Objective: To develop a model to store information in an electronic medical record (EMR) for the management of transplant patients. The model for storing donor information must be designed to allow clinicians to access donor information from the transplant recipient's record and to allow donor data to be stored without needlessly proliferating new Logical Observation Identifier Names and Codes (LOINC) codes for already-coded laboratory tests.
Design: Information required to manage transplant patients requires the use of a donor's medical information while caring for the transplant patient. Three strategies were considered: (1) link the transplant patient's EMR to the donor's EMR; (2) use pre-coordinated observation identifiers (i.e., LOINC codes with *(wedge)DONOR specified in the system axes) to identify donor data stored in the transplant patient's EMR; and (3) use an information model that allows donor information to be stored in the transplant patient's record by allowing the "source" of the data (donor) and the "name" of the result (e.g., blood type) to be post-coordinated in the transplant patient's EMR.
Results: We selected the third strategy and implemented a flexible post-coordinated information model. There was no need to create new LOINC codes for already-coded laboratory tests. The model required that the data structure in the EMR allow for the storage of the "subject" of the test.
Conclusion: The selected strategy met our design requirements and provided an extendable information model to store donor data. This model can be used whenever it is necessary to refer to one patient's data from another patient's EMR.
Download full-text PDF |
Source |
---|---|
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC1090468 | PMC |
http://dx.doi.org/10.1197/jamia.M1689 | DOI Listing |
Am J Sports Med
January 2025
Midwest Orthopaedics at Rush University Medical Center, Chicago, Illinois, USA.
Background: Mismatch between osteochondral allograft (OCA) donor and recipient sex has been shown to negatively affect outcomes. This study accounts for additional donor variables and clinically relevant outcomes.
Purpose: To evaluate whether donor sex, age, donor-recipient sex mismatch, and duration of graft storage affect clinical outcomes and failure rates after knee OCA transplantation.
Sci Rep
January 2025
Institute for System Dynamics, University of Stuttgart, Waldburgstr. 19, 70563, Stuttgart, Germany.
Including sensor information in medical interventions aims to support surgeons to decide on subsequent action steps by characterizing tissue intraoperatively. With bladder cancer, an important issue is tumor recurrence because of failure to remove the entire tumor. Impedance measurements can help to classify bladder tissue and give the surgeons an indication on how much tissue to remove.
View Article and Find Full Text PDFTransplant Cell Ther
January 2025
Dana-Farber Cancer Institute, Division of Transplantation and Cellular Therapy, Boston, MA. Electronic address:
Background: Post-transplant cyclophosphamide (PTCy) is a commonly used graft-vs-host disease (GVHD) prophylaxis, particularly in the setting of haploidentical (haplo) hematopoietic cell transplantation (HCT). The rate of graft failure has been reported to be as high as 12-20% in haplo-HCT recipients using PTCy. The objective of this study was to determine if donor type influenced the risk of late graft failure following RIC HCT using PTCy-based GVHD prophylaxis.
View Article and Find Full Text PDFAging (Albany NY)
January 2025
Department of Public Health Sciences, University of Chicago, Chicago, IL 60615, USA.
Background: DNA methylation (DNAm) data from human samples has been leveraged to develop "epigenetic clock" algorithms that predict age and other aging-related phenotypes. Some DNAm clocks were trained using DNAm obtained from blood cells, while other clocks were trained using data from diverse tissue/cell types. To assess how DNAm clocks perform across non-blood tissue types, we applied DNAm algorithms to DNAm data generated from 9 different human tissue types.
View Article and Find Full Text PDFMicrobiome
January 2025
Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia.
Background: Accurate classification of host phenotypes from microbiome data is crucial for advancing microbiome-based therapies, with machine learning offering effective solutions. However, the complexity of the gut microbiome, data sparsity, compositionality, and population-specificity present significant challenges. Microbiome data transformations can alleviate some of the aforementioned challenges, but their usage in machine learning tasks has largely been unexplored.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!