Postasphyxial hypoxic-ischemic encephalopathy in neonates: outcome prediction rule within 4 hours of birth |
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Authors: | Shah Prakesh S Beyene Joseph To Teresa Ohlsson Arne Perlman Max |
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Institution: | Department of Paediatrics, Mount Sinai Hospital, Toronto, Ontario, Canada. pshah@mtsinai.on.ca |
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Abstract: | OBJECTIVES: To construct and validate a model and derive a simple rule that is usable in any birth location for the prediction of outcome of term infants with severe asphyxia. DESIGN: Retrospective cohort study. SETTING: Regional outborn neonatal intensive care unit. PARTICIPANTS: Infants with postintrapartum asphyxial hypoxic-ischemic encephalopathy (n = 375). MAIN EXPOSURES: Clinical and laboratory predictors available at age 4 hours. MAIN OUTCOME MEASURES: A logistic regression model was developed and internally validated (with random sampling and based on the year of birth) for severe adverse outcome, which was defined as death or severe disability (severe cerebral palsy, severe developmental delay, sensorineural deafness, or cortical blindness singly or in combination). A simple prediction rule was derived from 3 variables. RESULTS: Complete data were available for 302 (92%) of the 345 infants with known outcomes (204 infants with severe adverse outcome). Six independent predictors of outcomes were identified. Using the 3 most significant predictors (chest compressions, age at onset of respiration, and base deficit), severe adverse outcome rates were 46% (95% confidence interval, 33%-58%) with none of the 3 predictors, 64% (95% confidence interval, 54%-73%) with any 1 predictor, 76% (95% confidence interval, 66%-85%) with any 2 predictors, and 93% (95% confidence interval, 81%-99%) with all of the 3 predictors present. The internal validations revealed a robust model. CONCLUSIONS: This predictive model for neonatal hypoxic-ischemic encephalopathy provides a sliding scale of probabilities that could be used for prognostication and to design eligibility criteria for decision making including neuroprotective therapy. |
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