Abstract:
To rapidly prognosticate and generate hypotheses on pathogenesis, leukocyte multi-cellularity
was evaluated in SARS-CoV-2 infected patients treated in India or the United States (152
individuals, 384 temporal observations). Within hospital (<90-day) death or discharge were
retrospectively predicted based on the admission complete blood cell counts (CBC). Two
methods were applied: (i) a “reductionist” one, which analyzes each cell type separately, and
(ii) a “non-reductionist” method, which estimates multi-cellularity. The second approach uses
a proprietary software package that detects distinct data patterns generated by complex and
hypothetical indicators and reveals each data pattern’s immunological content and associated
outcome(s). In the Indian population, the analysis of isolated cell types did not separate survivors
from non-survivors. In contrast, multi-cellular data patterns differentiated six groups of patients,
including, in two groups, 95.5% of all survivors. Some data structures revealed one data pointwide line of observations, which informed at a personalized level and identified 97.8% of all nonsurvivors. Discovery was also fostered: some non-survivors were characterized by low
monocyte/lymphocyte ratio levels. When both populations were analyzed with the nonreductionist method, they displayed results that suggested survivors and non-survivors
differed immunologically as early as hospitalization day 1.
