Prediction of Late-Onset Sepsis in Preterm Infants Using Monitoring Signals and Machine Learning

Abstract

Objectives: Prediction of late-onset sepsis (onset beyond day 3 of life) in preterm infants, based on multiple patient monitoring signals 24 hours before onset. Design: Continuous high-resolution electrocardiogram and respiration (chest impedance) data from the monitoring signals were extracted and used to create time-interval features representing heart rate variability, respiration, and body motion. For each infant with a blood culture-proven late-onset sepsis, a Cultures, Resuscitation, and Antibiotics Started Here moment was defined. The Cultures, Resuscitation, and Antibiotics Started Here moment served as an anchor point for the prediction analysis. In the group with controls (C), an “equivalent crash moment” was calculated as anchor point, based on comparable gestational and postnatal age. Three common machine learning approaches (logistic regressor, naive Bayes, and nearest mean classifier) were used to binary classify samples of late-onset sepsis from C. For training and evaluation of the three classifiers, a leave-k-subjects-out cross-validation was used. Setting: Level III neonatal ICU. Patients: The patient population consisted of 32 premature infants with sepsis and 32 age-matched control patients. Interventions: No interventions were performed. Measurements and Main Results: For the interval features representing heart rate variability, respiration, and body motion, differences between late-onset sepsis and C were visible up to 5 hours preceding the Cultures, Resuscitation, and Antibiotics Started Here moment. Using a combination of all features, classification of late-onset sepsis and C showed a mean accuracy of 0.79 ± 0.12 and mean precision rate of 0.82 ± 0.18 3 hours before the onset of sepsis. Conclusions: Information from routine patient monitoring can be used to predict sepsis. Specifically, this study shows that a combination of electrocardiogram-based, respiration-based, and motion-based features enables the prediction of late-onset sepsis hours before the clinical crash moment.

A Continuous Late-Onset Sepsis Prediction Algorithm for Preterm Infants using Multimodal Physiological Signals from a Patient Monitor

“…This diagram consists of three blocks including physiological signal processing, feature extraction, and prediction evaluation. Similar to our previous studies [15], [16], we applied a peak detection algorithm to detect R-peaks in ECG waveforms of all patients [21], followed by calculating R-R intervals (RRI), resulting in the HRV signal (i.e. RRI time series).…”

“…GA, BW, and PMA) between two groups. Details of characteristics can be found in our previous study [16]. Unless stated differently, we performed all the following machine learning experiments using both the full dataset and the matched subdataset.…”

“…Later studies also show the added value of other vital signs, such as respiration [4] and cross-correlation between different vitals [14] on the prediction performance, though the evaluation of its effectiveness is still needed [4], [7]. Only a few studies report using continuous measurement of body motion in CDSA to automatically detect lethargy preceding the onset of LOS [15], [16] since there is no accepted standard for measuring and quantifying motion patterns continuously.…”

Application and potential of artificial intelligence in neonatal medicine

“…This can lead to early recognition of disease states, sometimes hours before they would become clinically apparent [10]. ML algorithms can outperform HCPs in fulfilling this task, recognising the disease and supporting clinical decision making [7,[10][11][12].…”

“…Since LOS is often accompanied by changes in the infant's vital signs [10], it serves as an ideal use case for ML model development. AI algorithms can help recognise LOS before sepsis is clinically apparent [11]. The efficiency of these models is often evaluated on a small snapshot of data preceding sepsis diagnosis to identify the disease at a certain timepoint before the clinical sepsis call was made.…”