PDF(Pubmed)
Abstract：
Every year, 11% of infants are born preterm with significant health consequences, with the vaginal microbiome a risk factor for preterm birth. We crowdsource models to predict (1) preterm birth (PTB; <37 weeks) or (2) early preterm birth (ePTB; <32 weeks) from 9 vaginal microbiome studies representing 3,578 samples from 1,268 pregnant individuals, aggregated from public raw data via phylogenetic harmonization. The predictive models are validated on two independent unpublished datasets representing 331 samples from 148 pregnant individuals. The top-performing models (among 148 and 121 submissions from 318 teams) achieve area under the receiver operator characteristic (AUROC) curve scores of 0.69 and 0.87 predicting PTB and ePTB, respectively. Alpha diversity, VALENCIA community state types, and composition are important features in the top-performing models, most of which are tree-based methods. This work is a model for translation of microbiome data into clinically relevant predictive models and to better understand preterm birth.
摘要：
每年,11％的婴儿早产，具有重大的健康后果，阴道微生物组是早产的危险因素。我们从9个阴道微生物组研究中预测（1）早产（PTB;<37周）或（2）早期早产（ePTB;<32周），这些研究代表了1,268名孕妇的3,578个样本，通过系统发育协调从公共原始数据汇总。预测模型在代表来自148个怀孕个体的331个样品的两个独立的未发表的数据集上进行验证。表现最好的模型（在318个团队的148个和121个提交中）在接收者操作员特征（AUROC）曲线下的区域得分分别为0.69和0.87，预测PTB和ePTB，分别。阿尔法多样性，瓦伦西亚社区州类型，和构图是表现最好的模型的重要特征，其中大多数是基于树的方法。这项工作是将微生物组数据转化为临床相关预测模型并更好地了解早产的模型。
