Scott P Oltman1,2, Elizabeth E Rogers3, Rebecca J Baer4,5, Elizabeth A Jasper6, James G Anderson3, Martina A Steurer7,3, Matthew S Pantell3, Mark A Petersen3, J Colin Partridge3, Deborah Karasek4,8, Kharah M Ross9, Sky K Feuer4,8, Linda S Franck4,10, Larry Rand4,8, John M Dagle11, Kelli K Ryckman6,11, Laura L Jelliffe-Pawlowski4,7. 1. California Preterm Birth Initiative, University of California San Francisco, San Francisco, CA, USA. Scott.Oltman@ucsf.edu. 2. Department of Epidemiology & Biostatistics, University of California San Francisco, San Francisco, CA, USA. Scott.Oltman@ucsf.edu. 3. Department of Pediatrics, University of California San Francisco, San Francisco, CA, USA. 4. California Preterm Birth Initiative, University of California San Francisco, San Francisco, CA, USA. 5. Department of Pediatrics, University of California San Diego, La Jolla, CA, USA. 6. Department of Epidemiology, University of Iowa, Iowa City, IA, USA. 7. Department of Epidemiology & Biostatistics, University of California San Francisco, San Francisco, CA, USA. 8. Department of Obstetrics, Gynecology, & Reproductive Sciences, University of California San Francisco, San Francisco, CA, USA. 9. Owerko Centre, Alberta Children's Hospital Research Institute, University of Calgary, Calgary, AB, Canada. 10. School of Nursing, University of California San Francisco, San Francisco, CA, USA. 11. Department of Pediatrics, University of Iowa, Iowa City, IA, USA.
Abstract
BACKGROUND: Identifying preterm infants at risk for mortality or major morbidity traditionally relies on gestational age, birth weight, and other clinical characteristics that offer underwhelming utility. We sought to determine whether a newborn metabolic vulnerability profile at birth can be used to evaluate risk for neonatal mortality and major morbidity in preterm infants. METHODS: This was a population-based retrospective cohort study of preterm infants born between 2005 and 2011 in California. We created a newborn metabolic vulnerability profile wherein maternal/infant characteristics along with routine newborn screening metabolites were evaluated for their association with neonatal mortality or major morbidity. RESULTS: Nine thousand six hundred and thirty-nine (9.2%) preterm infants experienced mortality or at least one complication. Six characteristics and 19 metabolites were included in the final metabolic vulnerability model. The model demonstrated exceptional performance for the composite outcome of mortality or any major morbidity (AUC 0.923 (95% CI: 0.917-0.929). Performance was maintained across mortality and morbidity subgroups (AUCs 0.893-0.979). CONCLUSIONS: Metabolites measured as part of routine newborn screening can be used to create a metabolic vulnerability profile. These findings lay the foundation for targeted clinical monitoring and further investigation of biological pathways that may increase the risk of neonatal death or major complications in infants born preterm. IMPACT: We built a newborn metabolic vulnerability profile that could identify preterm infants at risk for major morbidity and mortality. Identifying high-risk infants by this method is novel to the field and outperforms models currently in use that rely primarily on infant characteristics. Utilizing the newborn metabolic vulnerability profile for precision clinical monitoring and targeted investigation of etiologic pathways could lead to reductions in the incidence and severity of major morbidities associated with preterm birth.
BACKGROUND: Identifying preterm infants at risk for mortality or major morbidity traditionally relies on gestational age, birth weight, and other clinical characteristics that offer underwhelming utility. We sought to determine whether a newborn metabolic vulnerability profile at birth can be used to evaluate risk for neonatal mortality and major morbidity in preterm infants. METHODS: This was a population-based retrospective cohort study of preterm infants born between 2005 and 2011 in California. We created a newborn metabolic vulnerability profile wherein maternal/infant characteristics along with routine newborn screening metabolites were evaluated for their association with neonatal mortality or major morbidity. RESULTS: Nine thousand six hundred and thirty-nine (9.2%) preterm infants experienced mortality or at least one complication. Six characteristics and 19 metabolites were included in the final metabolic vulnerability model. The model demonstrated exceptional performance for the composite outcome of mortality or any major morbidity (AUC 0.923 (95% CI: 0.917-0.929). Performance was maintained across mortality and morbidity subgroups (AUCs 0.893-0.979). CONCLUSIONS: Metabolites measured as part of routine newborn screening can be used to create a metabolic vulnerability profile. These findings lay the foundation for targeted clinical monitoring and further investigation of biological pathways that may increase the risk of neonatal death or major complications in infants born preterm. IMPACT: We built a newborn metabolic vulnerability profile that could identify preterm infants at risk for major morbidity and mortality. Identifying high-risk infants by this method is novel to the field and outperforms models currently in use that rely primarily on infant characteristics. Utilizing the newborn metabolic vulnerability profile for precision clinical monitoring and targeted investigation of etiologic pathways could lead to reductions in the incidence and severity of major morbidities associated with preterm birth.
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Authors: Kelli K Ryckman; Abhismitha Ramesh; Hyunkeun Cho; Scott P Oltman; Elizabeth E Rogers; John M Dagle; Laura L Jelliffe-Pawlowski Journal: Clin Biochem Date: 2021-10-22 Impact factor: 3.281
Authors: Alida Kindt; Yvonne Kraus; David Rasp; Kai M Foerster; Narges Ahmidi; Andreas W Flemmer; Susanne Herber-Jonat; Florian Heinen; Heike Weigand; Thomas Hankemeier; Berthold Koletzko; Jan Krumsiek; Juergen Babl; Anne Hilgendorff Journal: Nutrients Date: 2022-09-21 Impact factor: 6.706