A clinical prediction model for hospitalized COPD exacerbations based on "treatable traits"

Anthony C.A. Yii, C. H. Loh, P. Y. Tiew, Huiying Xu, Aza A.M. Taha, Jansen Koh, Jessica Tan, Therese S. Lapperre, Antonio R Anzueto, Augustine K.H. Tee

Research output: Contribution to journalArticle

Abstract

Background: Assessing risk of future exacerbations is an important component in COPD management. History of exacerbation is a strong and independent predictor of future exacerbations, and the criterion of ≥2 nonhospitalized or ≥1 hospitalized exacerbation is often used to identify high-risk patients in whom therapy should be intensified. However, other factors or "treatable traits" also contribute to risk of exacerbation. Objective: The objective of the study was to develop and externally validate a novel clinical prediction model for risk of hospitalized COPD exacerbations based on both exacerbation history and treatable traits. Patients and methods: A total of 237 patients from the COPD Registry of Changi General Hospital, Singapore, aged 75±9 years and with mean post-bronchodilator FEV1 60%±20% predicted, formed the derivation cohort. Hospitalized exacerbation rate was modeled using zero-inflated negative binomial regression. Calibration was assessed by graphically comparing the agreement between predicted and observed annual hospitalized exacerbation rates. Predictive (discriminative) accuracy of the model for identifying high-risk patients (defined as experiencing ≥1 hospitalized exacerbations) was assessed with area under the curve (AUC) and receiver operating characteristics analyses, and compared to other existing risk indices. We externally validated the prediction model using a multicenter dataset comprising 419 COPD patients. Results: The final model included hospitalized exacerbation rate in the previous year, history of acute invasive/noninvasive ventilation, coronary artery disease, bronchiectasis, and sputum nontuberculous mycobacteria isolation. There was excellent agreement between predicted and observed annual hospitalized exacerbation rates. AUC was 0.789 indicating good discriminative accuracy, and was significantly higher than the AUC of the Global Initiative for Chronic Obstructive Lung Disease (GOLD) risk assessment criterion (history of ≥1 hospitalized exacerbation in the previous year) and the age, dyspnea, and obstruction index. When applied to the independent multicenter validation cohort, the model was well-calibrated and discrimination was good. Conclusion: We have derived and externally validated a novel risk prediction model for COPD hospitalizations which outperforms several other risk indices. Our model incorporates several treatable traits which can be targeted for intervention to reduce risk of future hospitalized exacerbations.

Original languageEnglish (US)
Pages (from-to)719-728
Number of pages10
JournalInternational journal of chronic obstructive pulmonary disease
Volume14
DOIs
StatePublished - Jan 1 2019

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Chronic Obstructive Pulmonary Disease
Area Under Curve
Noninvasive Ventilation
Nontuberculous Mycobacteria
Bronchiectasis
Bronchodilator Agents
Singapore
Sputum
ROC Curve
General Hospitals
Dyspnea
Calibration
Registries
Coronary Artery Disease
Hospitalization
History

Keywords

  • clinical prediction model
  • exacerbations
  • risk assessment

ASJC Scopus subject areas

  • Pulmonary and Respiratory Medicine
  • Health Policy
  • Public Health, Environmental and Occupational Health

Cite this

A clinical prediction model for hospitalized COPD exacerbations based on "treatable traits". / Yii, Anthony C.A.; Loh, C. H.; Tiew, P. Y.; Xu, Huiying; Taha, Aza A.M.; Koh, Jansen; Tan, Jessica; Lapperre, Therese S.; Anzueto, Antonio R; Tee, Augustine K.H.

In: International journal of chronic obstructive pulmonary disease, Vol. 14, 01.01.2019, p. 719-728.

Research output: Contribution to journalArticle

Yii, Anthony C.A. ; Loh, C. H. ; Tiew, P. Y. ; Xu, Huiying ; Taha, Aza A.M. ; Koh, Jansen ; Tan, Jessica ; Lapperre, Therese S. ; Anzueto, Antonio R ; Tee, Augustine K.H. / A clinical prediction model for hospitalized COPD exacerbations based on "treatable traits". In: International journal of chronic obstructive pulmonary disease. 2019 ; Vol. 14. pp. 719-728.
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AU - Yii, Anthony C.A.

AU - Loh, C. H.

AU - Tiew, P. Y.

AU - Xu, Huiying

AU - Taha, Aza A.M.

