Hong Julian C, Niedzwiecki Donna, Palta Manisha, Tenenbaum Jessica D
All Authors: Duke University, Durham, NC.
JCO Clin Cancer Inform. 2018 Dec;2:1-11. doi: 10.1200/CCI.18.00037.
Patients undergoing radiotherapy (RT) or chemoradiotherapy (CRT) may require emergency department evaluation or hospitalization. Early identification may direct preventative supportive care, improving outcomes and reducing health care costs. We developed and evaluated a machine learning (ML) approach to predict these events.
A total of 8,134 outpatient courses of RT and CRT from a single institution from 2013 to 2016 were identified. Extensive pretreatment data were programmatically extracted and processed from the electronic health record (EHR). Training and internal validation cohorts were randomly generated (3:1 ratio). Gradient tree boosting (GTB), random forest, support vector machine, and least absolute shrinkage and selection operator logistic regression approaches were trained and internally validated based on area under receiver operating characteristic (AUROC) curve. The most predictive ML approach was also evaluated using only disease- and treatment-related factors to assess predictive gain of extensive EHR data.
All methods had high predictive accuracy, particularly GTB (validation AUROC, 0.798). Extensive EHR data beyond disease and treatment information improved accuracy (delta AUROC, 0.056). A Youden-based cutoff corresponded to validation sensitivity of 81.0% (175 of 216 courses with events) and specificity of 67.3% (1,218 of 1811 courses without events). Interpretability is an important advantage of GTB. Variable importance identified top predictive factors, including treatment (planned RT and systemic therapy), pretreatment encounters (emergency department visits and admissions in the year before treatment), vital signs (weight loss and pain score in the year before treatment), and laboratory values (albumin level at weeks before treatment).
ML predicts emergency visits and hospitalization during cancer therapy. Incorporating predictions into clinical care algorithms may help direct personalized supportive care, improve quality of care, and reduce costs. A prospective trial investigating ML-assisted direction of increased clinical assessments during RT is planned.
接受放射治疗（RT）或放化疗（CRT）的患者可能需要急诊科评估或住院治疗。早期识别可指导预防性支持治疗，改善治疗结果并降低医疗成本。我们开发并评估了一种机器学习（ML）方法来预测这些事件。
确定了2013年至2016年来自单一机构的总共8134例RT和CRT门诊疗程。通过编程从电子健康记录（EHR）中提取并处理了大量预处理数据。随机生成训练和内部验证队列（比例为3:1）。基于受试者操作特征（AUROC）曲线下面积，对梯度树提升（GTB）、随机森林、支持向量机以及最小绝对收缩和选择算子逻辑回归方法进行训练和内部验证。还仅使用疾病和治疗相关因素评估了预测性最强的ML方法，以评估大量EHR数据的预测增益。
所有方法均具有较高的预测准确性，尤其是GTB（验证AUROC为0.798）。疾病和治疗信息之外的大量EHR数据提高了准确性（AUROC增量为0.056）。基于约登指数的临界值对应的验证敏感性为81.0%（216例有事件的疗程中有175例），特异性为67.3%（1811例无事件的疗程中有1218例）。可解释性是GTB的一个重要优势。变量重要性确定了顶级预测因素，包括治疗（计划的RT和全身治疗）、预处理就诊情况（治疗前一年的急诊科就诊和住院）、生命体征（治疗前一年的体重减轻和疼痛评分）以及实验室值（治疗前几周的白蛋白水平）。
ML可预测癌症治疗期间的急诊就诊和住院情况。将预测纳入临床护理算法可能有助于指导个性化支持治疗，提高护理质量并降低成本。计划开展一项前瞻性试验，研究ML辅助指导在RT期间增加临床评估的情况。
