Prevalence of peri-intubation major adverse events among critically ill patients: A systematic review and meta analysis

Downing, Jessica; Yardi, Isha; Ren, Christine; Cardona, Stephanie; Zahid, Manahel; Tang, Kaitlyn; Bzhilyanskaya, Vera; Patel, Priya; Pourmand, Ali; Tran, Quincy K.

doi:10.1016/j.ajem.2023.06.046

Cited by 5 publications

(1 citation statement)

References 66 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…88% of patients admitted to critical care settings require tracheal intubation and mechanical ventilation (6,7). A total of 10%-15% of intubated ICU patients experience hypoxia, 18% experience cardiovascular instability, and 2% experience cardiac arrest (8,9). In addition, the first-pass success rate of tracheal intubation in the critically ill is often < 80%, with up to 20% of tracheal intubations taking > two attempts, while multiple intubation attempts are associated with a higher risk of adverse outcomes (10).…”

Section: /29mentioning

confidence: 99%

Development and Validation of an Interpretable Machine Learning Model for the Prediction of Intubation in the Intensive Care Unit

Liu,

Duan,

Duan

et al. 2023

Preprint

View full text Add to dashboard Cite

Background Since there is a limited ability to identify the need for intubation in the ICU, the objective of this study was to develop and validate an interpretable machine learning (ML) model to predict the need for intubation in ICU patients. Methods Seven widely used ML algorithms were applied to develop and validate prediction models. Adult patients from the Medical Information Mart for Intensive Care IV database who stayed in the ICU for longer than 24 hours were involved in developing the model. The model was then externally validated in the eICU-CRD database. In addition, the SHapley Additive exPlanations (SHAP) method was employed to illustrate the effects of the individual features attributed to the prediction model. Results A total of 11988 patients were included in the final cohort for this study. Among the seven constructed models, the CatBoost model achieved the best performance (AUC: 0.863). In the external validation, the efficacy of our model was also observed (AUC: 0.722). The Glasgow Coma Scale (GCS), body mass index (BMI), arterial partial pressure of oxygen (PaO2) and respiratory rate (RR) were the top 4 features of the CatBoost model with the greatest impact. Conclusions We have demonstrated the potential of machine-learning approaches for predicting the need for intubation in ICU patients. The SHAP method could improve the interpretability of machine-learning models and help clinicians better understand the reasoning behind the outcome. Trial registration: Not applicable.

show abstract

Section: /29mentioning

confidence: 99%

Development and Validation of an Interpretable Machine Learning Model for the Prediction of Intubation in the Intensive Care Unit

Liu,

Duan,

Duan

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

Prehospital hemodynamic instability: invasive blood pressure measurement—the future?

Ule,

Thiel,

Berwanger

et al. 2023

Notfall Rettungsmed

View full text Add to dashboard Cite

The Physiologically Difficult Airway and Management Considerations

Mosier

2024

Curr Anesthesiol Rep

View full text Add to dashboard Cite

Purpose of Review This paper evaluates the recent literature regarding the physiologically difficult airway. Recent Findings Adverse events mainly desaturation, cardiovascular collapse, and cardiac arrest remain common complications. This risk is greatly increased in patients with altered physiology prior to intubation. Studies published over the last 5 years have explored many aspects surrounding the epidemiology, risks, and approach to managing the physiologically difficult airway. Summary Important work has been done to identify directly modifiable risks of complications related to the physiologically difficult airway, but a large percentage of patients remain at high risk despite optimizing induction agents, preoxygenation, and first attempt success.

show abstract

Prevalence of peri-intubation major adverse events among critically ill patients: A systematic review and meta analysis

Cited by 5 publications

References 66 publications

Development and Validation of an Interpretable Machine Learning Model for the Prediction of Intubation in the Intensive Care Unit

Development and Validation of an Interpretable Machine Learning Model for the Prediction of Intubation in the Intensive Care Unit

Prehospital hemodynamic instability: invasive blood pressure measurement—the future?

The Physiologically Difficult Airway and Management Considerations

Contact Info

Product

Resources

About