Dataset Information

Cough Peak Flow Assessment Without Disconnection From the ICU Ventilator in Mechanically Ventilated Patients.

ABSTRACT:

Background

Because ICU ventilators incorporate flow velocity measurement, cough peak expiratory flow (CPF) can be assessed without disconnecting the patient from the ICU ventilator. Our goal was to estimate the correlation between CPF obtained with the built-in ventilator flow meter (ventilator CPF) and CPF obtained with an electronic portable handheld peak flow meter connected to the endotracheal tube.

Methods

Cooperative mechanically ventilated patients who entered the weaning process and who were ventilated with pressure support < 15 cm H₂O and PEEP < 9 cm H₂O were eligible for the study. Their CPF measurements obtained on the extubation day were kept for analysis.

Results

We analyzed CPF obtained in 61 subjects. The mean ± SD value of ventilator CPF and peak flow meter CPF were 72.6 ± 27.5 L/min and 31.1 ± 13.4 L/min. The Pearson correlation coefficient was 0.63 (95% CI 0.45-0.76), P < .001. The ventilator CPF had an area under the receiver operating characteristic curve of 0.84 (95% CI 0.75-0.93) to predict a peak flow meter CPF < 35 L/min. Neither ventilator CPF nor peak flow meter CPF differed significantly between subjects who were or were not re-intubated within 72 h (n = 5) and failed to predict re-intubation at 72 h (area under the receiver operating characteristic curve of 0.64 [95% CI 0.46-0.82] and 0.47 [95% CI 0.22-0.74]).

Conclusions

CPF measurements using a built-in ventilator flow meter were feasible in routine practice with cooperative ICU subjects who were intubated and correlated with CPF assessed by an electronic portable peak flow meter.

SUBMITTER: Fossat G

PROVIDER: S-EPMC10173113 | biostudies-literature | 2023 Apr

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Cough Peak Flow Assessment Without Disconnection From the ICU Ventilator in Mechanically Ventilated Patients.

Fossat Guillaume G Desmalles Emmanuelle E Courtes Léa L Fossat Cécile C Boulain Thierry T

Respiratory care 20230306 4

<h4>Background</h4>Because ICU ventilators incorporate flow velocity measurement, cough peak expiratory flow (CPF) can be assessed without disconnecting the patient from the ICU ventilator. Our goal was to estimate the correlation between CPF obtained with the built-in ventilator flow meter (ventilator CPF) and CPF obtained with an electronic portable handheld peak flow meter connected to the endotracheal tube.<h4>Methods</h4>Cooperative mechanically ventilated patients who entered the weaning p ...[more]

PMID: 36878644

Similar Datasets

Project description:ObjectivesThe purpose of this study was to evaluate the predictive value of the cough peak flow (CPF) for successful extubation in postcraniotomy critically ill patients.DesignThis was a single-centre prospective diagnostic study.SettingThe study was conducted in three intensive care units (ICUs) of a teaching hospital.ParticipantsPostcraniotomy patients who were 18 years or older, stayed in ICU for more than 24 hours and underwent mechanical ventilation for more than 24 hours were eligible for the study. Patients were excluded if one of the following was present: no extubation attempt during the ICU stay; underwent tracheostomy without extubation attempt; pregnant or lactating women; enrolled in other clinical trials; declined to participate in the study. A total of 4879 patients were screened and 1037 were eligible for the study, among whom 785 were included in the study.Outcome measuresCPF, including involuntary (CPF-invol) and voluntary CPF (CPF-vol), were measured before extubation. The area under the receiver operating characteristic curve (AUC) was calculated to explore the diagnostic accuracy of CPF in predicting successful extubation.ResultsThere were 641 successful extubation cases (81.7%). The AUC of CPF-invol for predicting successful extubation was 0.810 (95% CI 0.766 to 0.854), with a cut-off value of 63.2 L/min, a sensitivity of 87.4% and a specificity of 66.7%. For conscious patients, the AUC of CPF-invol for the prediction of successful extubation was 0.849 (95% CI 0.794 to 0.904), with a cut-off value of 63.2 L/min and the AUC of CPF-vol was 0.756 (95% CI 0.696 to 0.817), with a cut-off value of 68.2 L/min.ConclusionsThe CPF was much higher in patients with successful extubation than that in patients with failed extubation. CPF might be valuable for the prediction of extubation outcomes in postcraniotomy critically ill patients. Multicentre studies could be carried out to further validate the results of this study.Trial registration numberNCT04000997.

