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Objective: Develop and validate a resiliency score to predict survival and survival without neonatal morbidity in preterm neonates <32 weeks of gestation using machine learning., Study Design: Models using maternal, perinatal, and neonatal variables were developed using LASSO method in a population based Californian administrative dataset. Outcomes were survival and survival without severe neonatal morbidity. Discrimination was assessed in the derivation and an external dataset from a tertiary care center., Results: Discrimination in the internal validation dataset was excellent with a c-statistic of 0.895 (95% CI 0.882-0.908) for survival and 0.867 (95% CI 0.857-0.877) for survival without severe neonatal morbidity, respectively. Discrimination remained high in the external validation dataset (c-statistic 0.817, CI 0.741-0.893 and 0.804, CI 0.770-0.837, respectively)., Conclusion: Our successfully predicts survival and survival without major morbidity in preterm babies born at <32 weeks. This score can be used to adjust for multiple variables across administrative datasets., (© 2022. The Author(s).)

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.

Objectives: To investigate racial/ethnic differences in rehospitalization and mortality rates among premature infants over the first year of life., Study Design: A retrospective cohort study of infants born in California from 2011 to 2017 (n = 3,448,707) abstracted from a California Office of Statewide Health Planning and Development database. Unadjusted Kaplan-Meier tables and logistic regression controlling for health and sociodemographic characteristics were used to predict outcomes by race/ethnicity., Results: Compared to White infants, Hispanic and Black early preterm infants were more likely to be readmitted; Black late/moderate preterm (LMPT) infants were more likely to be readmitted and to die after discharge; Hispanic and Black early preterm infants with BPD were more likely to be readmitted; Black LMPT infants with RDS were more likely to be readmitted and die after discharge., Conclusions: Racial/ethnic disparities in readmission and mortality rates exist for premature infants across several co-morbidities. Future studies are needed to improve equitability of outcomes.

Background: Preterm infants suffer from respiratory morbidity especially during the first year of life., Objective: To investigate the association of air quality and sociodemographic indicators on hospital admission rates for respiratory causes., Methods: This is a retrospective cohort study. We identified all live-born preterm infants in California from 2007 to 2012 in a population-based administrative data set and linked them to a data set measuring several air quality and sociodemographic indicators at the census tract level. All sociodemographic and air quality predictors were divided into quartiles (first quartile most favourable to the fourth quartile least favourable). Mixed effect logistic models to account for clustering at the census tract level were used to investigate associations between chronic air quality and sociodemographic indicators respiratory hospital admission during the first year of life., Results: Of 205 178 preterm infants, 5.9% (n = 12 033) were admitted to the hospital for respiratory causes during the first year. In the univariate analysis, comparing the first to the fourth quartile of chronic ozone (risk ratio [RR] 1.29, 95% confidence interval [CI] 1.21, 1.37), diesel (RR 1.10, 95% CI 1.02, 1.17) and particulate matter 2.5 (RR 1.07, 95% CI 1.01, 1.14) exposure were associated with hospital admission during the first year. Following adjustment for confounders, the risk ratios for hospital admission during the first year were 1.53 (95% CI 1.37, 1.72) in relation to educational attainment (per cent of the population over age 25 with less than a high school education) and 1.23 (95% CI 1.09, 1.38) for poverty (per cent of the population living below two times the federal poverty level)., Conclusions: Among preterm infants, respiratory hospital admissions in the first year in California are associated with socioeconomic characteristics of the neighbourhood an individual is living in., (© 2020 John Wiley & Sons Ltd.)

Background: Disparities exist in the rates of preterm birth and infant mortality across different racial/ethnic groups. However, only a few studies have examined the impact of race/ethnicity on the outcomes of premature infants., Objective: To report the rates of mortality and severe neonatal morbidity among multiple gestational age (GA) groups stratified by race/ethnicity., Methods: A retrospective cohort study utilizing linked birth certificate, hospital discharge, readmission, and death records up to 1 year of life. Live-born infants ≤36 weeks born in the period 2007-2012 were included. Maternal self-identified race/ethnicity, as recorded on the birth certificate, was used. ICD-9 diagnostic and procedure codes captured neonatal morbidities (intraventricular hemorrhage, retinopathy of prematurity, periventricular leukomalacia, bronchopulmonary dysplasia, and necrotizing enterocolitis). Multiple logistic regression was performed to evaluate the impact of race/ethnicity on mortality and morbidity, adjusting for GA, birth weight, sex, and multiple gestation., Results: Our cohort totaled 245,242 preterm infants; 26% were white, 46% Hispanic, 8% black, and 12% Asian. At 22-25 weeks, black infants were less likely to die than white infants (odds ratio [OR] 0.76; 95% confidence interval [CI] 0.62-0.94). However, black infants born at 32-34 weeks (OR 1.64; 95% CI 1.15-2.32) or 35-36 weeks (OR 1.57; 95% CI 1.00-2.24) were more likely to die. Hispanic infants born at 35-36 weeks were less likely to die than white infants (OR 0.66; 95% CI 0.50-0.87). Racial disparities at different GAs were also detected for severe morbidities., Conclusions: The impact of race/ethnicity on mortality and severe morbidity varied across GA categories in preterm infants. Disparities persisted even after adjusting for important potential confounders., (© 2017 S. Karger AG, Basel.)