We studied whether cerebral blood pressure autoregulation and kidney and liver injuries are associated in neonatal encephalopathy (NE).
We monitored autoregulation of 75 newborns who received ...hypothermia for NE in the neonatal intensive care unit to identify the mean arterial blood pressure with optimized autoregulation (MAP
). Autoregulation parameters and creatinine, aspartate aminotransferase (AST) and alanine aminotransferase (ALT) were analyzed using adjusted regression models.
Greater time with blood pressure within MAP
during hypothermia was associated with lower creatinine in girls. Blood pressure below MAP
related to higher ALT and AST during normothermia in all neonates and boys. The opposite occurred in rewarming when more time with blood pressure above MAP
related to higher AST.
Blood pressures that optimize cerebral autoregulation may support the kidneys. Blood pressures below MAP
and liver injury during normothermia are associated. The relationship between MAP
and AST during rewarming requires further study.
Extremely preterm (EP) infants frequently receive opioids and/or benzodiazepines, but these drugs' association with neurodevelopmental outcomes is poorly understood.
To describe the use of opioids ...and benzodiazepines in EP infants during neonatal intensive care unit (NICU) hospitalization and to explore these drugs' association with neurodevelopmental outcomes at 2 years' corrected age.
This cohort study was a secondary analysis of data from the Preterm Erythropoietin Neuroprotection (PENUT) Trial, which was conducted among infants born between gestational ages of 24 weeks, 0 days, and 27 weeks, 6 days. Infants received care at 19 sites in the United States, and data were collected from December 2013 to September 2016. Data analysis for this study was conducted from March to December 2020.
Short (ie, ≤7 days) and prolonged (ie, >7 days) exposure to opioids and/or benzodiazepines during NICU stay.
Cognitive, language, and motor development scores were assessed using the Bayley Scales of Infant Development-Third Edition (BSID-III).
There were 936 EP infants (448 48% female infants; 611 65% White infants; mean SD gestational age, 181 8 days) included in the study, and 692 (74%) had neurodevelopmental outcome data available. Overall, 158 infants (17%) were not exposed to any drugs of interest, 297 (32%) received either opioids or benzodiazepines, and 481 (51%) received both. Infants exposed to both had adjusted odds ratios of 9.7 (95% CI, 2.9 to 32.2) for necrotizing enterocolitis and 1.7 (95% CI, 1.1 to 2.7) for severe bronchopulmonary dysplasia; they also had a longer estimated adjusted mean difference in length of stay of 34.2 (95% CI, 26.2 to 42.2) days compared with those who received neither drug. After adjusting for site and propensity scores derived for each exposure category, infants exposed to opioids and benzodiazepines had lower BSID-III cognitive, motor, and language scores compared with infants with no exposure (eg, estimated difference in mean scores on cognitive scale: -5.72; 95% CI, -8.88 to -2.57). Prolonged exposure to morphine, fentanyl, midazolam, or lorazepam was associated with lower BSID-III scores compared with infants without exposure (median interquartile range motor score, 85 73-97 vs 97 91-107). In contrast, BSID-III scores for infants with short exposure to both opioids and benzodiazepines were not different than those of infants without exposure.
In this study, prolonged combined use of opioids and benzodiazepines was associated with a risk of poorer neurodevelopmental outcomes as measured by BSID-III at 2 years' corrected age.
Infants born extremely preterm (<28 weeks’ gestation) are at high risk of neurodevelopmental impairment (NDI) with 50% of survivors showing moderate or severe NDI when at 2 years of age. We sought to ...develop novel models by which to predict neurodevelopmental outcomes, hypothesizing that combining baseline characteristics at birth with medical care and environmental exposures would produce the most accurate model.
Using a prospective database of 692 infants from the Preterm Epo Neuroprotection (PENUT) Trial, which was carried out between December 2013 and September 2016, we developed three predictive algorithms of increasing complexity using a Bayesian Additive Regression Trees (BART) machine learning approach to predict both NDI and continuous Bayley Scales of Infant and Toddler Development 3rd ed subscales at 2 year follow-up using: 1) the 5 variables used in the National Institute of Child Health and Human Development (NICHD) Extremely Preterm Birth Outcomes Tool, 2) 21 variables associated with outcomes in extremely preterm (EP) infants, and 3) a hypothesis-free approach using 133 potential variables available for infants in the PENUT database.
The NICHD 5-variable model predicted 3–4% of the variance in the Bayley subscale scores, and predicted NDI with an area under the receiver operator curve (AUROC, 95% CI) of 0.62 (0.56–0.69). Accuracy increased to 12–20% of variance explained and an AUROC of 0.77 (0.72–0.83) when using the 21 pre-selected clinical variables. Hypothesis-free variable selection using BART resulted in models that explained 20–31% of Bayley subscale scores and AUROC of 0.87 (0.83–0.91) for severe NDI, with good calibration across the range of outcome predictions. However, even with the most accurate models, the average prediction error for the Bayley subscale predictions was around 14–15 points, leading to wide prediction intervals. Higher total transfusion volume was the most important predictor of severe NDI and lower Bayley scores across all subscales.
While the machine learning BART approach meaningfully improved predictive accuracy above a widely used prediction tool (NICHD) as well as a model utilizing NDI-associated clinical characteristics, the average error remained approximately 1 standard deviation on either side of the true value. Although dichotomous NDI prediction using BART was more accurate than has been previously reported, and certain clinical variables such as transfusion exposure were meaningfully predictive of outcomes, our results emphasize the fact that the field is still not able to accurately predict the results of complex long-term assessments such as Bayley subscales in infants born EP even when using rich datasets and advanced analytic methods. This highlights the ongoing need for long-term follow-up of all EP infants.
