Utilizing principal component analysis in the identification of clinically relevant changes in patient HLA single antigen bead solid phase testing patterns Article Swipe
Caleb Cornaby
,
Eric T. Weimer
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1371/journal.pone.0288743
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1371/journal.pone.0288743
Background HLA antibody testing is essential for successful solid-organ allocation, patient monitoring post-transplant, and risk assessment for both solid-organ and hematopoietic transplant patients. Luminex solid-phase testing is the most common method for identifying HLA antibody specificities, making it one of the most complex immunoassays as each panel contains over 90 specificities for both HLA class I and HLA class II with most of the analysis being performed manually in the vendor-provided software. Principal component analysis (PCA), used in machine learning, is a feature extraction method often utilized to assess data with many variables. Methods & findings In our study, solid organ transplant patients who exhibited HLA donor-specific antibodies (DSAs) were used to characterize the utility of PCA-derived analysis when compared to a control group of post-transplant and pre-transplant patients. ROC analysis was utilized to determine a potential threshold for the PCA-derived analysis that would indicate a significant change in a patient’s single antigen bead pattern. To evaluate if the algorithm could identify differences in patterns on HLA class I and HLA class II single antigen bead results using the optimized threshold, HLA antibody test results were analyzed using PCA-derived analysis and compared to the clinical results for each patient sample. The PCA-derived algorithm had a sensitivity of 100% (95% CI, 73.54%-100%), a specificity of 75% (95% CI, 56.30%-92.54%), with a PPV of 65% (95% CI, 52.50%-83.90%) and an NPV of 100%, in identifying new reactivity that differed from the patients historic HLA antibody pattern. Additionally, PCA-derived analysis was utilized to assess the potential over-reactivity of single antigen beads for both HLA class I and HLA class II antibody panels. This assessment of antibody results identified several beads in both the HLA class I and HLA class II antibody panel which exhibit over reactivity from 2018 to the present time. Conclusions PCA-derived analysis would be ideal to help automatically identify patient samples that have an HLA antibody pattern of reactivity consistent with their history and those which exhibit changes in their antibody patterns which could include donor-specific antibodies, de novo HLA antibodies, and assay interference. A similar method could also be applied to evaluate the over-reactivity of beads in the HLA solid phase assays which would be beneficial for lot comparisons and instructive for transplant centers to better understand which beads are more prone to exhibiting over-reactivity and impact patient care.
Related Topics
Concepts
Human leukocyte antigen
Principal component analysis
Antigen
Medicine
Histocompatibility Testing
Immunology
Internal medicine
Computer science
Artificial intelligence
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1371/journal.pone.0288743
- https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0288743&type=printable
- OA Status
- gold
- Cited By
- 3
- References
- 22
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4387966095
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4387966095Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1371/journal.pone.0288743Digital Object Identifier
- Title
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Utilizing principal component analysis in the identification of clinically relevant changes in patient HLA single antigen bead solid phase testing patternsWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2023Year of publication
- Publication date
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2023-10-26Full publication date if available
- Authors
-
Caleb Cornaby, Eric T. WeimerList of authors in order
- Landing page
-
https://doi.org/10.1371/journal.pone.0288743Publisher landing page
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https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0288743&type=printableDirect link to full text PDF
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0288743&type=printableDirect OA link when available
- Concepts
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Human leukocyte antigen, Principal component analysis, Antigen, Medicine, Histocompatibility Testing, Immunology, Internal medicine, Computer science, Artificial intelligenceTop concepts (fields/topics) attached by OpenAlex
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3Total citation count in OpenAlex
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2025: 2, 2024: 1Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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