FHIR-PYrate: a data science friendly Python package to query FHIR servers.

BACKGROUND: We present FHIR-PYrate, a Python package to handle the full clinical data collection and extraction process. The software is to be plugged into a modern hospital domain, where electronic patient records are used to handle the entire patient's history. Most research institutes follow the same procedures to build study cohorts, but mainly in a non-standardized and repetitive way. As a result, researchers spend time writing boilerplate code, which could be used for more challenging tasks. METHODS: The package can improve and simplify existing processes in the clinical research environment. It collects all needed functionalities into a straightforward interface that can be used to query a FHIR server, download imaging studies and filter clinical documents. The full capacity of the search mechanism of the FHIR REST API is available to the user, leading to a uniform querying process for all resources, thus simplifying the customization of each use case. Additionally, valuable features like parallelization and filtering are included to make it more performant. RESULTS: As an exemplary practical application, the package can be used to analyze the prognostic significance of routine CT imaging and clinical data in breast cancer with tumor metastases in the lungs. In this example, the initial patient cohort is first collected using ICD-10 codes. For these patients, the survival information is also gathered. Some additional clinical data is retrieved, and CT scans of the thorax are downloaded. Finally, the survival analysis can be computed using a deep learning model with the CT scans, the TNM staging and positivity of relevant markers as input. This process may vary depending on the FHIR server and available clinical data, and can be customized to cover even more use cases. CONCLUSIONS: FHIR-PYrate opens up the possibility to quickly and easily retrieve FHIR data, download image data, and search medical documents for keywords within a Python package. With the demonstrated functionality, FHIR-PYrate opens an easy way to assemble research collectives automatically.

Allergen-specific immunotherapy induces the suppressive secretoglobin 1A1 in cells of the lower airways.

BACKGROUND: While several systemic immunomodulatory effects of allergen-specific immunotherapy (AIT) have been discovered, local anti-inflammatory mechanisms in the respiratory tract are largely unknown. We sought to elucidate local and epithelial mechanisms underlying allergen-specific immunotherapy in a genome-wide approach. METHODS: We induced sputum in hay fever patients and healthy controls during the pollen peak season and stratified patients by effective allergen immunotherapy or as untreated. Sputum was directly processed after induction and subjected to whole transcriptome RNA microarray analysis. Nasal secretions were analyzed for Secretoglobin1A1 (SCGB1A1) and IL-24 protein levels in an additional validation cohort at three defined time points during the 3-year course of AIT. Subsequently, RNA was extracted and subjected to an array-based whole transcriptome analysis. RESULTS: Allergen-specific immunotherapy inhibited pro-inflammatory CXCL8, IL24, and CCL26mRNA expression, while SCGB1A1, IL7, CCL5, CCL23, and WNT5BmRNAs were induced independently of the asthma status and allergen season. In our validation cohort, local increase of SCGB1A1 occurred concomitantly with the reduction of local IL-24 in upper airways during the course of AIT. Additionally, SCGB1A1 was identified as a suppressor of epithelial gene expression. CONCLUSIONS: Allergen-specific immunotherapy induces a yet unknown local gene expression footprint in the lower airways that on one hand appears to be a result of multiple regulatory pathways and on the other hand reveals SCGB1A1 as novel anti-inflammatory mediator of long-term allergen-specific therapeutic intervention in the local environment.