Construction and validation of an early prediction model of delirium in children after congenital heart surgery

A nomogram for predicting postoperative delirium in pediatric patients following cardiopulmonary bypass: A prospective observational study

OBJECTIVES

To create a nomogram for early delirium detection in pediatric patients following cardiopulmonary bypass.

RESEARCH METHODOLOGY/DESIGN

This prospective, observational study was conducted in the Cardiac Intensive Care Unit at a Children's Hospital, enrolling 501 pediatric patients from February 2022 to January 2023. Perioperative data were systematically collected through the hospital information system. Postoperative delirium was assessed using the Cornell Assessment of Pediatric Delirium (CAPD). For model development, Least Absolute Shrinkage and Selection Operator (LASSO) regression was employed to identify the most relevant predictors. These selected predictors were then incorporated into a multivariable logistic regression model to construct the predictive nomogram. The performance of the model was evaluated by Harrell's concordance index, receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis. External validity of the model was confirmed through the C-index and calibration plots.

RESULTS

Five independent predictors were identified: age, SpO2 levels, lymphocyte count, diuretic use, and midazolam administration, integrated into a predictive nomogram. This nomogram demonstrated strong predictive capacity (AUC 0.816, concordance index 0.815) with good model fit (Hosmer-Lemeshow test p = 0.826) and high accuracy. Decision curve analysis showed a significant net benefit, and external validation confirmed the nomogram's reliability.

CONCLUSIONS

The study successfully developed a precise and effective nomogram for identifying pediatric patients at high risk of post-cardiopulmonary bypass delirium, incorporating age, SpO2 levels, lymphocyte counts, diuretic use, and midazolam medication.

IMPLICATIONS FOR CLINICAL PRACTICE

This nomogram aids early delirium detection and prevention in critically ill children, improving clinical decisions and treatment optimization. It enables precise monitoring and tailored medication strategies, significantly contributes to reducing the incidence of delirium, thereby enhancing the overall quality of patient care.

Risk factors and incidence of postoperative delirium after cardiac surgery in children: a systematic review and meta-analysis

Delirium, a form of acute cerebral dysfunction, is a common complication of postoperative cardiac surgery in children. It is strongly associated with adverse outcomes, including prolonged hospitalization, increased mortality, and cognitive dysfunction. This study aimed to identify risk factors and incidence of delirium after cardiac surgery in children to facilitate early identification of delirium risk and provide a reference for the implementation of effective prevention and management. A systematic literature search was conducted in PubMed, Web of Science, Embase, Cochrane Library, Scopus, CNKI, Sinomed, and Wanfang for studies published in English or Chinese from the inception of each database to November 2023. The PRISMA guidelines were followed in all phases of this systematic review. The Risk of Bias Assessment for Nonrandomized Studies tool was used to assess methodological quality. A total of twelve studies were included in the analysis, with four studies classified as overall low risk of bias, seven studies as moderate risk of bias, and one study as high risk of bias. The studies reported 39 possible predictors of delirium, categorized into four broad groups: intrinsic and parent-related factors, disease-related factors, surgery and treatment-related factors, and clinical scores and laboratory parameters. By conducting qualitative synthesis and quantitative meta-analysis, we identified two definite factors, four possible factors, and 32 unclear factors related to delirium. Definite risk factors included age and mechanical ventilation duration. Possible factors included developmental delay, cyanotic heart disease, cardiopulmonary bypass time, and pain score. With only a few high-quality studies currently available, well-designed and more extensive prospective studies are still needed to investigate the risk factors affecting delirium and explore delirium prevention strategies in high-risk children.

Incidence, associated factors, and outcomes of delirium in critically ill children in china: a prospective cohort study

BACKGROUND

Delirium occurs frequently in critically ill children and has been reported in many countries, but delirium is not well-characterized in China. The aim of this study was to represent the incidence of delirium in critically ill children in China, its associated factors, and the influence of delirium on in-hospital outcomes.

METHODS

This observational prospective cohort study was set up in a large academic medical center with a 57-bed PICU in southwestern China. Critically ill children who required PICU stays over 24 h and were admitted between November 2019 and February 2022 were included in this study. The Cornell Assessment of Pediatric Delirium was used twice daily for delirium evaluation by bedside nurses, and twenty-four clinical features were collected from medical and nursing records during hospitalization.

RESULTS

The incidence of delirium was 26.0% (n = 410/1576). Multivariate analysis revealed that seven independent risk factors including days of mechanical ventilation and physical restraints, admission diagnosis (neurologic disorder), sleep deprivation, use of benzodiazepines and dexmedetomidine, liver failure/liver dysfunction associated with delirium in critically ill children. One potentially protective factor was the watching television /listening to music/playing with toys. Children with delirium had longer lengths of stay in the PICU (median 11 vs. 10 days, p < 0.001) and hospital (median 18 vs. 15 days, p < 0.001) compared to those without delirium. Additionally, the in-hospital mortality rates were 4.63% and 0.77% in patients with and without delirium (p < 0.05).

CONCLUSIONS

Delirium is common in critically ill children in China and related to poor outcomes. Interventional studies are warranted to determine the best practices to reduce delirium exposure in at-risk children.

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.