Evidence map›Paper›PMID 41605594›Full record

ArticleBMJ open2026

What is the ideal glucose range for a patient with sepsis in the ICU? A retrospective analysis of MIMIC-IV.

Tristan Struja, Lasse Hyldig Hansen, João Matos, Josep Gómez, Alex Pardo, Ismini Lourentzou, Nima Hejazi, Leo Anthony Celi, Andre Kurepa Waschka

Abstract read
In one paragraph

Article in BMJ open, 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

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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

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5 · Who and what money

Authors and funding

9 authors.

Tristan StrujaMedical University Clinic, Kantonsspital Aarau, Aarau, Switzerland tristan.struja@gmail.com.ORCID http://orcid.org/0000-0003-0199-0184
Lasse Hyldig HansenIMES, Labor for Computational Physiology, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA.
João MatosIMES, Labor for Computational Physiology, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA.
Josep GómezHospital Universitari de Tarragona Joan XXIII, Tarragona, Spain.
Alex PardoHospital Universitari de Tarragona Joan XXIII, Tarragona, Spain.
Ismini LourentzouSchool of Information Sciences, University of Illinois Urbana-Champaign, Urbana, Illinois, USA.
Nima HejaziDepartment of Biostatistics, Harvard University, Cambridge, Massachusetts, USA.ORCID http://orcid.org/0000-0002-7127-2789
Leo Anthony CeliIMES, Labor for Computational Physiology, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA.
Andre Kurepa WaschkaMathematics & Statistics, Elon University, Elon, North Carolina, USA.

Funding

Bridge2AI: Patient-Focused Collaborative Hospital Repository Uniting Standards (CHoRUS) for Equitable AIOT2OD032701 · OD · MASSACHUSETTS GENERAL HOSPITAL · PI BIHORAC, AZRA, CLERMONT, GILLES · 2022 to 2025
$24.6M
MUST Data Science Research Hub (MUDSReH) - Democratized Trusted Research Environment (dTRE)U54TW012043 · FIC · MBARARA UNIVERSITY/SCIENCE/ TECHNOLOGY · PI Leo Anthony G Celi, Jessica Elizabeth Haberer · 2021 to 2026
$6.9M
Critical Care Informatics: Ethical considerations around the use and sharing of health-related dataR01EB017205 · NIBIB · MASSACHUSETTS INSTITUTE OF TECHNOLOGY · PI CELI, LEO ANTHONY G, MARK, ROGER GREENWOOD · 2014 to 2021
$3.8M
FIC NIH HHS U54 TW012043NIBIB NIH HHS R01 EB017205NIH HHS OT2 OD032701
6 · The paper itself

Abstract

importanceClinical trials have produced inconclusive results regarding the optimal glucose range for a patient with sepsis in the intensive care unit (ICU) receiving insulin treatment.

objectiveTo investigate the optimal glucose range in patients with sepsis in the ICU independent of confounding covariates.

designTargeted trial emulation of glucose ranges using causal inference targeted maximum likelihood estimation and longitudinal mixed-effects models combined with survival models.

settingSingle-centre, academic referral hospital in Boston, Massachusetts, USA.

participantsAdults fulfilling sepsis 3 criteria with at least three glucose readings and insulin treatment from the Medical Information Mart for Intensive Care (MIMIC)-IV database (2008-2019). EXPOSURE: Five predefined glucose distributions with means at 100, 130, 160 (baseline), 190 and 220 mg/dL mimicking current guidelines' recommendations (140-180 mg/dL). MAIN OUTCOME AND MEASURE: The primary outcome was in-hospital mortality. Modified counterfactual treatment-policy risks across distinct time-weighted glucose ranges were estimated.

resultsOf 73 181 eligible patients, 8002 patients with a median age of 66 years (41% women, 67% white ethnicity, 57% diabetes) were included. There was a U-shaped curve between glucose range and mortality in patients without diabetes, but overall, this association was not significant (mean glucose at 100 mg/dL with 21% mortality and mean glucose at 220 mg/dL with 26% mortality, p-for-trend 0.26). Mortality was lowest at 17%, with mean glucose between 130 and 160 mg/dL. Hypoglycaemic events (<80 mg/dL) became increasingly more frequent with tighter glucose control 16% at 220 mg/dL compared with 77% at 100 mg/dL (p-for-trend 0.01). Joint modelling corroborated these results and did not identify covariates that would favour lower glucose ranges in subsets of patients. CONCLUSION AND RELEVANCE: Our data suggest a U-shaped association of glucose and mortality with an optimal average glucose between 160 and 190 mg/dL. These results confirm current guideline recommendations. Together with recent results from randomised controlled trials, intensivists should aim for a liberal glucose range in most patients.

Indexed as

Blood GlucoseInsulinIntensive Care UnitsSepsisAgedBostonFemaleHospital MortalityHumansHypoglycemiaHypoglycemic AgentsMaleMiddle AgedRetrospective StudiesBlood GlucoseHypoglycemic AgentsInsulinDIABETES & ENDOCRINOLOGYINTENSIVE & CRITICAL CARESepsis

Identifiers

PMID41605594
PMCPMC12853541

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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.