Evidence map›Paper›PMID 40528229›Full record

ArticleCritical care (London, England)2025

Optimizing microbiological surveillance during selective digestive decontamination in the intensive care unit: an in silico simulation study.

Jelle L G Haitsma Mulier, Fleur J van Dijk, Valentijn A Schweitzer, Marc J M Bonten, Lennie P G Derde, Olaf L Cremer

Abstract read
In one paragraph

Article in Critical care (London, England), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

What it found

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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

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3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

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5 · Who and what money

Authors and funding

6 authors.

Jelle L G Haitsma MulierJulius Centre for Health Sciences and Primary Care, University Medical Centre Utrecht, Utrecht, The Netherlands. j.l.g.haitsmamulier-2@umcutrecht.nl.
Fleur J van DijkDepartment of Intensive Care Medicine, University Medical Centre Utrecht, Utrecht, The Netherlands.
Valentijn A SchweitzerDepartment of Medical Microbiology, University Medical Centre Utrecht, Utrecht, the Netherlands.
Marc J M BontenJulius Centre for Health Sciences and Primary Care, University Medical Centre Utrecht, Utrecht, The Netherlands.
Lennie P G DerdeJulius Centre for Health Sciences and Primary Care, University Medical Centre Utrecht, Utrecht, The Netherlands.
Olaf L CremerDepartment of Intensive Care Medicine, University Medical Centre Utrecht, Utrecht, The Netherlands.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundSelective Digestive Decontamination (SDD) prevents infections and reduces mortality in the intensive care unit (ICU). Microbiological surveillance is considered essential for effective decontamination and detecting antibiotic resistance. However, its optimal frequency is unclear. We compared microbiological yield and costs of different surveillance intervals during SDD.

methodsIn a computational simulation study, using data from a Dutch ICU, three surveillance scenarios were compared: (A) twice-weekly, (B) once-weekly, and (C) no surveillance. The primary outcome was the number of clinically relevant potentially pathogenic microorganisms (PPMs) detected per scenario. Secondary outcomes included detection of colonisation persistence prompting SDD intensification and surveillance costs.

resultsWe included 8,499 ICU admissions, 52,553 clinical and 75,567 SDD cultures. Scenario A yielded 911 (95% CI 905-917) PPMs per 1,000 days, of which 90 (88-94) were clinically relevant: 9 (9-10) multidrug-resistant microorganisms, 68 (66-71) microorganisms resistant to standard therapy, and 13 (12-14) infection-related microorganisms. Scenarios B and C yielded 85 (82-88) and 77 (75-80) relevant PPMs, respectively (94% and 86% compared to scenario A). Scenario A identified 56 (55-58) cases of colonisation persistence per 1,000 days while scenarios B and C detected 43 (42-45) and 12 (11-12), respectively. Total costs of SDD surveillance were €78,774, €55,208, and €31,522 per 1,000 days for scenarios A, B and C.

conclusionCompared to twice-weekly surveillance, once-weekly microbiological surveillance reduces costs by 30% with 6% loss in clinically relevant PPM detections. No surveillance reduces costs by 60% with 14% detection loss.

Indexed as

DecontaminationComputer SimulationHumansIntensive Care UnitsNetherlandsCostsCulturesIntensive care unitMicrobiological surveillanceSelective digestive decontaminationSimulation

Identifiers

PMID40528229
PMCPMC12175364

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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.