbers, all predicted values per decile. Note that CRQ dyspnoea scores are relatively high, which is expected in this primary care cohort.
Mentions: Figure 2 visually displays calibration for the dyspnoea outcome. The other models were similar (see Supplementary Appendix Figures 1 and 2). For at least 90% of the deciles, the predicted CRQ values did not differ from those observed by >0.5, the minimal important difference.29 Note that CRQ dyspnoea scores were relatively high, which is to be expected in this primary care cohort.

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