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Predicting Hospitalization Due to COPD Exacerbations in Swedish Primary Care Patients Using Machine Learning – Based on the ARCTIC Study

Authors Ställberg B, Lisspers K, Larsson K, Janson C, Müller M, Łuczko M, Kjøller Bjerregaard B, Bacher G, Holzhauer B, Goyal P, Johansson G

Received 25 November 2020

Accepted for publication 4 March 2021

Published 16 March 2021 Volume 2021:16 Pages 677—688

DOI https://doi.org/10.2147/COPD.S293099

Checked for plagiarism Yes

Review by Single anonymous peer review

Peer reviewer comments 4

Editor who approved publication: Dr Richard Russell


Björn Ställberg,1 Karin Lisspers,1 Kjell Larsson,2 Christer Janson,3 Mario Müller,4 Mateusz Łuczko,5 Bine Kjøller Bjerregaard,6 Gerald Bacher,7 Björn Holzhauer,7 Pankaj Goyal,7 Gunnar Johansson1

1Department of Public Health and Caring Sciences, Family Medicine and Preventive Medicine, Uppsala University, Uppsala, Sweden; 2Integrative Toxicology, Karolinska Institutet, Stockholm, Sweden; 3Department of Medical Sciences: Respiratory, Allergy and Sleep Research, Uppsala University, Uppsala, Sweden; 4Department of Data Science and Advanced Analytics, IQVIA, Frankfurt Am Main, Germany; 5Department of Data Science and Advanced Analytics, IQVIA, Warsaw, Poland; 6Department of Real World Evidence Solutions, IQVIA, Copenhagen, Denmark; 7Department of Clinical Development and Analytics, Novartis Pharma AG, Basel, Switzerland

Correspondence: Björn Ställberg
Department of Public Health and Caring Sciences, Family Medicine and Preventive Medicine, Uppsala University, Box 564, Uppsala, SE-75122, Sweden
Tel +46-070-3149944
Email [email protected]

Purpose: Chronic obstructive pulmonary disease (COPD) exacerbations can negatively impact disease severity, progression, mortality and lead to hospitalizations. We aimed to develop a model that predicts a patient’s risk of hospitalization due to severe exacerbations (defined as COPD-related hospitalizations) of COPD, using Swedish patient level data.
Patients and Methods: Patient level data for 7823 Swedish patients with COPD was collected from electronic medical records (EMRs) and national registries covering healthcare contacts, diagnoses, prescriptions, lab tests, hospitalizations and socioeconomic factors between 2000 and 2013. Models were created using machine-learning methods to predict risk of imminent exacerbation causing patient hospitalization due to COPD within the next 10 days. Exacerbations occurring within this period were considered as one event. Model performance was assessed using the Area under the Precision-Recall Curve (AUPRC). To compare performance with previous similar studies, the Area Under Receiver Operating Curve (AUROC) was also reported. The model with the highest mean cross validation AUPRC was selected as the final model and was in a final step trained on the entire training dataset.
Results: The most important factors for predicting severe exacerbations were exacerbations in the previous six months and in whole history, number of COPD-related healthcare contacts and comorbidity burden. Validation on test data yielded an AUROC of 0.86 and AUPRC of 0.08, which was high in comparison to previously published attempts to predict COPD exacerbation.
Conclusion: Our work suggests that clinically available information on patient history collected via automated retrieval from EMRs and national registries or directly during patient consultation can form the basis for future clinical tools to predict risk of severe COPD exacerbations.

Keywords: COPD, machine learning, exacerbation, hospitalization

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