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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

Example of using the nomogram. FEV1, forced expiratory volume in 1 s; FT, feeling thermometer; CRQ, chronic respiratory questionnaire. From each predictor scale, draw a vertical line up through the points scale (upper scale) and sum all points, Next, fill in the sum value in the total points scale, draw a vertical line through the outcome variable (here dyspnoea at 6 months) and read off the predicted outcome.
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fig4: Example of using the nomogram. FEV1, forced expiratory volume in 1 s; FT, feeling thermometer; CRQ, chronic respiratory questionnaire. From each predictor scale, draw a vertical line up through the points scale (upper scale) and sum all points, Next, fill in the sum value in the total points scale, draw a vertical line through the outcome variable (here dyspnoea at 6 months) and read off the predicted outcome.

Mentions: Figure 3 shows the nomogram to predict dyspnoea at 6 months. Figure 4 illustrates how the nomogram should be used and read off. See Supplementary Appendix Figure 3 for the nomograms for the other outcomes. All predictors can be read from these nomograms, as well as their contribution to the prediction.


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)

Example of using the nomogram. FEV1, forced expiratory volume in 1 s; FT, feeling thermometer; CRQ, chronic respiratory questionnaire. From each predictor scale, draw a vertical line up through the points scale (upper scale) and sum all points, Next, fill in the sum value in the total points scale, draw a vertical line through the outcome variable (here dyspnoea at 6 months) and read off the predicted outcome.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

fig4: Example of using the nomogram. FEV1, forced expiratory volume in 1 s; FT, feeling thermometer; CRQ, chronic respiratory questionnaire. From each predictor scale, draw a vertical line up through the points scale (upper scale) and sum all points, Next, fill in the sum value in the total points scale, draw a vertical line through the outcome variable (here dyspnoea at 6 months) and read off the predicted outcome.
Mentions: Figure 3 shows the nomogram to predict dyspnoea at 6 months. Figure 4 illustrates how the nomogram should be used and read off. See Supplementary Appendix Figure 3 for the nomograms for the other outcomes. All predictors can be read from these nomograms, as well as their contribution to the prediction.

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