Observational Study

. 2023 Nov;11(11):965-974.

doi: 10.1016/S2213-2600(23)00237-0. Epub 2023 Aug 23.

Identifying molecular phenotypes in sepsis: an analysis of two prospective observational cohorts and secondary analysis of two randomised controlled trials

Pratik Sinha¹, V Eric Kerchberger², Andrew Willmore³, Julia Chambers⁴, Hanjing Zhuo⁵, Jason Abbott⁵, Chayse Jones⁵, Nancy Wickersham², Nelson Wu³, Lucile Neyton⁵, Charles R Langelier⁶, Eran Mick⁶, June He⁷, Alejandra Jauregui³, Matthew M Churpek⁸, Antonio D Gomez⁹, Carolyn M Hendrickson¹⁰, Kirsten N Kangelaris¹¹, Aartik Sarma³, Aleksandra Leligdowicz¹², Kevin L Delucchi¹³, Kathleen D Liu¹⁴, James A Russell¹⁵, Michael A Matthay¹⁶, Keith R Walley¹⁵, Lorraine B Ware¹⁷, Carolyn S Calfee¹⁶

Affiliations

¹ Division of Clinical and Translational Research, Division of Critical Care, Department of Anesthesiology, Washington University School of Medicine, Saint Louis, MO, USA. Electronic address: p.sinha@wustl.edu.
² Division of Allergy, Pulmonary, and Critical Care Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA.
³ Division of Pulmonary, Critical Care, Allergy and Sleep Medicine, Department of Medicine, University of California San Francisco, San Francisco, CA, USA.
⁴ Department of Medicine, University of California San Francisco, San Francisco, CA, USA.
⁵ Division of Pulmonary, Critical Care, Allergy and Sleep Medicine, Department of Medicine, University of California San Francisco, San Francisco, CA, USA; Cardiovascular Research Institute, University of California San Francisco, San Francisco, CA, USA.
⁶ Division of Infectious Diseases, Department of Medicine, University of California San Francisco, San Francisco, CA, USA; Chan Zuckerberg Biohub, San Francisco, CA, USA.
⁷ Division of Clinical and Translational Research, Division of Critical Care, Department of Anesthesiology, Washington University School of Medicine, Saint Louis, MO, USA.
⁸ Department of Medicine, University of Wisconsin-Madison, Madison, WI, USA.
⁹ Division of Pulmonary, Critical Care, Allergy and Sleep Medicine, Department of Medicine, University of California San Francisco, San Francisco, CA, USA; Zuckerberg San Francisco General Hospital and Trauma Center, San Francisco, CA, USA.
¹⁰ Zuckerberg San Francisco General Hospital and Trauma Center, San Francisco, CA, USA.
¹¹ Division of Hospital Medicine, Department of Medicine, University of California San Francisco, San Francisco, CA, USA.
¹² Department of Medicine, Division of Critical Care Medicine, Robarts Research Institute, University of Western Ontario, London, ON, Canada.
¹³ Department of Psychiatry, University of California San Francisco, San Francisco, CA, USA.
¹⁴ Division of Pulmonary, Critical Care, Allergy and Sleep Medicine, Department of Medicine, University of California San Francisco, San Francisco, CA, USA; Division of Nephrology, Department of Medicine, University of California San Francisco, San Francisco, CA, USA.
¹⁵ Division of Critical Care Medicine, St Paul's Hospital, University of British Columbia, Vancouver, BC, Canada; Centre for Heart Lung Innovation, University of British Columbia, Vancouver, BC, Canada.
¹⁶ Division of Pulmonary, Critical Care, Allergy and Sleep Medicine, Department of Medicine, University of California San Francisco, San Francisco, CA, USA; Department of Anesthesia, University of California San Francisco, San Francisco, CA, USA.
¹⁷ Division of Allergy, Pulmonary, and Critical Care Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA; Department of Pathology, Microbiology and Immunology, Vanderbilt University Medical Center, Nashville, TN, USA.

PMID: 37633303
PMCID: PMC10841178
DOI: 10.1016/S2213-2600(23)00237-0

Observational Study

Identifying molecular phenotypes in sepsis: an analysis of two prospective observational cohorts and secondary analysis of two randomised controlled trials

Pratik Sinha et al. Lancet Respir Med. 2023 Nov.

. 2023 Nov;11(11):965-974.

doi: 10.1016/S2213-2600(23)00237-0. Epub 2023 Aug 23.

