Identification of the risk factors in perioperative respiratory adverse events in children under general anesthesia and the development of a predictive model

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.

Predictors of perioperative respiratory adverse events among children with upper respiratory tract infection undergoing pediatric ambulatory ilioinguinal surgery: a prospective observational research

Objectives

Anesthesia for children with an upper respiratory tract infection (URI) has an increased risk of perioperative respiratory adverse events (PRAEs) that may be predicted according to the COLDS score. The aims of this study were to evaluate the validity of the COLDS score in children undergoing ilioinguinal ambulatory surgery with mild to moderate URI and to investigate new predictors of PRAEs.

Methods

This was a prospective observational study including children aged 1–5 years with mild to moderate symptoms of URI who were proposed for ambulatory ilioinguinal surgery. The anesthesia protocol was standardized. Patients were divided into two groups according to the incidence of PRAEs. Multivariate logistic regression was performed to assess predictors for PRAEs.

Results

In this observational study, 216 children were included. The incidence of PRAEs was 21%. Predictors of PRAEs were respiratory comorbidities (adjusted OR (aOR)=6.3, 95% CI 1.19 to 33.2; p=0.003), patients postponed before 15 days (aOR=4.3, 95% CI 0.83 to 22.4; p=0.029), passive smoking (aOR=5.31, 95% CI 2.07 to 13.6; p=0.001), and COLDS score of >10 (aOR=3.7, 95% CI 0.2 to 53.4; p=0.036).

Conclusions

Even in ambulatory surgery, the COLDS score was effective in predicting the risks of PRAEs. Passive smoking and previous comorbidities were the main predictors of PRAEs in our population. It seems that children with severe URI should be postponed to receive surgery for more than 15 days.

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).

Ad-hoc preoperative management and respiratory events in pediatric anesthesia during the first COVID-19 lockdown–an observational cohort study

Background

Early pre-anesthetic management for surgery is aimed at identifying risk factors, which notably in children are mostly airway related. The first COVID-19 lockdown opened a unique ‘window of opportunity’ to study what impact an ad-hoc management strategy would bring to bear on intraoperative respiratory events.

Methods

In this observational cohort study we included all patients with an American Society of Anesthesiology (ASA) Physical Status of I or II, aged 0 to ≤18 years, who underwent elective surgery at our center during the first national COVID-19 lockdown (March 15th to May 31st, 2020) and all analogue cases during the same calendar period of 2017−2019. The primary outcome parameter was a drop in peripheral oxygen saturation (SpO2) below 90% during anesthesia management. The study is completed and registered with the German Clinical Trials Register, DRKS00024128.

Results

Given 125 of 796 evaluable cases during the early 2020 lockdown, significant differences over the years did not emerge for the primary outcome or event counts (p>0.05). Events were exceedingly rare even under general anesthesia (n = 3) and non-existent under regional anesthesia (apart from block failures: n = 4). Regression analysis for SpO2 events <90% yielded no significant difference for ad-hoc vs standard preoperative management (p = 0.367) but more events based on younger patients (p = 0.007), endotracheal intubation (p = 0.007), and bronchopulmonary procedures (p = 0.001).

Conclusions

Early assessment may not add to the safety of pediatric anesthesia. As a potential caveat for other centers, the high rate of anesthesia without airway manipulation at our center may contribute to our low rate of respiratory events.

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

Purpose

The aim of this study was to explore the associated risk factors of perioperative respiratory adverse events (PRAEs) in children undergoing airway surgery and establish and validate a nomogram prediction model for PRAEs.

Patients and Methods

This study involved 709 children undergoing airway surgery between November 2020 and July 2021, aged ≤18 years in the affiliated hospital of Xuzhou Medical University. They were divided into training (70%; n = 496) and validation (30%; n = 213) cohorts. The least absolute shrinkage and selection operator (LASSO) was used to develop a risk nomogram model. Concordance index values, calibration plot, decision curve analysis, and the area under the curve (AUC) were examined.

Results

PRAEs were found in 226 of 496 patients (45.6%) and 88 of 213 patients (41.3%) in the training and validation cohorts, respectively. The perioperative risk factors associated with PRAEs were age, obesity, degree of upper respiratory tract infection, premedication, and passive smoking. The risk nomogram model showed good discrimination power, and the AUC generated to predict survival in the training cohort was 0.760 (95% confidence interval, 0.695–0.875). In the validation cohort, the AUC of survival predictions was 0.802 (95% confidence interval, 0.797–0.895). Calibration plots and decision curve analysis showed good model performance in both datasets. The sensitivity and specificity of the risk nomogram model were calculated, and the result showed the sensitivity of 69.5% and 64.8% and specificity of 73.3% and 81.6% for the training and validation cohorts, respectively.

Conclusion

The present study showed the proposed nomogram achieved an optimal prediction of PRAEs in patients undergoing airway surgery, which can provide a certain reference value for predicting the high-risk population of perioperative respiratory adverse events and can lead to reasonable preventive and treatment measures.

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.