Background

Cancer remains the second major cause of death in the United States over the last decade. Chemotherapy is a core component of nearly every cancer treatment plan. Chemotherapy-Induced Nausea and Vomiting (CINV) are the two most dreadful and unpleasant side-effects of chemotherapy for cancer patients. Several patient-specific factors affect the risk of CINV. However, none of the guidelines consider those factors. Not all of the patients have the similar emetic risk of CINV. Despite the improvements in CINV management, as many as two-thirds of chemotherapy patients still experience some degree of CINV. As a result, physicians use their personal experiences for CINV treatment, which leads to inconsistent managements of CINV.

Objective

The overall objective of this study is to improve the prevention of CINV using precise, personalized and evidence-based antiemetic treatment before chemotherapy. In CINV prediction, one of the interesting factors is that CINV has two distinct and complex pathophysiologic phases: acute and delayed. In addition, the risk factors and their associations are different for different emetogenic chemotherapies (e.g., low, moderate, and high). There are six contexts considering the combination of phases and emetogenicity levels. This will require the creation of six different models. Instead, our objective was to describe a single framework named “prediction engine” that can perform prediction query without losing the sensitivity to each context. The prediction engine discovers how the patient-related variables and the emetogenecity of chemotherapy are associated with the risk of CINV for each phase.

Methods

This was a single-center retrospective study. The data were collected by retrospective record review from the electronic medical record system used at the University of Missouri Ellis Fischel Cancer Center. An association rule-based dynamic and context-sensitive Prediction Engine has been developed. Physicians receive feedback about CINV risks of patients from the CINV decision support system based on patient-specific factors.

Results

The prediction performance of the system outperformed many popular prediction methods and all the results of CINV risk prediction published in the literature. Best prediction performance was achieved using the rule-ranking approach. The accuracy, sensitivity, and specificity were 87.85 %, 87.54 %, and 88.2 %, respectively.

Conclusions

The system used the patient-specific risk factors for making personalized treatment recommendations for CINV. It solved a real clinical problem that will shorten the gap between clinical practices and evidence-based guidelines for CINV management leading to the practice of personalized and precise treatment recommendation, better life quality of patient, and reduced healthcare cost. The approach presented in this article can be applied to any other clinical predictions.

中文翻译：

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防止化疗引起的恶心和呕吐的动态预测引擎

背景

在过去十年中，癌症仍然是美国的第二大死因。化疗是几乎所有癌症治疗计划的核心组成部分。化疗引起的恶心和呕吐 (CINV) 是化疗对癌症患者最可怕和最不愉快的两种副作用。几个患者特定的因素会影响 CINV 的风险。但是，这些指南都没有考虑这些因素。并非所有患者都具有相似的 CINV 呕吐风险。尽管 CINV 管理有所改进，但仍有多达三分之二的化疗患者经历了某种程度的 CINV。结果，医生利用他们的个人经验进行 CINV 治疗，这导致 CI​​NV 的管理不一致。

客观的

本研究的总体目标是在化疗前使用精确、个性化和循证的止吐治疗来改善 CINV 的预防。在 CINV 预测中，有趣的因素之一是 CINV 具有两个不同且复杂的病理生理阶段：急性和延迟。此外，不同致吐化学疗法（例如，低、中和高）的风险因素及其关联是不同的。考虑相位和致吐性水平的组合有六种情况。这将需要创建六个不同的模型。相反，我们的目标是描述一个名为“预测引擎”的单一框架，它可以在不失去对每个上下文的敏感性的情况下执行预测查询。

方法

这是一项单中心回顾性研究。这些数据是通过对密苏里大学埃利斯菲舍尔癌症中心使用的电子病历系统的回顾性记录审查收集的。已经开发了基于关联规则的动态和上下文敏感的预测引擎。医生根据患者特定因素从 CINV 决策支持系统接收有关患者 CINV 风险的反馈。

结果

该系统的预测性能优于许多流行的预测方法和文献中发表的CINV风险预测的所有结果。使用规则排序方法实现了最佳预测性能。准确度、灵敏度和特异性分别为 87.85 %、87.54 % 和 88.2 %。

结论

该系统使用患者特定的风险因素为 CINV 制定个性化的治疗建议。它解决了一个真正的临床问题，将缩短临床实践与 CINV 管理循证指南之间的差距，从而实现个性化和精确的治疗推荐，提高患者的生活质量，并降低医疗成本。本文中介绍的方法可以应用于任何其他临床预测。