Authors

Affiliations

¹ Division of Clinical and Translational Research, Division of Critical Care, Department of Anesthesiology, Washington University School of Medicine, Saint Louis, MO, USA. Electronic address: p.sinha@wustl.edu.
² Division of Allergy, Pulmonary, and Critical Care Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA.
³ Division of Pulmonary, Critical Care, Allergy and Sleep Medicine, Department of Medicine, University of California San Francisco, San Francisco, CA, USA.
⁴ Department of Medicine, University of California San Francisco, San Francisco, CA, USA.
⁵ Division of Pulmonary, Critical Care, Allergy and Sleep Medicine, Department of Medicine, University of California San Francisco, San Francisco, CA, USA; Cardiovascular Research Institute, University of California San Francisco, San Francisco, CA, USA.
⁶ Division of Infectious Diseases, Department of Medicine, University of California San Francisco, San Francisco, CA, USA; Chan Zuckerberg Biohub, San Francisco, CA, USA.
⁷ Division of Clinical and Translational Research, Division of Critical Care, Department of Anesthesiology, Washington University School of Medicine, Saint Louis, MO, USA.
⁸ Department of Medicine, University of Wisconsin-Madison, Madison, WI, USA.
⁹ Division of Pulmonary, Critical Care, Allergy and Sleep Medicine, Department of Medicine, University of California San Francisco, San Francisco, CA, USA; Zuckerberg San Francisco General Hospital and Trauma Center, San Francisco, CA, USA.
¹⁰ Zuckerberg San Francisco General Hospital and Trauma Center, San Francisco, CA, USA.
¹¹ Division of Hospital Medicine, Department of Medicine, University of California San Francisco, San Francisco, CA, USA.
¹² Department of Medicine, Division of Critical Care Medicine, Robarts Research Institute, University of Western Ontario, London, ON, Canada.
¹³ Department of Psychiatry, University of California San Francisco, San Francisco, CA, USA.
¹⁴ Division of Pulmonary, Critical Care, Allergy and Sleep Medicine, Department of Medicine, University of California San Francisco, San Francisco, CA, USA; Division of Nephrology, Department of Medicine, University of California San Francisco, San Francisco, CA, USA.
¹⁵ Division of Critical Care Medicine, St Paul's Hospital, University of British Columbia, Vancouver, BC, Canada; Centre for Heart Lung Innovation, University of British Columbia, Vancouver, BC, Canada.
¹⁶ Division of Pulmonary, Critical Care, Allergy and Sleep Medicine, Department of Medicine, University of California San Francisco, San Francisco, CA, USA; Department of Anesthesia, University of California San Francisco, San Francisco, CA, USA.
¹⁷ Division of Allergy, Pulmonary, and Critical Care Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA; Department of Pathology, Microbiology and Immunology, Vanderbilt University Medical Center, Nashville, TN, USA.

PMID: 37633303
PMCID: PMC10841178
DOI: 10.1016/S2213-2600(23)00237-0

Abstract

Background: In sepsis and acute respiratory distress syndrome (ARDS), heterogeneity has contributed to difficulty identifying effective pharmacotherapies. In ARDS, two molecular phenotypes (hypoinflammatory and hyperinflammatory) have consistently been identified, with divergent outcomes and treatment responses. In this study, we sought to derive molecular phenotypes in critically ill adults with sepsis, determine their overlap with previous ARDS phenotypes, and evaluate whether they respond differently to treatment in completed sepsis trials.

Methods: We used clinical data and plasma biomarkers from two prospective sepsis cohorts, the Validating Acute Lung Injury biomarkers for Diagnosis (VALID) study (N=1140) and the Early Assessment of Renal and Lung Injury (EARLI) study (N=818), in latent class analysis (LCA) to identify the optimal number of classes in each cohort independently. We used validated models trained to classify ARDS phenotypes to evaluate concordance of sepsis and ARDS phenotypes. We applied these models retrospectively to the previously published Prospective Recombinant Human Activated Protein C Worldwide Evaluation in Severe Sepsis and Septic Shock (PROWESS-SHOCK) trial and Vasopressin and Septic Shock Trial (VASST) to assign phenotypes and evaluate heterogeneity of treatment effect.

Findings: A two-class model best fit both VALID and EARLI (p<0·0001). In VALID, 804 (70·5%) of the 1140 patients were classified as hypoinflammatory and 336 (29·5%) as hyperinflammatory; in EARLI, 530 (64·8%) of 818 were hypoinflammatory and 288 (35·2%) hyperinflammatory. We observed higher plasma pro-inflammatory cytokines, more vasopressor use, more bacteraemia, lower protein C, and higher mortality in the hyperinflammatory than in the hypoinflammatory phenotype (p<0·0001 for all). Classifier models indicated strong concordance between sepsis phenotypes and previously identified ARDS phenotypes (area under the curve 0·87-0·96, depending on the model). Findings were similar excluding participants with both sepsis and ARDS. In PROWESS-SHOCK, 1142 (68·0%) of 1680 patients had the hypoinflammatory phenotype and 538 (32·0%) had the hyperinflammatory phenotype, and response to activated protein C differed by phenotype (p=0·0043). In VASST, phenotype proportions were similar to other cohorts; however, no treatment interaction with the type of vasopressor was observed (p=0·72).

Interpretation: Molecular phenotypes previously identified in ARDS are also identifiable in multiple sepsis cohorts and respond differently to activated protein C. Molecular phenotypes could represent a treatable trait in critical illness beyond the patient's syndromic diagnosis.

Funding: US National Institutes of Health.

