EPISYNC study: predictors of patient-ventilator asynchrony in a prospective cohort of patients under invasive mechanical ventilation - study protocol

IntroductionPatient-ventilator asynchrony is common during the entire period of invasive mechanical ventilation (MV) and is associated with worse clinical outcomes. However, risk factors associated with asynchrony are not completely understood. The main objectives of this study are to estimate the i...

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Veröffentlicht in:BMJ open 2019-05, Vol.9 (5), p.e028601-e028601
Hauptverfasser: Sousa, Mayson Laercio de Araujo, Magrans, Rudys, Hayashi, Fátima K, Blanch, Lluis, Kacmarek, R M, Ferreira, Juliana C
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creator Sousa, Mayson Laercio de Araujo
Magrans, Rudys
Hayashi, Fátima K
Blanch, Lluis
Kacmarek, R M
Ferreira, Juliana C
description IntroductionPatient-ventilator asynchrony is common during the entire period of invasive mechanical ventilation (MV) and is associated with worse clinical outcomes. However, risk factors associated with asynchrony are not completely understood. The main objectives of this study are to estimate the incidence of asynchrony during invasive MV and its association with respiratory mechanics and other baseline patient characteristics.Methods and analysisWe designed a prospective cohort study of patients admitted to the intensive care unit (ICU) of a university hospital. Inclusion criteria are adult patients under invasive MV initiated for less than 72 hours, and with expectation of remaining under MV for more than 24 hours. Exclusion criteria are high flow bronchopleural fistula, inability to measure respiratory mechanics and previous tracheostomy. Baseline assessment includes clinical characteristics of patients at ICU admission, including severity of illness, reason for initiation of MV, and measurement of static mechanics of the respiratory system. We will capture ventilator waveforms during the entire MV period that will be analysed with dedicated software (Better Care, Barcelona, Spain), which automatically identifies several types of asynchrony and calculates the asynchrony index (AI). We will use a linear regression model to identify risk factors associated with AI. To assess the relationship between survival and AI we will use Kaplan-Meier curves, log rank tests and Cox regression. The calculated sample size is 103 patients. The statistical analysis will be performed by the software R Programming (www.R-project.org) and will be considered statistically significant if the p value is less than 0.05.Ethics and disseminationThe study was approved by the Ethics Committee of Instituto do Coração, School of Medicine, University of São Paulo, Brazil, and informed consent was waived due to the observational nature of the study. We aim to disseminate the study findings through peer-reviewed publications and national and international conference presentations.Trial registration number NCT02687802; Pre-results.
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However, risk factors associated with asynchrony are not completely understood. The main objectives of this study are to estimate the incidence of asynchrony during invasive MV and its association with respiratory mechanics and other baseline patient characteristics.Methods and analysisWe designed a prospective cohort study of patients admitted to the intensive care unit (ICU) of a university hospital. Inclusion criteria are adult patients under invasive MV initiated for less than 72 hours, and with expectation of remaining under MV for more than 24 hours. Exclusion criteria are high flow bronchopleural fistula, inability to measure respiratory mechanics and previous tracheostomy. Baseline assessment includes clinical characteristics of patients at ICU admission, including severity of illness, reason for initiation of MV, and measurement of static mechanics of the respiratory system. We will capture ventilator waveforms during the entire MV period that will be analysed with dedicated software (Better Care, Barcelona, Spain), which automatically identifies several types of asynchrony and calculates the asynchrony index (AI). We will use a linear regression model to identify risk factors associated with AI. To assess the relationship between survival and AI we will use Kaplan-Meier curves, log rank tests and Cox regression. The calculated sample size is 103 patients. The statistical analysis will be performed by the software R Programming (www.R-project.org) and will be considered statistically significant if the p value is less than 0.05.Ethics and disseminationThe study was approved by the Ethics Committee of Instituto do Coração, School of Medicine, University of São Paulo, Brazil, and informed consent was waived due to the observational nature of the study. We aim to disseminate the study findings through peer-reviewed publications and national and international conference presentations.Trial registration number NCT02687802; Pre-results.</description><identifier>ISSN: 2044-6055</identifier><identifier>EISSN: 2044-6055</identifier><identifier>DOI: 10.1136/bmjopen-2018-028601</identifier><identifier>PMID: 31123002</identifier><language>eng</language><publisher>England: BMJ Publishing Group LTD</publisher><subject>Accuracy ; Airway Resistance ; Anesthesia ; Chronic obstructive pulmonary disease ; Clinical outcomes ; Cohort Studies ; Data collection ; Disease ; Dyspnea ; Epidemiology ; Hospitals ; Humans ; Incidence ; Intensive Care ; Intubation ; Kaplan-Meier Estimate ; Lung Compliance ; Mechanics ; Mortality ; Ostomy ; Patients ; Positive-Pressure Respiration, Intrinsic ; Proportional Hazards Models ; Prospective Studies ; Respiration, Artificial - methods ; Respiratory Mechanics ; Respiratory system ; Risk Factors ; Ventilators</subject><ispartof>BMJ open, 2019-05, Vol.9 (5), p.e028601-e028601</ispartof><rights>Author(s) (or their employer(s)) 2019. 