POPCORN: A web service for individual PrognOsis prediction based on multi-center clinical data CollabORatioN without patient-level data sharing
POPCORN: A web service for individual PrognOsis prediction based on multi-center clinical data CollabORatioN without patient-level data sharing
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POPCORN：基于多中心临床数据协作的个体预后预测网络服务，无需患者级数据共享
DOI：
10.1016/j.jbi.2018.08.008
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发表时间：
2018-10
影响因子：
4.5
通讯作者：
Li Jing-Song
中科院分区：
文献类型：
--
作者：
Tian Yu;Shang Yong;Tong Dan-Yang;Chi Sheng-Qiang;Li Jun;Kong Xiang-Xing;Ding Ke-Feng;Li Jing-Song
背景与目的：临床预后预测在临床研究和实践中占有重要地位。基于电子健康档案数据的预测模型的构建已成为近年来的研究热点。由于缺乏外部验证，基于单中心、特定于医院的数据集的预测模型可能不能很好地与来自其他医疗机构的数据集一起使用。因此，研究基于多中心电子病历数据协同分析的预后预测模型构建，可以增加模型训练的患者数量和覆盖面，丰富患者的预后特征，最终提高预后预测的准确性和泛化能力。材料和方法：提出了一种基于多中心临床数据协作而无需患者级数据共享的个体预后预测Web服务(爆米花)。着重解决基于电子健康档案系统的多中心协作研究中的关键问题，包括临床数据表达的标准化、模型训练过程中患者隐私的保护以及病例组合变化对预测模型构建和应用的影响。爆米花基于多变量荟萃分析和贝叶斯框架，可以构建适用于多种临床场景的预测模型，能够有效地适应复杂的临床应用环境。结果：爆米花通过中国和美国联合多中心合作研究网络对被诊断为结直肠癌的患者进行了验证。基于爆米花的模型的性能与标准预后预测模型的性能相当；然而，爆米花不会暴露患者的原始数据。预测模型具有相似的AUC，但BMA模型的ECI在所有预测模型中最低，这表明该模型比其他模型具有更好的校准性能，尤其是对中国医院的患者。结论：爆米花系统可以构建在复杂的临床应用场景中表现良好的预测模型，并可以为个体患者的预后预测提供有效的决策支持。
Background and objective: Clinical prognosis prediction plays an important role in clinical research and practice. The construction of prediction models based on electronic health record data has recently become a research focus. Due to the lack of external validation, prediction models based on single-center, hospital-specific datasets may not perform well with datasets from other medical institutions. Therefore, research investigating prognosis prediction model construction based on a collaborative analysis of multi-center electronic health record data could increase the number and coverage of patients used for model training, enrich patient prognostic features and ultimately improve the accuracy and generalization of prognosis prediction. Materials and methods: A web service for individual prognosis prediction based on multi-center clinical data collaboration without patient-level data sharing (POPCORN) was proposed. POPCORN focuses on solving key issues in multi-center collaborative research based on electronic health record systems; these issues include the standardization of clinical data expression, the preservation of patient privacy during model training and the effect of case mix variance on the prediction model construction and application. POPCORN is based on a multivariable meta-analysis and a Bayesian framework and can construct suitable prediction models for multiple clinical scenarios that can effectively adapt to complex clinical application environments. Results: POPCORN was validated using a joint, multi-center collaborative research network between China and the United States with patients diagnosed with colorectal cancer. The performance of the models based on POPCORN was comparable-to that-of-the standard prognosis prediction model; however, POPCORN did not expose raw patient data. The prediction models had similar AUC, but the BMA model had the lowest ECI across all prediction models, indicating that this model had better calibration performance than the other models, especially for patients in Chinese hospitals. Conclusions: The POPCORN system can build prediction models that perform well in complex clinical application scenarios and can provide effective decision support for individual patient prognostic predictions.
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