Risk factors for extubation failure in the intensive care unit

Silva-Cruz, Aracely; Velarde-Jacay, Karina; Carreazo, Nilton Yhuri; Escalante-Kanashiro, Raffo

doi:10.5935/0103-507x.20180046

Cited by 32 publications

(45 citation statements)

References 26 publications

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“…This result was similar to other studies that estimate that the process of MV removal takes up about half of the total time of ventilatory support (40)(41)(42) . However, both the weaning time and the total MV time, concerning the date of intubation until the day of extubation, did not show statistically significant differences between the groups.…”

Section: Discussionsupporting

confidence: 92%

“…In this study, the weaning time corresponding to the replacement date of the controlled ventilation mode for an assisted modality until the day of extubation consisted of 37.32% of the total MV time. This result was similar to other studies that estimate that the process of MV removal takes up about half of the total time of ventilatory support ( 40 - 42 ) . However, both the weaning time and the total MV time, concerning the date of intubation until the day of extubation, did not show statistically significant differences between the groups.…”

Section: Discussionsupporting

confidence: 92%

See 1 more Smart Citation

Clinical study of Dysfunctional Ventilatory Weaning Response in critically ill patients

Silva

Tonelli²,

Costa³

et al. 2020

Rev. Latino-Am. Enfermagem

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Objective: to clinically validate the nursing diagnosis of Dysfunctional Ventilatory Weaning Response in adult patients admitted to Intensive Care Units. Method: a concurrent cohort performed with 93 patients admitted to Intensive Care Units. The incidence and incidence density of the diagnosis were estimated, its related factors were identified based on bivariate analysis and clinical indicators for determining its occurrence, according to the global and temporal presentation. Results: the overall incidence of the diagnosis was 44.09% and the incidence density was 14.49 occurrences for every 100 extubations/day. The factors related to the diagnosis were the following: age, clinical severity, fluid balance, oliguria, hemodialysis, edema in upper/lower limbs, anasarca, number of antibiotics, hypothermia, hyperthermia, amount of secretion, muscle retraction, anxiety score, heart rate, use of vasopressors and non-invasive ventilation after extubation. The clinical indicators most frequently identified for determining the diagnosis were the following: tachypnea, drop of saturation and tachycardia. Temporal progression in the severity of these manifestations was found. Conclusion: the Dysfunctional Ventilatory Weaning Response is a common finding in critically ill patients. Some components of the diagnosis of the NANDA-International (2018) version could be clinically validated. It is noteworthy that there are variables not yet described in the taxonomy, demonstrating the need to review this nursing diagnosis.

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Section: Discussionsupporting

confidence: 92%

Section: Discussionsupporting

confidence: 92%

Clinical study of Dysfunctional Ventilatory Weaning Response in critically ill patients

Silva

Tonelli²,

Costa³

et al. 2020

Rev. Latino-Am. Enfermagem

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“…As expected, SBT success time and parameters of MV such as tidal volume, PEEP and mean airway pressure, helped to accurately predict EF in our study. By assessing SHAP values, we found that MV duration and PSV level were the most important features for prediction, which is consistent with previous studies (7,(38)(39)(40)(41). Additionally, fluid balance (only urine output, crystalloid amount in our study) and antibiotic types were included in the final model.…”

Section: Discussionsupporting

confidence: 89%

“…Many factors have been assessed by prior studies for EF prediction, including Rapid Shallow Breathing Index (RSBI, f/Vt) (6), prolonged MV (7,8), and cough strength (9,10). Unfortunately, it was shown that these factors as well as physicians' judgments were not as accurate as expected (11,12).…”

Section: Introductionmentioning

confidence: 99%

Development and Validation of a Machine-Learning Model for Prediction of Extubation Failure in Intensive Care Units

et al. 2021

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Background: Extubation failure (EF) can lead to an increased chance of ventilator-associated pneumonia, longer hospital stays, and a higher mortality rate. This study aimed to develop and validate an accurate machine-learning model to predict EF in intensive care units (ICUs).Methods: Patients who underwent extubation in the Medical Information Mart for Intensive Care (MIMIC)-IV database were included. EF was defined as the need for ventilatory support (non-invasive ventilation or reintubation) or death within 48 h following extubation. A machine-learning model called Categorical Boosting (CatBoost) was developed based on 89 clinical and laboratory variables. SHapley Additive exPlanations (SHAP) values were calculated to evaluate feature importance and the recursive feature elimination (RFE) algorithm was used to select key features. Hyperparameter optimization was conducted using an automated machine-learning toolkit (Neural Network Intelligence). The final model was trained based on key features and compared with 10 other models. The model was then prospectively validated in patients enrolled in the Cardiac Surgical ICU of Zhongshan Hospital, Fudan University. In addition, a web-based tool was developed to help clinicians use our model.Results: Of 16,189 patients included in the MIMIC-IV cohort, 2,756 (17.0%) had EF. Nineteen key features were selected using the RFE algorithm, including age, body mass index, stroke, heart rate, respiratory rate, mean arterial pressure, peripheral oxygen saturation, temperature, pH, central venous pressure, tidal volume, positive end-expiratory pressure, mean airway pressure, pressure support ventilation (PSV) level, mechanical ventilation (MV) durations, spontaneous breathing trial success times, urine output, crystalloid amount, and antibiotic types. After hyperparameter optimization, our model had the greatest area under the receiver operating characteristic (AUROC: 0.835) in internal validation. Significant differences in mortality, reintubation rates, and NIV rates were shown between patients with a high predicted risk and those with a low predicted risk. In the prospective validation, the superiority of our model was also observed (AUROC: 0.803). According to the SHAP values, MV duration and PSV level were the most important features for prediction.Conclusions: In conclusion, this study developed and prospectively validated a CatBoost model, which better predicted EF in ICUs than other models.

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“…[ 1 ] Although risk factors for extubation failures have been well described, which include young age, prolonged mechanical ventilation, sedative usage, and recently diaphragm atrophy, the etiologies underlying extubation failures are still poorly understood. [ 2 ] We describe a case report of a toddler with multiple failed extubation attempts secondary to rare complications of an enterovirus infection, reported in only two other pediatric patients.…”

Section: Introductionmentioning

confidence: 99%

Multiple extubation failures following a rhino-enteroviral infection

Annamalai

Bhalala

2021

Journal of Postgraduate Medicine

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This case report reviews the hospital course of a 15-month-old girl admitted to the PICU for acute respiratory failure due to enterovirus infection; who subsequently had multiple extubation failures secondary to acute transverse myelitis. This rare presentation highlights the importance of assessing the neurological status in a patient with rhino-enteroviral respiratory infection and of considering acute transverse myelitis as an etiology for difficulty with extubation.

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Risk factors for extubation failure in the intensive care unit

Cited by 32 publications

References 26 publications

Clinical study of Dysfunctional Ventilatory Weaning Response in critically ill patients

Clinical study of Dysfunctional Ventilatory Weaning Response in critically ill patients

Development and Validation of a Machine-Learning Model for Prediction of Extubation Failure in Intensive Care Units

Multiple extubation failures following a rhino-enteroviral infection

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