ArticleCritical care explorations2021
Prediction of Late-Onset Sepsis in Preterm Infants Using Monitoring Signals and Machine Learning.
Article in Critical care explorations, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
23 citing papers in PubMed, 1 synthesis or guideline pooled it, 60 citations in OpenAlex.
- Heart Rate Characteristics Monitoring for Late-Onset Sepsis in Preterm Infants: A Systematic Review.Neonatology · 2023Pooled it
- Machine learning models for predicting neonatal bacterial infections: a retrospective cohort study.European journal of pediatrics · 2026Article
- On the usage of artificial intelligence for identifying main attributes and predicting neonatal sepsis.Scientific reports · 2026Article
- Neonatal sepsis and cardiovascular dysfunction II: assessment.Pediatric research · 2026Review
- AI models in clinical neonatology: a review of modeling approaches and a consensus proposal for standardized reporting of model performance.Pediatric research · 2025Review
- Predictive model development for premature infant extubation outcomes: development and analysis.Pediatric research · 2025Article
- Machine Learning Models as Early Warning Systems for Neonatal Infection.Clinics in perinatology · 2025Review
- Continuous non-contact monitoring of neonatal activity.BMC pediatrics · 2025Article
- Enhancing bronchopulmonary dysplasia prediction in preterm infants using artificial intelligence and multimodal data integration.Frontiers in pediatrics · 2025Review
- Detection of neurologic changes in critically ill infants using deep learning on video data: a retrospective single center cohort study.EClinicalMedicine · 2024Article
- Exploring Computational Techniques in Preprocessing Neonatal Physiological Signals for Detecting Adverse Outcomes: Scoping Review.Interactive journal of medical research · 2024Article
- Accurate prediction of neurologic changes in critically ill infants using pose AI.medRxiv : the preprint server for health sciences · 2024Article
- Artificial Intelligence-Based Diagnostic Support System for Patent Ductus Arteriosus in Premature Infants.Journal of clinical medicine · 2024Article
- Transforming neonatal care with artificial intelligence: challenges, ethical consideration, and opportunities.Journal of perinatology : official journal of the California Perinatal Association · 2024Review
- Heart rate and oxygen saturation patterns in very low birth weight infants with early onset sepsis and histologic chorioamnionitis.Journal of neonatal-perinatal medicine · 2024Article
- The use of probabilistic graphical models in pediatric sepsis: a feasibility and scoping review.Translational pediatrics · 2023Article
- The past, current, and future of neonatal intensive care units with artificial intelligence: a systematic review.NPJ digital medicine · 2023Review
- Review
- Machine learning applications on neonatal sepsis treatment: a scoping review.BMC infectious diseases · 2023Article
- Cardiorespiratory signature of neonatal sepsis: development and validation of prediction models in 3 NICUs.Pediatric research · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesPrediction of late-onset sepsis (onset beyond day 3 of life) in preterm infants, based on multiple patient monitoring signals 24 hours before onset.
designContinuous 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.
settingLevel III neonatal ICU. PATIENTS: The patient population consisted of 32 premature infants with sepsis and 32 age-matched control patients.
interventionsNo interventions were performed. MEASUREMENTS AND MAIN
resultsFor 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.
conclusionsInformation 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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.