Supported by the National Institute of Neurological Disorders and StrokeU01NS077953 and U01NS077955.
Despite numerous attempts to increase the neutrophil count of infants with alloimmune neonatal neutropenia, no therapy has been consistently effective. We describe two infants with alloimmune ...neutropenia who had a rapid and prolonged increase in neutrophil number after treatment with granulocyte colony-stimulating factor (G-CSF). Patient 1 had antibody directed against the neutrophil antigen NA2. He received three daily doses of G-CSF, and within 2 days his neutrophil count increased from 0.350 x 10(9) to 3.584 x 10(9)/L (350 to 3584/mm3). Despite cessation of treatment the neutrophil count remained in the normal range. Patient 2 had antibody to the neutrophil antigen NA1, and received six daily doses of G-CSF. Within 4 days his neutrophil count increased from 0.477 x 10(9) to 4.320 x 10(9)/L (477 to 4320/mm3) and remained in the normal range for 11 days after the last dose of G-CSF. We recommend that treatment with G-CSF be considered for selected infants with alloimmune neutropenia.
Abstract
Human epidermal growth factor receptor 4 (HER4/ERBB4) belongs to the family of four receptor tyrosine kinases (HER1-4). While HER1, HER2 and HER3 are often overexpressed in breast cancer and ...predict poor prognosis, the role of HER4 in cancer is still unclear. Some studies suggest a tumor suppressor role of HER4, while contradictory reports suggest its oncogenic potential. HER4 gene produces multiple isoforms as a result of alternative splicing, in contrast to other members of this receptor family. We have used a gain-of-function MMTV-HER4 model isoforms, to evaluate potential developmental and carcinogenic roles of HER4-CYT1 and CYT2. Mammary gland ductal morphogenesis was significantly suppressed by expressing one of the transgene isoforms, strongly suggesting a role of HER4 in normal mammary ductal morphogenesis in addition to its role in lactation described previously. Interestingly, sustained expression of one HER4 transgene isoform resulted in formation of mammary tumor lesions, whereas no significant findings were observed in the control group. Formation of tumor lesions in these transgenic mice strongly suggests an oncogenic function of a HER4 isoform. These novel findings may help improve the rational design of targeted breast cancer therapy. Supported by NIH grant RO1 CA80065.
Citation Format: Vikram B. Wali, Maureen Gilmore-Hebert, Klaus Elenius, David F. Stern. Overexpression of HER4 isoforms in transgenic mice reveal isoform-specific role in mammary gland development and carcinogenesis. abstract. In: Proceedings of the 104th Annual Meeting of the American Association for Cancer Research; 2013 Apr 6-10; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2013;73(8 Suppl):Abstract nr 1401. doi:10.1158/1538-7445.AM2013-1401
Background: Infants born extremely preterm (<28 weeks’ gestation) are at high risk of neurodevelopmental impairment (NDI) with 50% of survivors showing moderate or severe NDI when at 2 years of age. ...We sought to develop novel models by which to predict neurodevelopmental outcomes, hypothesizing that combining baseline characteristics at birth with medical care and environmental exposures would produce the most accurate model. Methods: Using a prospective database of 692 infants from the Preterm Epo Neuroprotection (PENUT) Trial, which was carried out between December 2013 and September 2016, we developed three predictive algorithms of increasing complexity using a Bayesian Additive Regression Trees (BART) machine learning approach to predict both NDI and continuous Bayley Scales of Infant and Toddler Development 3rd ed subscales at 2 year follow-up using: 1) the 5 variables used in the National Institute of Child Health and Human Development (NICHD) Extremely Preterm Birth Outcomes Tool, 2) 21 variables associated with outcomes in extremely preterm (EP) infants, and 3) a hypothesis-free approach using 133 potential variables available for infants in the PENUT database. Findings: The NICHD 5-variable model predicted 3–4% of the variance in the Bayley subscale scores, and predicted NDI with an area under the receiver operator curve (AUROC, 95% CI) of 0.62 (0.56–0.69). Accuracy increased to 12–20% of variance explained and an AUROC of 0.77 (0.72–0.83) when using the 21 pre-selected clinical variables. Hypothesis-free variable selection using BART resulted in models that explained 20–31% of Bayley subscale scores and AUROC of 0.87 (0.83–0.91) for severe NDI, with good calibration across the range of outcome predictions. However, even with the most accurate models, the average prediction error for the Bayley subscale predictions was around 14–15 points, leading to wide prediction intervals. Higher total transfusion volume was the most important predictor of severe NDI and lower Bayley scores across all subscales. Interpretation: While the machine learning BART approach meaningfully improved predictive accuracy above a widely used prediction tool (NICHD) as well as a model utilizing NDI-associated clinical characteristics, the average error remained approximately 1 standard deviation on either side of the true value. Although dichotomous NDI prediction using BART was more accurate than has been previously reported, and certain clinical variables such as transfusion exposure were meaningfully predictive of outcomes, our results emphasize the fact that the field is still not able to accurately predict the results of complex long-term assessments such as Bayley subscales in infants born EP even when using rich datasets and advanced analytic methods. This highlights the ongoing need for long-term follow-up of all EP infants. Funding: Supported by the National Institute of Neurological Disorders and Stroke U01NS077953 and U01NS077955.
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