Project description:IntroductionAlthough several parameters have been proposed to predict the hemodynamic response to fluid expansion in critically ill patients, most of them are invasive or require the use of special monitoring devices. The aim of this study is to determine whether noninvasive evaluation of respiratory variation of brachial artery peak velocity flow measured using Doppler ultrasound could predict fluid responsiveness in mechanically ventilated patients.MethodsWe conducted a prospective clinical research in a 17-bed multidisciplinary ICU and included 38 mechanically ventilated patients for whom fluid administration was planned due to the presence of acute circulatory failure. Volume expansion (VE) was performed with 500 mL of a synthetic colloid. Patients were classified as responders if stroke volume index (SVi) increased >or= 15% after VE. The respiratory variation in Vpeakbrach (DeltaVpeakbrach) was calculated as the difference between maximum and minimum values of Vpeakbrach over a single respiratory cycle, divided by the mean of the two values and expressed as a percentage. Radial arterial pressure variation (DeltaPPrad) and stroke volume variation measured using the FloTrac/Vigileo system (DeltaSVVigileo), were also calculated.ResultsVE increased SVi by >or= 15% in 19 patients (responders). At baseline, DeltaVpeakbrach, DeltaPPrad and DeltaSVVigileo were significantly higher in responder than nonresponder patients [14 vs 8%; 18 vs. 5%; 13 vs 8%; P < 0.0001, respectively). A DeltaVpeakbrach value >10% predicted fluid responsiveness with a sensitivity of 74% and a specificity of 95%. A DeltaPPrad value >10% and a DeltaSVVigileo >11% predicted volume responsiveness with a sensitivity of 95% and 79%, and a specificity of 95% and 89%, respectively.ConclusionsRespiratory variations in brachial artery peak velocity could be a feasible tool for the noninvasive assessment of fluid responsiveness in patients with mechanical ventilatory support and acute circulatory failure.Trial registrationClinicalTrials.gov ID: NCT00890071.

Project description:BackgroundVentilator-associated pneumonia (VAP) is one of the common critical complications of nosocomial infection (NI) in invasive mechanically ventilated intensive care unit (ICU) patients. The efficacy of total parenteral nutrition (TPN), enteral nutrition and/or adjuvant peripheral parenteral nutrition (EPN) supplemented with or without probiotic, prebiotic, and synbiotic therapies in preventing VAP among these patients has been questioned. We aimed to systematically and comprehensively summarize all available studies to generate the best evidence of VAP prevention for invasive mechanically ventilated ICU patients.MethodsRandomized controlled trials (RCTs) for the administration of TPN, EPN, probiotics-supplemented EPN, prebiotics-supplemented EPN, and synbiotics-supplemented EPN for VAP prevention in invasive mechanically ventilated ICU patients were systematically retrieved from four electronic databases. The incidence of VAP was the primary outcome and was determined by the random-effects model of a Bayesian framework. The secondary outcomes were NI, ICU and hospital mortality, ICU and hospital length of stay, and mechanical ventilation duration. The registration number of Prospero is CRD42020195773.ResultsA total of 8339 patients from 31 RCTs were finally included in network meta-analysis. The primary outcome showed that probiotic-supplemented EPN had a higher correlation with the alleviation of VAP than EPN in critically invasive mechanically ventilated patients (odds ratio [OR] 0.75; 95% credible intervals [CrI] 0.58-0.95). Subgroup analyses showed that probiotic-supplemented EPN prevented VAP in trauma patients (OR 0.30; 95% CrI 0.13-0.83), mixed probiotic strain therapy was more effective in preventing VAP than EPN therapy (OR 0.55; 95% CrI 0.31-0.97), and low-dose probiotic therapy (less than 1010 CFU per day) was more associated with lowered incidence of VAP than EPN therapy (OR 0.16; 95% CrI 0.04-0.64). Secondary outcomes indicated that synbiotic-supplemented EPN therapy was more significantly related to decreased incidence of NI than EPN therapy (OR 0.34; 95% CrI 0.11-0.85). Prebiotic-supplemented EPN administration was the most effective in preventing diarrhea (OR 0.05; 95% CrI 0.00-0.71).ConclusionProbiotic supplementation shows promise in reducing the incidence of VAP in critically invasive mechanically ventilated patients. Currently, low quality of evidence reduces strong clinical recommendations. Further high-quality RCTs are needed to conclusively prove these findings.Systamatic review registration[https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42020195773], identifier [CRD42020195773].

