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ESICM guidelines on acute respiratory distress syndrome: definition, phenotyping and respiratory support strategies.

ABSTRACT: The aim of these guidelines is to update the 2017 clinical practice guideline (CPG) of the European Society of Intensive Care Medicine (ESICM). The scope of this CPG is limited to adult patients and to non-pharmacological respiratory support strategies across different aspects of acute respiratory distress syndrome (ARDS), including ARDS due to coronavirus disease 2019 (COVID-19). These guidelines were formulated by an international panel of clinical experts, one methodologist and patients' representatives on behalf of the ESICM. The review was conducted in compliance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement recommendations. We followed the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) approach to assess the certainty of evidence and grade recommendations and the quality of reporting of each study based on the EQUATOR (Enhancing the QUAlity and Transparency Of health Research) network guidelines. The CPG addressed 21 questions and formulates 21 recommendations on the following domains: (1) definition; (2) phenotyping, and respiratory support strategies including (3) high-flow nasal cannula oxygen (HFNO); (4) non-invasive ventilation (NIV); (5) tidal volume setting; (6) positive end-expiratory pressure (PEEP) and recruitment maneuvers (RM); (7) prone positioning; (8) neuromuscular blockade, and (9) extracorporeal life support (ECLS). In addition, the CPG includes expert opinion on clinical practice and identifies the areas of future research.

SUBMITTER: Grasselli G

PROVIDER: S-EPMC10354163 | biostudies-literature | 2023 Jul

REPOSITORIES: biostudies-literature

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ESICM guidelines on acute respiratory distress syndrome: definition, phenotyping and respiratory support strategies.

Grasselli Giacomo G Calfee Carolyn S CS Camporota Luigi L Poole Daniele D Amato Marcelo B P MBP Antonelli Massimo M Arabi Yaseen M YM Baroncelli Francesca F Beitler Jeremy R JR Bellani Giacomo G Bellingan Geoff G Blackwood Bronagh B Bos Lieuwe D J LDJ Brochard Laurent L Brodie Daniel D Burns Karen E A KEA Combes Alain A D'Arrigo Sonia S De Backer Daniel D Demoule Alexandre A Einav Sharon S Fan Eddy E Ferguson Niall D ND Frat Jean-Pierre JP Gattinoni Luciano L Guérin Claude C Herridge Margaret S MS Hodgson Carol C Hough Catherine L CL Jaber Samir S Juffermans Nicole P NP Karagiannidis Christian C Kesecioglu Jozef J Kwizera Arthur A Laffey John G JG Mancebo Jordi J Matthay Michael A MA McAuley Daniel F DF Mercat Alain A Meyer Nuala J NJ Moss Marc M Munshi Laveena L Myatra Sheila N SN Ng Gong Michelle M Papazian Laurent L Patel Bhakti K BK Pellegrini Mariangela M Perner Anders A Pesenti Antonio A Piquilloud Lise L Qiu Haibo H Ranieri Marco V MV Riviello Elisabeth E Slutsky Arthur S AS Stapleton Renee D RD Summers Charlotte C Thompson Taylor B TB Valente Barbas Carmen S CS Villar Jesús J Ware Lorraine B LB Weiss Björn B Zampieri Fernando G FG Azoulay Elie E Cecconi Maurizio M

Intensive care medicine 20230616 7

The aim of these guidelines is to update the 2017 clinical practice guideline (CPG) of the European Society of Intensive Care Medicine (ESICM). The scope of this CPG is limited to adult patients and to non-pharmacological respiratory support strategies across different aspects of acute respiratory distress syndrome (ARDS), including ARDS due to coronavirus disease 2019 (COVID-19). These guidelines were formulated by an international panel of clinical experts, one methodologist and patients' repr ...[more]

PMID: 37326646

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Project description:Accurate identification of acute respiratory distress syndrome is essential for understanding its epidemiology, patterns of care, and outcomes. We aimed to design a computable phenotyping strategy to detect acute respiratory distress syndrome in electronic health records of critically ill patients.DesignThis is a retrospective cohort study. Using a near real-time copy of the electronic health record, we developed a computable phenotyping strategy to detect acute respiratory distress syndrome based on the Berlin definition.SettingTwenty multidisciplinary ICUs in Mayo Clinic Health System.SubjectsThe phenotyping strategy was applied to 196,487 consecutive admissions from year 2009 to 2019.InterventionsThe acute respiratory distress syndrome cohort generated by this novel strategy was compared with the acute respiratory distress syndrome cohort documented by clinicians during the same period. The sensitivity and specificity of the phenotyping strategy were calculated in randomly selected patient cohort (50 patients) using the results from manual medical record review as gold standard.Measurements and main resultsAmong the patients who did not have acute respiratory distress syndrome documented, the computable phenotyping strategy identified 3,169 adult patients who met the Berlin definition, 676 patients (21.3%) were classified to have severe acute respiratory distress syndrome (Pao2/Fio2 ratio ≤ 100), 1,535 patients (48.4%) had moderate acute respiratory distress syndrome (100 < Pao2/Fio2 ratio ≤ 200), and 958 patients (30.2%) had mild acute respiratory distress syndrome (200 < Pao2/Fio2 ratio ≤ 300). The phenotyping strategy achieved a sensitivity of 94.4%, specificity of 96.9%, positive predictive value of 94.4%, and negative predictive value of 96.9% in a randomly selected patient cohort. The clinicians documented acute respiratory distress syndrome in 1,257 adult patients during the study period. The clinician documentation rate of acute respiratory distress syndrome was 28.4%. Compared with the clinicians' documentation, the phenotyping strategy identified a cohort that had higher acuity and complexity of illness suggested by higher Sequential Organ Failure Assessment score (9 vs 7; p < 0.0001), higher Acute Physiology and Chronic Health Evaluation score (76 vs 63; p < 0.0001), higher rate of requiring invasive mechanical ventilation (99.1% vs 71.8%; p < 0.0001), higher ICU mortality (20.6% vs 16.8%; p < 0.0001), and longer ICU length of stay (5.1 vs 4.2 d; p < 0.0001).ConclusionsOur rule-based computable phenotyping strategy can accurately detect acute respiratory distress syndrome in critically ill patients in the setting of high clinical complexity. This strategy can be applied to enhance early recognition of acute respiratory distress syndrome and to facilitate best-care delivery and clinical research in acute respiratory distress syndrome.

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ESICM guidelines on acute respiratory distress syndrome: definition, phenotyping and respiratory support strategies.

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ESICM guidelines on acute respiratory distress syndrome: definition, phenotyping and respiratory support strategies.

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