Department of Pediatrics, University of Alabama at Birmingham, Birmingham, Alabama 35249, USA.
Pediatrics. 2012 Jul;130(1):e115-25. doi: 10.1542/peds.2011-3693. Epub 2012 Jun 11.
Methods are required to predict prognosis with changes in clinical course. Death or neurodevelopmental impairment in extremely premature neonates can be predicted at birth/admission to the ICU by considering gender, antenatal steroids, multiple birth, birth weight, and gestational age. Predictions may be improved by using additional information available later during the clinical course. Our objective was to develop serial predictions of outcome by using prognostic factors available over the course of NICU hospitalization.
Data on infants with birth weight ≤ 1.0 kg admitted to 18 large academic tertiary NICUs during 1998-2005 were used to develop multivariable regression models following stepwise variable selection. Models were developed by using all survivors at specific times during hospitalization (in delivery room [n = 8713], 7-day [n = 6996], 28-day [n = 6241], and 36-week postmenstrual age [n = 5118]) to predict death or death/neurodevelopmental impairment at 18 to 22 months.
Prediction of death or neurodevelopmental impairment in extremely premature infants is improved by using information available later during the clinical course. The importance of birth weight declines, whereas the importance of respiratory illness severity increases with advancing postnatal age. The c-statistic in validation models ranged from 0.74 to 0.80 with misclassification rates ranging from 0.28 to 0.30.
Dynamic models of the changing probability of individual outcome can improve outcome predictions in preterm infants. Various current and future scenarios can be modeled by input of different clinical possibilities to develop individual "outcome trajectories" and evaluate impact of possible morbidities on outcome.
需要有方法来预测临床病程变化后的预后。通过考虑性别、产前类固醇、多胎妊娠、出生体重和胎龄，可在极早产儿出生时或入住 ICU 时预测其死亡或神经发育损伤。通过使用临床病程中获得的其他信息，预测结果可能会得到改善。我们的目的是通过使用在新生儿重症监护病房住院期间获得的预后因素来对结果进行连续预测。
使用 1998-2005 年期间入住 18 家大型学术性三级新生儿重症监护病房、出生体重≤1.0kg 的婴儿数据，通过逐步变量选择建立多变量回归模型。通过使用住院期间特定时间的所有存活者（分娩室[8713 例]、7 天[6996 例]、28 天[6241 例]和 36 周校正胎龄[5118 例]）的模型来预测 18 至 22 个月时的死亡或死亡/神经发育损伤。
通过使用临床病程中获得的信息，可改善对极早产儿死亡或神经发育损伤的预测。出生体重的重要性随着出生后年龄的增加而降低，而呼吸疾病严重程度的重要性则增加。验证模型的 c 统计量范围为 0.74 至 0.80，错误分类率范围为 0.28 至 0.30。
个体预后变化概率的动态模型可以提高早产儿的预后预测。通过输入不同的临床可能性来模拟各种当前和未来的情况，可以建立个体“预后轨迹”并评估可能的并发症对预后的影响。
