Development and validation of a postoperative delirium prediction model for pediatric patients: A prospective, observational, single-center study

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.

Association of preoperative neutrophil-lymphocyte ratios with the emergence delirium in pediatric patients after tonsillectomy and adenoidectomy: an observational prospective study

PURPOSE

The study aimed to investigate potential risk factors for emergence delirium (ED) in pediatric patients after tonsillectomy and adenoidectomy (T&A).

METHODS

This prospective, single-center observational study enrolled children aged 3-7 years who underwent T&A under general anesthesia. ED was assessed according to DSM-IV or V criteria. Receiver operating characteristic curve analysis was performed to evaluate the predicative and cut-off values of risk factors, including age, preoperative anxiety level, postoperative pain and neutrophil-lymphocyte ratio (NLR) for ED. Univariate and multivariate logistic regression analyses were performed to investigate risk factors for ED.

RESULTS

94 pediatric patients who underwent T&A were enrolled and 19 developed ED (an incidence of 25.3%). Receiver operating characteristic analysis indicated that preoperative NLR was a significant predictor of ED with a cut-off value of 0.8719 and an area under the curve (AUC) of 0.671 (95% confidence interval (CI) 0.546-0.796, P = 0.022). Preoperative NLR (< 0.8719) and postoperative pain were independent risk factors associated with ED (odds ratio: 0.168, 95% CI 0.033-0.858, P = 0.032; odds ratio: 7.298, 95% CI 1.563-34.083, P = 0.011) according to multivariate logistic regression analysis.

CONCLUSIONS

Preoperative NLR level and postoperative pain were independent risk factors for ED in pediatric patients undergoing T&A.

Predictors of postoperative delirium in paediatric patients undergoing surgery under general anaesthesia at Amhara Regional State Tertiary Hospitals: a multicenter prospective study

Introduction

Postoperative delirium in paediatric patients is a recognised issue. Nevertheless, in low- and middle-income nations, researchers have had luck in determining its extent and predictors. Identifying predictors of postoperative delirium in paediatric patients having general anaesthesia at Tertiary Hospitals in Ethiopia was the aim of this study.

Methods

A multicenter, prospective follow up study was conducted from April 15 to June 15, 2023 at the study settings. During the study period a total of 424 paediatric surgical patients treated under general anaesthesia in all study locations, ranging in age from birth to sixteen were candidates for this study. Charts and direct observation of patient's with assessment tool [Cornell Assessment of Pediatric Delirium (CAPD)] were used from each available patient. Binary logistic regression analysis was performed to determine predictors of postoperative delirium in paediatric patients undergoing surgery under general anaesthesia.

Results

Postoperative delirium occurred in 160 of the 404 paediatric patients who underwent surgery under general anaesthesia. Ophthalmic surgery, corticosteroid use, anticholinergic use, severe postoperative pain, and preoperative anxiety were found to be predictors of postoperative delirium; whereas, sedative medication premedication and paracetamol used for analgesia were found to be protective against postoperative delirium.

Inference and recommendation

The postoperative delirium in paediatric patients undergoing surgery under general anaesthesia was higher compared to developed countries. Ophthalmic surgery, corticosteroids, anticholinergic medications, postoperative pain, and preoperative anxiety were found to be predictors. The impact of postoperative delirium might be lessened by concentrating on its screening and factor control.

Machine learning-based prediction of delirium 24 h after pediatric intensive care unit admission in critically ill children: A prospective cohort study

BACKGROUND

Accurately identifying patients at high risk of delirium is vital for timely preventive intervention measures. Approaches for identifying the risk of developing delirium among critically ill children are not well researched.

OBJECTIVE

To develop and validate machine learning-based models for predicting delirium among critically ill children 24 h after pediatric intensive care unit (PICU) admission.

DESIGN

A prospective cohort study.

SETTING

A large academic medical center with a 57-bed PICU in southwestern China from November 2019 to February 2022.

PARTICIPANTS

One thousand five hundred and seventy-six critically ill children requiring PICU stay over 24 h.

METHODS

Five machine learning algorithms were employed. Delirium was screened by bedside nurses twice a day using the Cornell Assessment of Pediatric Delirium. Twenty-four clinical features from medical and nursing records during hospitalization were used to inform the models. Model performance was assessed according to numerous learning metrics, including the area under the receiver operating characteristic curve (AUC).

RESULTS

Of the 1576 enrolled patients, 929 (58.9 %) were boys, and the age ranged from 28 days to 15 years with a median age of 12 months (IQR 3 to 60 months). Among them, 1126 patients were assigned to the training cohort, and 450 were assigned to the validation cohort. The AUCs ranged from 0.763 to 0.805 for the five models, among which the eXtreme Gradient Boosting (XGB) model performed best, achieving an AUC of 0.805 (95 % CI, 0.759-0.851), with 0.798 (95 % CI, 0.758-0.834) accuracy, 0.902 sensitivity, 0.839 positive predictive value, 0.640 F1-score and a Brier score of 0.144. Almost all models showed lower predictive performance in children younger than 24 months than in older children. The logistic regression model also performed well, with an AUC of 0.789 (95 % CI, 0.739, 0.838), just under that of the XGB model, and was subsequently transformed into a nomogram.

CONCLUSIONS

Machine learning-based models can be established and potentially help identify critically ill children who are at high risk of delirium 24 h after PICU admission. The nomogram may be a beneficial management tool for delirium for PICU practitioners at present.

Perioperative neurocognitive disorders: A narrative review focusing on diagnosis, prevention, and treatment

Perioperative neurocognitive disorders (NCDs) refer to neurocognitive abnormalities detected during the perioperative periods, including preexisting cognitive impairment, preoperative delirium, delirium occurring up to 7 days after surgery, delayed neurocognitive recovery, and postoperative NCD. The Diagnostic and Statistical Manual of Mental Disorders‐5th edition (DSM‐5) is the golden standard for diagnosing perioperative NCDs. Given the impracticality of using the DSM‐5 by non‐psychiatric practitioners, many diagnostic tools have been developed and validated for different clinical scenarios. The etiology of perioperative NCDs is multifactorial and includes predisposing and precipitating factors. Identifying these risk factors is conducive to preoperative risk stratification and perioperative risk reduction. Prevention for perioperative NCDs should include avoiding possible contributors and implementing nonpharmacologic and pharmacological interventions. The former generally includes avoiding benzodiazepines, anticholinergics, prolonged liquid fasting, deep anesthesia, cerebral oxygen desaturation, and intraoperative hypothermia. Nonpharmacologic measures include preoperative cognitive prehabilitation, comprehensive geriatric assessment, implementing fast‐track surgery, combined use of regional block, and sleep promotion. Pharmacological measures including dexmedetomidine, nonsteroidal anti‐inflammatory drugs, and acetaminophen are found to have beneficial effects. Nonpharmacological treatments are the first‐line measures for established perioperative NCDs. Pharmacological treatments are still limited to severely agitated or distressed patients.