Cargando…

364. Individualized Prognostics in COVID-19 Facilitated by Computer Recognition of Blood Leukocyte Subsets

BACKGROUND: To determine whether CBC differentials of COVID+ inpatients can predict, at admission, both maximum oxygen requirements (MOR) and 30-day mortality. METHODS: Based on an approved IRB protocol, CBC differentials from the first 3 days of hospitalization of 12 SARS CoV-2 infected patients we...

Descripción completa

Detalles Bibliográficos
Autores principales: Libertin, Claudia R, Kempaiah, Prakash, Durvasula, Ravindra, Rivas, Ariel
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8643946/
http://dx.doi.org/10.1093/ofid/ofab466.565
_version_ 1784609971667730432
author Libertin, Claudia R
Kempaiah, Prakash
Durvasula, Ravindra
Rivas, Ariel
author_facet Libertin, Claudia R
Kempaiah, Prakash
Durvasula, Ravindra
Rivas, Ariel
author_sort Libertin, Claudia R
collection PubMed
description BACKGROUND: To determine whether CBC differentials of COVID+ inpatients can predict, at admission, both maximum oxygen requirements (MOR) and 30-day mortality. METHODS: Based on an approved IRB protocol, CBC differentials from the first 3 days of hospitalization of 12 SARS CoV-2 infected patients were retrospectively extracted from hospital records and analyzed with a privately owned Pattern Recognition Software (PRS, US Patent 10,429,389 B2) previously validated in sepsis, HIV, and hantavirus infections. PRS partitions the data into subsets immunologically dissimilar from one another, although internally similar. RESULTS: Regardless of the angle considered, the classic analysis −which measured the percentages of lymphocytes, monocytes, and neutrophils− did not distinguish outcomes (A). In contrast, non-overlapping patterns generated by the PRS differentiated 3 (left, vertical, and right) groups of patients (B). One subset was only composed of survivors (B). The remaining subsets included the highest oxygenation requirements (B). At least two immunologically interpretable, multi-cellular indicators distinguished the 3 data subsets with statistically significant differences (C, p≤ 0.05). Survivors (the left subset) showed lower N/L and/or higher M/L ratios than non-survivors (the vertical subset, C).Therefore, PRS partitioned the data into subsets that displayed both biological and significant differences. Because it offers visually explicit information, clinicians do not require a specialized training to interpret PRS-generated results. CBCs vs. outcomes - Software-analyzed CBCs vs. outcomes [Image: see text] CONCLUSION: (1) Analysis of blood leukocyte data predicts MOR and 30-d mortality. (2) Real time PRS analysis facilitates personalized medical decisions. (3) PRS measures two dimensions rarely assessed: multi-cellularity and dynamics. (4) Even with very small datasets, PRS may achieve statistical significance. (5) Larger COVID+ infected cohort is being analyzed for potential commercialization. DISCLOSURES: Claudia R. Libertin, MD, Gilead (Grant/Research Support)
format Online
Article
Text
id pubmed-8643946
institution National Center for Biotechnology Information
language English
publishDate 2021
publisher Oxford University Press
record_format MEDLINE/PubMed
spelling pubmed-86439462021-12-06 364. Individualized Prognostics in COVID-19 Facilitated by Computer Recognition of Blood Leukocyte Subsets Libertin, Claudia R Kempaiah, Prakash Durvasula, Ravindra Rivas, Ariel Open Forum Infect Dis Poster Abstracts BACKGROUND: To determine whether CBC differentials of COVID+ inpatients can predict, at admission, both maximum oxygen requirements (MOR) and 30-day mortality. METHODS: Based on an approved IRB protocol, CBC differentials from the first 3 days of hospitalization of 12 SARS CoV-2 infected patients were retrospectively extracted from hospital records and analyzed with a privately owned Pattern Recognition Software (PRS, US Patent 10,429,389 B2) previously validated in sepsis, HIV, and hantavirus infections. PRS partitions the data into subsets immunologically dissimilar from one another, although internally similar. RESULTS: Regardless of the angle considered, the classic analysis −which measured the percentages of lymphocytes, monocytes, and neutrophils− did not distinguish outcomes (A). In contrast, non-overlapping patterns generated by the PRS differentiated 3 (left, vertical, and right) groups of patients (B). One subset was only composed of survivors (B). The remaining subsets included the highest oxygenation requirements (B). At least two immunologically interpretable, multi-cellular indicators distinguished the 3 data subsets with statistically significant differences (C, p≤ 0.05). Survivors (the left subset) showed lower N/L and/or higher M/L ratios than non-survivors (the vertical subset, C).Therefore, PRS partitioned the data into subsets that displayed both biological and significant differences. Because it offers visually explicit information, clinicians do not require a specialized training to interpret PRS-generated results. CBCs vs. outcomes - Software-analyzed CBCs vs. outcomes [Image: see text] CONCLUSION: (1) Analysis of blood leukocyte data predicts MOR and 30-d mortality. (2) Real time PRS analysis facilitates personalized medical decisions. (3) PRS measures two dimensions rarely assessed: multi-cellularity and dynamics. (4) Even with very small datasets, PRS may achieve statistical significance. (5) Larger COVID+ infected cohort is being analyzed for potential commercialization. DISCLOSURES: Claudia R. Libertin, MD, Gilead (Grant/Research Support) Oxford University Press 2021-12-04 /pmc/articles/PMC8643946/ http://dx.doi.org/10.1093/ofid/ofab466.565 Text en © The Author(s) 2021. Published by Oxford University Press on behalf of Infectious Diseases Society of America. https://creativecommons.org/licenses/by/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Poster Abstracts
Libertin, Claudia R
Kempaiah, Prakash
Durvasula, Ravindra
Rivas, Ariel
364. Individualized Prognostics in COVID-19 Facilitated by Computer Recognition of Blood Leukocyte Subsets
title 364. Individualized Prognostics in COVID-19 Facilitated by Computer Recognition of Blood Leukocyte Subsets
title_full 364. Individualized Prognostics in COVID-19 Facilitated by Computer Recognition of Blood Leukocyte Subsets
title_fullStr 364. Individualized Prognostics in COVID-19 Facilitated by Computer Recognition of Blood Leukocyte Subsets
title_full_unstemmed 364. Individualized Prognostics in COVID-19 Facilitated by Computer Recognition of Blood Leukocyte Subsets
title_short 364. Individualized Prognostics in COVID-19 Facilitated by Computer Recognition of Blood Leukocyte Subsets
title_sort 364. individualized prognostics in covid-19 facilitated by computer recognition of blood leukocyte subsets
topic Poster Abstracts
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8643946/
http://dx.doi.org/10.1093/ofid/ofab466.565
work_keys_str_mv AT libertinclaudiar 364individualizedprognosticsincovid19facilitatedbycomputerrecognitionofbloodleukocytesubsets
AT kempaiahprakash 364individualizedprognosticsincovid19facilitatedbycomputerrecognitionofbloodleukocytesubsets
AT durvasularavindra 364individualizedprognosticsincovid19facilitatedbycomputerrecognitionofbloodleukocytesubsets
AT rivasariel 364individualizedprognosticsincovid19facilitatedbycomputerrecognitionofbloodleukocytesubsets