Evidence map›Paper›PMID 34772497›Full record

SynthesisBritish journal of anaesthesia2022

Artificial intelligence for mechanical ventilation: systematic review of design, reporting standards, and bias.

Jack Gallifant, Joe Zhang, Maria Del Pilar Arias Lopez, Tingting Zhu, Luigi Camporota, Leo A Celi, Federico Formenti

Abstract readSystematic Review
PubMed Full text at PMC Publisher
In one paragraph

Synthesis in British journal of anaesthesia, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
34citing papers in PubMed, 3 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

34 citing papers in PubMed, 3 syntheses or guidelines pooled it.

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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

7 authors.

Jack GallifantCentre for Human and Applied Physiological Sciences, School of Basic and Medical Biosciences, King's College London, London, UK. Electronic address: jack.gallifant@kcl.ac.uk.
Joe ZhangDepartment of Adult Critical Care, Guy's and St Thomas' NHS Foundation Trust, King's Health Partners, London, UK; Institute of Global Health Innovation, Imperial College London, London, UK.
Maria Del Pilar Arias LopezSATI-Q Program, Argentine Society of Intensive Care, Buenos Aires, Argentina.
Tingting ZhuInstitute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford, UK.
Luigi CamporotaCentre for Human and Applied Physiological Sciences, School of Basic and Medical Biosciences, King's College London, London, UK; Department of Adult Critical Care, Guy's and St Thomas' NHS Foundation Trust, King's Health Partners, London, UK.
Leo A CeliLaboratory for Computational Physiology, Massachusetts Institute of Technology, Cambridge, MA, USA; Division of Pulmonary, Critical Care and Sleep Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA; Department of Biostatistics, Harvard T.H. Chan School of Public Health, Harvard University, Boston, MA, USA. Electronic address: lceli@mit.edu.
Federico FormentiCentre for Human and Applied Physiological Sciences, School of Basic and Medical Biosciences, King's College London, London, UK; Nuffield Division of Anaesthetics, University of Oxford, Oxford, UK; Department of Biomechanics, University of Nebraska Omaha, Omaha, NE, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundArtificial intelligence (AI) has the potential to personalise mechanical ventilation strategies for patients with respiratory failure. However, current methodological deficiencies could limit clinical impact. We identified common limitations and propose potential solutions to facilitate translation of AI to mechanical ventilation of patients.

methodsA systematic review was conducted in MEDLINE, Embase, and PubMed Central to February 2021. Studies investigating the application of AI to patients undergoing mechanical ventilation were included. Algorithm design and adherence to reporting standards were assessed with a rubric combining published guidelines, satisfying the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis [TRIPOD] statement. Risk of bias was assessed by using the Prediction model Risk Of Bias ASsessment Tool (PROBAST), and correspondence with authors to assess data and code availability.

resultsOur search identified 1,342 studies, of which 95 were included: 84 had single-centre, retrospective study design, with only one randomised controlled trial. Access to data sets and code was severely limited (unavailable in 85% and 87% of studies, respectively). On request, data and code were made available from 12 and 10 authors, respectively, from a list of 54 studies published in the last 5 yr. Ethnicity was frequently under-reported 18/95 (19%), as was model calibration 17/95 (18%). The risk of bias was high in 89% (85/95) of the studies, especially because of analysis bias.

conclusionsDevelopment of algorithms should involve prospective and external validation, with greater code and data availability to improve confidence in and translation of this promising approach. TRIAL REGISTRATION NUMBER: PROSPERO - CRD42021225918.

Indexed as

Artificial IntelligenceAlgorithmsBiasHumansModels, TheoreticalRandomized Controlled Trials as TopicResearch DesignResearch ReportRespiration, ArtificialRespiratory Insufficiencyartificial intelligencebiascritical caredecision supportmechanical ventilation respiratory failure

Identifiers

PMID34772497
PMCPMC8792831

What OpenQuestion holds

Textmetadata
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.