Development of a Prediction Score for the Occurrence of Death or Lung Transplantation in Patients With Emphysema Secondary to Alpha-1-anti-tripsin Deficiency

Alpha-1-antitrypsin Deficiency, Artificial Intelligence (AI), Emphysema, Lung Transplantation, Mortality Prediction, Predictive Learning Models

Emphysema linked to alpha-1-antitrypsin deficiency (DAAT): towards a better prediction of risks Emphysema caused by alpha-1-antitrypsin deficiency (DAAT) is a rare genetic disorder that can lead to serious complications, such as the need for a lung transplant or death, affecting up to 15% of patients. The only specific treatment available is a weekly infusion of alpha-1-antitrypsin (IV-AAT), an expensive and burdensome therapy. Currently, there is no reliable model to predict the course of the disease in these patients. Our study, conducted in several French hospitals, aims to develop a prediction tool combining clinical, biological, functional data and advanced medical image analysis (lung CT). This model will make it possible to identify the most at-risk patients, in order to better adapt their care, anticipate transplant needs and avoid unnecessary treatments for low-risk patients. Ultimately, this approach could also improve access to care for patients who need it most, while optimizing health system resources.

Context. Emphysema secondary to alpha-1-anti-trypsin deficiency (AATD) is a genetic rare disease, but with pejorative events such as death or lung transplantation (LT) which affect up to 15% of patients. Aside from standard medical care for COPD management, the only specific treatment as augmentation therapy for severe AATD, is weekly intravenous alpha-1 antitrypsin (IV-AAT) which is still expensive and restrictive. Several prognostic scores for tobacco-related COPD have been developed but, none of them have been validated in AATD, neither have included quantitative chest CT imaging while they have been associated with mortality in emphysema. Therefore, we aim to develop a prediction model combining clinical, biological, functional and quantitative CT data (including radiomics) for mortality or LT to identify at-risk patients with emphysema secondary to AATD. Methods: From several French hospitals, we'll conduct a multicenter retrospective study based on data collected in usual care among patients over 18-year-old, with an available chest CT. We'll collect clinical data (BMI, mMRC dyspnea scale), respiratory function parameters and quantitative imaging data (including radiomics) from the initial CT from AATD patients secondary to ZZ, Znull, ZMalton, Z and rare mutations. We'll then validate our results on an independent external database from the European AADT cohort (EARCO), with specific dedicated funding. Perspectives: To develop a prediction model to identify the AATD patients at risk of clinical deterioration, defined by death or LT allowing for personalized care, in order to: 1. anticipate registration on the transplant list, 2. optimize overall management, including pharmacological (IV-AAT) and non-pharmacological management. 3. to avoid cost-prohibitive and constraining IV-AAT infusion for low risk patients For Health policy, finding a way to target high-risk patients may help a better access for IV-AAT to appropriate individuals.

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France