Zernikow B, Holtmannspoetter K, Michel E, Theilhaber M, Pielemeier W, Hennecke K H
Vestische Kinderklinik, Witten/Herdecke University, Datteln, Germany.
Acta Paediatr. 1998 Sep;87(9):969-75. doi: 10.1080/080352598750031644.
Intraventricular haemorrhage (IVH) incidence is used to assess peri-/neonatal therapy, and to make intra- and inter-hospital quality assessments. Unbiased assessment is complicated by the amount of confounding factors. Is an artificial neural network (ANN) able to early and accurately forecast the occurrence of severe IVH in an individual patient? Is it superior to classic multiple logistic regression? We conducted an observational study on pre-existing routine data. Admission data were available from 890 preterm neonates (gestational age < 32 weeks, birthweight < 1500 g). Patients were randomly assigned to either a training, or a validation set (50%/50%). Using the training set data an ANN was trained. A second predictive model was developed by stepwise multiple logistic regression analysis. Using the validation set input data both models delivered estimates of the probability for severe IVH to occur in each individual patient. Receiver operating characteristic (ROC) curves were used to compare prognostic performance. The optimal ANN processed 13 input variables, whereas stepwise logistic regression analysis only identified five independent predictor variables. The area under the ROC curve was 0.935 for the ANN and 0.884 for the logistic regression model (p = 0.001). Adjusted for 95%, 90%, 85%, 80% and 75% specificity, the sensitivity of the ANN was significantly superior to that of the logistic regression model. Due to its ability to give an accurate prognosis based solely on admission data, a trained ANN qualifies as a tool for local quality control.
脑室内出血（IVH）发生率用于评估围产期/新生儿期治疗情况，并进行院内和院间质量评估。由于存在大量混杂因素，无偏倚评估变得复杂。人工神经网络（ANN）能否早期且准确地预测个体患者发生严重IVH的情况？它是否优于经典的多元逻辑回归？我们对现有的常规数据进行了一项观察性研究。收集了890例早产新生儿（胎龄<32周，出生体重<1500 g）的入院数据。患者被随机分配到训练集或验证集（各占50%）。利用训练集数据训练了一个人工神经网络。通过逐步多元逻辑回归分析建立了第二个预测模型。利用验证集输入数据，两个模型都给出了每个个体患者发生严重IVH概率的估计值。采用受试者工作特征（ROC）曲线比较预后性能。最优的人工神经网络处理13个输入变量，而逐步逻辑回归分析仅识别出5个独立预测变量。人工神经网络的ROC曲线下面积为0.935，逻辑回归模型为0.884（p = 0.001）。在特异性调整为95%、90%、85%、80%和75%时，人工神经网络的敏感性显著优于逻辑回归模型。由于其仅基于入院数据就能给出准确预后的能力，经过训练的人工神经网络可作为局部质量控制的工具。
