Lopez-Leon Sandra, Wen Xuerong, Gaitonde Sneha, Afonso Ana Sofia, Colas Sandrine, DiSantostefano Rachael L, Kürzinger Marie-Laure, Le Noan-Lainé Maryline, Mitter Vera Ruth, Murray Gayle, Sabidó Meritxell, Scotto Julie, Jacobson Melanie H, Bromley Rebecca L, Sarayani Amir
Quantitative Safety & Epidemiology, Novartis Pharmaceuticals, East Hanover, New Jersey, USA.
Rutgers Center for Pharmacoepidemiology and Treatment Science, Rutgers University, New Brunswick, New Jersey, USA.
Pharmacoepidemiol Drug Saf. 2025 Sep;34(9):e70196. doi: 10.1002/pds.70196.
Investigating pediatric neurodevelopmental outcomes (NDO) in studies using secondary data is often challenging due to heterogeneous clinical definitions and medical coding systems. This study aims to identify the algorithms used to define NDO in studies using electronic healthcare data through a systematic literature review.
A search strategy was developed to identify studies on NDO that describe phenotype algorithms from January 1, 2010, to March 10, 2025. The search strategy included terms to identify studies containing algorithms for NDO as an outcome, routinely collected healthcare data, epidemiologic designs likely to incorporate algorithms, and pregnant individuals and/or infants/children. Two independent reviewers assessed eligibility criteria and performed data extraction, with inconsistencies reviewed by a third reviewer. Descriptive statistics were used to summarize categorical and continuous variables appropriately.
The review included 156 publications that implemented algorithms for NDO, with 18 of these studies validating the outcomes. Most publications studied autism spectrum disorder (ASD) (n = 103, 65.6%) and attention deficit hyperactivity disorder (ADHD) (n = 72, 45.9%) either as a single outcome or as a composite.
Instead of presenting NDO as a composite outcome, it is recommended to present multiple single outcomes. Validated outcomes in data from Nordic countries demonstrate a high positive predictive value when using one code for diagnoses, while more complex algorithms are required for US data. Clearly detailing and establishing the time of assessment for each NDO is critical to inform valid epidemiological estimates.
在使用二手数据的研究中，由于临床定义和医学编码系统的异质性，调查儿科神经发育结局（NDO）往往具有挑战性。本研究旨在通过系统的文献综述，确定在使用电子医疗数据的研究中用于定义NDO的算法。
制定了一项检索策略，以识别2010年1月1日至2025年3月10日期间关于NDO且描述表型算法的研究。检索策略包括用于识别包含以NDO为结局的算法、常规收集的医疗数据、可能纳入算法的流行病学设计以及孕妇和/或婴儿/儿童的研究的术语。两名独立评审员评估纳入标准并进行数据提取，如有不一致则由第三名评审员进行审核。使用描述性统计方法对分类变量和连续变量进行适当汇总。
该综述纳入了156篇实施NDO算法的出版物，其中18项研究对结局进行了验证。大多数出版物将自闭症谱系障碍（ASD）（n = 103，65.6%）和注意力缺陷多动障碍（ADHD）（n = 72，45.9%）作为单一结局或复合结局进行研究。
建议呈现多个单一结局，而不是将NDO作为复合结局呈现。北欧国家数据中的验证结局表明，使用一个代码进行诊断时具有较高的阳性预测价值，而美国数据则需要更复杂的算法。明确详细地说明并确定每个NDO的评估时间对于得出有效的流行病学估计至关重要。
