Evidence map›Paper›PMID 40637495›Full record

ArticleCritical care medicine2025

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

Lukas Ronner, Heather M Giannini, Todd A Miano, Caroline A G Ittner, Alexandra P Turner, Thomas G Dunn, Roseline S Agyekum, Anushka Dasgupta, Kirstin West, Tiffanie K Jones and 3 more

Abstract read
In one paragraph

Article in Critical care medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

5 citing papers in PubMed.

  1. Review
  2. Article
  3. Observational
  4. Article
  5. Observational
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

13 authors.

Lukas RonnerDivision of Hematology and Oncology, Hospital of the University of Pennsylvania, Philadelphia, PA.ORCID 0000-0002-2310-5345
Heather M GianniniDivision of Pulmonary and Critical Care Medicine and Center for Translational Lung Biology, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA.
Todd A MianoDepartment of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA.
Caroline A G IttnerDivision of Pulmonary and Critical Care Medicine and Center for Translational Lung Biology, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA.
Alexandra P TurnerDivision of Pulmonary and Critical Care Medicine and Center for Translational Lung Biology, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA.
Thomas G DunnDivision of Pulmonary and Critical Care Medicine and Center for Translational Lung Biology, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA.
Roseline S AgyekumDivision of Pulmonary and Critical Care Medicine and Center for Translational Lung Biology, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA.
Anushka DasguptaDivision of Pulmonary and Critical Care Medicine and Center for Translational Lung Biology, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA.
Kirstin WestDivision of Pulmonary and Critical Care Medicine and Center for Translational Lung Biology, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA.
Tiffanie K JonesDivision of Pulmonary and Critical Care Medicine and Center for Translational Lung Biology, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA.
Michael G S ShashatyDivision of Pulmonary and Critical Care Medicine and Center for Translational Lung Biology, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA.
John P ReillyDivision of Pulmonary and Critical Care Medicine and Center for Translational Lung Biology, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA.
Nuala J MeyerDivision of Pulmonary and Critical Care Medicine and Center for Translational Lung Biology, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA.

Funding

TRAINING IN PULMONARY IMMUNOLOGYT32HL007586 · NHLBI · UNIVERSITY OF PENNSYLVANIA · PI MEYER, NUALA JENNINGS · 1985 to 2025
$8.8M
Investigating Individual Susceptibility and Host Response in Acute Respiratory Distress SyndromeR35HL161196 · NHLBI · UNIVERSITY OF PENNSYLVANIA · PI Nuala Jennings Meyer · 2022 to 2026
$4.8M
An ABO Blood Type Defined ARDS Endotype in SepsisR01HL155159 · NHLBI · UNIVERSITY OF PENNSYLVANIA · PI REILLY, JOHN PATRICK · 2021 to 2025
$2.7M
The Penn-StARR Program for Research in ResidencyR38HL143613 · NHLBI · UNIVERSITY OF PENNSYLVANIA · PI KLEIN, PETER S · 2018 to 2021
$1.4M
MESO QuikPlex SQ 120 for the multiplex quantification of proteins in human biospecimensS10OD025172 · OD · UNIVERSITY OF PENNSYLVANIA · PI MEYER, NUALA JENNINGS · 2018 to 2018
$50k
NHLBI NIH HHS R01 HL155159NHLBI NIH HHS R35 HL161196NHLBI NIH HHS R38 HL143613NHLBI NIH HHS T32 HL007586NIH HHS S10 OD025172
6 · The paper itself

Abstract

objectivesLatent 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.

designProspective 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.

interventionsNone. MEASUREMENTS AND MAIN

resultsWe 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).

conclusionsAs 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.

Indexed as

AlgorithmsHematologic NeoplasmsSepsisAgedBiomarkersCritical IllnessFemaleHumansIntensive Care UnitsInterleukin-6Interleukin-8MaleMiddle AgedPhenotypeProspective StudiesRespiratory Distress SyndromeBiomarkersInterleukin-6Interleukin-8critical illness subphenotypinghematologic malignancyoncologic intensive care unitsepsis

Identifiers

PMID40637495
PMCPMC12323692

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LicenceCC BY-NC-ND
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.