Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2025 Jul 10;53(12):e2415-e2428.
doi: 10.1097/CCM.0000000000006774. Online ahead of print.

Parsimonious Subphenotyping Algorithms Perform Differently in Patients With Sepsis and Hematologic Malignancy

Affiliations

Parsimonious Subphenotyping Algorithms Perform Differently in Patients With Sepsis and Hematologic Malignancy

Lukas Ronner et al. Crit Care Med. .

Abstract

Objectives: Latent class assignment-derived subphenotyping algorithms may identify treatment-responsive subgroups of critically ill patients with sepsis and acute respiratory distress syndrome. It is unclear if these algorithms are generalizable to patients with comorbid malignancy, a state which may perturb influential inflammatory biomarkers. This study aimed to test whether malignancy or neutropenia modified the effect of subphenotype assignment by two algorithms as applied to a prospective cohort enriched for ICU patients with active malignancy.

Design: Prospective cohort study at a single U.S. quaternary referral center.

Setting/patients: ICU patients older than 18 admitted to an ICU with a primary admission indication of sepsis.

Interventions: None.

Measurements and main results: We applied two published subphenotyping algorithms utilizing either interleukin (IL)-6 or IL-8 (in addition to soluble tumor necrosis factor receptor 1 and bicarbonate) to our cohort of 930 patients with sepsis, 396 (42%) of whom had active malignancy. A greater proportion of hematologic malignancy patients were assigned the "hyperinflammatory" subphenotype by the IL-8-utilizing algorithm than the IL-6 algorithm (58% vs. 32%). Patients with leukemia and neutropenia were overrepresented among those classified as hyperinflammatory by IL-8 algorithm. We constructed Cox proportional hazards models to assess for interaction between the presence of solid malignancy, hematologic malignancy, and severe neutropenia and the subphenotype/mortality association. Hematologic malignancy uniquely appeared to attenuate the associated mortality of the IL-6-assigned hyperinflammatory subphenotype (interaction; p = 0.037), but not the IL-8-assigned hyperinflammatory subphenotype (interaction; p = 0.260), which retained an independent association with mortality in hematologic malignancy subjects (hazard ratio, 1.50; 95% CI, 1.08-2.07; p = 0.014).

Conclusions: As subphenotyping algorithms are being tested as point-of-care prognostic tools, it is important to understand their generalizability to patients with comorbid malignancy, which constitute an increasing proportion of ICU patients. The differential behavior of these algorithms in patients with hematologic malignancy suggests a need for independent derivation and validation in this specific population.

Keywords: critical illness subphenotyping; hematologic malignancy; oncologic intensive care unit; sepsis.

PubMed Disclaimer

Conflict of interest statement

Drs. Ronner, Miano, Dasgupta, West, Jones, Reilly, and Meyer received support for article research from the National Institutes of Health (NIH). Dr. Ronner reports stock ownership in Verona Pharma. Dr. Giannini reports medical advising for TellHealth. Dr. Reilly’s and Meyer’s institutions received funding from the NIH. Dr. Meyer’s institution received funding from the National Heart, Lung, and Blood Institute and Quantum Leap Healthcare Collaborative; she received funding from Endpoint Health, Novartis, and AstraZeneca. The remaining authors have disclosed that they do not have any potential conflicts of interest.

Figures

Figure 1.
Figure 1.
Consolidated Standards of Reporting Trials (CONSORT) diagram depicting inclusion into Molecular Epidemiology of SepsiS in the ICU (MESSI) cohort and selection of phenotyping subcohort. ED = emergency department.
Figure 2.
Figure 2.
Survival of sepsis patients stratified by subphenotype and presence of hematologic malignancy. IL = interleukin.

References

    1. Calfee CS, Delucchi K, Parsons PE, et al. ; NHLBI ARDS Network: Subphenotypes in acute respiratory distress syndrome: Latent class analysis of data from two randomised controlled trials. Lancet Respir Med. 2014; 2:611–620 - PMC - PubMed
    1. Sinha P, Delucchi KL, Chen Y, et al. : Latent class analysis-derived subphenotypes are generalisable to observational cohorts of acute respiratory distress syndrome: A prospective study. Thorax. 2022; 77:13–21 - PMC - PubMed
    1. Bos LD, Schouten LR, Vught LA van, et al. : Identification and validation of distinct biological phenotypes in patients with acute respiratory distress syndrome by cluster analysis. Thorax. 2017; 72:876–883 - PMC - PubMed
    1. Sinha P, Calfee CS, Cherian S, et al. : Prevalence of phenotypes of acute respiratory distress syndrome in critically ill patients with COVID-19: A prospective observational study. Lancet Respir Med. 2020; 8:1209–1218 - PMC - PubMed
    1. Kitsios GD, Yang L, Manatakis DV, et al. : Host-response subphenotypes offer prognostic enrichment in patients with or at risk for acute respiratory distress syndrome. Crit Care Med. 2019; 47:1724–1734 - PMC - PubMed