Hospital-Associated Venous Thromboembolism in a Pediatric Cardiac ICU: A Multivariable Predictive Algorithm to Identify Children at High Risk

The Pediatric Data Science and Analytics Subgroup of the Pediatric Acute Lung Injury and Sepsis Investigators Network: Use of Supervised Machine Learning Applications in Pediatric Critical Care Medicine Research

OBJECTIVE

Perform a scoping review of supervised machine learning in pediatric critical care to identify published applications, methodologies, and implementation frequency to inform best practices for the development, validation, and reporting of predictive models in pediatric critical care.

DESIGN

Scoping review and expert opinion.

SETTING

We queried CINAHL Plus with Full Text (EBSCO), Cochrane Library (Wiley), Embase (Elsevier), Ovid Medline, and PubMed for articles published between 2000 and 2022 related to machine learning concepts and pediatric critical illness. Articles were excluded if the majority of patients were adults or neonates, if unsupervised machine learning was the primary methodology, or if information related to the development, validation, and/or implementation of the model was not reported. Article selection and data extraction were performed using dual review in the Covidence tool, with discrepancies resolved by consensus.

SUBJECTS

Articles reporting on the development, validation, or implementation of supervised machine learning models in the field of pediatric critical care medicine.

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

Of 5075 identified studies, 141 articles were included. Studies were primarily (57%) performed at a single site. The majority took place in the United States (70%). Most were retrospective observational cohort studies. More than three-quarters of the articles were published between 2018 and 2022. The most common algorithms included logistic regression and random forest. Predicted events were most commonly death, transfer to ICU, and sepsis. Only 14% of articles reported external validation, and only a single model was implemented at publication. Reporting of validation methods, performance assessments, and implementation varied widely. Follow-up with authors suggests that implementation remains uncommon after model publication.

CONCLUSIONS

Publication of supervised machine learning models to address clinical challenges in pediatric critical care medicine has increased dramatically in the last 5 years. While these approaches have the potential to benefit children with critical illness, the literature demonstrates incomplete reporting, absence of external validation, and infrequent clinical implementation.

Management of venous thromboembolism in pediatric patients: Quality assessment of clinical practice guidelines and variations in recommendations

INTRODUCTION

We performed a quality assessment and comparison of clinical practice guidelines (CPGs) for the prevention and treatment of venous thromboembolism (VTE) in pediatric patients and to provide a clinical reference.

MATERIALS AND METHODS

Electronic databases, guideline development organizations, and professional societies were searched to identify CPGs for VTE in pediatric patients between January 1, 2012, and April 7, 2022. The Appraisal of Guidelines Research & Evaluation (AGREE) II instrument was used to evaluate quality. Recommendations for preventing and treating VTE in pediatric patients were extracted via descriptive synthesis.

RESULTS

Six CPGs were included. The median scores (interquartile range [IQR]) for each AGREE II domain were as follows: scope and purpose, 88.89 % (IQR: 8.33 %); stakeholder involvement, 88.89 % (25 %); rigor of development, 67.71 % (24.47 %); clarity and presentation, 88.89 % (0 %); applicability, 50 % (42.71 %); and editorial independence, 66.67 % (50.00 %). In total, 268 key recommendations were extracted, and traditional anticoagulants (heparin and warfarin) remain the standard treatment. However, in recent years direct oral anticoagulants (DOACs) have shown similar efficacy and safety results for the treatment of VTE in children to those reported in adults; therefore, this practice is recommended in recent guidelines.

CONCLUSIONS

Variability exists in the development and reporting of CPGs for VTE in pediatric patients. There may be changes to the recommendations for the prevention and treatment of VTE in pediatrics in the future due to the efficacy of DOACs in children, and recommendations should be revised periodically as new evidence emerges.