ArticleNursing research
Predicting Infection in Very Preterm Infants: A Study Protocol.
Article in Nursing research. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed, 7 citations in OpenAlex.
- Evaluating a blood warming device for packed red blood cell transfusions to decrease hypothermia in very preterm infants: A randomised control trial protocol.Transfusion medicine (Oxford, England) · 2025Article
- Neonatal autonomic regulation as a predictor of autism symptoms in very preterm infants.Journal of perinatology : official journal of the California Perinatal Association · 2025Article
- Prenatal and Postnatal Disparities in Very-Preterm Infants in a Study of Infections between 2018-2023 in Southeastern US.Tropical medicine and infectious disease · 2024Article
- Artificial and human intelligence for early identification of neonatal sepsis.Pediatric research · 2023Review
- Vital signs as physiomarkers of neonatal sepsis.Pediatric research · 2022Review
- Conducting Neonatal Intensive Care Unit Research During a Pandemic: Challenges and Lessons Learned.Nursing researchArticle
Corrections and comments
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Authors and funding
8 authors at 5 institutions in 1 country.
Funding
Abstract
backgroundNeonatal sepsis causes morbidity and mortality in preterm infants. Clinicians need a predictive tool for the onset of neonatal infection to expedite treatment and prevent morbidity. Abnormal thermal gradients, a central-peripheral temperature difference (CPtd) of >2°C or <0°C, and elevated heart rate characteristic (HRC) scores are associated with infection.
objectiveThis article presents the protocol for the Predictive Analysis Using Temperature and Heart Rate Study.
methodsThis observational trial will enroll 440 very preterm infants to measure abdominal temperature and foot temperature every minute and HRC scores hourly for 28 days to compare infection data. Time with abnormal thermal gradients (Model 1) and elevated HRC scores (Model 2) will be compared to the onset of infections. For data analysis, CPtd (abdominal temperature - foot temperature) will be investigated as two derived variables, high CPtd (number/percentage of minutes with CPtd of >2°C) and low CPtd (number/percentage of minutes with CPtd of <0°C). In the infant-level model, the outcome yi will be an indicator of whether the infant was diagnosed with an infection in the first 28 days of life, and the high CPtd and low CPtd variables will be the average over the entire observation period, logit(yi) = β0 + xiβ1 + ziγ. For the day-level model, the outcome yit will be an indicator of whether the ith infant was diagnosed with an infection on the tth day from t = 4 through t = 28 or the day that infection is diagnosed (25 possible repeated measures), logit(yit) = β0 + xitβ1 + zitγ. It will be determined whether a model with only high CPtd or only low CPtd is superior in predicting infection. Also, the correlation of abnormal HRC scores with high CPtd and low CPtd values will be assessed. DISCUSSION: Study results will inform the design of an interventional study using temperatures and/or heart rate as a predictive tool to alert clinicians of cardiac and autonomic instability present with infection.
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