Comparisons of recursive partitioning analysis and conventional methods for selection of uncuffed endotracheal tubes for pediatric patients

Advances in pediatric perioperative care using artificial intelligence

PURPOSE OF THIS REVIEW

This article explores how artificial intelligence (AI) can be used to evaluate risks in pediatric perioperative care. It will also describe potential future applications of AI, such as models for airway device selection, controlling anesthetic depth and nociception during surgery, and contributing to the training of pediatric anesthesia providers.

RECENT FINDINGS

The use of AI in healthcare has increased in recent years, largely due to the accessibility of large datasets, such as those gathered from electronic health records. Although there has been less focus on pediatric anesthesia compared to adult anesthesia, research is on- going, especially for applications focused on risk factor identification for adverse perioperative events. Despite these advances, the lack of formal external validation or feasibility testing results in uncertainty surrounding the clinical applicability of these tools.

SUMMARY

The goal of using AI in pediatric anesthesia is to assist clinicians in providing safe and efficient care. Given that children are a vulnerable population, it is crucial to ensure that both clinicians and families have confidence in the clinical tools used to inform medical decision- making. While not yet a reality, the eventual incorporation of AI-based tools holds great potential to contribute to the safe and efficient care of our patients.

Use of artificial intelligence in paediatric anaesthesia: a systematic review

Objectives

Although the development of artificial intelligence (AI) technologies in medicine has been significant, their application to paediatric anaesthesia is not well characterised. As the paediatric operating room is a data-rich environment that requires critical clinical decision-making, this systematic review aims to characterise the current use of AI in paediatric anaesthesia and to identify barriers to the successful integration of such technologies.

Methods

This review was registered with PROSPERO (CRD42022304610), the international registry for systematic reviews. The search strategy was prepared by a librarian and run in five electronic databases (Embase, Medline, Central, Scopus, and Web of Science). Collected articles were screened by two reviewers. Included studies described the use of AI for paediatric anaesthesia (<18 yr old) within the perioperative setting.

Results

From 3313 records identified in the initial search, 40 were included in this review. Identified applications of AI were described for patient risk factor prediction (24 studies; 60%), anaesthetic depth estimation (2; 5%), anaesthetic medication/technique decision guidance (2; 5%), intubation assistance (1; 2.5%), airway device selection (3; 7.5%), physiological variable monitoring (6; 15%), and operating room scheduling (2; 5%). Multiple domains of AI were discussed including machine learning, computer vision, fuzzy logic, and natural language processing.

Conclusion

There is an emerging literature regarding applications of AI for paediatric anaesthesia, and their clinical integration holds potential for ultimately improving patient outcomes. However, multiple barriers to their clinical integration remain including a lack of high-quality input data, lack of external validation/evaluation, and unclear generalisability to diverse settings.

Systematic review protocol

CRD42022304610 (PROSPERO).

Applications of Airway Ultrasound for Endotracheal Intubation in Pediatric Patients: A Systematic Review

Endotracheal intubation is a challenging procedure for pediatric patients. Airway ultrasound as a new technology is suitable for aiding this process, but its diagnostic value remains unclear. We searched MEDLINE, EMBASE, Cochrane Central Register of Controlled Trials, and the Chinese biomedical literature database to summarize specific applications of airway ultrasound in each step of endotracheal intubation in pediatric patients. Diagnostic accuracy and 95% confidence interval were used as outcomes. In total, 33 studies (6 randomized controlled trials and 27 diagnostic studies) with 1934 airway ultrasound examinations were included. Population included neonates, infants, and older children. Airway ultrasound could be used to determine the endotracheal tube size and confirm endotracheal intubation and intubation depth; the diagnostic accuracy for all these factors was 23.3-100%, 90.6-100%, and 66.7-100%, respectively. Furthermore, the accuracy of airway ultrasound in predicting endotracheal tube size was consistently higher than traditional methods, such as height formula, age formula, and the width of the little finger. In conclusion, airway ultrasound has unique advantages for confirming successful endotracheal intubation in pediatric patients, and it may become an effective auxiliary tool in this field. There is a need to develop a unified airway ultrasound protocol to conduct clinical trials and practice in the future.

Frequency of the requirement of inappropriate uncuffed tracheal tube size for pediatric patients: a retrospective observational analysis

BACKGROUND

The insertion of inappropriately sized uncuffed endotracheal tubes (ETTs) with a tight seal or presence of air leakage may be necessary in children. This study aimed to analyze the frequency of the requirement of inappropriately sized uncuffed ETT insertion, air leakage after the ETT was replaced with one of a larger size, and factors associated with air leakage after ETT replacement.

