MétaCan
Menu
← Back to cohort

Global Burden of Mechanical Ventilation (GEMINI Study): An Epidemiological and Geo-economic Modelling Study

2025· article· en· W4410276722 on OpenAlexaff
Óscar Peñuelas, Alfonso Muriel, Laura del Campo-Albendea, L. Chinh Quoc, Do Ngoc Son, F.J. Molina Saldarriaga, Ilias Ι. Siempos, Bruno Valle Pinheiro, Yaseen M. Arabi, Pedro David Wendel‐Garcia, Nicole P. Juffermans, John G. Laffey, C. Ming-Cheng, Nahit Çakar, Arnaud W. Thille, Fernando Ríos, H. Aguirre, Manuel Jibaja, Salvatore Maurizio Maggiore, Lorenzo Del Sorbo, L.J. Brochard, Nicolás Nín, Martha Romero, Bin Du, G. Bugedo, Brijesh Patel, Mayur Murali, Pedro Póvoa, L. Nobile, Daisuke Kasugai, Sean G. Young, N. Martínez, Amine Ali Zeggwagh, Benjamin Seeliger, Spyridon Fortis, P Dostál, Andréa Rodrigues Ávila, Matej Podbregar, Fekri Abroug, Sheila Nainan Myatra, Mohamed Fakher, Yuda Sutherasan, Antonio Anzueto, Fernando Frutos–Vivar, VENTILAGROUP

Bibliographic record

VenueAmerican Journal of Respiratory and Critical Care Medicine · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicClimate Change and Health Impacts
Canadian institutionsSt. Michael's HospitalToronto General HospitalUniversity of TorontoUniversity Health Network
Fundersnot available
KeywordsMedicineEpidemiologyMechanical ventilationIntensive care medicineEnvironmental healthPathologyInternal medicine

Abstract

fetched live from OpenAlex

Abstract RATIONALE: A routine clinical practice in adult critically ill patients receiving invasive mechanical ventilation (IMV) may be difficult to define. Our goal was to report updated global, and country-specific estimates of incidence, mortality, and case-fatality rates. METHODS: An observational prospective cohort of consecutive adult patients admitted between October 1, 2022, and April 30, 2023 to 457 intensive care units (ICU) from 42 countries who received IMV longer than 12 hours. Data were collected on each patient at initiation of mechanical ventilation and daily throughout the course of IMV for up to 28 days. RESULTS: During the period of recruitment, 8,350 patients were enrolled. A total of 6,998 patients from lower-middle income countries (1,428 patients), upper-middle countries (1930 patients) and high-income countries (3,640 patients) were included. Patients were predominantly males (63%) with a median age of 64 years (IQR 50,74) and median SAPS3 64 points (IQR 52-77). Main reasons for IMV were: neurologic disease (20%), postoperative respiratory insufficiency (16%), community acquired pneumonia (10%), sepsis (9%), cardiac failure (7%), ARDS (6%), COPD (5%), nosocomial pneumonia (5%), COVID (3%). Ventilator setting registered were: Tidal volume (median, IQR 7.4; 6.6-8.3 ml/kgPBW), plateau pressure (18; 15-22 cmH2O), applied PEEP (6; 5-8 cmH2O), driving pressure (12; 9-15 cmH2O), mechanical power (15.7; 11.9-20.8 joules/min). A lung protective strategy was applied on 79% of monitoring days and an open Lung Approach on 25% of monitoring days. Patients received sedation on 80% of the monitored days, analgesia on 77% and neuromuscular blocking on 9%. Most prevalent complications during the course of mechanical ventilation were sepsis (17%), delirium (14%), ventilator-associated pneumonia (8%), ARDS (8%) and ICU acquired weakness (8%). Other complications as thromboembolic events, tracheobronchitis, bleeding ulcus stress or Clostridium infection had a prevalence lower than 2%. 73% of the patients had at least one organ dysfunction, the most frequent of which were cardiovascular failure (66%), renal failure (24%), hematological failure (12%) and hepatic failure (9%). Comparison of outcomes according to income country is shown in table 1. CONCLUSIONS: After the pandemic COVID-19, we found significant geo-economic differences in the clinical outcomes of critically ill patients requiring IMV. Further adjusted models will provide information about the usual care of mechanically ventilated patients and variables related with poor outcomes.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.018
Threshold uncertainty score0.036

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.076
GPT teacher head0.403
Teacher spread0.326 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

Explore more

Same venueAmerican Journal of Respiratory and Critical Care Medicine→Same topicClimate Change and Health Impacts→French-language works237,207→