1353: Early Prediction of Pediatric in-Hospital Cardiac Arrest Using a Tabular Foundation Model

Introduction: In-hospital cardiac arrest (IHCA) is an important cause of morbidity and mortality in children. IHCA prediction to prevent events remains a challenge in the pediatric intensive care unit (PICU). Machine learning algorithms have been used successfully for clinical predictions tasks. An emerging approach is the use of foundation models, pre-trained on unrelated datasets, for this purpose. Tabular Prior-data Fitted Network (TabPFN) is a recently published foundation model designed for tabular data, such as electronic health record data. Our objective was to evaluate the ability of TabPFN to predict IHCA in PICU patients 6 hours after admission compared to traditional machine learning algorithms. Methods: Retrospective cohort study of PICU patients from 1/6/2016-30/5/2022. Features included demographics, vital signs, assessments, laboratory values and medications from the first 6 hours of admission. We compared TabPFN to multiple traditional machine learning algorithms (K-Nearest Neighbor, Random Forest, LightGBM, XGBoost and Tabular Neural Network) for model development. Results: IHCA occurred in 175/14,050 encounters (1.2%). TabPFN had the best performance (AUROC 0.84, AUPRC 0.16) followed by Random Forest (AUROC 0.83, AUPRC 0.12). Negative predictive value was >98.8% for all algorithms, however positive predictive value was low (2.9-5.1%). Conclusions: We have demonstrated a modest ability to identify children at risk of IHCA early in their ICU admission, this may be improved by developing models that re-evaluate risk over time. Though the TabPFN model was not developed using pediatric ICU data, it outperformed the other algorithms highlighting the potential of this approach.