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Published by BMJ. 2019</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-b472t-732cfdf2bcd25ceceab9f22550cb396afe93f0ff65c3a298b52891f61b572eae3</citedby><cites>FETCH-LOGICAL-b472t-732cfdf2bcd25ceceab9f22550cb396afe93f0ff65c3a298b52891f61b572eae3</cites><orcidid>0000-0003-4236-5107</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://bmjopen.bmj.com/content/9/5/e028601.full.pdf$$EPDF$$P50$$Gbmj$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://bmjopen.bmj.com/content/9/5/e028601.full$$EHTML$$P50$$Gbmj$$Hfree_for_read</linktohtml><link.rule.ids>230,314,723,776,780,860,881,27526,27527,27901,27902,53766,53768,77343,77374</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/31123002$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Sousa, Mayson Laercio de Araujo</creatorcontrib><creatorcontrib>Magrans, Rudys</creatorcontrib><creatorcontrib>Hayashi, Fátima K</creatorcontrib><creatorcontrib>Blanch, Lluis</creatorcontrib><creatorcontrib>Kacmarek, R M</creatorcontrib><creatorcontrib>Ferreira, Juliana C</creatorcontrib><title>EPISYNC study: predictors of patient-ventilator asynchrony in a prospective cohort of patients under invasive mechanical ventilation - study protocol</title><title>BMJ open</title><addtitle>BMJ Open</addtitle><description>IntroductionPatient-ventilator asynchrony is common during the entire period of invasive mechanical ventilation (MV) and is associated with worse clinical outcomes. However, risk factors associated with asynchrony are not completely understood. The main objectives of this study are to estimate the incidence of asynchrony during invasive MV and its association with respiratory mechanics and other baseline patient characteristics.Methods and analysisWe designed a prospective cohort study of patients admitted to the intensive care unit (ICU) of a university hospital. Inclusion criteria are adult patients under invasive MV initiated for less than 72 hours, and with expectation of remaining under MV for more than 24 hours. Exclusion criteria are high flow bronchopleural fistula, inability to measure respiratory mechanics and previous tracheostomy. Baseline assessment includes clinical characteristics of patients at ICU admission, including severity of illness, reason for initiation of MV, and measurement of static mechanics of the respiratory system. We will capture ventilator waveforms during the entire MV period that will be analysed with dedicated software (Better Care, Barcelona, Spain), which automatically identifies several types of asynchrony and calculates the asynchrony index (AI). We will use a linear regression model to identify risk factors associated with AI. To assess the relationship between survival and AI we will use Kaplan-Meier curves, log rank tests and Cox regression. The calculated sample size is 103 patients. The statistical analysis will be performed by the software R Programming (www.R-project.org) and will be considered statistically significant if the p value is less than 0.05.Ethics and disseminationThe study was approved by the Ethics Committee of Instituto do Coração, School of Medicine, University of São Paulo, Brazil, and informed consent was waived due to the observational nature of the study. 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However, risk factors associated with asynchrony are not completely understood. The main objectives of this study are to estimate the incidence of asynchrony during invasive MV and its association with respiratory mechanics and other baseline patient characteristics.Methods and analysisWe designed a prospective cohort study of patients admitted to the intensive care unit (ICU) of a university hospital. Inclusion criteria are adult patients under invasive MV initiated for less than 72 hours, and with expectation of remaining under MV for more than 24 hours. Exclusion criteria are high flow bronchopleural fistula, inability to measure respiratory mechanics and previous tracheostomy. Baseline assessment includes clinical characteristics of patients at ICU admission, including severity of illness, reason for initiation of MV, and measurement of static mechanics of the respiratory system. We will capture ventilator waveforms during the entire MV period that will be analysed with dedicated software (Better Care, Barcelona, Spain), which automatically identifies several types of asynchrony and calculates the asynchrony index (AI). We will use a linear regression model to identify risk factors associated with AI. To assess the relationship between survival and AI we will use Kaplan-Meier curves, log rank tests and Cox regression. The calculated sample size is 103 patients. The statistical analysis will be performed by the software R Programming (www.R-project.org) and will be considered statistically significant if the p value is less than 0.05.Ethics and disseminationThe study was approved by the Ethics Committee of Instituto do Coração, School of Medicine, University of São Paulo, Brazil, and informed consent was waived due to the observational nature of the study. We aim to disseminate the study findings through peer-reviewed publications and national and international conference presentations.Trial registration number NCT02687802; Pre-results.</abstract><cop>England</cop><pub>BMJ Publishing Group LTD</pub><pmid>31123002</pmid><doi>10.1136/bmjopen-2018-028601</doi><orcidid>https://orcid.org/0000-0003-4236-5107</orcidid><oa>free_for_read</oa></addata></record>
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subjects Accuracy
Airway Resistance
Anesthesia
Chronic obstructive pulmonary disease
Clinical outcomes
Cohort Studies
Data collection
Disease
Dyspnea
Epidemiology
Hospitals
Humans
Incidence
Intensive Care
Intubation
Kaplan-Meier Estimate
Lung Compliance
Mechanics
Mortality
Ostomy
Patients
Positive-Pressure Respiration, Intrinsic
Proportional Hazards Models
Prospective Studies
Respiration, Artificial - methods
Respiratory Mechanics
Respiratory system
Risk Factors
Ventilators
title EPISYNC study: predictors of patient-ventilator asynchrony in a prospective cohort of patients under invasive mechanical ventilation - study protocol
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