Edwin S.H. Leong Centre for Health Children, The Hospital for Sick Children, University of Toronto, Toronto, ON, Canada; Child Health Evaluative Sciences, The Hospital for Sick Children, Toronto, ON, Canada; Transplant and Regenerative Medicine Centre, The Hospital for Sick Children, Toronto, ON, Canada.
Department of Clinical Epidemiology, Aarhus University Hospital and Aarhus University, Aarhus, Denmark.
J Pediatr. 2024 Jul;270:114013. doi: 10.1016/j.jpeds.2024.114013. Epub 2024 Mar 15.
To define major congenital anomaly (CA) subgroups and assess outcome variability based on defined subgroups.
This population-based cohort study used registries in Denmark for children born with a major CA between January 1997 and December 2016, with follow-up until December 2018. We performed a latent class analysis (LCA) using child and family clinical and sociodemographic characteristics present at birth, incorporating additional variables occurring until age of 24 months. Cox proportional hazards regression models estimated hazard ratios (HRs) of pediatric mortality and intensive care unit (ICU) admissions for identified LCA classes.
The study included 27 192 children born with a major CA. Twelve variables led to a 4-class solution (entropy = 0.74): (1) children born with higher income and fewer comorbidities (55.4%), (2) children born to young mothers with lower income (24.8%), (3) children born prematurely (10.0%), and (4) children with multiorgan involvement and developmental disability (9.8%). Compared with those in Class 1, mortality and ICU admissions were highest in Class 4 (HR = 8.9, 95% CI = 6.4-12.6 and HR = 4.1, 95% CI = 3.6-4.7, respectively). More modest increases were observed among the other classes for mortality and ICU admissions (Class 2: HR = 1.7, 95% CI = 1.1-2.5 and HR = 1.3, 95% CI = 1.1-1.4, respectively; Class 3: HR = 2.5, 95% CI = 1.5-4.2 and HR = 1.5, 95% CI = 1.3-1.9, respectively).
Children with a major CA can be categorized into meaningful subgroups with good discriminative ability. These groupings may be useful for risk-stratification in outcome studies.
定义主要先天异常（CA）亚组，并根据定义的亚组评估结果变异性。
本基于人群的队列研究使用丹麦的登记处，纳入了 1997 年 1 月至 2016 年 12 月间出生的患有主要 CA 的儿童，并随访至 2018 年 12 月。我们使用出生时儿童和家庭的临床及社会人口学特征，以及 24 个月龄前出现的其他变量进行潜在类别分析（LCA）。Cox 比例风险回归模型估计了为确定的 LCA 类别确定的儿科死亡率和重症监护病房（ICU）入院的风险比（HR）。
该研究共纳入了 27192 名患有主要 CA 的儿童。12 个变量导致了 4 类解决方案（熵=0.74）：（1）出生于高收入家庭且合并症较少的儿童（55.4%）；（2）出生于年轻母亲、低收入家庭的儿童（24.8%）；（3）早产儿（10.0%）；（4）多器官受累和发育障碍的儿童（9.8%）。与第 1 类相比，第 4 类的死亡率和 ICU 入院率最高（HR=8.9，95%CI=6.4-12.6；HR=4.1，95%CI=3.6-4.7）。其他类别死亡率和 ICU 入院率也有适度增加（第 2 类：HR=1.7，95%CI=1.1-2.5；HR=1.3，95%CI=1.1-1.4；第 3 类：HR=2.5，95%CI=1.5-4.2；HR=1.5，95%CI=1.3-1.9）。
患有主要 CA 的儿童可以分为具有良好判别能力的有意义的亚组。这些分组可能有助于结局研究中的风险分层。
