CALM-QE: Personalised diagnostics and risk assessment in asthma and COPD
The MHH Children’s Hospital is participating in a nationwide project to better predict individual disease courses in non-infectious lung diseases using standardised data from health care and environmental data.
Bronchial asthma and chronic obstructive pulmonary disease (COPD) are the most common non-infectious diseases of the lungs. The main symptoms are varied. In addition, the causes and progression of the disease and the response to medication and therapy vary from person to person. In order to better understand the influencing factors and their interaction for the course of the disease, especially for acute exacerbations, twelve university hospitals want to collect data from as many patients as possible from outpatient and inpatient care and make it available for joint research. The CALM-QE project combines expertise from adult and pediatric pneumology. It is being funded by the Federal Ministry of Education and Research (BMBF) with more than ten million euros over four years as part of the Medical Informatics Initiative. The Philipps University of Marburg is the overall coordinator, and the Hannover Medical School (MHH) is participating as a data integration centre via the Peter L. Reichertz Institute (PLRI). The Department of Paediatric Pneumology, Allergology and Neonatology at the MHH Children’s Hospital is also in charge of the paediatric and adolescent medicine section. The sub-project receives funding of more than one million euros.
Participants are not specifically selected
“With our partner institutions, we want to bring together data not only from the hospital information systems but also from surrounding practices, since after all 90 per cent of the treatments take place outside the University Medical Center,” says Professor Dr Anna-Maria Dittrich, senior physician at the paediatric clinic. In order to reflect the reality of care, the existing data sets are to be expanded to include lung functions, imaging and medication data. “For the first time, we are also including environmental data relevant to lung diseases, such as pollen count, environmental pollution and climate, and investigating which influences affect the course of the disease.”
Patients play an active role in CALM-QE, as the project will also incorporate data from the participants’ everyday lives that reflect stress situations, stress or sleep patterns. This is done using smartwatches, “intelligent” wristwatches that measure and store physical parameters such as physical activity, pulse rate or oxygen saturation in real time.
Predicting disease development with structured data
However, in order to make the data from the clinics, practices and patient-generated data usable and comparable for all researchers, it must first be standardized and structured. At the MHH, this is the task of the team led by Dr. Matthias Gietzelt, research associate at the PLRI. “We have already prepared the infrastructure for this,” says the Head of the Medical Data Integration Center at MHH. Complex modeling with the help of artificial intelligence and machine learning should make it possible to predict severe courses of COPD and asthma. “In this way, we hope to determine individual risk factors, influence the respective disease development, prevent short-term deteriorations such as asthma attacks or improve long-term effects on lung function,” hopes Clinic Director Professor Dr Gesine Hansen. “The aim is to be able to make a diagnosis adapted to each patient and to give more individualised therapy recommendations that effectively counteract the personal risk factors.”
Text: Kirsten Pötzke