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1.
ATS Sch ; 3(2): 204-219, 2022 Jun.
Artigo em Inglês | MEDLINE | ID: mdl-35924199

RESUMO

Background: Mechanical ventilation (MV) skills are essential for clinicians caring for critically ill patients, yet few training programs use structured curricula and appropriate assessments. Objective structured clinical exams (OSCEs) have been used to assess clinical competency in many areas, but there are no OSCE models focused on MV. Objective: To develop and validate a simulation-based assessment (SBA) with an OSCE structure to assess baseline MV competence among residents and identify knowledge gaps. Methods: We developed an SBA using a lung simulator and a mechanical ventilator, and an OSCE structure, with six clinical scenarios in MV. We included internal medicine residents at the beginning of their rotation in the respiratory intensive care unit (ICU) of a university-affiliated hospital. A subset of residents was also evaluated with a validated multiple-choice exam (MCE) at the beginning and at the end of the ICU rotation. Scores on both assessments were normalized to range from 0 to 10. We used Cronbach's α coefficient to assess reliability and Spearman correlation to estimate the correlation between the SBA and the MCE. Results: We included 80 residents, of whom 42 also completed the MCE examinations. The final version of the SBA had 32 items, and the Cronbach's α coefficient was 0.72 (95% confidence interval [CI], 0.64-0.81). The average SBA score was 6.2 ± 1.3, and performance was variable across items, with 80% correctly adjusting initial ventilatory settings and only 12% correctly identifying asynchrony. The MCE had 24 questions, and the average score was 7.6 ± 2.4 at the beginning of the rotation and 8.2 ± 2.3 at the end of the rotation (increase of 0.6 points; 95% CI, 0.30-0.90; P < 0.001). There was moderate correlation between the SBA and the MCE (rho = 0.41; P = 0.002). Conclusion: We developed and validated an objective structured assessment on MV using a pulmonary simulator and a mechanical ventilator addressing the main competencies in MV. The performance of residents in the SBA at the beginning of an ICU rotation was lower than the performance in MCE, highlighting the need for greater emphasis on practical skills in MV during residency.

2.
Crit Care Med ; 49(9): 1460-1469, 2021 09 01.
Artigo em Inglês | MEDLINE | ID: mdl-33883458

RESUMO

OBJECTIVES: To measure the impact of clusters of double triggering on clinical outcomes. DESIGN: Prospective cohort study. SETTING: Respiratory ICU in Brazil. PATIENTS: Adult patients under recent mechanical ventilation and with expectation of mechanical ventilation for more than 24 hours after enrollment. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: We used a dedicated software to analyze ventilator waveforms throughout the entire period of mechanical ventilation and detect double triggering. We defined a cluster of double triggering as a period of time containing at least six double triggering events in a 3-minute period. Patients were followed until hospital discharge. We addressed the association between the presence and the duration of clusters with clinical outcomes. A total of 103 patients were enrolled in the study and 90 (87%) had at least one cluster of double triggering. The median number of clusters per patient was 19 (interquartile range, 6-41), with a median duration of 8 minutes (6-12 min). Compared with patients who had no clusters, patients with at least one cluster had longer duration of mechanical ventilation (7 d [4-11 d] vs 2 d [2-3 d]) and ICU length of stay (9 d [7-16 d] vs 13 d [2-8 d]). Thirty-three patients had high cumulative duration of clusters of double triggering (≥ 12 hr), and it was associated with longer duration of mechanical ventilation, fewer ventilator-free days, and longer ICU length of stay. Adjusted by duration of mechanical ventilation and severity of illness, high cumulative duration of clusters was associated with shorter survival at 28 days (hazard ratio, 2.09 d; 95% CI, 1.04-4.19 d). CONCLUSIONS: Clusters of double triggering are common and were associated with worse clinical outcomes. Patients who had a high cumulative duration of clusters had fewer ventilator-free days, longer duration of mechanical ventilation, longer ICU length of stay, and shorter survival than patients with low cumulative duration of cluster.


Assuntos
Respiração Artificial/estatística & dados numéricos , Insuficiência Respiratória/terapia , Adulto , Brasil , Estudos de Coortes , Feminino , Humanos , Unidades de Terapia Intensiva/organização & administração , Unidades de Terapia Intensiva/estatística & dados numéricos , Tempo de Internação/estatística & dados numéricos , Masculino , Pessoa de Meia-Idade , Modelos de Riscos Proporcionais , Estudos Prospectivos , Respiração Artificial/métodos , Insuficiência Respiratória/epidemiologia , Escore Fisiológico Agudo Simplificado
4.
J Crit Care ; 57: 30-35, 2020 06.
Artigo em Inglês | MEDLINE | ID: mdl-32032901

RESUMO

PURPOSE: To investigate if respiratory mechanics and other baseline characteristics are predictors of patient-ventilator asynchrony and to evaluate the relationship between asynchrony during assisted ventilation and clinical outcomes. METHODS: We performed a prospective cohort study in patients under mechanical ventilation (MV). Baseline measurements included severity of illness and respiratory mechanics. The primary outcome was the Asynchrony Index (AI), defined as the number of asynchronous events divided by the number of ventilator cycles and wasted efforts. We recorded ventilator waveforms throughout the entire period of MV. RESULTS: We analyzed 11,881 h of MV from 103 subjects. Median AI during the entire period of MV was 5.1% (IQR:2.6-8.7). Intrinsic PEEP was associated with AI (OR:1.72, 95%CI:1.1-2.68), but static compliance and airway resistance were not. Simplified Acute Physiology Score 3 (OR:1.03, 95%CI:1-1.06) was also associated with AI. Median AI was higher during assisted (5.4%, IQR:2.9-9.1) than controlled (2%, IQR:0.6-4.9) ventilation, and 22% of subjects had high incidence of asynchrony (AI≥10%). Subjects with AI≥10% had more extubation failure (33%) than patients with AI<10% (6%), p = .01. CONCLUSIONS: Predictors of high incidence of asynchrony were severity of illness and intrinsic PEEP. High incidence of asynchrony was associated with extubation failure, but not mortality. TRIAL REGISTRATION: ClinicalTrials.gov, NCT02687802.


Assuntos
Respiração com Pressão Positiva , Respiração Artificial/métodos , Mecânica Respiratória , Ventiladores Mecânicos , Adulto , Idoso , Extubação , Feminino , Humanos , Incidência , Inalação , Estimativa de Kaplan-Meier , Masculino , Pessoa de Meia-Idade , Estudos Prospectivos , Escore Fisiológico Agudo Simplificado , Resultado do Tratamento
5.
BMJ Open ; 9(5): e028601, 2019 05 22.
Artigo em Inglês | MEDLINE | ID: mdl-31123002

RESUMO

INTRODUCTION: Patient-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 ANALYSIS: We 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 DISSEMINATION: The 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.


Assuntos
Respiração Artificial/métodos , Mecânica Respiratória , Resistência das Vias Respiratórias , Estudos de Coortes , Humanos , Incidência , Estimativa de Kaplan-Meier , Complacência Pulmonar , Respiração por Pressão Positiva Intrínseca , Modelos de Riscos Proporcionais , Estudos Prospectivos , Fatores de Risco
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