Prospective Development and Validation of a Weight Prediction Tool for Patients With Obesity Undergoing Critical Care Transport Using Width and Arm Circumference.

OBJECTIVE: Obesity rates are rising, affecting health care systems and causing potential delays in critical care transfers. Accurate weights are important for critical care transport to ensure the appropriate transport asset is selected and dispatched in a timely fashion. Inaccurate weight measurements can lead to various unnecessary delays, including not sending appropriate nonbariatric assets, delays due to secondary dispatch, and delays due to systemic overuse of these bariatric capable assets. The primary objective of this study was to develop and validate a weight prediction tool for patients with obesity undergoing critical care transport. The secondary objective was to compare the performance of this model to the Crandall weight prediction tool. METHODS: A prospective observational study was conducted, collecting data from patients transported by Ornge air ambulance between May 2022 and April 2023. Adults weighing >100 kg undergoing interfacility transfers were included. Four predictive models, using height, arm circumference, width, and girth measurements, were evaluated against actual patient weights and analyzed separately for males and females. Model performance was evaluated by mean squared error, mean absolute error (MAE), mean absolute percentage error, R2, and F-statistic. RESULTS: The Ornge model using arm circumference and width demonstrated the highest accuracy and stability in predicting patient weight for both males and females. This model exhibited the lowest MAE of 12.2 kg, was within a margin of 20% error 91.2% of the time, and had an overall false negative error of 6.9%, outperforming all other models. CONCLUSION: The Ornge arm circumference and width-based model offers a reliable method for predicting patient weight in air ambulance settings. Implementing this tool could improve the efficiency and safety of patient transfers by reducing delays caused by inaccurate weight estimations, thereby expediting access to critical care. Further research is recommended to validate these findings in larger and more diverse populations.