METHODS

Patients under 2 years of age who underwent oral surgery under general anesthesia with uncuffed ETTs between December 2013 and May 2015 were enrolled. The ETT size was selected at the discretion of the attending anesthesiologists. A leak test was performed after intubation. The ETT was replaced when considered necessary. Data regarding the leak pressure (P_Leak) and inspiratory and expiratory tidal volumes were extracted from anesthesia records. We considered a P_Leak of 10 < P_Leak ≤ 30 cmH₂O to be appropriate. The frequencies of the requirement of inappropriately sized ETTs, absence of leakage after ETT replacement, ETT size difference, and leak rate were calculated. A logistic regression was performed, with P_Leak, leak rate, and size difference included as explanatory variables and presence of leakage after replacement as the outcome variable.

RESULTS

Out of the 156 patients enrolled, 109 underwent ETT replacement, with the requirement of inappropriately sized ETTs being observed in 25 patients (23%). ETT replacement was performed in patients with P_Leak ≤ 10 cmH₂O; leakage was absent after replacement (P_Leak < 30 cmH₂O) in 52% of patients (25/48). In the multivariate logistic model, the leak rate before ETT replacement was significantly associated with the presence of leakage after replacement (p = 0.021).

CONCLUSIONS

Inappropriately sized ETTs were inserted in approximately 23% of the patients. The leak rate may be useful to guide ETT replacement.

Prediction of appropriate formula for nasotracheal tube size in developmental disability children

OBJECTIVES

Developmental disability children have differences in growth. Therefore, tube size selection is important for nasotracheal intubation. In our previous study for healthy children undergoing dental surgery, height was the most suitable factor to predict nasotracheal tube size. The aim of this study was to find the most suitable formula for selection of nasotracheal tube size for them, retrospectively.

MATERIAL AND METHODS

Developmental disability children aged 2 to 10 years were included in this study. They were intubated nasotracheally from April 2012 until May 2017. Their actually intubated tube sizes were checked. The predicted tube sizes were calculated according to the formulas by the backgrounds: the diameter of the trachea at the 6th cervical (C6), 7th cervical (C7), and 2nd thoracic vertebrae (T2) in X-ray. The actually intubated tube sizes were compared with predicted sizes. Data were analyzed using Spearman's regression analysis.

RESULTS

The tube sizes with 5.0, 5.5, and 6.0 mm ID were intubated in 75 patients. The age-based formula was the most suitable; the correlation coefficients (r²) were 0.9027 (vs age), 0.5434 (vs height), 0.3779 (vs weight), 0.0785 (vs C6), 0.2279 (vs C7), and 0.3065 (Th2) (p < 0.01). However, 0.5-mm smaller size tubes were more frequently intubated actually. Their correspondence rate to the predicted size was 48% (5.0 mm), 52% (5.5 mm), and 39% (6.0 mm), respectively.

CONCLUSION

The age-based formula could be the most suitable for predicting nasotracheal tube size in developmental disability children aged 2 to 10 years. One smaller size by the age formula was most suitable at first trial tube.

CLINICAL RELEVANCE

The present data indicate that the selection of nasotracheal tube using one smaller size by the age formula (ID = 4 + age [years]/4) might be useful for developmental disability children.

Derivation and validation of a formula for paediatric tracheal tube size using bootstrap resampling procedure

BACKGROUND AND AIMS

The accuracy of age-, length- and weight-based formulae to predict optimal size of uncuffed tracheal tubes (TTs) in children varies widely. We determined the accuracy of age, length and weight in predicting the size of TT in Indian children, and derived and validated a formula using the best predictor.

METHODS

In the derivation phase, 100 children aged 1-8 years undergoing general anaesthesia and tracheal intubation with an uncuffed tube were prospectively studied. The correct size of the TT used was confirmed using the leak test. A bootstrap resampling procedure was used to estimate the accuracy of the predictors (age, weight, or length alone; length and age; length and weight; and length, weight and age). The best predictor was used to derive a formula (Paediatric Tube Size Predictor, PTSP) to calculate the size of TT. The accuracy of PTSP was tested in 150 children of the same age group in the validation phase.

RESULTS

Length (L (in meters), R 2 = 0.61) was the best single predictor of the size of TT and was used to derive the PTSP as internal diameter = 3L + 2.5. In the validation phase, the PTSP predicted the size of TT correctly in 75% of children. Re-intubation was associated with a higher incidence of respiratory morbidity than one-time tracheal intubation.

CONCLUSION

Length of the child predicts the size of an uncuffed TT better than age and weight. The PTSP formula based on length correctly predicts the size of uncuffed TT in 75% of children.