利用易得的纵向数据预测克罗恩病手术的可能性。
The Use of Readily Available Longitudinal Data to Predict the Likelihood of Surgery in Crohn Disease.
机构信息
Department of Internal Medicine, Division of Gastroenterology and Hepatology, University of Michigan Medical School, Ann Arbor, Michigan, USA.
Michigan Integrated Center for Health Analytics and Medical Prediction, Ann Arbor, Michigan, USA.
出版信息
Inflamm Bowel Dis. 2021 Jul 27;27(8):1328-1334. doi: 10.1093/ibd/izab035.
BACKGROUND
Although imaging, endoscopy, and inflammatory biomarkers are associated with future Crohn disease (CD) outcomes, common laboratory studies may also provide prognostic opportunities. We evaluated machine learning models incorporating routinely collected laboratory studies to predict surgical outcomes in U.S. Veterans with CD.
METHODS
Adults with CD from a Veterans Health Administration, Veterans Integrated Service Networks (VISN) 10 cohort examined between 2001 and 2015 were used for analysis. Patient demographics, medication use, and longitudinal laboratory values were used to model future surgical outcomes within 1 year. Specifically, data at the time of prediction combined with historical laboratory data characteristics, described as slope, distribution statistics, fluctuation, and linear trend of laboratory values, were considered and principal component analysis transformations were performed to reduce the dimensionality. Lasso regularized logistic regression was used to select features and construct prediction models, with performance assessed by area under the receiver operating characteristic using 10-fold cross-validation.
RESULTS
We included 4950 observations from 2809 unique patients, among whom 256 had surgery, for modeling. Our optimized model achieved a mean area under the receiver operating characteristic of 0.78 (SD, 0.002). Anti-tumor necrosis factor use was associated with a lower probability of surgery within 1 year and was the most influential predictor in the model, and corticosteroid use was associated with a higher probability of surgery. Among the laboratory variables, high platelet counts, high mean cell hemoglobin concentrations, low albumin levels, and low blood urea nitrogen values were identified as having an elevated influence and association with future surgery.
CONCLUSIONS
Using machine learning methods that incorporate current and historical data can predict the future risk of CD surgery.
背景
尽管影像学、内镜检查和炎症生物标志物与克罗恩病（CD）的未来结局相关，但常规实验室研究也可能提供预后机会。我们评估了纳入常规收集的实验室研究的机器学习模型，以预测美国退伍军人 CD 患者的手术结局。
方法
使用退伍军人健康管理局（VA）退伍军人综合服务网络（VISN）10 队列中 2001 年至 2015 年间接受检查的成年 CD 患者进行分析。患者人口统计学、药物使用情况和纵向实验室值用于预测 1 年内的未来手术结局。具体来说，预测时的数据与历史实验室数据特征（如斜率、分布统计、波动和实验室值的线性趋势）相结合，并进行主成分分析转换以降低维度。使用套索正则化逻辑回归选择特征并构建预测模型，使用 10 折交叉验证的接收器操作特征曲线下面积评估性能。
结果
我们纳入了 2809 名患者中的 4950 个观测值进行建模，其中 256 人接受了手术。我们优化后的模型在接收器操作特征曲线下的平均面积为 0.78（SD，0.002）。使用抗肿瘤坏死因子与 1 年内手术的可能性降低相关，是模型中最具影响力的预测因子，而使用皮质类固醇与手术的可能性增加相关。在实验室变量中，血小板计数高、平均红细胞血红蛋白浓度高、白蛋白水平低和血尿素氮值低被确定为具有较高的影响和与未来手术的关联。
结论
使用纳入当前和历史数据的机器学习方法可以预测 CD 手术的未来风险。
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