Abstract
To seek new signatures of illness in heart rate and oxygen saturation vital signs from Neonatal Intensive Care Unit (NICU) patients, we implemented highly comparative time-series analysis to discover features of all-cause mortality in the next 7 days. We collected 0.5 Hz heart rate and oxygen saturation vital signs of infants in the University of Virginia NICU from 2009 to 2019. We applied 4998 algorithmic operations from 11 mathematical families to random daily 10 min segments from 5957 NICU infants, 205 of whom died. We clustered the results and selected a representative from each, and examined multivariable logistic regression models. 3555 operations were usable; 20 cluster medoids held more than 81% of the information, and a multivariable model had AUC 0.83. New algorithms outperformed others: moving threshold, successive increases, surprise, and random walk. We computed provenance of the computations and constructed a software library with links to the data. We conclude that highly comparative time-series analysis revealed new vital sign measures to identify NICU patients at the highest risk of death in the next week.
Highlights
Monitored vital signs of patients in intensive care units hold untapped information about risk for adverse events and outcomes[1]
From January 2009 to December 2019, 6837 infants were admitted to the UVa Neonatal Intensive Care Unit (NICU), with median gestational age (GA) 35 weeks
We tested the idea that we might improve the current art through a systematic study of our very large set of time series using an exhaustive number of analytical measures
Summary
Monitored vital signs of patients in intensive care units hold untapped information about risk for adverse events and outcomes[1]. The display of a score based on analysis of abnormal heart rate characteristics was shown by our group to reduce sepsis-associated mortality by 40% in preterm infants in the Neonatal Intensive Care Unit (NICU)[2,3,4,5]. The fundamental idea is to extract features from many time series, using many algorithms, most operating with many sets of parameter values. We apply this ensemble to a dataset to determine which algorithms perform best for predicting a specific outcome. Clustering of algorithms can, eventually, simplify this approach for clinical applications[13,14]
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