Title: Predicting Individual Physiologically Acceptable States for Discharge from a Pediatric Intensive Care Unit,
Abstract: Objective: Predict patient-specific vitals deemed medically acceptable for
discharge from a pediatric intensive care unit (ICU). Design: The means of each
patient's hr, sbp and dbp measurements between their medical and physical
discharge from the ICU were computed as a proxy for their physiologically
acceptable state space (PASS) for successful ICU discharge. These individual
PASS values were compared via root mean squared error (rMSE) to population
age-normal vitals, a polynomial regression through the PASS values of a
Pediatric ICU (PICU) population and predictions from two recurrent neural
network models designed to predict personalized PASS within the first twelve
hours following ICU admission. Setting: PICU at Children's Hospital Los Angeles
(CHLA). Patients: 6,899 PICU episodes (5,464 patients) collected between 2009
and 2016. Interventions: None. Measurements: Each episode data contained 375
variables representing vitals, labs, interventions, and drugs. They also
included a time indicator for PICU medical discharge and physical discharge.
Main Results: The rMSEs between individual PASS values and population
age-normals (hr: 25.9 bpm, sbp: 13.4 mmHg, dbp: 13.0 mmHg) were larger than the
rMSEs corresponding to the polynomial regression (hr: 19.1 bpm, sbp: 12.3 mmHg,
dbp: 10.8 mmHg). The rMSEs from the best performing RNN model were the lowest
(hr: 16.4 bpm; sbp: 9.9 mmHg, dbp: 9.0 mmHg). Conclusion: PICU patients are a
unique subset of the general population, and general age-normal vitals may not
be suitable as target values indicating physiologic stability at discharge.
Age-normal vitals that were specifically derived from the medical-to-physical
discharge window of ICU patients may be more appropriate targets for
'acceptable' physiologic state for critical care patients. Going beyond simple
age bins, an RNN model can provide more personalized target values.