UCLA Center for Inflammatory Bowel Diseases, Vatche and Tamar Manoukian Division of Digestive Disease, David Geffen School of Medicine, University of California at Los Angeles, 10945 Le Conte Ave #2338, Los Angeles, CA, 90095, USA.
OptumLabs Visiting Fellow, Eden Prairie, MN, USA.
Dig Dis Sci. 2022 Oct;67(10):4874-4885. doi: 10.1007/s10620-022-07506-8. Epub 2022 Apr 27.
Inflammatory Bowel Diseases with its complexity and heterogeneity could benefit from the increased application of Artificial Intelligence in clinical management.
To accurately predict adverse outcomes in patients with IBD using advanced computational models in a nationally representative dataset for potential use in clinical practice.
We built a training model cohort and validated our result in a separate cohort. We used LASSO and Ridge regressions, Support Vector Machines, Random Forests and Neural Networks to balance between complexity and interpretability and analyzed their relative performances and reported the strongest predictors to the respective models. The participants in our study were patients with IBD selected from The OptumLabs® Data Warehouse (OLDW), a longitudinal, real-world data asset with de-identified administrative claims and electronic health record (EHR) data.
We included 72,178 and 69,165 patients in the training and validation set, respectively. In total, 4.1% of patients in the validation set were hospitalized, 2.9% needed IBD-related surgeries, 17% used long-term steroids and 13% of patients were initiated with biological therapy. Of the AI models we tested, the Random Forest and LASSO resulted in high accuracies (AUCs 0.70-0.92). Our artificial neural network performed similarly well in most of the models (AUCs 0.61-0.90).
This study demonstrates feasibility of accurately predicting adverse outcomes using complex and novel AI models on large longitudinal data sets of patients with IBD. These models could be applied for risk stratification and implementation of preemptive measures to avoid adverse outcomes in a clinical setting.
炎症性肠病具有复杂性和异质性，可受益于人工智能在临床管理中的应用增加。
使用全国代表性数据集的先进计算模型准确预测 IBD 患者的不良结局，以便在临床实践中潜在应用。
我们构建了一个训练模型队列，并在单独的队列中验证了我们的结果。我们使用 LASSO 和 Ridge 回归、支持向量机、随机森林和神经网络在复杂性和可解释性之间取得平衡，并分析了它们的相对性能，并向各自的模型报告了最强的预测因素。我们的研究参与者是从 OptumLabs®Data Warehouse（OLDW）中选择的患有 IBD 的患者，OLDW 是一个具有去识别管理索赔和电子健康记录（EHR）数据的纵向真实世界数据资产。
我们分别在训练集和验证集中纳入了 72178 名和 69165 名患者。验证集中共有 4.1%的患者住院，2.9%需要 IBD 相关手术，17%使用长期类固醇，13%的患者开始接受生物治疗。在我们测试的 AI 模型中，随机森林和 LASSO 产生了较高的准确率（AUC 为 0.70-0.92）。我们的人工神经网络在大多数模型中表现良好（AUC 为 0.61-0.90）。
这项研究表明，在大型 IBD 患者纵向数据集上使用复杂和新颖的 AI 模型准确预测不良结局是可行的。这些模型可应用于风险分层和实施预防措施，以避免临床环境中的不良结局。
