Prediction of optimal endotracheal tube cuff volume from tracheal diameter and from patient height and age: a prospective cohort trial

The Relationship between Cuff Pressure and Air Injection Volume of Endotracheal Tube: A Study with Sheep Trachea Ex Vivo

Background

Endotracheal intubation is a widely used treatment. Excessive pressure of the endotracheal tube cuff leads to a series of complications. Here, we used tracheae of sheep to analyze the relationship between the air injection volume and endotracheal tube cuff pressure so as to guide the doctors and nurses in controlling the pressure of the endotracheal tube cuff during clinical work and minimise the risk of complications.

Materials and Methods

Forty sheep tracheae were utilised and were divided into five groups according to their diameters. Different sizes of endotracheal tubes were inserted into each trachea, and the cuff pressure with the increase of air injection volume was recorded. The formulas that reflect the relationship between air injection volume and cuff pressure were obtained. Then, sheep tracheae were randomly selected; different types of tubes were inserted, and the stipulated volume of air was injected. The actual pressure was measured and compared with the pressure predicted from the formulas. Statistical analysis was conducted to verify whether the formulas obtained from the first part of the experiment were in accordance with the expert evaluation table, which consists of opinions of several experts.

Results

After obtaining 15 formulas, we collected the differences between the theoretical cuff pressure and the actual cuff pressure that satisfied the expert evaluation. Relying on the formulas, the medical turntable was obtained, which is a tool that consists of two round cards with data on them. The top card has a notch. The two cards are stacked together, and as the top card rotates, the data on the bottom card can be easily seen in a one-to-one relationship.

Conclusion

The formulas are capable of showing the relationship between the cuff air injection volume and pressure of endotracheal tube cuff. The medical turntable can estimate the air injection volume to ensure that the pressure stays in an acceptable range.

Assessing the accuracy of ultrasound measurements of tracheal diameter: an in vitro experimental study

Background

Recent studies indicate that ultrasound can detect changes in tracheal diameter during endotracheal tube (ETT) cuff inflation. We sought to assess the accuracy of ultrasound measurement of tracheal diameter, and to determine the relationship between tracheal wall pressure (TWP), cuff inflation volume (CIV), and the degree of tracheal deformation.

Methods

Our study comprised two parts: the first included 45 porcine tracheas, the second 41 porcine tracheas. Each trachea was intubated with a cuffed ETT, which was connected to an injector and the manometer via a three-way tap. The cuff was inflated and the cuff pressure recorded before and after intubation. The tracheal diameter was measured using ultrasound. This included three separate measurements: outer transverse diameter (OTD), internal transverse diameter (ITD), and anterior tracheal wall thicknesses (ATWT). A precision electronic Vernier caliper was also used to measure tracheal diameter. We calculated TWP and the percentage change of tracheal diameter. The Bland–Altman method, linear regression, and locally weighted regression (LOESS) were used to analyze the data.

Results

There were strong correlation and agreement for OTD (r = 0.97, P < 0.001) and ITD (r = 0.90, P < 0.001) as measured by ultrasound and by precision electronic Vernier caliper, but a poor correlation for ATWT (r = 0.58, P < 0.001). There was a strong correlation between the percentage change of OTD (OTD%, r = 0.75, P < 0.001) and CIV, the percentage change of ITD (ITD%, r = 0.77, P < 0.001) and CIV, TWP (r = 0.75, P < 0.001) and CIV. And a strong correlation was also found between TWP and OTD% (r = 0.84, P < 0.001), TWP and ITD% (r = 0.84, P < 0.001).

Conclusions

Use of ultrasound to measure OTD and ITD is accurate, but is less accurate for ATWT. There is a close correlation between OTD%, ITD%, CIV and TWP.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12871-021-01398-3.

Interactive Cohort Identification of Sleep Disorder Patients Using Natural Language Processing and i2b2

Nationwide Children's Hospital established an i2b2 (Informatics for Integrating Biology & the Bedside) application for sleep disorder cohort identification. Discrete data were gleaned from semistructured sleep study reports. The system showed to work more efficiently than the traditional manual chart review method, and it also enabled searching capabilities that were previously not possible.

OBJECTIVE

We report on the development and implementation of the sleep disorder i2b2 cohort identification system using natural language processing of semi-structured documents.

METHODS

We developed a natural language processing approach to automatically parse concepts and their values from semi-structured sleep study documents. Two parsers were developed: a regular expression parser for extracting numeric concepts and a NLP based tree parser for extracting textual concepts. Concepts were further organized into i2b2 ontologies based on document structures and in-domain knowledge.

RESULTS

26,550 concepts were extracted with 99% being textual concepts. 1.01 million facts were extracted from sleep study documents such as demographic information, sleep study lab results, medications, procedures, diagnoses, among others. The average accuracy of terminology parsing was over 83% when comparing against those by experts. The system is capable of capturing both standard and non-standard terminologies. The time for cohort identification has been reduced significantly from a few weeks to a few seconds.

CONCLUSION

Natural language processing was shown to be powerful for quickly converting large amount of semi-structured or unstructured clinical data into discrete concepts, which in combination of intuitive domain specific ontologies, allows fast and effective interactive cohort identification through the i2b2 platform for research and clinical use.