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On the predictability of postoperative complications for cancer patients: a Portuguese cohort study

机译：论癌症患者术后并发症的可预测性：葡萄牙队列研究

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Postoperative complications are still hard to predict despite the efforts towards the creation of clinical risk scores. The published scores contribute for the creation of specialized tools, but with limited predictive performance and reusability for implementation in the oncological context. This work aims to predict postoperative complications risk for cancer patients, offering two major contributions. First, to develop and evaluate a machine learning-based risk score, specific for the Portuguese population using a retrospective cohort of 847 cancer patients undergoing surgery between 2016 and 2018, for 4 outcomes of interest: (1) existence of postoperative complications, (2) severity level of complications, (3) number of days in the Intermediate Care Unit (ICU), and (4) postoperative mortality within 1?year. An additional cohort of 137 cancer patients from the same center was used for validation. Second, to improve the interpretability of the predictive models. In order to achieve these objectives, we propose an approach for the learning of risk predictors, offering new perspectives and insights into the clinical decision process. For postoperative complications the Receiver Operating Characteristic Curve (AUC) was 0.69, for complications’ severity AUC was 0.65, for the days in the ICU the mean absolute error was 1.07 days, and for 1-year postoperative mortality the AUC was 0.74, calculated on the development cohort. In this study, predictive models which could help to guide physicians at organizational and clinical decision making were developed. Additionally, a web-based decision support tool is further provided to this end.
机译：尽管努力建立临床风险评分，但术后并发症仍然很难预测。已发布的分数有助于创建专业工具，但在肿瘤学背景下的实施有限的预测性能和可重用性。这项工作旨在预测癌症患者的术后并发症风险，提供两项主要贡献。首先，要制定和评估基于机器学习的风险评分，具体使用2016年和2018年在2016年至2018年间手术中进行的847名癌症患者的葡萄牙语群体，为4次兴趣结果：（1）存在术后并发症（2 ）严重性的并发症水平，（3）中级护理单位（ICU）的天数，（4）术后死亡率在1年内。额外的137名来自同一中心的癌症患者用于验证。其次，提高预测模型的可解释性。为了实现这些目标，我们提出了一种学习风险预测因子的方法，提供新的观点和洞察临床决策过程。对于术后并发症，接收器操作特征曲线（AUC）为0.69，对于并发症的严重性AUC为0.65，对于ICU的日子，平均绝对误差为1.07天，并且术后死亡率为0.74，计算为0.74，计算发展队列。在这项研究中，开发了有助于引导医生在组织和临床决策中引导医生的预测模型。另外，还提供了基于网络的决策支持工具。

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