Prevalence of PErioperAtive CHildhood obesitY in children undergoing general anaesthesia in the UK: a prospective, multicentre, observational cohort study

Burton, Zoë; Lewis, Robert J.; Bennett, Tom; McLernon, David J.; Engelhardt, Thomas; Brooks, Peter; Edwards, Mark

doi:10.1016/j.bja.2021.07.034

Cited by 13 publications

(13 citation statements)

References 39 publications

(42 reference statements)

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“…This prohibited the use of BMI in model development, despite obesity being a common and known risk factor for PAEs. Obese and severely obese children are also frequently under‐classified in the ASA‐PS system 31 and may inappropriately be allocated to a non‐tertiary center for surgery. Additional examples of more granular data that may improve the performance of a predictive model include details of acute symptoms, duration of fasting, liver and pancreatic diseases, diabetes, congenital heart disease, kidney injury, immunosuppression, and preoperative use of corticosteroids.…”

Section: Discussionmentioning

confidence: 99%

A machine‐learning approach for decision support and risk stratification of pediatric perioperative patients based on the APRICOT dataset

Gray

Ahumada

Rehman

et al. 2023

Pediatric Anesthesia

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Background Pediatric anesthesia has evolved to a high level of patient safety, yet a small chance remains for serious perioperative complications, even in those traditionally considered at low risk. In practice, prediction of at‐risk patients currently relies on the American Society of Anesthesiologists Physical Status (ASA‐PS) score, despite reported inconsistencies with this method. Aims The goal of this study was to develop predictive models that can classify children as low risk for anesthesia at the time of surgical booking and after anesthetic assessment on the procedure day. Methods Our dataset was derived from APRICOT, a prospective observational cohort study conducted by 261 European institutions in 2014 and 2015. We included only the first procedure, ASA‐PS classification I to III, and perioperative adverse events not classified as drug errors, reducing the total number of records to 30 325 with an adverse event rate of 4.43%. From this dataset, a stratified train:test split of 70:30 was used to develop predictive machine learning algorithms that could identify children in ASA‐PS class I to III at low risk for severe perioperative critical events that included respiratory, cardiac, allergic, and neurological complications. Results Our selected models achieved accuracies of >0.9, areas under the receiver operating curve of 0.6–0.7, and negative predictive values >95%. Gradient boosting models were the best performing for both the booking phase and the day‐of‐surgery phase. Conclusions This work demonstrates that prediction of patients at low risk of critical PAEs can be made on an individual, rather than population‐based, level by using machine learning. Our approach yielded two models that accommodate wide clinical variability and, with further development, are potentially generalizable to many surgical centers.

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Section: Discussionmentioning

confidence: 99%

A machine‐learning approach for decision support and risk stratification of pediatric perioperative patients based on the APRICOT dataset

Gray

Ahumada

Rehman

et al. 2023

Pediatric Anesthesia

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“…We will not be addressing large multicenter observational studies with direct observation of clinical practice that are typically carried out for a prespecified short period of time, or studies utilizing datasets collected for other purposes, such as those utilizing national databases. There are notable examples of large observational studies from Europe, the United Kingdom, Denmark, and South Africa that have yielded important insights, but their inherent challenges are different than those addressed here 1–5 . We will describe some limitations and problems with long‐term sustainability of registries and propose a new model to overcome these challenges.…”

Section: Perioperative Pediatric Registriesmentioning

confidence: 99%

“…There are notable examples of large observational studies from Europe, the United Kingdom, Denmark, and South Africa that have yielded important insights, but their inherent challenges are different than those addressed here. [1][2][3][4][5] We will…”

mentioning

confidence: 99%

Registries in pediatric anesthesiology: A brief history and a new way forward

Nelson,

Wang,

Matava

et al. 2023

Pediatric Anesthesia

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Clinical registries are multicenter prospective observational datasets that have been used to examine current perioperative practices in pediatric anesthesia. These datasets have proven useful in quantifying the incidence of rare adverse outcomes. Data from registries can highlight associations between severe patient safety events and patient and procedure‐related risk factors. Registries are an effective tool to delineate practices and outcomes in niche patient populations. They have been used to quantify uncommon complications of medications and procedures. Registries can be used to generate knowledge and to support quality improvement. Multicenter engagement can promote best clinical practices and foster professional networks. Registries are limited by their observational nature, which entails a lack of randomization as well as selection and treatment bias. The maintenance of registries over time can be challenging due to difficulties in modifying the included variables, collaborator fatigue, and continued outlay of resources to maintain the database and onboard new sites. These latter issues can lead to decreased data quality. In this article, we discuss key insights from several pediatric anesthesia registries and propose a new type of registry that addresses some shortcomings of the current paradigm.

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“…A number of studies in the anesthesia literature suggest that children with obesity are more likely to experience intraoperative adverse events, including airway obstruction and oxygen desaturations, than their healthyweighted peers. [8][9][10][11][12][13][14][15] It remains unclear whether these observations extend to all surgical interventions, or whether there are specific BMI limits above which standard pediatric anesthesia protocols should be adapted. In contrast, there are several recent studies that suggest the majority of children with obesity complete surgical treatment without experiencing adverse events.…”

Section: Introductionmentioning

confidence: 99%

Body Mass Index (BMI) Impact on Anesthetic Safety among Adolescents Undergoing Bilateral Reduction Mammaplasty

Cappitelli

McNamara

Nuzzi

et al. 2022

Plastic and Reconstructive Surgery - Global Open

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Introduction: Obesity is common in adolescents with macromastia seeking surgery, prompting concerns over anesthesia-related complications due to obesity. This may lead to weight-based surgical policies, despite limited research. This study’s purpose is to examine the impact of obesity status on perioperative anesthetic-related adverse events/surgical complications in adolescents/young adults undergoing bilateral reduction mammaplasty. Methods: A medical record query was performed to retrospectively identify patients who underwent bilateral reduction mammaplasty at our institution between January 2021 and December 2021. Patient demographics and clinical/surgical data were obtained from medical records. Pearson’s chi-square, Fisher exact, and Median tests were used to examine associations between body mass index (BMI) category and clinical/demographic data. Results: Two hundred patients were included in analyses, with a median age at surgery of 18.0 years. The majority of patients were obese (63.5%, n = 127), and none were underweight. The most common comorbidity was asthma (27.0%, n = 54). There were no anesthetic-related intraoperative complications or delayed waking. The following did not significantly differ by BMI category: proportion of patients who experienced oxygen desaturation (pre-, intra-, or postoperatively), frequency of postoperative complications, and length of postanesthesia care unit and total hospital stays (P > 0.05, all). Conclusions: While institutions may implement weight-based surgical policies due to anesthesia-related complication concerns in patients with obesity, our study showed that BMI category did not significantly impact anesthetic-related adverse events/surgical complications in our sample. This suggests that such policies are not warranted in all situations, and that decisions regarding their necessity be data-driven.

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Prevalence of PErioperAtive CHildhood obesitY in children undergoing general anaesthesia in the UK: a prospective, multicentre, observational cohort study

Cited by 13 publications

References 39 publications

A machine‐learning approach for decision support and risk stratification of pediatric perioperative patients based on the APRICOT dataset

A machine‐learning approach for decision support and risk stratification of pediatric perioperative patients based on the APRICOT dataset

Registries in pediatric anesthesiology: A brief history and a new way forward

Body Mass Index (BMI) Impact on Anesthetic Safety among Adolescents Undergoing Bilateral Reduction Mammaplasty

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