ArticlePediatric research2023
Cardiorespiratory signature of neonatal sepsis: development and validation of prediction models in 3 NICUs.
Article in Pediatric research, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers, 1 of them a synthesis that pooled it.
What it found
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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
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Who cites it
22 citing papers in PubMed, 1 synthesis or guideline pooled it, 30 citations in OpenAlex.
- Opportunities and Challenges of Using Artificial Intelligence in Predicting Clinical Outcomes and Length of Stay in Neonatal Intensive Care Units: Systematic Review.Journal of medical Internet research · 2025Pooled it
- The developing relationship between circadian rhythm and heart rate variability in premature infants.Clinical autonomic research : official journal of the Clinical Autonomic Research Society · 2026Article
- Cardiorespiratory signatures of necrotizing enterocolitis: a 4-NICU study of very low birth weight infants.Pediatric research · 2026Article
- Predicting pulmonary hemorrhage in very low birth weight infants.Pediatric research · 2026Article
- A machine learning system enables just-in-time risk-stratified sepsis evaluations in the neonatal intensive care unit.Journal of perinatology : official journal of the California Perinatal Association · 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
- Vital signs, demographics, and clinical events for low-birth-weight infants from four intensive care units.medRxiv : the preprint server for health sciences · 2026Article
- Modeling heart rate patterns to quantify neonatal opioid withdrawal syndrome.Pediatric research · 2026Article
- Toward governance of artificial intelligence in pediatric healthcare.NPJ digital medicine · 2025Review
- Comparing machine learning techniques for neonatal mortality prediction: insights from a modeling competition.Pediatric research · 2025Article
- Clinical correlates of a high cardiorespiratory risk score for very low birth weight infants.Pediatric research · 2025Article
- Aligning prediction models with clinical information needs: infant sepsis case study.JAMIA open · 2025Article
- Machine Learning Models as Early Warning Systems for Neonatal Infection.Clinics in perinatology · 2025Review
- Heart rate analysis in neonatal sepsis: a complex equation.Pediatric research · 2025Article
- Exploring Computational Techniques in Preprocessing Neonatal Physiological Signals for Detecting Adverse Outcomes: Scoping Review.Interactive journal of medical research · 2024Article
- Apnea, Intermittent Hypoxemia, and Bradycardia Events Predict Late-Onset Sepsis in Infants Born Extremely Preterm.The Journal of pediatrics · 2024Observational
- Highly comparative time series analysis of oxygen saturation and heart rate to predict respiratory outcomes in extremely preterm infants.Physiological measurement · 2024Observational
- Editorial: Neonatal sepsis: current insights and challenges.Frontiers in pediatrics · 2024Article
- Transforming neonatal care with artificial intelligence: challenges, ethical consideration, and opportunities.Journal of perinatology : official journal of the California Perinatal Association · 2024Review
Corrections and comments
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Authors and funding
12 authors at 3 institutions in 1 country.
Funding
Abstract
backgroundHeart rate characteristics aid early detection of late-onset sepsis (LOS), but respiratory data contain additional signatures of illness due to infection. Predictive models using cardiorespiratory data may improve early sepsis detection. We hypothesized that heart rate (HR) and oxygenation (SpO
methodsWe analyzed cardiorespiratory data from very low birth weight (VLBW, <1500 g) infants admitted to three NICUs. We developed and externally validated four machine learning models to predict LOS using features calculated every 10 m: mean, standard deviation, skewness, kurtosis of HR and SpO
resultsPerformance, feature importance, and calibration were similar among modeling methods. All models had favorable external validation performance. The HR-SpO
conclusionsCardiorespiratory signatures detect LOS in VLBW infants at 3 NICUs. Demographics risk-stratify, but predictive modeling with both HR and SpO IMPACT: Heart rate characteristics aid early detection of late-onset sepsis, but respiratory data contain signatures of illness due to infection. Predictive models using both heart rate and respiratory data may improve early sepsis detection. A cardiorespiratory early warning score, analyzing heart rate from electrocardiogram or pulse oximetry with SpO
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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.