A Continuous Late-Onset Sepsis Prediction Algorithm for Preterm Infants Using Multi-Channel Physiological Signals From a Patient Monitor

Abstract

The aim of this study is to develop an explainable late-onset sepsis (LOS) prediction algorithm based on continuously measured multi-channel physiological signals that can be applied to a bedside patient monitor for preterm infants in a neonatal intensive care unit (NICU). The study highlights the complementary predictive value of motion information for LOS prediction when combined with cardiorespiratory information. The algorithm uses features that contain information on heart rate variability (HRV), respiration, and motion, based on continuously measured physiological waveforms including electrocardiogram (ECG) and chest impedance (CI). In this study, 127 preterm infants were included, of whom 59 were bloodculture-proven LOS patients and 68 were control patients. Features in 24 hours before the onset of sepsis (for the LOS group), and an age-matched onset time point (for the control group) were extracted and fed into machine learning classifiers together with gestational age (GA) and birth weight. We compared the prediction performance of several well-known classifiers using features extracted from different signal channels (HRV, respiration, and motion) individually as well as their combinations. The prediction performance was evaluated using the area under the receiver-operating-characteristics curve (AUC). The best performance for LOS prediction was achieved by an XGB classifier combining features from all signal channels, with an AUC of 0.88, a positive predictive value of 0.80, and a negative predictive value of 0.83 during the 6 hours preceding LOS onset. This feasibility study demonstrates the complementary predictive value of motion information in addition to cardiorespiratory information for LOS prediction. Furthermore, visualization of how each feature measuredThis research was supported by grant from e/MTIC.

Cardiorespiratory signature of neonatal sepsis: development and validation of prediction models in 3 NICUs

“…Thus, we hypothesize that abnormal patterns in pulse oximetry might add to HR characteristics in early detection of sepsis. While studies in small cohorts have explored this hypothesis 18 – 20 , none have analyzed the added value of continuous SpO 2 data for sepsis prediction in large data sets with external validation. Here, we used data from three tertiary NICUs to develop and validate statistical models combining HR and SpO 2 analytics for sepsis detection in very low birth weight (VLBW, <1500g) infants.…”

Detecting central apneas using multichannel signals in premature infants

“…Using demographic data such as GA, PMA and birth weight could also help in the task of performing apnea detection, since this information has already played an important role in the detection of other conditions occurring to premature infants (Peng et al 2022). However, this information could also hinder direct implementation into clinical patient monitors, since typically this demographic data is not added into the system as it is stored in the electronic medical records but not in the patient monitor.…”

Continuous sensing and quantification of body motion in infants: A systematic review

“…One point worth mentioning is that the detection of periods of reduced motion (e.g. in sepsis detection [ 48 , 49 ]), periodicity or changes in the motion patterns over time (e.g. in seizure detection [ [30] , [31] , [32] ]), startles during apnea [ 45 , 46 ], and motion counts in sleep assessment [ 4 , 12 , 44 ] can usually be acquired from a sensing technology providing 1D output signal where specific features can be extracted (e.g.…”

“…To better characterize the diagnostic information from quantified motion signals, various features based on domain knowledge have been extracted to detect specific pathological statuses such as seizures, cerebral palsy, and sepsis as aforementioned in Refs. [ 30 , 49 ]. However, more studies investigating the association between motion patterns and health conditions in infants are needed to enhance knowledge and insight in this domain, verification by more clinical trials is also needed before implementation in clinical practice.…”

“…Later, Peng et al [ 47 ] compared and evaluated these artifact-based motion quantification methods (signal instability index, low-frequency power) and their combination of three physiological signals (ECG, CI, and PPG) with video-based annotation, showing the combined measure outperformed every single measure for motion detection in infants. Afterwards, this improved motion quantification method was utilized in a more recent study to highlight the complementary predictive value of motion information in relation to cardiorespiratory information in sepsis prediction in preterm infants [ 49 ].…”