Singh, Dave;
Hurst, John R;
Martinez, Fernando J;
Rabe, Klaus F;
Bafadhel, Mona;
Jenkins, Martin;
Salazar, Domingo;
... Darken, Patrick; + view all
(2022)
Predictive modeling of COPD exacerbation rates using baseline risk factors.
Therapeutic Advances in Respiratory Disease
, 16
pp. 1-15.
10.1177/17534666221107314.
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Abstract
BACKGROUND: Demographic and disease characteristics have been associated with the risk of chronic obstructive pulmonary disease (COPD) exacerbations. Using previously collected multinational clinical trial data, we developed models that use baseline risk factors to predict an individual's rate of moderate/severe exacerbations in the next year on various pharmacological treatments for COPD. METHODS: Exacerbation data from 20,054 patients in the ETHOS, KRONOS, TELOS, SOPHOS, and PINNACLE-1, PINNACLE-2, and PINNACLE-4 studies were pooled. Machine learning was used to identify predictors of moderate/severe exacerbation rates. Important factors were selected for generalized linear modeling, further informed by backward variable selection. An independent test set was held back for validation. RESULTS: Prior exacerbations, eosinophil count, forced expiratory volume in 1 s percent predicted, prior maintenance treatments, reliever medication use, sex, COPD Assessment Test score, smoking status, and region were significant predictors of exacerbation risk, with response to inhaled corticosteroids (ICSs) increasing with higher eosinophil counts, more prior exacerbations, or additional prior treatments. Model fit was similar in the training and test set. Prediction metrics were ~10% better in the full model than in a simplified model based only on eosinophil count, prior exacerbations, and ICS use. CONCLUSION: These models predicting rates of moderate/severe exacerbations can be applied to a broad range of patients with COPD in terms of airway obstruction, eosinophil counts, exacerbation history, symptoms, and treatment history. Understanding the relative and absolute risks related to these factors may be useful for clinicians in evaluating the benefit: risk ratio of various treatment decisions for individual patients.Clinical trials registered with www.clinicaltrials.gov (NCT02465567, NCT02497001, NCT02766608, NCT02727660, NCT01854645, NCT01854658, NCT02343458, NCT03262012, NCT02536508, and NCT01970878).
| Type: | Article |
|---|---|
| Title: | Predictive modeling of COPD exacerbation rates using baseline risk factors |
| Location: | England |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1177/17534666221107314 |
| Publisher version: | https://publons.com/publon/10.1177/175346662211073... |
| Language: | English |
| Additional information: | This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third-party material in this article are included in the Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ |
| Keywords: | ICS/LAMA/LABA, chronic obstructive pulmonary disease, exacerbations, machine learning, prediction model, triple therapy, Administration, Inhalation, Adrenal Cortex Hormones, Adrenergic beta-2 Receptor Agonists, Bronchodilator Agents, Clinical Trials as Topic, Disease Progression, Drug Therapy, Combination, Forced Expiratory Volume, Humans, Pulmonary Disease, Chronic Obstructive, Risk Factors |
| UCL classification: | UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Medical Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Medical Sciences > Div of Medicine > Respiratory Medicine UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences UCL UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Medical Sciences > Div of Medicine |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10152843 |
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