AU - Koh, Jansen

AU - Tan, Jessica

AU - Lapperre, Therese S.

AU - Anzueto, Antonio R

AU - Tee, Augustine K.H.

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N2 - Background: Assessing risk of future exacerbations is an important component in COPD management. History of exacerbation is a strong and independent predictor of future exacerbations, and the criterion of ≥2 nonhospitalized or ≥1 hospitalized exacerbation is often used to identify high-risk patients in whom therapy should be intensified. However, other factors or "treatable traits" also contribute to risk of exacerbation. Objective: The objective of the study was to develop and externally validate a novel clinical prediction model for risk of hospitalized COPD exacerbations based on both exacerbation history and treatable traits. Patients and methods: A total of 237 patients from the COPD Registry of Changi General Hospital, Singapore, aged 75±9 years and with mean post-bronchodilator FEV1 60%±20% predicted, formed the derivation cohort. Hospitalized exacerbation rate was modeled using zero-inflated negative binomial regression. Calibration was assessed by graphically comparing the agreement between predicted and observed annual hospitalized exacerbation rates. Predictive (discriminative) accuracy of the model for identifying high-risk patients (defined as experiencing ≥1 hospitalized exacerbations) was assessed with area under the curve (AUC) and receiver operating characteristics analyses, and compared to other existing risk indices. We externally validated the prediction model using a multicenter dataset comprising 419 COPD patients. Results: The final model included hospitalized exacerbation rate in the previous year, history of acute invasive/noninvasive ventilation, coronary artery disease, bronchiectasis, and sputum nontuberculous mycobacteria isolation. There was excellent agreement between predicted and observed annual hospitalized exacerbation rates. AUC was 0.789 indicating good discriminative accuracy, and was significantly higher than the AUC of the Global Initiative for Chronic Obstructive Lung Disease (GOLD) risk assessment criterion (history of ≥1 hospitalized exacerbation in the previous year) and the age, dyspnea, and obstruction index. When applied to the independent multicenter validation cohort, the model was well-calibrated and discrimination was good. Conclusion: We have derived and externally validated a novel risk prediction model for COPD hospitalizations which outperforms several other risk indices. Our model incorporates several treatable traits which can be targeted for intervention to reduce risk of future hospitalized exacerbations.

AB - Background: Assessing risk of future exacerbations is an important component in COPD management. History of exacerbation is a strong and independent predictor of future exacerbations, and the criterion of ≥2 nonhospitalized or ≥1 hospitalized exacerbation is often used to identify high-risk patients in whom therapy should be intensified. However, other factors or "treatable traits" also contribute to risk of exacerbation. Objective: The objective of the study was to develop and externally validate a novel clinical prediction model for risk of hospitalized COPD exacerbations based on both exacerbation history and treatable traits. Patients and methods: A total of 237 patients from the COPD Registry of Changi General Hospital, Singapore, aged 75±9 years and with mean post-bronchodilator FEV1 60%±20% predicted, formed the derivation cohort. Hospitalized exacerbation rate was modeled using zero-inflated negative binomial regression. Calibration was assessed by graphically comparing the agreement between predicted and observed annual hospitalized exacerbation rates. Predictive (discriminative) accuracy of the model for identifying high-risk patients (defined as experiencing ≥1 hospitalized exacerbations) was assessed with area under the curve (AUC) and receiver operating characteristics analyses, and compared to other existing risk indices. We externally validated the prediction model using a multicenter dataset comprising 419 COPD patients. Results: The final model included hospitalized exacerbation rate in the previous year, history of acute invasive/noninvasive ventilation, coronary artery disease, bronchiectasis, and sputum nontuberculous mycobacteria isolation. There was excellent agreement between predicted and observed annual hospitalized exacerbation rates. AUC was 0.789 indicating good discriminative accuracy, and was significantly higher than the AUC of the Global Initiative for Chronic Obstructive Lung Disease (GOLD) risk assessment criterion (history of ≥1 hospitalized exacerbation in the previous year) and the age, dyspnea, and obstruction index. When applied to the independent multicenter validation cohort, the model was well-calibrated and discrimination was good. Conclusion: We have derived and externally validated a novel risk prediction model for COPD hospitalizations which outperforms several other risk indices. Our model incorporates several treatable traits which can be targeted for intervention to reduce risk of future hospitalized exacerbations.

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