Predicting Hospitalization Due to COPD Exacerbations in Swedish Primary Care Patients Using Machine Learning – Based on the ARCTIC Study - StÃ¤llberg,BjÃ¶rn; Lisspers,Karin; Larsson,Kjell; Janson,Christer; MÃ¼ller,Mario; Åuczko,Mateusz; KjÃ¸ller Bjerregaard,Bine; Bacher,Gerald; Holzhauer,BjÃ¶rn; Goyal,Pankaj; Johansson,Gunnar (International Journal of Chronic Obstructive Pulmonary Disease, 2021)
Björn Ställberg,1 Karin Lisspers,1 Kjell Larsson,2 Christer Janson,3 Mario Müller,4 Mateusz Åuczko,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, SwitzerlandCorrespondence: Björn StällbergDepartment of Public Health and Caring Sciences, Family Medicine and Preventive Medicine, Uppsala University, Box 564, Uppsala, SE-75122, SwedenTel +46-070-3149944Email b.stallberg@telia.comPurpose: 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 ...