Leveraging electronic health records to predict the risk of acute kidney injury after allogeneic hematopoietic cell transplantation

• Verfasst von: Bischoff, Elena [VerfasserIn]
• Verfasst von: Kirilov, Nikola [VerfasserIn]
• Titel: Leveraging electronic health records to predict the risk of acute kidney injury after allogeneic hematopoietic cell transplantation
• Verf.angabe: Elena Bischoff and Nikola Kirilov
• E-Jahr: 2024
• Jahr: 8 August 2024
• Umfang: 13 S.
• Illustrationen: Illustrationen
• Fussnoten: Gesehen am 10.02.2025
• Titel Quelle: Enthalten in: Life
• Ort Quelle: Basel : MDPI, 2011
• Jahr Quelle: 2024
• Band/Heft Quelle: 14(2024), 8, Artikel-ID 987, Seite 1-13
• ISSN Quelle: 2075-1729
• DOI: doi:10.3390/life14080987
• Datenträger: Online-Ressource
• Sprache: eng
• Sach-SW: acute kidney injury
• Sach-SW: allogeneic hematopoietic cell transplantation
• Sach-SW: electronic health record
• Sach-SW: risk
• Sach-SW: score
• K10plus-PPN: 1916760147

Abstract

Background: The objective of this study is to assess the electronic health records (EHRs), which are potential risk factors for acute kidney injury (AKI) after allogenic hematopoietic cell transplantation (allo-HCT), and to propose a basic dataset and score for the calculation of HCT-acute kidney injury risk (HCT-AKIR). Methods: We undertook a retrospective analysis of the EHRs of 312 patients. Pre- and post-transplant factors were assessed, recognizing the following structured entries: laboratory data, encounters, medication, imaging studies, diagnoses, and procedures. Composite variables were used to create patient groups by combining two or more multivariate significant risk factors for AKI. The EHRs dataset and HCT-AKIR score were created based on the multivariate analysis of the composite variables. Results: A multivariate analysis showed that previous CKD and once-impaired pre-transplant kidney function, sepsis, imaging procedures with contrast media, and cumulative length of intensive care unit stay after transplantation were significant risk factors. A new unit-weighted composite score based on the combination of significant risk factors contained in common EHR resources was proposed. Conclusions: Using our novel HCT-AKIR score calculated from the basic EHR dataset could be an easy way to increase awareness of post-transplant AKI and provide risk stratification.