Rivas, Ariel L.

Background: While ‘immuno-competence’ is a well-known term, it lacks an operational definition. To address this omission, this study explored whether the temporal and structured data of the complete blood cell count (CBC) can rapidly estimate immuno-competence. To this end, one or more ratios that included data on all monocytes, lymphocytes and neutrophils were investigated. Materials and methods: Longitudinal CBC data collected from 101 COVID-19 patients (291 observations) were analyzed. Dynamics were estimated with several approaches, which included non-structured (the classic CBC format) and structured data. Structured data were assessed as complex ratios that capture multicellular interactions among leukocytes. In comparing survivors with non-survivors, the hypothesis that immuno-competence may exhibit feedback-like (oscillatory or cyclic) responses was tested. Results: While non-structured data did not distinguish survivors from non-survivors, structured data revealed immunological and statistical differences between outcomes: while survivors exhibited oscillatory data patterns, non-survivors did not. In survivors, many variables (including IL-6, hemoglobin and several complex indicators) showed values above or below the levels observed on day 1 of the hospitalization period, displaying L-shaped data distributions (positive kurtosis). In contrast, non-survivors did not exhibit kurtosis. Three immunologically defined data subsets included only survivors. Because information was based on visual patterns generated in real time, this method can, potentially, provide information rapidly. Discussion: The hypothesis that immuno-competence expresses feedback-like loops when immunological data are structured was not rejected. This function seemed to be impaired in immuno-suppressed individuals. While this method rapidly informs, it is only a guide that, to be confirmed, requires additional tests. Despite this limitation, the fact that three protective (survival-associated) immunological data subsets were observed since day 1 supports many clinical decisions, including the early and personalized prognosis and identification of targets that immunomodulatory therapies could pursue. Because it extracts more information from the same data, structured data may replace the century-old format of the CBC.

Abstract 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 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)