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2. Generalizability of Risk Stratification Algorithms for Exacerbations in COPD

Author

Bansback, Nick, Bottorff, Joan L., Bryan, Stirling, Burns, Paloma, Carlsten, Chris, Conklin, Annalijn I., De Vera, Mary, Gershon, Andrea, Gupta, Samir, Gustafson, Paul, Harvard, Stephanie, Hoens, Alison M., Mokhtaran, Mehrshad, Johnson, Jim, Joshi, Phalgun, Leung, Janice, Lynd, Larry D., Metcalfe, Rebecca K., Michaux, Kristina D., Sadatsafavi, Mohsen, Simmers, Brian, Sin, Don D., Smith, Daniel, Struik, Laura, Vinay, Dhingra, Ho, Joseph Khoa, Safari, Abdollah, Adibi, Amin, and Johnson, Kate

Published
2023
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7. Generalizability of risk stratification algorithms for acute exacerbation of chronic obstructive pulmonary disease

Author

Ho, Joseph (Khoa Nguyen)

Abstract

Background: Contemporary management guidelines for chronic obstructive pulmonary disease (COPD) rely on exacerbation history to risk-stratify patients and guide therapy for the prevention of future exacerbations. However, exacerbation history alone may not reliably predict future exacerbations due to random variability in frequency. To address this problem, multivariable prediction models have been developed to improve predictive accuracy. Objective: The objective of this thesis was to assess the generalizability of COPD exacerbation risk stratification algorithms and assess whether the inclusion of race improves the performance of such algorithms. Methods: I evaluated three algorithms: the Acute COPD Exacerbation Prediction Tool (ACCEPT), a prediction model by Bertens et al., and exacerbation history alone, using data from three COPD clinical trials representing different levels of exacerbation risk. I examined discrimination, calibration, and clinical utility as measures for model performance. I then recalibrated the models using the setting-specific exacerbation risk for comparison. I explored race as a variable that could convey information on background risk and assessed whether adjusting for race with a random-effects approach could improve model performance. Results: Both prediction models had better discrimination compared to exacerbation history alone with Δ area under the curves (AUCs) ranging from 0.05 to 0.10 (P-values 0.17. There were also no notable improvements to calibration, clinical utility, or goodness-of-fit (P-value 0.15) after race-adjustment. Conclusions: Risk stratification algorithms for COPD exacerbations are not universally applicable across all settings. However, the flexibility of clinical prediction models allows them to be updated to accommodate setting differences.

Published
2023
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9. Generalizability of Risk Stratification Algorithms for Exacerbations in COPD.

Author

Ho, Joseph Khoa, Safari, Abdollah, Adibi, Amin, Sin, Don D., Johnson, Kate, and Sadatsafavi, Mohsen

Subjects
*RECEIVER operating characteristic curves, *CHRONIC obstructive pulmonary disease, *DISEASE exacerbation, *PREDICTION models
Abstract

Contemporary management of COPD relies on exacerbation history to risk-stratify patients for future exacerbations. Multivariable prediction models can improve the performance of risk stratification. However, the clinical utility of risk stratification can vary from one population to another. How do two validated exacerbation risk prediction models (Acute COPD Exacerbation Prediction Tool [ACCEPT] and the Bertens model) compared with exacerbation history alone perform in different patient populations? We used data from three clinical studies representing populations at different levels of moderate to severe exacerbation risk: the Study to Understand Mortality and Morbidity in COPD (SUMMIT; N = 2,421; annual risk, 0.22), the Long-term Oxygen Treatment Trial (LOTT; N = 595; annual risk, 0.38), and Towards a Revolution in COPD Health (TORCH; N = 1,091; annual risk, 0.52). We compared the area under the receiver operating characteristic curve (AUC) and net benefit (measure of clinical utility) among three risk stratification algorithms for predicting exacerbations in the next 12 months. We also evaluated the effect of model recalibration on clinical utility. Compared with exacerbation history, ACCEPT showed better performance in all three samples (change in AUC, 0.08, 0.07, and 0.10, in SUMMIT, LOTT, and TORCH, respectively; P ≤.001 for all). The Bertens model showed better performance compared with exacerbation history in SUMMIT and TORCH (change in AUC, 0.10 and 0.05, respectively; P <.001 for both), but not in LOTT. No algorithm was superior in clinical utility across all samples. Before recalibration, the Bertens model generally outperformed the other algorithms in low-risk settings, whereas ACCEPT outperformed others in high-risk settings. All three algorithms showed the risk of harm (providing lower net benefit than not using any risk stratification). After recalibration, risk of harm was mitigated substantially for both prediction models. Exacerbation history alone is unlikely to provide clinical utility for predicting COPD exacerbations in all settings and could be associated with a risk of harm. Prediction models have superior predictive performance, but require setting-specific recalibration to confer higher clinical utility. [ABSTRACT FROM AUTHOR]

Published
2023
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