Development and Validation of a Risk Nomogram Model for Perioperative Respiratory Adverse Events in Children Undergoing Airway Surgery: An Observational Prospective Cohort Study

Impact of varied anesthesia maintenance strategies on postoperative respiratory complications in pediatric patients undergoing tonsillectomy and adenoidectomy (AmPRAEC study): study protocol for a multicenter randomized, double-blind clinical trial

BACKGROUND

Postoperative respiratory adverse events are the most common perioperative complications in pediatric anesthesia, particularly prevalent in children undergoing tonsillectomy and adenoidectomy, with an incidence rate as high as 50%. The choice of anesthetic induction regimen directly influences the incidence of respiratory adverse events during the induction period. However, this effect seems to have minimal impact on postoperative outcomes. The occurrence rate of postoperative respiratory adverse events is likely more closely associated with the anesthetic maintenance phase, yet this relationship remains uncertain at present.

METHODS

The objective of this study was to assess the impact of different anesthetic maintenance regimens on postoperative respiratory adverse events in pediatric patients undergoing tonsillectomy and adenoidectomy. The AmPRAEC study is a multicenter, randomized, double-blind controlled trial. A total of 717 pediatric patients were recruited from 12 medical centers and randomly assigned to three groups: group A (intravenous maintenance group, receiving propofol infusion); group B (intravenous-inhalational combination group, maintained with 1% sevoflurane combined with propofol); and group C (inhalational maintenance group, maintained with 2-3% sevoflurane inhalation). The primary outcome measure was the incidence rate of postoperative respiratory adverse events.

DISCUSSION

This clinical trial aims to elucidate the impact of various anesthetic maintenance regimens on postoperative respiratory adverse events in pediatric patients. The outcomes of this study are anticipated to facilitate anesthesiologists in devising more comprehensive perioperative management strategies, enhancing comfort, and improving the clinical outcomes for this patient population.

TRIAL REGISTRATION

Chinese Clinical Trial Registry ( http://www.chictr.org.cn ) ChiCTR2300074803. Registered on August 16, 2023.

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.

A Predictive Model of Major Postoperative Respiratory Adverse Events in Pediatric Patients Undergoing Rigid Bronchoscopy for Exploration and Foreign Body Removal

Background: No nomogram has been established to predict the incidence of major postoperative respiratory adverse events (mPRAEs) in children undergoing rigid bronchoscopy for airway foreign bodies (AFB) removal and exploration of the airway, though some studies have confirmed the risk factors. Methods: 1214 pediatric patients (≤3 years old) undergoing rigid bronchoscopy for AFB from June 2014 to December 2020 were enrolled in this study. The primary outcome was the occurrence of mPRAEs, including laryngospasm and bronchospasm. Following that, a nomogram prediction model for the mPRAEs was developed. Results: The incidence of mPRAEs was 84 (6.9%) among 1214 subjects. American Society of Anesthesiologists physical status (ASA-PS), intraoperative desaturation (SpO2 < 90%), procedural duration and ventilatory approach were all independent risk factors of mPRAEs. The area under the receiver operating characteristic curve (AUC) value of the nomogram for predicting mPRAEs was 0.815 (95% CI: 0.770-0.861), and the average AUC for ten-fold cross-validation was 0.799. These nomograms were well calibrated by Hosmer-Lemshow (p = 0.607). Decision curve analysis showed that the nomogram prediction model is effective in clinical settings. Conclusions: Combining ASA-PS, intraoperative desaturation, procedural duration, and ventilatory approach, the nomogram model is adequate for predicting the risk of developing mPRAEs, followed by rigid bronchoscopy for AFB removal and exploration.

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).

A novel nomogram for predicting respiratory adverse events during transport after interventional cardiac catheterization in children

OBJECTIVE

The rate and predictors of respiratory adverse events (RAEs) during transport discharged from operating room after interventional cardiac catheterization in children remain unclear. This study aimed to investigate the incidence and predictors, and to construct a nomogram for predicting RAEs during transport in this pediatric surgical treatment.

METHODS

This prospective cohort study enrolled 290 consecutive pediatric patients who underwent ventricular septal defects (VSD), atrial septal defects (ASD), and patent ductus arteriosus (PDA) between February 2019 and December 2020. Independent predictors were used to develop a nomogram, and a bootstrap resampling approach was used to conduct internal validation. Composite RAEs were defined as the occurrence of at least 1 complication regarding laryngospasm, bronchospasm, apnea, severe cough, airway secretions, airway obstruction, and oxygen desaturation.

RESULTS

The rate of RAEs during transport was 23.1% (67 out of 290). Multivariate analysis identified age (vs. ≤3 years, adjusted odds ratio (aOR) = 0.507, 95% confidence interval (CI), 0.268-0.958, P = 0.036), preoperative upper respiratory tract infections (URI, aOR = 2.335, 95% CI, 1.223-4.460, P = 0.01), type of surgery (vs. VSD, for ASD, aOR =  2.856, 95% CI, 1.272-6.411, P = 0.011; for PDA, aOR = 5.518, 95% CI, 2.425-12.553, P < 0.001), morphine equivalent (vs. ≤0.153 mg/kg, aOR = 2.904, 95% CI, 1.371-6.150, P = 0.005), atropine usage (aOR = 0.463, 95% CI, 0.244-0.879, P = 0.019), and RAEs during extubation to transport (aOR = 5.004, 95% CI, 2.633-9.511, P < 0.001) as independent predictors of RAEs during transport. These six candidate predictors were used to develop a nomogram, which showed a C-statistic value of 0.809 and good calibration (P = 0.844). Internal validation revealed similarly good discrimination (C-statistic, 0.782; 95% CI, 0.726-0.837) and calibration. Decision curve analysis (DCA) also demonstrated the clinical usefulness of the nomogram.

CONCLUSION

The high rate of RAEs during transport reminds us of the need for more medical care and attention. The proposed nomogram can reliably identify pediatric patients at high risk of RAEs during transport and guide clinicians to make proper transport plans. Our findings have important and meaningful implications for RAEs risk prediction, clinical intervention and healthcare quality control.