Evidence map›Paper›PMID 40920119›Full record

ArticleInternational journal of cancer2026

A causal inference framework to compare the effectiveness of life-sustaining ICU therapies-using the example of cancer patients with sepsis.

João Matos, Tristan Struja, Naira Link Woite, David Restrepo, Andre Kurepa Waschka, Leo A Celi, Christopher M Sauer

Abstract readComparative Study
In one paragraph

Article in International journal of cancer, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Article
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

7 authors.

João MatosFaculty of Engineering, University of Porto, Porto, Portugal.
Tristan StrujaLaboratory for Computational Physiology, Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA.ORCID https://orcid.org/0000-0003-0199-0184
Naira Link WoiteLaboratory for Computational Physiology, Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA.
David RestrepoLaboratory for Computational Physiology, Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA.
Andre Kurepa WaschkaCollege of Liberal Arts and Sciences, Mercer University, Macon, Georgia, USA.
Leo A CeliLaboratory for Computational Physiology, Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA.
Christopher M SauerLaboratory for Computational Physiology, Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA.

Funding

Integrating Data, Models, and Reasoning in Critical CareR01EB001659 · NIBIB · MASSACHUSETTS INSTITUTE OF TECHNOLOGY · PI MARK, ROGER GREENWOOD · 2003 to 2012
$9.8M
Deutsche Forschungsgemeinschaft FU356/12-2Fulbright Portugal AY 2022/2023NIBIB NIH HHS EB001659NIBIB NIH HHS R01 EB001659Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung P400PM_194497
6 · The paper itself

Abstract

The rise in cancer patients could lead to an increase in intensive care units (ICUs) admissions. We explored differences in treatment practices and outcomes of invasive therapies between patients with sepsis with and without cancer. Adults from 2008 to 2019 admitted to the ICU for sepsis were extracted from the databases MIMIC-IV and eICU-CRD. Using Extreme Gradient Boosting, we estimated the odds for invasive mechanical ventilation (IMV) or vasopressors. Targeted maximum likelihood estimation (TMLE) models estimated treatment effects of IMV and vasopressors on in-hospital mortality and 28 hospital-free days. 58,988 adult septic patients were included, of which 6145 had cancer. In-hospital mortality was higher for cancer patients (30.3% vs. 16.1%). Patients with cancer had lower odds of receiving IMV (aOR [95%CI], 0.94 [0.90-0.97]); pronounced for hematologic patients (aOR 0.89 [0.84-0.93]). Odds for vasopressors were also lower for hematologic patients (aOR 0.89 [0.84-0.94]). TMLE models found IMV to be overall associated with higher in-hospital mortality for solid and hematological patients (ATE 3% [1%-5%], 6% [3%-9%], respectively), while vasopressors were associated with higher in-hospital mortality for patients with solid and metastatic cancer (ATE 6% [4%-8%], 3% [1%-6%], respectively). We utilized US-wide ICU data to estimate a relationship between mortality and the use of common therapies. With the exception of hematologic patients being less likely to receive IMV, we did not find differential treatment patterns. We did not demonstrate an average survival benefit for therapies, underscoring the need for a more granular analysis to identify subgroups who benefit from these interventions.

Indexed as

Intensive Care UnitsNeoplasmsRespiration, ArtificialSepsisAdultAgedFemaleHospital MortalityHumansMaleMiddle AgedVasoconstrictor AgentsVasoconstrictor Agentscancercritical careeICU‐CRDhealth equityMIMIC‐IVsepsisTMLE

Identifiers

PMID40920119
PMCPMC12670347

What Socratic holds

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LicenceCC BY
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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 Socratic graph.