Secondary data analysis of universal autism screening in a large primary care network: The impact of early autism screening on age of diagnosis, referral, and entry into intervention (R03)

项目总结/摘要 美国预防服务工作组（USPSTF）最近强调， 研究初级保健中的普遍自闭症筛查，以及它是否会导致早期诊断和治疗。 这一差距使他们无法推荐普遍的自闭症筛查，直到有更多的 证据CHOP在初级保健中成功实施了普遍的自闭症筛查- 83%的筛查 在2016年合规，并在电子健康中提供筛查和后续初级保健 记录本建议是对这些数据进行二次分析，以直接回答以下问题： 普遍筛查导致自闭症儿童的早期识别和早期治疗。 对13，503名儿童的初步分析发现，M-CHAT的敏感性令人惊讶地低- 40% 特异性94%，阳性预测值16%，阴性预测值98%。这些结果表明， 在仅进行一次筛查的情况下，筛查阴性的自闭症儿童（n=198）多于筛查阳性的自闭症儿童（n=128）。 该项目将包括一个两倍于此规模的样本（随着越来越多的儿童进入4-6岁年龄范围）， 在EHR中包含丰富的数据，以实现两个目标。目标1将确定诊断年龄 并在一个普遍的自闭症筛查项目中对儿童进行早期干预。许多因素促成了 到诊断和干预的年龄，但这个数据集可以回答三个重要的问题：第一， 确定诊断年龄和进入干预的自闭症儿童谁筛选阳性， 幼儿在第一次与第二次屏幕（“真阳性”）。二、要及早确定诊断年龄 对筛查结果为阴性但随后被诊断为自闭症（“假阴性”）的儿童进行干预， 因为负面的筛选结果仍然会导致对里程碑的认识提高。第三，探索时代 筛查与未筛查儿童的诊断和干预，因为一部分儿童没有 筛查（约17%的健康儿童就诊，以及一些接受疾病护理的儿童） 但没有为好孩子的访问）。目标2将确定提高筛查准确性的因素， 然后创建并验证临床决策树。儿科医生使用临床判断来解释筛查 结果-该项目可以提供急需的证据。自闭症风险因素等变量（例如，家庭 历史）;儿童人口统计学、医学和发育因素;家庭变量，包括社会经济因素 状态和父母的担忧;以及实践水平变量，包括患者人群人口统计学、提供者 多年的经验，提供者对筛查的依从性，以及提供者的转诊率，可以告知临床 判断成功完成后，该项目将提供来自全球的确切类型的证据， USPSTF要求的初级保健筛查，以及为儿科医生提供如何 微调他们对筛查结果的解释。这可能会严重影响诊断年龄， 干预，通过揭示筛查对谁有效，对谁无效。

PROJECT SUMMARY/ABSTRACT The United States Preventive Services Task Force (USPSTF) recently highlighted the lack of studies examining universal autism screening in primary care and whether it leads to earlier diagnosis and treatment. This gap prevented them from being able to recommend universal autism screening until there is more evidence. CHOP has successfully implemented universal autism screening in primary care – 83% screening compliance in 2016, and has both screening and follow-up primary care available in the electronic health record. This proposal is for a secondary analysis of this data to directly answer the question of whether universal screening results in earlier identification and earlier treatment entry for children with autism. Preliminary analyses on 13,503 children found the sensitivity of the M-CHAT was surprisingly low -- 40% (specificity 94%, positive predictive value 16%, and negative predictive value of 98%). These results show that, with only one screen, more children with autism screened negative (n=198) than screened positive (n=128). This project will include a sample double this size (as more children age into the 4-6 year age range), and include the wealth of data within the EHR to accomplish two aims. Aim 1 will determine the age of diagnosis and entry into early intervention for children in a universal autism screening program. Many factors contribute to the age of diagnosis and intervention, but this dataset can answer three important questions: First, to determine the age of diagnosis and entry to intervention for children with autism who screened positive as toddlers at the first v. second screen (“true positives”). Second, to determine the age of diagnosis and early intervention for children who screened negative but went on to be diagnosed with autism (“false negatives”), since negative screening results can still lead to heightened awareness of milestones. Third, to explore the age of diagnosis and intervention for screened v. unscreened children, since a subset of children were not screened (approximately 17% of well-child visits, as well as some number of children who received sick care but did not present for well-child visits). Aim 2 will identify factors that would improve screening accuracy, and then create and validate a clinical decision tree. Pediatricians use clinical judgment to interpret screening results—this project can provide much needed evidence. Variables such as autism risk factors (e.g., family history); child demographic, medical, and developmental factors; family variables including socio-economic status and parent concerns; and practice-level variables including patient population demographics, provider years of experience, provider compliance with screening, and provider rates of referrals, could inform clinical judgment. Upon successful completion, this project will contribute the exact type of evidence from universal screening in primary care requested by the USPSTF, as well as provide guidance for pediatricians on how to fine tune their interpretation of screening results. This can significantly impact the age of diagnosis and intervention, by revealing for whom screening is and is not working well.