From numbers to medical knowledge: harnessing combinatorial data patterns to predict COVID-19 resource needs and distinguish patient subsets

Parthkumar H Satashia; Pablo Moreno Franco; Ariel L Rivas; Shahin Isha; Abby Hanson; Sai Abhishek Narra; Kawaljeet Singh; Anna Jenkins; Anirban Bhattacharyya; Pramod Guru; Sanjay Chaudhary; Sean Kiley; Anna Shapiro; Archer Martin; Mathew Thomas; Basar Sareyyupoglu; Claudia R Libertin; Devang K Sanghavi

doi:10.3389/fmed.2023.1240426

From numbers to medical knowledge: harnessing combinatorial data patterns to predict COVID-19 resource needs and distinguish patient subsets

Front Med (Lausanne). 2023 Nov 8:10:1240426. doi: 10.3389/fmed.2023.1240426. eCollection 2023.

Authors

Parthkumar H Satashia^#¹, Pablo Moreno Franco^#¹, Ariel L Rivas², Shahin Isha¹, Abby Hanson¹, Sai Abhishek Narra¹, Kawaljeet Singh¹, Anna Jenkins³, Anirban Bhattacharyya¹, Pramod Guru¹, Sanjay Chaudhary¹, Sean Kiley¹, Anna Shapiro¹, Archer Martin⁴, Mathew Thomas⁵, Basar Sareyyupoglu⁵, Claudia R Libertin^#⁶, Devang K Sanghavi^#¹

Affiliations

¹ Department of Critical Care Medicine, Mayo Clinic, Jacksonville, FL, United States.
² Center for Global Health-Department of Internal Medicine, School of Medicine, University of New Mexico, Albuquerque, NM, United States.
³ Mayo Clinic Alix School of Medicine, Jacksonville, FL, United States.
⁴ Division of Cardiovascular and Thoracic Anesthesiology, Department of Anesthesiology and Perioperative Medicine, Mayo Clinic, Jacksonville, FL, United States.
⁵ Department of Cardiothoracic Surgery, Mayo Clinic, Jacksonville, FL, United States.
⁶ Division of Infectious Diseases, Mayo Clinic, Jacksonville, FL, United States.

^# Contributed equally.

Abstract

Background: The COVID-19 pandemic intensified the use of scarce resources, including extracorporeal membrane oxygenation (ECMO) and mechanical ventilation (MV). The combinatorial features of the immune system may be considered to estimate such needs and facilitate continuous open-ended knowledge discovery.

Materials and methods: Computer-generated distinct data patterns derived from 283 white blood cell counts collected within five days after hospitalization from 97 COVID-19 patients were used to predict patient's use of hospital resources.

Results: Alone, data on separate cell types-such as neutrophils-did not identify patients that required MV/ECMO. However, when structured as multicellular indicators, distinct data patterns displayed by such markers separated patients later needing or not needing MV/ECMO. Patients that eventually required MV/ECMO also revealed increased percentages of neutrophils and decreased percentages of lymphocytes on admission.

Discussion/conclusion: Future use of limited hospital resources may be predicted when combinations of available blood leukocyte-related data are analyzed. New methods could also identify, upon admission, a subset of COVID-19 patients that reveal inflammation. Presented by individuals not previously exposed to MV/ECMO, this inflammation differs from the well-described inflammation induced after exposure to such resources. If shown to be reproducible in other clinical syndromes and populations, it is suggested that the analysis of immunological combinations may inform more and/or uncover novel information even in the absence of pre-established questions.

Keywords: combinatorial immunology; early patient partitioning; personalized and prognostic medicine; resource allocation; structured blood leukocyte data.