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首页> 外文期刊>Pediatrics: Official Publication of the American Academy of Pediatrics >Prediction of death for extremely low birth weight neonates.
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Prediction of death for extremely low birth weight neonates.

机译：出生体重极低的新生儿的死亡预测。

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OBJECTIVE: To compare multiple logistic regression and neural network models in predicting death for extremely low birth weight neonates at 5 time points with cumulative data sets, as follows: scenario A, limited prenatal data; scenario B, scenario A plus additional prenatal data; scenario C, scenario B plus data from the first 5 minutes after birth; scenario D, scenario C plus data from the first 24 hours after birth; scenario E, scenario D plus data from the first 1 week after birth. METHODS: Data for all infants with birth weights of 401 to 1000 g who were born between January 1998 and April 2003 in 19 National Institute of Child Health and Human Development Neonatal Research Network centers were used (n = 8608). Twenty-eight variables were selected for analysis (3 for scenario A, 15 for scenario B, 20 for scenario C, 25 for scenario D, and 28 for scenario E) from those collected routinely. Data sets censored for prior death or missing data were created for each scenario and divided randomly into training (70%) and test (30%) data sets. Logistic regression and neural network models for predicting subsequent death were created with training data sets and evaluated with test data sets. The predictive abilities of the models were evaluated with the area under the curve of the receiver operating characteristic curves. RESULTS: The data sets for scenarios A, B, and C were similar, and prediction was best with scenario C (area under the curve: 0.85 for regression; 0.84 for neural networks), compared with scenarios A and B. The logistic regression and neural network models performed similarly well for scenarios A, B, D, and E, but the regression model was superior for scenario C. CONCLUSIONS: Prediction of death is limited even with sophisticated statistical methods such as logistic regression and nonlinear modeling techniques such as neural networks. The difficulty of predicting death should be acknowledged in discussions with families and caregivers about decisions regarding initiation or continuation of care.
机译：目的：比较多重逻辑回归和神经网络模型在五个时间点的极低出生体重新生儿的死亡情况下，其累积数据集如下：情景A，有限的产前数据;方案B，方案A以及其他产前数据;方案C，方案B以及出生后前5分钟的数据;情况D，情况C以及出生后前24小时的数据;方案E，方案D以及出生后前1周的数据。方法：使用1998年1月至2003年4月在19个国家儿童健康和人类发展研究所新生儿研究网络中心出生的所有出生体重为401至1000 g的婴儿的数据（n = 8608）。从常规收集的变量中选择28个变量进行分析（方案A为3个，方案B为15个，方案C为20个，方案D为25个，方案E为28个）。为每种情况创建了针对先前死亡或丢失数据进行检查的数据集，并将其随机分为训练（70％）和测试（30％）数据集。使用训练数据集创建用于预测随后死亡的逻辑回归和神经网络模型，并使用测试数据集进行评估。用接收器工作特性曲线的曲线下方的面积评估模型的预测能力。结果：与方案A和B相比，方案A，B和C的数据集相似，并且方案C的预测最佳（曲线下面积：回归系数为0.85;神经网络为0.84）。神经网络模型在场景A，B，D和E中的表现相似，但回归模型在场景C方面更好。结论：即使采用复杂的统计方法（如逻辑回归和神经网络等非线性建模技术），死亡的预测也受到限制网络。在与家人和看护人讨论有关开始或继续治疗的决定时，应认识到预测死亡的困难。

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