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Artificial intelligence for mechanical ventilation: systematic review of design, reporting standards, and bias.
Gallifant, Jack; Zhang, Joe; Del Pilar Arias Lopez, Maria; Zhu, Tingting; Camporota, Luigi; Celi, Leo A; Formenti, Federico.
Afiliación
  • Gallifant J; Centre 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.
  • Zhang J; Department 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.
  • Del Pilar Arias Lopez M; SATI-Q Program, Argentine Society of Intensive Care, Buenos Aires, Argentina.
  • Zhu T; Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford, UK.
  • Camporota L; Centre 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.
  • Celi LA; Laboratory 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 Universi
  • Formenti F; Centre 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.
Br J Anaesth ; 128(2): 343-351, 2022 Feb.
Article en En | MEDLINE | ID: mdl-34772497
BACKGROUND: Artificial 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. METHODS: A 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. RESULTS: Our 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. CONCLUSIONS: Development 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.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Respiración Artificial / Insuficiencia Respiratoria / Inteligencia Artificial Tipo de estudio: Clinical_trials / Guideline / Observational_studies / Prognostic_studies / Risk_factors_studies / Systematic_reviews Límite: Humans Idioma: En Revista: Br J Anaesth Año: 2022 Tipo del documento: Article Pais de publicación: Reino Unido
Texto completo
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Respiración Artificial / Insuficiencia Respiratoria / Inteligencia Artificial Tipo de estudio: Clinical_trials / Guideline / Observational_studies / Prognostic_studies / Risk_factors_studies / Systematic_reviews Límite: Humans Idioma: En Revista: Br J Anaesth Año: 2022 Tipo del documento: Article Pais de publicación: Reino Unido