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 point-wide line of observations, which informed at a personalized level and identified 97.8% of all non-survivors. Discovery was also fostered: some non-survivors were characterized by low monocyte/lymphocyte ratio levels. When both populations were analyzed with the non-reductionist method, they displayed results that suggested survivors and non-survivors differed immunologically as early as hospitalization day 1.
Highlights
One consequence of biological complexity is that bottom-up approaches cannot anticipate outcomes associated with emergent properties –to that end, top-down methods are needed [32]
This study aimed to evaluate whether a non-reductionist method can (i) extract more information than alternatives and prevent errors, such as confounding; (ii) predict outcomes, such as survival or non-survival to SARS-CoV-2 infection; and (iii) provide information that promotes personalized medicine
A non-interventional, observational, and retrospective cohort study was based on hematological data collected from laboratory-confirmed COVID-19 individuals admitted to the Vardhman Mahavir Medical College (VMMC) and Safdarjung Hospital of New Delhi, India
Summary
The rapid extraction of more or new biologically interpretable information from the same data is a classic priority of clinical medicine and biomedical research. This goal is pursued by integrative approaches, which analyze several biological levels ‒including but not limited to genetic, molecular, cellular, and supra-cellular relationships [1, 2]. Technologists feel pressed to “reduce dimensions” ‒so the time and cost involved in data analysis are reduced. This situation is driven by ‘the curse of dimensionality’: datasets may not be statistically treatable because there may be more parameters than data points [5]. Inferences can be reduced to or explained by a few “low-level” variables [6, 7]
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
