Machine Learning-Based Systems for the Anticipation of Adverse Events After Pediatric Cardiac Surgery

Abstract

Pediatric congenital heart disease (CHD) patients are at higher risk of postoperative complications and clinical deterioration either due to their underlying pathology or due to the cardiac surgery, contributing significantly to mortality, morbidity, hospital and family costs, and poor quality of life. In current clinical practice, clinical deterioration is detected, in most of the cases, when it has already occurred. Several early warning scores (EWS) have been proposed to assess children at risk of clinical deterioration using vital signs and risk indicators, in order to intervene in a timely manner to reduce the impact of deterioration and risk of death among children. However, EWS are based on measurements performed at a single time point without incorporating trends nor providing information about patient's risk trajectory. Moreover, some of these measurements rely on subjective assessment making them susceptible to different interpretations. All these limitations could explain why the implementation of EWS in high-resource settings failed to show a significant decrease in hospital mortality. By means of machine learning (ML) based algorithms we could integrate heterogeneous and complex data to predict patient's risk of deterioration. In this perspective article, we provide a brief overview of the potential of ML technologies to improve the identification of pediatric CHD patients at high-risk for clinical deterioration after cardiac surgery, and present the CORTEX traffic light, a ML-based predictive system that Sant Joan de Déu Barcelona Children's Hospital is implementing, as an illustration of the application of an ML-based risk stratification system in a relevant hospital setting.

Artificial Intelligence in Pediatric Cardiology: A Scoping Review

“…The subsequent post-operative management of CHDs is imperative for maintaining the structural fluid dynamics and thus the prognosis. This could be done seamlessly with the help of recurrent neural networks based on ML models and deep learning models, allowing for an integrated system that can enhance survival outcomes in pediatric patients [ 101 ].…”

Applying machine learning to consumer wearable data for the early detection of complications after pediatric appendectomy

“…This study sought to generate clinically relevant ML models for the detection of abnormal recovery and complications after appendectomy in children. Although there have been efforts in adult patients after surgery, little has been done in children 60 , 61 . The ability of a model to detect up to 83% of abnormal events up to 2 days before they are reported by the caregiver has the potential to dramatically improve patient outcomes.…”

How Will Artificial Intelligence Shape the Future of Decision-Making in Congenital Heart Disease?

“…Furthermore, AI-driven outcome prediction models analyse preoperative patient data, surgical parameters and postoperative outcomes to identify predictive factors associated with surgical success or complications. Moreover, ML algorithms can assist surgeons in preoperative planning and risk stratification, enhancing patient selection and optimising surgical outcomes [15,29].…”

“…For instance, a study by P.G. Canadilla et al, employed ML techniques to predict the risk of adverse events in paediatric patients undergoing cardiac surgery, achieving high accuracy and specificity [15]. Thus, by incorporating clinical data, imaging findings and genetic markers, ML models can enhance risk stratification and aid in early intervention strategies.…”