Jo Yong-Yeon, Han JaiHong, Park Hyun Woo, Jung Hyojung, Lee Jae Dong, Jung Jipmin, Cha Hyo Soung, Sohn Dae Kyung, Hwangbo Yul
Healthcare AI Team, National Cancer Center, Goyang, Republic of Korea.
Department of Surgery, National Cancer Center, Goyang, Republic of Korea.
JMIR Med Inform. 2021 Feb 22;9(2):e23147. doi: 10.2196/23147.
Postoperative length of stay is a key indicator in the management of medical resources and an indirect predictor of the incidence of surgical complications and the degree of recovery of the patient after cancer surgery. Recently, machine learning has been used to predict complex medical outcomes, such as prolonged length of hospital stay, using extensive medical information.
The objective of this study was to develop a prediction model for prolonged length of stay after cancer surgery using a machine learning approach.
In our retrospective study, electronic health records (EHRs) from 42,751 patients who underwent primary surgery for 17 types of cancer between January 1, 2000, and December 31, 2017, were sourced from a single cancer center. The EHRs included numerous variables such as surgical factors, cancer factors, underlying diseases, functional laboratory assessments, general assessments, medications, and social factors. To predict prolonged length of stay after cancer surgery, we employed extreme gradient boosting classifier, multilayer perceptron, and logistic regression models. Prolonged postoperative length of stay for cancer was defined as bed-days of the group of patients who accounted for the top 50% of the distribution of bed-days by cancer type.
In the prediction of prolonged length of stay after cancer surgery, extreme gradient boosting classifier models demonstrated excellent performance for kidney and bladder cancer surgeries (area under the receiver operating characteristic curve [AUC] >0.85). A moderate performance (AUC 0.70-0.85) was observed for stomach, breast, colon, thyroid, prostate, cervix uteri, corpus uteri, and oral cancers. For stomach, breast, colon, thyroid, and lung cancers, with more than 4000 cases each, the extreme gradient boosting classifier model showed slightly better performance than the logistic regression model, although the logistic regression model also performed adequately. We identified risk variables for the prediction of prolonged postoperative length of stay for each type of cancer, and the importance of the variables differed depending on the cancer type. After we added operative time to the models trained on preoperative factors, the models generally outperformed the corresponding models using only preoperative variables.
A machine learning approach using EHRs may improve the prediction of prolonged length of hospital stay after primary cancer surgery. This algorithm may help to provide a more effective allocation of medical resources in cancer surgery.
术后住院时间是医疗资源管理中的一个关键指标，也是癌症手术后手术并发症发生率和患者恢复程度的间接预测指标。最近，机器学习已被用于利用广泛的医疗信息预测复杂的医疗结果，如延长住院时间。
本研究的目的是使用机器学习方法开发一种预测癌症手术后延长住院时间的模型。
在我们的回顾性研究中，2000年1月1日至2017年12月31日期间在单一癌症中心接受17种癌症初次手术的42751例患者的电子健康记录（EHR）被纳入研究。EHR包含众多变量，如手术因素、癌症因素、基础疾病、功能实验室评估、一般评估、用药情况和社会因素。为了预测癌症手术后延长住院时间，我们采用了极端梯度提升分类器、多层感知器和逻辑回归模型。癌症术后延长住院时间定义为按癌症类型划分的住院天数分布前50%患者组的住院天数。
在预测癌症手术后延长住院时间方面，极端梯度提升分类器模型在肾癌和膀胱癌手术中表现出色（受试者操作特征曲线下面积[AUC]>0.85）。在胃癌、乳腺癌、结肠癌、甲状腺癌、前列腺癌、子宫颈癌、子宫体癌和口腔癌中观察到中等性能（AUC 0.70 - 0.85）。对于每种病例数均超过4000例的胃癌、乳腺癌、结肠癌、甲状腺癌和肺癌，极端梯度提升分类器模型的表现略优于逻辑回归模型，尽管逻辑回归模型也表现良好。我们确定了每种癌症术后延长住院时间预测的风险变量，且变量的重要性因癌症类型而异。在将手术时间添加到基于术前因素训练的模型后，这些模型总体上优于仅使用术前变量的相应模型。
使用EHR的机器学习方法可能会改善对原发性癌症手术后延长住院时间的预测。该算法可能有助于在癌症手术中更有效地分配医疗资源。
