Division of Biostatistics, Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada; ICES, Ontario, Canada; Institute of Health Policy, Management and Evaluation, University of Toronto, Toronto, Ontario.
Division of Biostatistics, Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada.
J Pain Symptom Manage. 2019 Nov;58(5):745-755. doi: 10.1016/j.jpainsymman.2019.07.007. Epub 2019 Jul 16.
Prior work shows measurements of symptom severity using the Edmonton Symptom Assessment System (ESAS) which are associated with emergency department (ED) visits in patients with cancer; however, it is not known if symptom severity improves the ability to predict ED visits.
To determine whether information on symptom severity improves the ability to predict ED visits among patients with cancer.
This was a population-based study of patients who were diagnosed with cancer and had at least one ESAS assessment completed between 2007 and 2015 in Ontario, Canada. After splitting the cohort into training and test sets, two ED visit risk prediction models using logistic regression were developed on the training cohort, one without ESAS and one with ESAS. The predictive performance of each risk model was assessed on the test cohort and compared with respect to area under the curve and calibration.
The full cohort consisted of 212,615 unique patients with a total of 1,267,294 ESAS assessments. The risk prediction model including ESAS was superior in sensitivity, specificity, accuracy, and discrimination. The area under the curve was 73.7% under the model with ESAS, whereas it was 70.1% under the model without ESAS. The model with ESAS was also better calibrated. This improvement in calibration was particularly noticeable among patients in the higher deciles of predicted risk.
This study demonstrates the importance of incorporating symptom measurements when developing an ED visit risk calculator for patients with cancer. Improved predictive models for ED visits using measurements of symptom severity may serve as an important clinical tool to prompt timely interventions by the cancer care team before an ED visit is necessary.
先前的研究表明，使用 Edmonton 症状评估系统（ESAS）测量的症状严重程度与癌症患者的急诊科就诊相关；然而，目前尚不清楚症状严重程度是否能提高预测急诊科就诊的能力。
确定症状严重程度信息是否能提高预测癌症患者急诊科就诊的能力。
这是一项基于人群的研究，纳入了 2007 年至 2015 年期间在加拿大安大略省被诊断患有癌症且至少完成过一次 ESAS 评估的患者。在将队列分为训练集和测试集后，在训练集上使用逻辑回归开发了两个急诊科就诊风险预测模型，一个没有 ESAS，一个有 ESAS。在测试集上评估每个风险模型的预测性能，并比较曲线下面积和校准。
全队列由 212615 名独特的患者组成，共有 1267294 次 ESAS 评估。包含 ESAS 的风险预测模型在敏感性、特异性、准确性和区分度方面表现更好。包含 ESAS 的模型的曲线下面积为 73.7%，而不包含 ESAS 的模型为 70.1%。包含 ESAS 的模型的校准也更好。在预测风险较高的患者中，这种校准的改善尤其明显。
本研究表明，在为癌症患者开发急诊科就诊风险计算器时，纳入症状测量非常重要。使用症状严重程度测量值改进的急诊科就诊预测模型可以作为一个重要的临床工具，以便在需要急诊科就诊之前及时提醒癌症护理团队进行干预。