Project description:Mechanical ventilation (MV) is vital for critically ill ICU patients but carries significant mortality risks. This study aims to develop a predictive model to estimate hospital mortality among MV patients, utilizing comprehensive health data to assist ICU physicians with early-stage alerts. We developed a Machine Learning (ML) framework to predict hospital mortality in ICU patients receiving MV. Using the MIMIC-III database, we identified 25,202 eligible patients through ICD-9 codes. We employed backward elimination and the Lasso method, selecting 32 features based on clinical insights and literature. Data preprocessing included eliminating columns with over 90% missing data and using mean imputation for the remaining missing values. To address class imbalance, we used the Synthetic Minority Over-sampling Technique (SMOTE). We evaluated several ML models, including CatBoost, XGBoost, Decision Tree, Random Forest, Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and Logistic Regression, using a 70/30 train-test split. The CatBoost model was chosen for its superior performance in terms of accuracy, precision, recall, F1-score, AUROC metrics, and calibration plots. The study involved a cohort of 25,202 patients on MV. The CatBoost model attained an AUROC of 0.862, an increase from an initial AUROC of 0.821, which was the best reported in the literature. It also demonstrated an accuracy of 0.789, an F1-score of 0.747, and better calibration, outperforming other models. These improvements are due to systematic feature selection and the robust gradient boosting architecture of CatBoost. The preprocessing methodology significantly reduced the number of relevant features, simplifying computational processes, and identified critical features previously overlooked. Integrating these features and tuning the parameters, our model demonstrated strong generalization to unseen data. This highlights the potential of ML as a crucial tool in ICUs, enhancing resource allocation and providing more personalized interventions for MV patients.

Project description:BackgroundIn most countries, patients receiving mechanical ventilation (MV) are treated in intensive care units (ICUs). However, in some countries, including Japan, many patients on MV are not treated in ICUs. There are insufficient epidemiological data on these patients. Here, we sought to describe the epidemiology of patients on MV in Japan by comparing and contrasting patients on MV treated in ICUs and in non-ICU settings. A preliminary comparison of patient outcomes between ICU and non-ICU patients was a secondary objective.MethodsData on adult patients receiving MV for at least 3 days in ICUs or non-ICU settings from April 2010 through March 2012 were obtained from the Quality Indicator/Improvement Project, a voluntary data-administration project covering more than 400 acute-care hospitals in Japan. We excluded patients with cancer-related diagnoses. Patient demographic data and the critical care provided were compared between groups.ResultsOver the study period, 17,775 patients on MV were treated only in non-ICU settings, whereas 20,516 patients were treated at least once in ICUs (46.4% vs. 53.6%). Average age was higher in non-ICU patients than in ICU patients (72.8 vs. 70.2, P < 0.001). Mean number of ventilation days was greater in non-ICU patients (11.7 vs. 9.5, P < 0.001). Hospital mortality was higher in non-ICU patients (41.4% vs. 38.8%, P < 0.001). Standard critical care (e.g., arterial line placement, enteral nutrition, and stress-ulcer prevention) was provided significantly less often in non-ICU patients. Multivariate analysis showed that ICU admission significantly decreased hospital mortality (adjusted odds ratio 0.713, 95% CI 0.676 to 0.753).ConclusionsA large proportion of Japanese patients on MV were treated in non-ICU settings. Analysis of administrative data indicated preliminarily that hospital mortality rates in these patients were higher in non-ICU settings than in ICUs. Prospective analyses comparing non-ICU and ICU patients on MV by severity scoring are needed.

Dataset Information

Cough Peak Flow Assessment Without Disconnection From the ICU Ventilator in Mechanically Ventilated Patients.

Background

Methods

Results

Conclusions

Publications

Cough Peak Flow Assessment Without Disconnection From the ICU Ventilator in Mechanically Ventilated Patients.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets