• Danny Eytan, Anusha Jegatheeswaran, Mjaye L Mazwi, Azadeh Assadi, Andrew J Goodwin, Robert W Greer, Sebastian D Goodfellow, and Peter C Laussen.
• Department of Critical Care Medicine, The Hospital for Sick Children, Toronto, ON, Canada.
• Pediatr Crit Care Me. 2019 Jul 1; 20 (7): e333-e341.

ObjectivesPhysiologic signals are typically measured continuously in the critical care unit, but only recorded at intermittent time intervals in the patient health record. Low frequency data collection may not accurately reflect the variability and complexity of these signals or the patient's clinical state. We aimed to characterize how increasing the temporal window size of observation from seconds to hours modifies the measured variability and complexity of basic vital signs.DesignRetrospective analysis of signal data acquired between April 1, 2013, and September 30, 2015.SettingCritical care unit at The Hospital for Sick Children, Toronto.PatientsSeven hundred forty-seven patients less than or equal to 18 years old (63,814,869 data values), within seven diagnostic/surgical groups.InterventionsNone.Measurements And Main ResultsMeasures of variability (SD and the absolute differences) and signal complexity (multiscale sample entropy and detrended fluctuation analysis [expressed as the scaling component α]) were calculated for systolic blood pressure, heart rate, and oxygen saturation. The variability of all vital signs increases as the window size increases from seconds to hours at the patient and diagnostic/surgical group level. Significant differences in the magnitude of variability for all time scales within and between groups was demonstrated (p < 0.0001). Variability correlated negatively with patient age for heart rate and oxygen saturation, but positively with systolic blood pressure. Changes in variability and complexity of heart rate and systolic blood pressure from time of admission to discharge were found.ConclusionsIn critically ill children, the temporal variability of physiologic signals supports higher frequency data capture, and this variability should be accounted for in models of patient state estimation.

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