Abstract：
Osteogenesis imperfecta (OI) is a genetic disease with an estimated prevalence of 1 in 13,500 and 1 in 9700. The classification into subtypes of OI is important for prognosis and management. In this study, we established a clinical severity prediction model depending on multiple features of variants in COL1A1/2 genes.
BACKGROUND: Ninety percent of OI cases are caused by pathogenic variants in the COL1A1/COL1A2 gene. The Sillence classification describes four OI types with variable clinical features ranging from mild symptoms to lethal and progressively deforming symptoms.
METHODS: We established a prediction model of the clinical severity of OI based on the random forest model with a training set obtained from the Human Gene Mutation Database, including 790 records of the COL1A1/COL1A2 genes. The features used in the prediction model were respectively based on variant-type features only, and the optimized features.
RESULTS: With the training set, the prediction results showed that the area under the receiver operating characteristic curve (AUC) for predicting lethal to severe OI or mild/moderate OI was 0.767 and 0.902, respectively, when using variant-type features only and optimized features for COL1A1 defects, 0.545 and 0.731, respectively, for COL1A2 defects. For the 17 patients from our hospital, prediction accuracy for the patient with the COL1A1 and COL1A2 defects was 76.5% (95% CI: 50.1-93.2%) and 88.2% (95% CI: 63.6-98.5%), respectively.
CONCLUSIONS: We established an OI severity prediction model depending on multiple features of the specific variants in COL1A1/2 genes, with a prediction accuracy of 76-88%. This prediction algorithm is a promising alternative that could prove to be valuable in clinical practice.
BACKGROUND: Ninety percent of OI cases are caused by pathogenic variants in the COL1A1/COL1A2 gene. The Sillence classification describes four OI types with variable clinical features ranging from mild symptoms to lethal and progressively deforming symptoms.
METHODS: We established a prediction model of the clinical severity of OI based on the random forest model with a training set obtained from the Human Gene Mutation Database, including 790 records of the COL1A1/COL1A2 genes. The features used in the prediction model were respectively based on variant-type features only, and the optimized features.
RESULTS: With the training set, the prediction results showed that the area under the receiver operating characteristic curve (AUC) for predicting lethal to severe OI or mild/moderate OI was 0.767 and 0.902, respectively, when using variant-type features only and optimized features for COL1A1 defects, 0.545 and 0.731, respectively, for COL1A2 defects. For the 17 patients from our hospital, prediction accuracy for the patient with the COL1A1 and COL1A2 defects was 76.5% (95% CI: 50.1-93.2%) and 88.2% (95% CI: 63.6-98.5%), respectively.
CONCLUSIONS: We established an OI severity prediction model depending on multiple features of the specific variants in COL1A1/2 genes, with a prediction accuracy of 76-88%. This prediction algorithm is a promising alternative that could prove to be valuable in clinical practice.
摘要：
成骨不全症（OI）是一种遗传性疾病，估计患病率为13,500中的1和9700中的1。OI亚型的分类对于预后和管理很重要。在这项研究中,我们根据COL1A1/2基因变异的多个特征建立了临床严重程度预测模型.
背景：90%的OI病例是由COL1A1/COL1A2基因的致病变异引起的。Sillence分类描述了四种OI类型，具有从轻度症状到致命和逐渐变形症状的可变临床特征。
方法：我们基于随机森林模型，使用从人类基因突变数据库获得的训练集，建立了OI临床严重程度的预测模型，包括COL1A1/COL1A2基因的790条记录。预测模型中使用的特征分别仅基于变体类型特征，和优化的功能。
结果：使用训练集，预测结果表明，预测重度OI或轻度/中度OI致死性的受试者工作特征曲线下面积（AUC）分别为0.767和0.902，当仅使用变体类型特征和COL1A1缺陷的优化特征时，分别为0.545和0.731，COL1A2缺陷。对于我们医院的17名患者，COL1A1和COL1A2缺陷患者的预测准确率分别为76.5%(95%CI:50.1-93.2%)和88.2%(95%CI:63.6-98.5%),分别。
结论：我们根据COL1A1/2基因中特定变异的多个特征建立了OI严重程度预测模型，预测准确率为76-88%。这种预测算法是一种有前途的替代方案，可以证明在临床实践中很有价值。
背景：90%的OI病例是由COL1A1/COL1A2基因的致病变异引起的。Sillence分类描述了四种OI类型，具有从轻度症状到致命和逐渐变形症状的可变临床特征。
方法：我们基于随机森林模型，使用从人类基因突变数据库获得的训练集，建立了OI临床严重程度的预测模型，包括COL1A1/COL1A2基因的790条记录。预测模型中使用的特征分别仅基于变体类型特征，和优化的功能。
结果：使用训练集，预测结果表明，预测重度OI或轻度/中度OI致死性的受试者工作特征曲线下面积（AUC）分别为0.767和0.902，当仅使用变体类型特征和COL1A1缺陷的优化特征时，分别为0.545和0.731，COL1A2缺陷。对于我们医院的17名患者，COL1A1和COL1A2缺陷患者的预测准确率分别为76.5%(95%CI:50.1-93.2%)和88.2%(95%CI:63.6-98.5%),分别。
结论：我们根据COL1A1/2基因中特定变异的多个特征建立了OI严重程度预测模型，预测准确率为76-88%。这种预测算法是一种有前途的替代方案，可以证明在临床实践中很有价值。
