To identify the factors that predict mortality post-transfer and develop a comprehensive mortality prediction model capable of supporting pre-transfer decision making.
Electronic health record data from the Medical Transport Data Repository of a large health system hospital in Northeast Ohio that consists of a main campus and 11 affiliated medical centers. We retrospectively analyzed patient data from the referring hospital encounter prior to interhospital transfer. All patient data including diagnoses, laboratory results, medication, and medical and social history were analyzed to predict in-hospital mortality post-transfer. We employed a multi-method approach including logistic regression, gradient boosting, and multiple correspondence analysis to identify significant predictors of mortality as well as variables that are clinically useful to inform clinical decision support development. We identified all patients aged 21 and older that underwent critical care transfer in the health system between 2010 and 2017.
We found that age, laboratory results (albumin, INR, platelets, BUN, leukocyte, hemoglobin, glucose), vital signs (temperature, respirations, pulse, systolic blood pressure, pulse oximetry), and ventilator usage are the most predictive variables of post-interhospital transfer mortality. Using structured data from the EHR we achieved the same performance as APACHE IV within our health system (0.85 vs. 0.85). Lastly, mode of transport alone was not a significant predictor for the general population in any of the outcome models.
Our findings provide a foundation for the development of decision support tools to guide transport referrals and identified the need for further inquiry to discern the role of mode of transport to enable future inclusion in decision support approaches. Further inquiry is needed to identify factors that differentiate patients not triaged as time-sensitive transfers but still require helicopter intervention to maintain or improve post-interhospital transfer morbidity and mortality.