Center of Research in Epidemiology and Statistics, Obstetrical, Perinatal and Pediatric Epidemiology Research Team, INSERM UMR 1153, Université de Paris, Paris, France.
Center of Research in Epidemiology and Statistics, Obstetrical, Perinatal and Pediatric Epidemiology Research Team, INSERM UMR 1153, Université de Paris, Paris, France; Department of General Pediatrics and Pediatric Infectious Diseases, Necker Sick Children Hospital, Assistance Publique-Hôpitaux de Paris, Université de Paris, Paris, France.
J Pediatr. 2021 Aug;235:212-219. doi: 10.1016/j.jpeds.2021.03.072. Epub 2021 Apr 6.
To assess the diagnostic accuracy of existing clinical criteria and to develop prediction tools for iron deficiency in 2-year-old children.
In a national cross-sectional study conducted in primary care pediatricians' practices throughout France, 2-year-old children were consecutively included (2016-2017). Multivariable logistic regression modeling and bootstrapping were used to develop several clinical models to predict iron deficiency (serum ferritin <12 μg/L). These models used the best criteria and combinations among the American Academy of Pediatrics' (AAP) criteria adapted to the European context (n = 10), then all potential predictors (n = 19). One model was then simplified into a simple prediction tool.
Among 568 included infants, 38 had iron deficiency (6.7%). In univariable analyses, no significant association with iron deficiency was observed for 8 of the 10 adapted AAP criteria. Three criteria (both parents born outside the European Union, low weight at 1 year old, and weaning to cow's milk without supplemental iron) were retained in the AAP model, which area under the receiver operating characteristic curve, sensitivity, and specificity were 0.62 (95% CI, 0.58-0.67), 30% (95% CI, 22%-39%), and 95% (95% CI, 92%-97%), respectively. Four criteria were retained in a newly derived simple prediction tool (≥1 criterion among the 3 previous plus duration of iron-rich formula consumption <12 months), which area under the receiver operating characteristic curve, sensitivity, and specificity were 0.72 (95% CI, 0.65-0.79), 63% (95% CI, 47%-80%), and 81% (95% CI, 70%-91%), respectively.
All prediction tools achieved acceptable diagnostic accuracy. The newly derived simple prediction tool offered potential ease of use.
ClinicalTrials.gov NCT02484274.
评估现有的临床标准的诊断准确性,并为 2 岁儿童的缺铁症开发预测工具。
在法国各地的初级保健儿科医生诊所进行的全国性横断面研究中,连续纳入 2 岁儿童(2016-2017 年)。多变量逻辑回归模型和引导法用于开发几种预测缺铁症(血清铁蛋白<12μg/L)的临床模型。这些模型使用了美国儿科学会(AAP)标准中最适合欧洲情况的最佳标准和组合(n=10),然后使用所有潜在的预测因素(n=19)。然后,将一个模型简化为一个简单的预测工具。
在纳入的 568 名婴儿中,有 38 名患有缺铁症(6.7%)。在单变量分析中,10 项适应性 AAP 标准中没有 8 项与缺铁症有显著关联。3 项标准(父母均出生于欧盟以外地区、1 岁时体重较低、未添加铁的牛奶断奶)保留在 AAP 模型中,其受试者工作特征曲线下面积、敏感性和特异性分别为 0.62(95%CI,0.58-0.67)、30%(95%CI,22%-39%)和 95%(95%CI,92%-97%)。在一个新的简单预测工具中保留了 4 项标准(3 项以前的标准中至少有 1 项,加上富含铁的配方奶消费时间<12 个月),其受试者工作特征曲线下面积、敏感性和特异性分别为 0.72(95%CI,0.65-0.79)、63%(95%CI,47%-80%)和 81%(95%CI,70%-91%)。
所有预测工具均达到可接受的诊断准确性。新开发的简单预测工具具有潜在的易用性。
ClinicalTrials.gov NCT02484274。