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Yao L, Rey DA, Bulgarelli L, Kast R, Osborn J, Van Ark E, Fang LT, Lau B, Lam H, Teixeira LM, Neto AS, Bellomo R, Deliberato RO. Gene Expression Scoring of Immune Activity Levels for Precision Use of Hydrocortisone in Vasodilatory Shock. Shock 2022; 57:384-391. [PMID: 35081076 PMCID: PMC8868213 DOI: 10.1097/shk.0000000000001910] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2021] [Revised: 12/06/2021] [Accepted: 01/07/2022] [Indexed: 11/26/2022]
Abstract
PURPOSE Among patients with vasodilatory shock, gene expression scores may identify different immune states. We aimed to test whether such scores are robust in identifying patients' immune state and predicting response to hydrocortisone treatment in vasodilatory shock. MATERIALS AND METHODS We selected genes to generate continuous scores to define previously established subclasses of sepsis. We used these scores to identify a patient's immune state. We evaluated the potential for these states to assess the differential effect of hydrocortisone in two randomized clinical trials of hydrocortisone versus placebo in vasodilatory shock. RESULTS We initially identified genes associated with immune-adaptive, immune-innate, immune-coagulant functions. From these genes, 15 were most relevant to generate expression scores related to each of the functions. These scores were used to identify patients as immune-adaptive prevalent (IA-P) and immune-innate prevalent (IN-P). In IA-P patients, hydrocortisone therapy increased 28-day mortality in both trials (43.3% vs 14.7%, P = 0.028) and (57.1% vs 0.0%, P = 0.99). In IN-P patients, this effect was numerically reversed. CONCLUSIONS Gene expression scores identified the immune state of vasodilatory shock patients, one of which (IA-P) identified those who may be harmed by hydrocortisone. Gene expression scores may help advance the field of personalized medicine.
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Affiliation(s)
- Lijing Yao
- Department of Clinical Data Science, Endpoint Health Inc, Palo Alto, California
| | - Diego Ariel Rey
- Department of Clinical Data Science, Endpoint Health Inc, Palo Alto, California
| | - Lucas Bulgarelli
- Department of Clinical Data Science, Endpoint Health Inc, Palo Alto, California
| | - Rachel Kast
- Department of Clinical Data Science, Endpoint Health Inc, Palo Alto, California
| | - Jeff Osborn
- Department of Clinical Data Science, Endpoint Health Inc, Palo Alto, California
| | - Emily Van Ark
- Department of Clinical Data Science, Endpoint Health Inc, Palo Alto, California
| | - Li Tai Fang
- Department of Clinical Data Science, Endpoint Health Inc, Palo Alto, California
| | - Bayo Lau
- Bioinformatics Department, HypaHub Inc, San Jose, California, USA
| | - Hugo Lam
- Bioinformatics Department, HypaHub Inc, San Jose, California, USA
| | | | - Ary Serpa Neto
- Department of Critical Care Medicine, Hospital Israelita Albert Einstein, São Paulo, Brazil
- Australian and New Zealand Intensive Care Research Centre (ANZIC-RC), School of Public Health and Preventive Medicine, Monash University, Melbourne, Australia
- Department of Critical Care, Melbourne Medical School, University of Melbourne, Austin Hospital, Melbourne, Australia
- Data Analytics Research and Evaluation (DARE) Centre, Austin Hospital, Melbourne, Australia
| | - Rinaldo Bellomo
- Australian and New Zealand Intensive Care Research Centre (ANZIC-RC), School of Public Health and Preventive Medicine, Monash University, Melbourne, Australia
- Department of Critical Care, Melbourne Medical School, University of Melbourne, Austin Hospital, Melbourne, Australia
- Data Analytics Research and Evaluation (DARE) Centre, Austin Hospital, Melbourne, Australia
- Department of Intensive Care, Austin Hospital, Melbourne, Australia
- Department of Intensive Care, Royal Melbourne Hospital, Melbourne, Australia
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