Department of Decision Sciences and Information Management, Faculty of Business and Economics, KU Leuven, Brussels Campus, Brussel, Belgium.
Faculty of Industrial Engineering and Management, Technion, Haifa, Israel.
PLoS One. 2019 Mar 20;14(3):e0211694. doi: 10.1371/journal.pone.0211694. eCollection 2019.
Patients with hematological malignancies are susceptible to life-threatening infections after chemotherapy. The current study aimed to evaluate whether management of such patients in dedicated inpatient and emergency wards could provide superior infection prevention and outcome.
We have developed an approach allowing to retrieve infection-related information from unstructured electronic medical records of a tertiary center. Data on 2,330 adults receiving 13,529 chemotherapy treatments for hematological malignancies were identified and assessed. Infection and mortality hazard rates were calculated with multivariate models. Patients were randomly divided into 80:20 training and validation cohorts. To develop patient-tailored risk-prediction models, several machine-learning methods were compared using area under the curve (AUC).
Of the tested algorithms, the probit model was found to most accurately predict the evaluated hazards and was implemented in an online calculator. The infection-prediction model identified risk factors for infection based on patient characteristics, treatment and history. Observation of patients with a high predicted infection risk in general wards appeared to increase their infection hazard (p = 0.009) compared to similar patients observed in hematology units. The mortality-risk model demonstrated that for infection events starting at home, admission through hematology services was associated with a lower mortality hazard compared to admission through the general emergency department (p = 0.007). Both models show that dedicated hematological facilities and emergency services improve patient outcome post-chemotherapy. The calculated numbers needed to treat were 30.27 and 31.08 for the dedicated emergency and observation facilities, respectively. Infection hazard risks were found to be non-monotonic in time.
The accuracy of the proposed mortality and infection risk-prediction models was high, with the AUC of 0.74 and 0.83, respectively. Our results demonstrate that temporal assessment of patient risks is feasible. This may enable physicians to move from one-point decision-making to a continuous dynamic observation, allowing a more flexible and patient-tailored admission policy.
化疗后，血液系统恶性肿瘤患者易发生危及生命的感染。本研究旨在评估将此类患者安置在专门的住院病房和急诊病房是否能更好地预防感染和改善预后。
我们开发了一种从三级中心的非结构化电子病历中检索感染相关信息的方法。确定并评估了 2330 名接受 13529 次血液系统恶性肿瘤化疗治疗的成年人的数据。使用多变量模型计算感染和死亡率风险率。患者被随机分为 80:20 的训练和验证队列。为了开发针对患者的风险预测模型，使用曲线下面积（AUC）比较了几种机器学习方法。
在所测试的算法中，发现概率模型最能准确预测评估的风险，并在在线计算器中实现。感染预测模型根据患者特征、治疗和病史确定感染的危险因素。在普通病房观察到高预测感染风险的患者似乎比在血液科病房观察到的类似患者感染风险更高（p=0.009）。死亡率风险模型表明，对于从家中开始的感染事件，通过血液科服务入院与通过普通急诊入院相比，死亡率风险较低（p=0.007）。这两个模型都表明，专门的血液科设施和急诊服务可改善化疗后患者的预后。分别为 30.27 和 31.08。感染风险随着时间的推移是非单调的。
提出的死亡率和感染风险预测模型的准确性很高，AUC 分别为 0.74 和 0.83。我们的结果表明，对患者风险进行时间评估是可行的。这可能使医生能够从单点决策转变为连续动态观察，从而实现更灵活和针对患者的入院政策。
