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Diagnostic Yield of Genetic Testing in Cerebral Palsy

医学 基因检测 病因学 荟萃分析 逻辑回归 观察研究 儿科 研究异质性 遗传异质性 产量(工程) 一致性 系统回顾 数据提取 诊断试验 多重比较问题 内科学 磁共振成像 样本量测定 卡帕 优势比 队列 邦费罗尼校正 遗传模型 科恩卡帕 遗传力 人口
作者
Sun-Young Joo,Eun Jae Ko,Bo Ryun Kim,Jaewon Kim,김지용,Hye Jung Park,Jin A. Yoon,You Gyoung Yi,Hyun Jung Lee,Hoo Young Lee,Ah-Ra Cho,Ja Young Choi,Seungbeen Hong,홍준택,Dae-Hyun Jang
出处
期刊:Archives of pediatrics & adolescent medicine [American Medical Association]
标识
DOI:10.1001/jamapediatrics.2026.4471
摘要

Importance: Genetic etiologies are increasingly recognized in cerebral palsy (CP), yet diagnostic yields across different clinical subgroups and phenotypes remain inconsistent, limiting evidence-based prioritization of genetic testing. Objective: To determine the pooled diagnostic yield of genetic testing in CP and identify clinical and phenotypic predictors that influence diagnostic yields. Data Sources: A systematic search of PubMed, Embase, and the Cochrane Library was conducted for studies published between January 2010 and August 25, 2025. Study Selection: Peer-reviewed observational studies reporting genetic testing results in cohorts of at least 10 patients with CP. Data Extraction and Synthesis: Data were extracted independently by 2 reviewers following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines. A random-effects model with logit transformation was used to calculate pooled diagnostic yields with 95% CIs. Heterogeneity was evaluated using the I2 statistic and Cochran Q test. Main Outcomes and Measures: The primary outcome was the pooled diagnostic yield of genetic testing stratified by CP etiology (cryptogenic, noncryptogenic, and mixed/unselected cohorts). Secondary outcomes included diagnostic yields across phenotype-based subgroups defined by brain magnetic resonance imaging (MRI) findings, motor types, comorbidities, facial dysmorphism, congenital anomalies, and clinical trajectory. Results: A total of 31 studies met inclusion criteria, of which 20 were included in the quantitative meta-analysis. The overall pooled diagnostic yield in unselected CP cohorts was 0.19 (95% CI, 0.11-0.32; I2 = 95.1%). In patients with cryptogenic CP, the yield was 0.44 (95% CI, 0.37-0.50; I2 = 72.2%), compared with 0.13 (95% CI, 0.07-0.21; I2 = 85.1%) in noncryptogenic cases. Higher yields were observed in patients with normal brain MRI findings (25%-80%), facial dysmorphism (66%-71%), congenital anomalies (85%), and neurological atypical features (50%). Combined next-generation sequencing and chromosomal microarray analysis provided the highest pooled yield of 0.53 (95% CI, 0.43-0.62) in the cryptogenic subgroup. Conclusions and Relevance: In this systematic review and meta-analysis, genetic testing demonstrated a high diagnostic yield in CP, particularly in cryptogenic cases and in patients with specific clinical phenotypes. These findings suggest the integration of genomic evaluation into the standard diagnostic workup for CP, guided by clinical indicators, to facilitate timely diagnosis and personalized management.
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