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Conflict of interest statement

Declaration of interests PS reports funding from the US National Institutes of Health (NIH) and National Institute of General Medical Sciences; and consulting fees from AstraZeneca. LBW reports funding from NIH, Department of Defense (DoD), Genentech, Boehringer Ingelheim, and CSL Behring; consulting fees from Akebia Therapeutics, Santhera, Global Blood Therapeutics, and Boehringer Ingelheim; and stock options in Virtuoso Surgical. CSC reports funding from NIH; research grants from Roche Genentech and Quantum Leap Healthcare Collaborative; consulting fees from Vasomune Therapeutics, GEn1E Lifesciences, NGM Bio, Cellenkos, and Janssen; and a patent on metagenomic sequencing for sepsis diagnosis (co-recipient). MAM reports funding from Roche Genentech, Quantum Therapeutics, NIH/National Heart, Lung, and Blood Institute/National Institute of Allergy and Infectious Diseases, DoD, and California Institute for Regenerative Medicine; and consulting fees from Johnson & Johnson, Gilead Sciences, and Novartis. MMC reports funding from NIH and DoD; and intellectual property royalties from an issued patent (#11 410 777). JAR reports an investigator-initiated grant from Grifols provided to and administered by the University of British Columbia, Canadian Institutes of Health Research; three grants from the St Paul's Foundation; patents owned by the University of British Columbia related to the use of PCSK9 inhibitor(s) in sepsis and the use of vasopressin in septic shock, and by Ferring Pharmaceuticals for use of selepressin in septic shock; formerly being a founder, Director, and shareholder in Cyon Therapeutics (now closed); being a shareholder in Molecular You; receiving consulting fees in the last 3 years from SIB, Ferring Pharmaceuticals, and Par Pharmaceutical; and having been a funded member of the Data and Safety Monitoring Board of an NIH-sponsored trial of plasma in COVID-19 (PASS-IT-ON). KDL reports grants from NIH: National Institute of Diabetes and Digestive and Kidney Diseases; consulting fees from bioMérieux, UpToDate, SeaStar Medical, AM-Pharma, and Baxter; and stock or stock options in Amgen. All other authors declare no competing interests.

Figures

**Figure 1:. Summary of the Analysis Plan.**
Latent class analysis (LCA) was used in VALID and EARLI because sufficient number of biomarkers were available across the entire cohort to recapitulate prior work used for discovering molecular phenotypes. In comparison, in PROWESS-SHOCK and VASST protein biomarker availability was insufficient for performing LCA as per our prior procedures. VALID: Validating Acute Lung Injury markers for Diagnosis; EARLI: Early Assessment of Renal and Lung Injury; PROWESS-SHOCK: Prospective Recombinant Human Activated Protein C Worldwide Evaluation in Severe Sepsis and Septic Shock trial; VASST: Vasopressin and Septic Shock trial; ICAM-1: intercellular adhesion molecule-1, IL-6: interleukin 6; IL-8: interleukin 8; PAI-1: plasminogen activator inhibitor-1; sTNFR-1: soluble tumour necrosis factor receptor-1; CCM = clinical classifier model; PCM = Parsimonious classifier model .

**Figure 2:. Mean standardized values for continuous class-defining variables used in the latent class analysis models.**
The variables are sorted from left to right in descending order for the highest values in the Hyperinflammatory phenotype. Standardized values were calculated by assigning the mean of the variables as 0 and standard deviation as 1. All variables were collected on the day of study enrolment. **Panel A:** VALID cohort. **Panel B:** EARLI Cohort. BMI: body mass index, SBP: systolic blood pressure, ICAM-1: intercellular adhesion molecule-1, IL-6: interleukin 6, IL-8: interleukin 8, PAI-1: plasminogen activator inhibitor-1, sTNFR-1: soluble tumour necrosis factor receptor-1, WBC: white blood cell count, RR = Respiratory Rate, HR = Heart Rate.

**Figure 3:. Differential treatment response to activated protein C (APC) according to probability of belonging to the Hyperinflammatory phenotype.**
A logistic regression model was fit to predict mortality at Day 28 in the PROWESS-SHOCK trial, with the probability of belonging to the Hyperinflammatory phenotype (x-axis), treatment allocation, and their interaction term as predictor variables. The lines plot the estimated mortality in either placebo (red) or activated protein C (green) with 95% confidence intervals over a range of probabilities. P-value was generated using Wald test for the interaction term of probability and treatment allocation in the logistic regression model.

See this image and copyright information in PMC

Comment in

Beyond patterns: how to assign biological meaning to ARDS and sepsis phenotypes.
de Grooth HJ, Cremer OL. de Grooth HJ, et al. Lancet Respir Med. 2023 Nov;11(11):946-947. doi: 10.1016/S2213-2600(23)00266-7. Epub 2023 Aug 23. Lancet Respir Med. 2023. PMID: 37633305 No abstract available.

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Identifying molecular phenotypes in sepsis: an analysis of two prospective observational cohorts and secondary analysis of two randomised controlled trials

Affiliations

Identifying molecular phenotypes in sepsis: an analysis of two prospective observational cohorts and secondary analysis of two randomised controlled trials

Authors

Affiliations

Abstract

Conflict of interest statement

Figures

Comment in

References

Publication types

MeSH terms

Substances

Grants and funding

LinkOut - more resources

Full Text Sources

Medical

Research Materials

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