Development of a prediction model to target screening for high blood pressure in children
Development of a prediction model to target screening for high blood pressure in children
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DOI：
10.1016/j.ypmed.2020.105997
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发表时间：
2020-03-01
影响因子：
5.1
通讯作者：
de Kroon, Marlou L. A.
中科院分区：
文献类型：
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作者：
Hamoen, Marleen;Welten, Marieke;de Kroon, Marlou L. A.
在所有儿童中，对儿童高血压进行有针对性的筛查可能比常规血压测量更可行，以避免不必要的伤害、过度诊断或成本。例如，目标可能基于超重，但有关其他预测因素的信息也可能有用。因此，我们的目标是开发一个多变量诊断预测模型，以选择9-10岁的儿童进行血压测量。在一项基于人群的前瞻性队列研究中，使用了5359名儿童的数据。高血压被定义为收缩压或舒张压=性别、年龄和身高的第95个百分位数。使用Logistic回归和反向选择来确定与怀孕、孩子和父母特征相关的最强预测因素。使用Bootstrapping进行内部验证。227名儿童(4.2%)患有高血压。诊断模型包括母亲孕期高血压疾病、母亲体重指数、母亲文化程度、父母高血压、父母吸烟、儿童出生体重标准差得分、儿童BMI、儿童种族。ROC曲线下面积为0.73，而使用独生子女超重时为0.65。使用该模型，以5%作为预测风险的临界值，灵敏度和特异度分别为59%和76%；仅使用儿童超重，灵敏度和特异度分别为47%和84%。总而言之，我们的诊断预测模型使用容易获得的信息来识别高血压风险增加的儿童，为有针对性的筛查提供了机会。与仅基于儿童超重的策略相比，该模型能够检测出更高比例的高血压儿童。
Targeted screening for childhood high blood pressure may be more feasible than routine blood pressure measurement in all children to avoid unnecessary harms, overdiagnosis or costs. Targeting maybe based e.g. on being overweight, but information on other predictors may also be useful. Therefore, we aimed to develop a multivariable diagnostic prediction model to select children aged 9-10 years for blood pressure measurement. Data from 5359 children in a population-based prospective cohort study were used. High blood pressure was defined as systolic or diastolic blood pressure >= 95th percentile for gender, age, and height. Logistic regression with backward selection was used to identify the strongest predictors related to pregnancy, child, and parent characteristics. Internal validation was performed using bootstrapping. 227 children (4.2%) had high blood pressure. The diagnostic model included maternal hypertensive disease during pregnancy, maternal BMI, maternal educational level, parental hypertension, parental smoking, child birth weight standard deviation score (SDS), child BMI SDS, and child ethnicity. The area under the ROC curve was 0.73, compared to 0.65 when using only child overweight. Using the model and a cut-off of 5% for predicted risk, sensitivity and specificity were 59% and 76%; using child overweight only, sensitivity and specificity were 47% and 84%. In conclusion, our diagnostic prediction model uses easily obtainable information to identify children at increased risk of high blood pressure, offering an opportunity for targeted screening. This model enables to detect a higher proportion of children with high blood pressure than a strategy based on child overweight only.