Prediction of neurologic morbidity in extremely low birth weight infants

被引：45

作者：

Ambalavanan N. ^{[1
,4
]}

Nelson K.G. ^{[1
]}

Alexander G. ^{[2
]}

Johnson S.E. ^{[1
]}

Biasini F. ^{[3
]}

Carlo W.A. ^{[1
]}

机构：

[1] Department of Pediatrics, University of Alabama at Birmingham, Birmingham, AL

[2] School of Public Health, University of Alabama at Birmingham, Birmingham, AL

[3] Division of Neonatology, Department of Pediatrics, University of Alabama at Birmingham, Birmingham

来源：

Journal of Perinatology | 2000年 / 20卷 / 8期

关键词：

D O I：

10.1038/sj.jp.7200419

中图分类号：

学科分类号：

摘要：

OBJECTIVE: (1) Identify major determinants of adverse neurodevelopmental outcome in extremely low birth weight (ELBW) infants. (2) Compare neural networks and regression analysis in the prediction of major handicaps and Bayley scores (MDI and PDI) in individual ELBW neonates followed to 18 months. STUDY DESIGN: Retrospective cohort study of regional tertiary care NICU database. A database with 21 selected variables was divided into training (n=144) and test sets (n=74). The training set was used to train a neural network and develop regression equations to predict outcomes in the test set. RESULTS: Determinants (descending order of contribution to variance) : Major handicap: intraventricular hemorrhage (IVH) grade, necrotizing enterocolitis≥stage II, black race, and no chorioamnionitis; low MDI: IVH grade, plurality, bronchopulmonary dysplasia (BPD), lower maternal grade, and no chorioamnionitis; low PDI: IVH grade, BPD, periventricular leukomalacia, lower maternal grade, and no chorioamnionitis. Regression techniques and neural networks were comparable and had relatively low sensitivity and correlation with adverse outcomes. CONCLUSION: Much of the variance in ELBW neurologic outcome cannot be explained by either regression analysis or neural network approaches.

引用

页码：496 / 503

页数：7

共 44 条

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