Abstract：
OBJECTIVE: The objective of this study was to develop a simple tool for general physicians to promptly identify and refer pediatric patients with a higher probability of having a genetic condition.
METHODS: This retrospective, descriptive study was conducted at a tertiary pediatric hospital\'s Clinical Genetics Unit from June 2019 to January 2020. We included patients under 18 years of age who visited the unit, excluding those without genetic testing. Epidemiological, clinical, and genetic variables were collected from electronic medical records. The primary outcome was the diagnosis of a genetic condition based on genetic testing.
RESULTS: Among 445 patients, 304 were included; 163 (53.6%) were male, and mean age was 7.4 years (SD 5.1 years). A genetic condition was diagnosed in 139 patients (45.7%). Using a multiple logistic regression model, five variables significantly contributed to reaching a diagnosis: suspected diagnosis at referral (OR 3.45, P < 0.001), short stature (OR 3.11, P < 0.001), global developmental delay/intellectual disability (OR 2.65, P < 0.001), dysmorphic craniofacial features (OR 1.99, P = 0.035), and multiple congenital anomalies (OR 2.54, P = 0.033). The association strength (OR) increased when these variables were paired with each other. The study\'s findings are presented in the form of a triangle, known as the Clinical Genetics Assessment Triangle (CGAT), which summarizes the results. A decision tree model is applied to guide clinical department referrals based on the affected sides of the triangle.
CONCLUSIONS: The CGAT has the potential to enable general physicians to promptly identify pediatric patients with an increased probability of having a genetic condition.
METHODS: This retrospective, descriptive study was conducted at a tertiary pediatric hospital\'s Clinical Genetics Unit from June 2019 to January 2020. We included patients under 18 years of age who visited the unit, excluding those without genetic testing. Epidemiological, clinical, and genetic variables were collected from electronic medical records. The primary outcome was the diagnosis of a genetic condition based on genetic testing.
RESULTS: Among 445 patients, 304 were included; 163 (53.6%) were male, and mean age was 7.4 years (SD 5.1 years). A genetic condition was diagnosed in 139 patients (45.7%). Using a multiple logistic regression model, five variables significantly contributed to reaching a diagnosis: suspected diagnosis at referral (OR 3.45, P < 0.001), short stature (OR 3.11, P < 0.001), global developmental delay/intellectual disability (OR 2.65, P < 0.001), dysmorphic craniofacial features (OR 1.99, P = 0.035), and multiple congenital anomalies (OR 2.54, P = 0.033). The association strength (OR) increased when these variables were paired with each other. The study\'s findings are presented in the form of a triangle, known as the Clinical Genetics Assessment Triangle (CGAT), which summarizes the results. A decision tree model is applied to guide clinical department referrals based on the affected sides of the triangle.
CONCLUSIONS: The CGAT has the potential to enable general physicians to promptly identify pediatric patients with an increased probability of having a genetic condition.
摘要：
目的：这项研究的目的是为普通医师开发一种简单的工具，以迅速识别和转诊具有较高遗传状况概率的儿科患者。
方法：本回顾性研究，描述性研究于2019年6月至2020年1月在三级儿科医院的临床遗传学部门进行。我们包括18岁以下访问该单位的患者，不包括那些没有基因检测的人。流行病学,临床,和遗传变量是从电子病历中收集的。主要结果是基于基因检测的遗传状况的诊断。
结果：在445名患者中，包括304;163（53.6％）为男性，平均年龄为7.4岁（SD5.1岁）。139例患者（45.7%）被诊断为遗传病。使用多元逻辑回归模型，五个变量显着有助于达到诊断：转诊时可疑诊断（OR3.45，P<0.001），身材矮小（OR3.11，P<0.001），全球发育迟缓/智力残疾（OR2.65，P<0.001），畸形颅面特征（OR1.99，P=0.035），和多种先天性异常（OR2.54，P=0.033）。当这些变量彼此配对时，关联强度（OR）增加。这项研究的发现以三角形的形式呈现，被称为临床遗传学评估三角形(CGAT)，其中总结了结果。基于三角形的受影响边，应用决策树模型来指导临床科室转诊。
结论：CGAT有可能使普通医生能够及时识别出患有遗传疾病的可能性增加的儿科患者。
方法：本回顾性研究，描述性研究于2019年6月至2020年1月在三级儿科医院的临床遗传学部门进行。我们包括18岁以下访问该单位的患者，不包括那些没有基因检测的人。流行病学,临床,和遗传变量是从电子病历中收集的。主要结果是基于基因检测的遗传状况的诊断。
结果：在445名患者中，包括304;163（53.6％）为男性，平均年龄为7.4岁（SD5.1岁）。139例患者（45.7%）被诊断为遗传病。使用多元逻辑回归模型，五个变量显着有助于达到诊断：转诊时可疑诊断（OR3.45，P<0.001），身材矮小（OR3.11，P<0.001），全球发育迟缓/智力残疾（OR2.65，P<0.001），畸形颅面特征（OR1.99，P=0.035），和多种先天性异常（OR2.54，P=0.033）。当这些变量彼此配对时，关联强度（OR）增加。这项研究的发现以三角形的形式呈现，被称为临床遗传学评估三角形(CGAT)，其中总结了结果。基于三角形的受影响边，应用决策树模型来指导临床科室转诊。
结论：CGAT有可能使普通医生能够及时识别出患有遗传疾病的可能性增加的儿科患者。
