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  • Springer Science and Business Media LLC  (2)
  • Koitka, Sven  (2)
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  • Springer Science and Business Media LLC  (2)
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  • 1
    Online Resource
    Online Resource
    Springer Science and Business Media LLC ; 2023
    In:  BMC Health Services Research Vol. 23, No. 1 ( 2023-07-06)
    In: BMC Health Services Research, Springer Science and Business Media LLC, Vol. 23, No. 1 ( 2023-07-06)
    Abstract: 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.
    Type of Medium: Online Resource
    ISSN: 1472-6963
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2023
    detail.hit.zdb_id: 2050434-2
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  • 2
    In: Scientific Reports, Springer Science and Business Media LLC, Vol. 12, No. 1 ( 2022-09-30)
    Abstract: The complex process of manual biomarker extraction from body composition analysis (BCA) has far restricted the analysis of SARS-CoV-2 outcomes to small patient cohorts and a limited number of tissue types. We investigate the association of two BCA-based biomarkers with the development of severe SARS-CoV-2 infections for 918 patients (354 female, 564 male) regarding disease severity and mortality (186 deceased). Multiple tissues, such as muscle, bone, or adipose tissue are used and acquired with a deep-learning-based, fully-automated BCA from computed tomography images of the chest. The BCA features and markers were univariately analyzed with a Shapiro–Wilk and two-sided Mann–Whitney-U test. In a multivariate approach, obtained markers were adjusted by a defined set of laboratory parameters promoted by other studies. Subsequently, the relationship between the markers and two endpoints, namely severity and mortality, was investigated with regard to statistical significance. The univariate approach showed that the muscle volume was significant for female ( p severity  ≤ 0.001, p mortality  ≤ 0.0001) and male patients ( p severity  = 0.018, p mortality  ≤ 0.0001) regarding the severity and mortality endpoints. For male patients, the intra- and intermuscular adipose tissue (IMAT) ( p  ≤ 0.0001), epicardial adipose tissue (EAT) ( p  ≤ 0.001) and pericardial adipose tissue (PAT) ( p  ≤ 0.0001) were significant regarding the severity outcome. With the mortality outcome, muscle ( p  ≤ 0.0001), IMAT ( p  ≤ 0.001), EAT ( p  = 0.011) and PAT ( p  = 0.003) remained significant. For female patients, bone ( p  ≤ 0.001), IMAT ( p  = 0.032) and PAT ( p  = 0.047) were significant in univariate analyses regarding the severity and bone ( p  = 0.005) regarding the mortality. Furthermore, the defined sarcopenia marker ( p  ≤ 0.0001, for female and male) was significant for both endpoints. The cardiac marker was significant for severity (p female  = 0.014, p male  ≤ 0.0001) and for mortality (p female  ≤ 0.0001, p male  ≤ 0.0001) endpoint for both genders. The multivariate logistic regression showed that the sarcopenia marker was significant ( p severity  = 0.006, p mortality  = 0.002) for both endpoints (OR severity  = 0.42, 95% CI severity : 0.23–0.78, OR mortality  = 0.34, 95% CI mortality : 0.17–0.67). The cardiac marker showed significance (p = 0.018) only for the severity endpoint (OR = 1.42, 95% CI 1.06–1.90). The association between BCA-based sarcopenia and cardiac biomarkers and disease severity and mortality suggests that these biomarkers can contribute to the risk stratification of SARS-CoV-2 patients. Patients with a higher cardiac marker and a lower sarcopenia marker are at risk for a severe course or death. Whether those biomarkers hold similar importance for other pneumonia-related diseases requires further investigation.
    Type of Medium: Online Resource
    ISSN: 2045-2322
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2022
    detail.hit.zdb_id: 2615211-3
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