INTRODUCTION Previous work had shown that machine learning models can predict inflammatory bowel disease (IBD)-related hospitalizations and outpatient corticosteroid use based on patient demographic and laboratory data in a cohort of United States Veterans. This study aimed to replicate this modeling framework in a nationally representative cohort. METHODS A retrospective cohort design using Optum Electronic Health Records (EHR) were used to identify IBD patients, with at least 12 months of follow-up between 2007 and 2018. IBD flare was defined as an inpatient/emergency visit with a diagnosis of IBD or an outpatient corticosteroid prescription for IBD. Predictors included demographic and laboratory data. Logistic regression and random forest (RF) models were used to predict IBD flare within 6 months of each visit. A 70% training and 30% validation approach was used. RESULTS A total of 95,878 patients across 780,559 visits were identified. Of these, 22,245 (23.2%) patients had at least one IBD flare. Patients were predominantly White (87.7%) and female (57.1%), with a mean age of 48.0 years. The logistic regression model had an area under the receiver operating curve (AuROC) of 0.66 (95% CI: 0.65-0.66), sensitivity of 0.69 (95% CI: 0.68-0.70), and specificity of 0.74 (95% CI: 0.73-0.74) in the validation cohort. The RF model had an AuROC of 0.80 (95% CI: 0.80-0.81), sensitivity of 0.74 (95% CI: 0.73-0.74), and specificity of 0.72 (95% CI: 0.72-0.72) in the validation cohort. Important predictors of IBD flare in the RF model were the number of previous flares, age, potassium, and white blood cell count. CONCLUSION The machine learning modeling framework was replicated and results showed a similar predictive accuracy in a nationally representative cohort of IBD patients. This modeling framework could be embedded in routine practice as a tool to distinguish high-risk patients for disease activity.

中文翻译：

---

复制炎症性肠病患者住院和皮质类固醇使用的预测算法。

引言 先前的工作表明，机器学习模型可以根据一组美国退伍军人的患者人口统计和实验室数据预测炎症性肠病 (IBD) 相关的住院和门诊皮质类固醇的使用。本研究旨在在具有全国代表性的队列中复制该建模框架。方法 使用 Optum 电子健康记录 (EHR) 的回顾性队列设计来识别 IBD 患者，并在 2007 年至 2018 年期间进行至少 12 个月的随访。 IBD 突发定义为诊断为 IBD 或用于 IBD 的门诊皮质类固醇处方。预测因素包括人口统计和实验室数据。Logistic 回归和随机森林 (RF) 模型用于预测每次访问后 6 个月内的 IBD 发作。使用了 70% 的训练和 30% 的验证方法。结果 共确定了 780,559 次访问中的 95,878 名患者。其中，22,245 (23.2%) 名患者至少有一次 IBD 发作。患者主要是白人 (87.7%) 和女性 (57.1%)，平均年龄为 48.0 岁。Logistic 回归模型的受试者工作曲线下面积 (AuROC) 为 0.66（95% CI：0.65-0.66），敏感性为 0.69（95% CI：0.68-0.70），特异性为 0.74（95% CI：0.73） -0.74) 在验证队列中。在验证队列中，RF 模型的 AuROC 为 0.80（95% CI：0.80-0.81），灵敏度为 0.74（95% CI：0.73-0.74），特异性为 0.72（95% CI：0.72-0.72）。RF 模型中 IBD 爆发的重要预测因素是先前爆发的次数、年龄、钾和白细胞计数。结论 复制了机器学习建模框架，结果在具有全国代表性的 IBD 患者队列中显示出类似的预测准确性。该建模框架可以嵌入到日常实践中，作为区分疾病活动高危患者的工具。