{"id":6915,"date":"2025-09-22T13:21:30","date_gmt":"2025-09-22T11:21:30","guid":{"rendered":"https:\/\/alpha1-deutschland.org\/?p=6915"},"modified":"2025-09-22T13:57:33","modified_gmt":"2025-09-22T11:57:33","slug":"calm%e2%80%91qe","status":"publish","type":"post","link":"https:\/\/alpha1-deutschland.org\/en\/calm%e2%80%91qe","title":{"rendered":"CALM\u2011QE \u2013 Artificial intelligence for a better understanding of asthma and COPD"},"content":{"rendered":"
<\/div><\/div><\/div>
\n\t
\n\t\t
\n\t\t\t
\n\t\t\t\t
<\/span><\/span>

Author<\/h4><\/span><\/span>\n<\/div>\n
\n\t

Marion Wilkens, as appeared in Alpha1 Journal 1\/2025<\/a>.<\/strong><\/p>\n<\/div>\n\n\n\n

<\/div><\/div>\n
\n\t

CALM-QE stands for \u201eCOPD and Asthma: longitudinal and cross-sectoral Real-World data for machine learning application for quality improvement and knowledge acquisition\u201c.<\/p>\n

This nationwide project is developing AI models that, using real-world data (routine healthcare data), enable deeper insights and better prediction of individual disease progression in COPD and asthma. The goal is to make treatment decisions more targeted and tailored to each patient.<\/p>\n

The project is funded by the Federal Ministry of Education and Research (BMBF) as part of the Medical Informatics Initiative.<\/p>\n

The relevance of the study stems from the following three pillars:<\/p>\n

1) Data collection:<\/strong> CALM-QE analyzes data from numerous patients and evaluates it using specialized computer systems. Twelve university hospitals contribute data from outpatient and inpatient care and make it available for collaborative research. Data from surrounding practices are also included. This reveals the wide variety of clinical presentations, including rare forms such as those that can occur in alpha-1 antitrypsin deficiency.<\/p>\n

2) Numerous influencing factors:<\/strong> In addition to clinical data, environmental factors such as air quality, climate, or even smartwatch measurements (e.g., oxygen saturation, pulse, sleep patterns) are included. Such variability helps to identify individual risk profiles \u2013 including those for Alpha-1 \u2013 at an early stage.<\/p>\n

3) Personalized medicine:<\/strong> The generated models are intended to help predict exacerbations and guide therapies more precisely. This could significantly improve the quality of life, especially for Alpha-1 patients, who more frequently experience atypical disease courses.<\/p>\n

As a patient representative in the CALM-QE consortium, I directly contribute the perspectives and needs of people with COPD, asthma, and alpha-1 diabetic nephropathy to the project. My primary task is to ensure that even rare patient groups like ours are included in the data and that relevant patient questions are addressed \u2013 for example, the impact of environmental factors on exacerbations. I also help to present study content and results in a way that is understandable and accessible to patients.<\/p>\n<\/div>\n\n\n\n\n\t\t\t<\/div> \n\t\t<\/div>\n\t<\/div> \n<\/div><\/div>\n\t\t

<\/div><\/div><\/div>
\n\t
\n\t\t
\n\t\t\t
\n\t\t\t\t\n
\n\t

In my view, patients benefit from the CALM-QE study for the following reasons:<\/p>\n