Predicting Infection in Very Preterm Infants: A Study Protocol

Neonatal 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.

This article presents the protocol for the Predictive Analysis Using Temperature and Heart Rate Study.

This 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.

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.