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Objective: Predict individual septic children's personalized physiologicresponses to vasoactive titrations by training a Recurrent Neural Network (RNN)using EMR data.  Materials and Methods: This study retrospectively analyzed EMR of patientsadmitted to a pediatric ICU from 2009 to 2017. Data included charted timeseries vitals, labs, drugs, and interventions of children with septic shocktreated with dopamine, epinephrine, or norepinephrine. A RNN was trained topredict responses in heart rate (HR), systolic blood pressure (SBP), diastolicblood pressure (DBP) and mean arterial pressure (MAP) to 8,640 titrationsduring 652 septic episodes and evaluated on a holdout set of 3,883 titrationsduring 254 episodes. A linear regression model using titration data as its soleinput was also developed and compared to the RNN model. Evaluation methodsincluded the correlation coefficient between actual physiologic responses andRNN predictions, mean absolute error (MAE), and area under the receiveroperating characteristic curve (AUC).  Results: The actual physiologic responses displayed significant variabilityand were more accurately predicted by the RNN model than by titration alone(r=0.20 vs r=0.05, p<0.01). The RNN showed MAE and AUC improvements over thelinear model. The RNN's MAEs associated with dopamine and epinephrine were 1-3%lower than the linear regression model MAE for HR, SBP, DBP, and MAP. Acrossall vitals vasoactives, the RNN achieved 1-19% AUC improvement over the linearmodel.  Conclusion: This initial attempt in pediatric critical care to predictindividual physiologic responses to vasoactive dose changes in children withseptic shock demonstrated an RNN model showed some improvement over a linearmodel. While not yet clinically applicable, further development may assistclinical administration of vasoactive medications in children with septicshock.

Predicting Individual Responses to Vasoactive Medications in Children with Septic Shock