Evidence map›Paper›PMID 39946423›Full record

ArticlePloS one2025

Optimizing mechanical ventilation: Personalizing mechanical power to reduce ICU mortality - a retrospective cohort study.

Ahmed S Alkhalifah, Kenny Rumindo, Edgar Brincat, Florian Blanchard, Johan Helleberg, David Clarke, Benjamin Popoff, Olivier Duranteau, Zubair Umer Mohamed, Abdelrahman Senosy

Abstract read
In one paragraph

Article in PloS one, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed, 1 pooled it
–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

6 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. Review
  4. Review
  5. Review
  6. Review
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

10 authors.

Ahmed S AlkhalifahPediatric Critical Care Unit, Qatif Central Hospital, Dammam, Saudi Arabia.ORCID https://orcid.org/0000-0001-9277-8511
Kenny RumindoResearch & Development, Getinge Acute Care Therapies, Solna, Sweden.
Edgar BrincatPediatric Intensive Care Unit, Royal Hospital for Children, Glasgow, United Kingdom.
Florian BlanchardDepartment of Anesthesiology and Critical Care, Sorbonne University, GRC 29, AP-HP, DMU DREAM, Pitié-Salpêtrière Hôpital, Paris, France.ORCID https://orcid.org/0000-0001-6545-9566
Johan HellebergDepartment of Perioperative Medicine and Intensive Care, Karolinska University Hospital, Stockholm, Sweden.
David ClarkeOxford Critical Care, Oxford University Healthcare Trust, Oxford, United Kingdom.
Benjamin PopoffDepartment of Anesthesiology and Critical Care, Rouen University Hospital, Rouen, France.ORCID https://orcid.org/0000-0003-2854-0909
Olivier DuranteauAnesthesiology Department, Erasmus Hospital, Brussels, Belgium.ORCID https://orcid.org/0000-0002-3028-2104
Zubair Umer MohamedAdult Critical Care Unit, King Faisal Specialist Hospital and Research Centre, Madinah, Saudi Arabia.
Abdelrahman SenosyAdult Intensive Care Unit, Hayat National Hospital, Medina, Saudi Arabia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundMechanical ventilation, a crucial intervention for acute respiratory distress syndrome (ARDS), can lead to ventilator-induced lung injury (VILI). This study focuses on individualizing mechanical power (MP) in mechanically ventilated patients to minimize VILI and reduce ICU mortality.

methodsA retrospective analysis was conducted using the Amsterdam University Medical Centers Database (AmsterdamUMCdb) data. The study included patients aged 18 and older who needed at least 48 hours of pressure-controlled mechanical ventilation. Patients who died or were extubated within 48 hours and those with inadequate data were excluded. Patients were categorized into hypoxemia groups based on their PaO2/FiO2 ratio. MP was calculated using a surrogate formula and normalized to ideal body weight (IBW). Statistical analyses and machine learning models, including logistic regression and random forest, were used to predict ICU mortality and establish safe upper limits for IBW-adjusted MP.

resultsOut of 23,106 admissions, 2,338 met the criteria. Nonsurvivors had a significantly higher time-weighted average MP (TWA-MP) than survivors. Safe upper limits for IBW-adjusted MP varied across hypoxemia groups. The XGBoost model showed the highest predictive accuracy for ICU mortality. An individualization method for mechanical ventilation settings, based on real-time physiological variables, demonstrated reduced predicted mortality in a subset of patients. DISCUSSION: Elevated TWA-MP is associated with increased ICU mortality, underscoring the need for personalized mechanical ventilation strategies. The study highlights the complexity of VILI and the multifactorial nature of ICU mortality. Further studies to define a safe upper limit for IBW-adjusted MP may help clinicians optimize mechanical ventilation settings and decrease the risk of VILI and mortality.

conclusionsDespite the fact that the study's retrospective design and reliance on a single-center database may limit the generalizability of findings, this study offers valuable insights into the relationship between mechanical power and ICU mortality, emphasizing the need for individualized mechanical ventilation strategies. The findings suggest a potential for more personalized, data-driven approach in managing mechanically ventilated patients, which could improve patient outcomes in critical care settings.

Indexed as

Hospital MortalityIntensive Care UnitsRespiration, ArtificialRespiratory Distress SyndromeVentilator-Induced Lung InjuryAdultAgedFemaleHumansMaleMiddle AgedRetrospective Studies

Identifiers

PMID39946423
PMCPMC11825045

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.