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      The development of neonatal neurointensive care

      Pediatric Research
      Springer Science and Business Media LLC

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          Neonatal MRI to predict neurodevelopmental outcomes in preterm infants.

          Very preterm infants are at high risk for adverse neurodevelopmental outcomes. Magnetic resonance imaging (MRI) has been proposed as a means of predicting neurodevelopmental outcomes in this population. We studied 167 very preterm infants (gestational age at birth, 30 weeks or less) to assess the associations between qualitatively defined white-matter and gray-matter abnormalities on MRI at term equivalent (gestational age of 40 weeks) and the risks of severe cognitive delay, severe psychomotor delay, cerebral palsy, and neurosensory (hearing or visual) impairment at 2 years of age (corrected for prematurity). At two years of age, 17 percent of infants had severe cognitive delay, 10 percent had severe psychomotor delay, 10 percent had cerebral palsy, and 11 percent had neurosensory impairment. Moderate-to-severe cerebral white-matter abnormalities present in 21 percent of infants at term equivalent were predictive of the following adverse outcomes at two years of age: cognitive delay (odds ratio, 3.6; 95 percent confidence interval, 1.5 to 8.7), motor delay (odds ratio, 10.3; 95 percent confidence interval, 3.5 to 30.8), cerebral palsy (odds ratio, 9.6; 95 percent confidence interval, 3.2 to 28.3), and neurosensory impairment (odds ratio, 4.2; 95 percent confidence interval, 1.6 to 11.3). Gray-matter abnormalities (present in 49 percent of infants) were also associated, but less strongly, with cognitive delay, motor delay, and cerebral palsy. Moderate-to-severe white-matter abnormalities on MRI were significant predictors of severe motor delay and cerebral palsy after adjustment for other measures during the neonatal period, including findings on cranial ultrasonography. Abnormal findings on MRI at term equivalent in very preterm infants strongly predict adverse neurodevelopmental outcomes at two years of age. These findings suggest a role for MRI at term equivalent in risk stratification for these infants. Copyright 2006 Massachusetts Medical Society.
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            Is Open Access

            Rapid whole-genome sequencing decreases infant morbidity and cost of hospitalization

            Genetic disorders are a leading cause of morbidity and mortality in infants. Rapid whole-genome sequencing (rWGS) can diagnose genetic disorders in time to change acute medical or surgical management (clinical utility) and improve outcomes in acutely ill infants. We report a retrospective cohort study of acutely ill inpatient infants in a regional children’s hospital from July 2016–March 2017. Forty-two families received rWGS for etiologic diagnosis of genetic disorders. Probands also received standard genetic testing as clinically indicated. Primary end-points were rate of diagnosis, clinical utility, and healthcare utilization. The latter was modelled in six infants by comparing actual utilization with matched historical controls and/or counterfactual utilization had rWGS been performed at different time points. The diagnostic sensitivity of rWGS was 43% (eighteen of 42 infants) and 10% (four of 42 infants) for standard genetic tests (P = .0005). The rate of clinical utility of rWGS (31%, thirteen of 42 infants) was significantly greater than for standard genetic tests (2%, one of 42; P = .0015). Eleven (26%) infants with diagnostic rWGS avoided morbidity, one had a 43% reduction in likelihood of mortality, and one started palliative care. In six of the eleven infants, the changes in management reduced inpatient cost by $800,000–$2,000,000. These findings replicate a prior study of the clinical utility of rWGS in acutely ill inpatient infants, and demonstrate improved outcomes and net healthcare savings. rWGS merits consideration as a first tier test in this setting.
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              The cost of preterm birth throughout childhood in England and Wales.

              Infants born preterm are at increased risk of adverse health and developmental outcomes. Mortality and morbidity after preterm birth impose a burden on finite public sector resources. This study considers the economic consequences of preterm birth from birth to adult life and compares the costs accruing to those born preterm with those born at term. A decision-analytic model was constructed to estimate the costs to the public sector over the first 18 years after birth, stratified by week of gestational age at birth. Costs were discounted and reported in UK pounds at 2006 prices. Probabilistic sensitivity analysis was used to examine uncertainty in the model parameters and generate confidence intervals surrounding the cost estimates. The model estimates the costs associated with a hypothetical cohort of 669601 children and is based on live birth and preterm birth data from England and Wales in 2006. The total cost of preterm birth to the public sector was estimated to be pound2.946 billion (US $4.567 billion), and an inverse relationship was identified between gestational age at birth and the average public sector cost per surviving child. The incremental cost per preterm child surviving to 18 years compared with a term survivor was estimated at pound22885 (US $35471). The corresponding estimates for a very and extremely preterm child were substantially higher at pound61781 (US $95760) and pound94740 (US $146847), respectively. Despite concerns about ongoing costs after discharge from perinatal services, the largest contribution to the economic implications of preterm birth are hospital inpatient costs after birth, which are responsible for 92.0% of the incremental costs per preterm survivor.
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                Author and article information

                Journal
                Pediatric Research
                Pediatr Res
                Springer Science and Business Media LLC
                0031-3998
                1530-0447
                December 18 2019
                Article
                10.1038/s41390-019-0729-5
                31852010
                ae55089a-3ed2-491d-8b0f-e21f2bc71e2e
                © 2019

                http://www.springer.com/tdm

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