The journal of maternal-fetal & neonatal medicine : the official journal of the European Association of Perinatal Medicine, the Federation of Asia and Oceania Perinatal Societies, the International Society of Perinatal Obstetricians · 2019
A decision tree model of cerebral palsy based on risk factors.
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Xiang Shiting, Li Liping, Wang Lili, Liu Juan, Tan Yaqiong, Hu Jihong
Аннотация
OBJECTIVE: A risk prediction model of cerebral palsy (CP) was established by a decision tree model to predict the individual risk of CP.
METHODS: A hospital-based case-control study was conducted with 109 cases of CP and 327 controls without CP. The cases and the controls were obtained from Hunan Children's Hospital. A questionnaire was administered to collect the variables relevant to CP by face to face interviews. Chi-square test was used to identify the factors associated with CP, and a decision tree model was used to construct the prediction model.
RESULTS: Univariate analysis showed that there were significant differences between cases group and controls group on maternal age, weight gain during pregnancy, medical treatment during pregnancy, preterm birth, low birth weight and birth asphyxia (all p-values <.05). Three factors, including preterm birth, birth asphyxia, and maternal age >35 years old, entered the decision tree model. The area under the receiver operating characteristic curve (AUC) was 0.722 (95%CI: 0.659-0.784, p < .001).
CONCLUSION: The decision tree prediction model can be used for predicting the individual risk of CP. Further large-scale, population-based cerebral palsy studies are needed to improve the model.
METHODS: A hospital-based case-control study was conducted with 109 cases of CP and 327 controls without CP. The cases and the controls were obtained from Hunan Children's Hospital. A questionnaire was administered to collect the variables relevant to CP by face to face interviews. Chi-square test was used to identify the factors associated with CP, and a decision tree model was used to construct the prediction model.
RESULTS: Univariate analysis showed that there were significant differences between cases group and controls group on maternal age, weight gain during pregnancy, medical treatment during pregnancy, preterm birth, low birth weight and birth asphyxia (all p-values <.05). Three factors, including preterm birth, birth asphyxia, and maternal age >35 years old, entered the decision tree model. The area under the receiver operating characteristic curve (AUC) was 0.722 (95%CI: 0.659-0.784, p < .001).
CONCLUSION: The decision tree prediction model can be used for predicting the individual risk of CP. Further large-scale, population-based cerebral palsy studies are needed to improve the model.