Erasmus MC, University Medical Center Rotterdam, Department of Public Health, Rotterdam, Netherlands; Erasmus MC, University Medical Center Rotterdam, The Generation R Study Group, Rotterdam, Netherlands.
Department of Epidemiology and Biostatistics, Amsterdam Public Health research institute, VU University Medical Center, Amsterdam, Netherlands.
Prev Med. 2020 Mar;132:105997. doi: 10.1016/j.ypmed.2020.105997. Epub 2020 Jan 23.
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.
针对儿童高血压的目标筛查可能比在所有儿童中常规测量血压更可行，以避免不必要的伤害、过度诊断或成本。目标可能基于超重等，但其他预测因素的信息也可能有用。因此，我们旨在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。该数据来自一项基于人群的前瞻性队列研究中的 5359 名儿童。高血压定义为性别、年龄和身高的收缩压或舒张压≥第 95 百分位数。使用向后选择的逻辑回归来识别与妊娠、儿童和父母特征相关的最强预测因素。使用自举法进行内部验证。227 名儿童（4.2%）患有高血压。诊断模型包括母亲怀孕期间的高血压疾病、母亲 BMI、母亲教育水平、父母高血压、父母吸烟、儿童出生体重标准差评分（SDS）、儿童 BMI SDS 和儿童种族。ROC 曲线下面积为 0.73，而仅使用儿童超重时为 0.65。使用该模型和预测风险 5%的截断值，敏感性和特异性分别为 59%和 76%；仅使用儿童超重，敏感性和特异性分别为 47%和 84%。总之，我们的诊断预测模型使用易于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，该模型能够检测到更高比例的高血压儿童。
