Published online November 1, 2006
PEDIATRICS Vol. 118 No. 5 November 2006, pp. 2084-2093 (doi:10.1542/peds.2006-1591)

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ARTICLE

Predicting Outcomes of Neonates Diagnosed With Hypoxemic-Ischemic Encephalopathy

Namasivayam Ambalavanan, MD^a, Waldemar A. Carlo, MD^a, Seetha Shankaran, MD^b, Carla M. Bann, PhD^c, Steven L. Emrich, MS^c, Rosemary D. Higgins, MD^d, Jon E. Tyson, MD, MPH^e, T. Michael O'Shea, MD, MPH^f, Abbot R. Laptook, MD^g, Richard A. Ehrenkranz, MD^h, Edward F. Donovan, MDⁱ, Michele C. Walsh, MD, MS^j, Ronald N. Goldberg, MD^k, Abhik Das, PhD^c for the National Institute of Child Health and Human Development Neonatal Research Network

^a Department of Pediatrics, University of Alabama at Birmingham, Birmingham, Alabama
^b Department of Pediatrics, Wayne State University, Detroit, Michigan
^c RTI International, Research Triangle Park, North Carolina
^d National Institute of Child Health and Human Development Neonatal Research Network, Bethesda, Maryland
^e Department of Pediatrics, University of Texas Health Science Center at Houston, Houston, Texas
^f Department of Pediatrics, Wake Forest University School of Medicine, Winston-Salem, North Carolina
^g Department of Pediatrics, Women and Infants Hospital, Providence, Rhode Island
^h Department of Pediatrics, Yale University School of Medicine, New Haven, Connecticut
ⁱ Department of Pediatrics, University of Cincinnati, Cincinnati, Ohio
^j Department of Pediatrics, Case Western Reserve University, Cleveland, Ohio
^k Department of Pediatrics, Duke University, Durham, North Carolina

OBJECTIVE. The goals were to identify predictor variables and to develop scoring systems and classification trees to predict death/disability or death in infants with hypoxic-ischemic encephalopathy.

METHODS. Secondary analysis of data from the multicenter, randomized, controlled, National Institute of Child Health and Human Development Neonatal Research Network trial of hypothermia in hypoxic-ischemic encephalopathy was performed. Data for 205 neonates diagnosed as having hypoxic-ischemic encephalopathy were studied. Logistic regression analysis was performed by using clinical and laboratory variables available within 6 hours of birth, with death or moderate/severe disability at 18 to 22 months or death as the outcomes. By using the identified variables and odds ratios, scoring systems to predict death/disability or death were developed, weighting each predictor in proportion to its odds ratio. In addition, classification and regression tree analysis was performed, with recursive partitioning and automatic selection of optimal cutoff points for variables. Correct classification rates for the scoring systems, classification and regression tree models, and early neurologic examination were compared.

RESULTS. Correct classification rates were 78% for death/disability and 71% for death with the scoring systems, 80% and 77%, respectively, with the classification and regression tree models, and 67% and 73% with severe encephalopathy in early neurologic examination. Correct classification rates were similar in the hypothermia and control groups.

CONCLUSIONS. Among neonates diagnosed as having hypoxic-ischemic encephalopathy, the classification and regression tree model, but not the scoring system, was superior to early neurologic examination in predicting death/disability. The 3 models were comparable in predicting death. Only a few components of the early neurologic examination were associated with poor outcomes. These scoring systems and classification trees, if validated, may help in assessments of prognosis and may prove useful for risk-stratification of infants with hypoxic-ischemic encephalopathy for clinical trials.

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Key Words: logistic models • decision trees • asphyxia neonatorum • hypoxia-ischemia • brain • predictive value of tests

Abbreviations: CART—classification and regression tree • GMFCS—gross motor function classification system • HIE—hypoxic-ischemic encephalopathy • ROC—receiver operating characteristic • NICHD—National Institute of Child Health and Human Development • CI—confidence interval

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Accepted Jul 26, 2006.

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