WeChat Mini Program
Old Version Features

Difficult Airway Prediction in Infants with Apparently Normal Face and Neck Features

JOURNAL OF CLINICAL MEDICINE(2024)

Univ Childrens Hosp | Univ Belgrade | Univ Clin Ctr Serbia

Cited 0|Views5
Abstract
Background/Objectives: Prediction of a difficult airway during pre-anesthetic evaluation is of great importance because it enables an adequate anesthetic approach and airway management. As there is a scarcity of prospective studies evaluating the role of anthropometric measures of the face and neck in predicting difficult airways in infants with an apparently normal airway, we aimed to identify the aforementioned predictors of difficult facemask ventilation and intubation in infants. Methods: A prospective, observational study that included 97 infants requiring general endotracheal anesthesia was conducted. Anthropometric and specific facial measurements were obtained before ventilation and intubation. Results: The incidence of difficult facemask ventilation was 15.5% and 38.1% for difficult intubation. SMD (sternomental distance), TMA (tragus-to-mouth angle distance), NL (neck length) and mouth opening were significantly lower in the difficult facemask ventilation group. HMDn (hyomental distance in neutral head position), HMDe (hyomental distance in neck extension), TMD (thyromental distance), SMD, mandibular development and mouth opening were significantly different in the intubation difficulty group compared to the non-difficult group. HMDn and HMDe showed significantly greater specificities for difficult intubation (83.8% and 76.7%, respectively), while higher sensitivities were observed in TMD, SMD and RHSMD (ratio of height to SMD) (89.2%, 75.7%, and 70.3%, respectively). Regarding difficult facemask ventilation, TMA showed greater sensitivity (86.7%) and SMD showed greater specificity (80%) compared to other anthropometric parameters. In a multivariate model, BMI (body mass index), COPUR (Colorado Pediatric Airway Score), BOV (best oropharyngeal view) and TMA were found to be independent predictors of difficult intubation, while BMI, ASA (The American Society Physical Status Classification System), CL (Cormack–Lehane Score), TMA and SMD predicted difficult facemask ventilation. Conclusions: Preoperative airway assessment is of great importance for ventilation and intubation. Patient’s overall condition and facial measurements can be used as predictors of difficult intubation and ventilation.
More
Translated text
Key words
infants,airway assessment,difficult airway predictors,difficult facemask ventilation,difficult intubation
求助PDF
上传PDF
Bibtex
AI Read Science
AI Summary
AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
Example
Background
Key content
Introduction
Methods
Results
Related work
Fund
Key content
  • Pretraining has recently greatly promoted the development of natural language processing (NLP)
  • We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
  • We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
  • The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
  • Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
Upload PDF to Generate Summary
Must-Reading Tree
Example
Generate MRT to find the research sequence of this paper
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper
Summary is being generated by the instructions you defined