Background: Cancer patients who are admitted to hospitals are at high risk of short-term deterioration due to treatment-related or cancer-specific complications. A rapid response system (RRS) is initiated when patients who are deteriorating or at risk of deteriorating are identified. This study was conducted to develop a deep learning-based early warning score (EWS) for cancer patients (Can-EWS) using delta values in vital signs. Methods: A retrospective cohort study was conducted on all oncology patients who were admitted to the general ward between 2016 and 2020. The data were divided into a training set (January 2016–December 2019) and a held-out test set (January 2020–December 2020). The primary outcome was clinical deterioration, defined as the composite of in-hospital cardiac arrest (IHCA) and unexpected intensive care unit (ICU) transfer. Results: During the study period, 19,739 cancer patients were admitted to the general wards and eligible for this study. Clinical deterioration occurred in 894 cases. IHCA and unexpected ICU transfer prevalence was 1.77 per 1000 admissions and 43.45 per 1000 admissions, respectively. We developed two models: Can-EWS V1, which used input vectors of the original five input variables, and Can-EWS V2, which used input vectors of 10 variables (including an additional five delta variables). The cross-validation performance of the clinical deterioration for Can-EWS V2 (AUROC, 0.946; 95% confidence interval [CI], 0.943–0.948) was higher than that for MEWS of 5 (AUROC, 0.589; 95% CI, 0.587–0.560;p< 0.001) and Can-EWS V1 (AUROC, 0.927; 95% CI, 0.924–0.931). As a virtual prognostic study, additional validation was performed on held-out test data. The AUROC and 95% CI were 0.588 (95% CI, 0.588–0.589), 0.890 (95% CI, 0.888–0.891), and 0.898 (95% CI, 0.897–0.899), for MEWS of 5, Can-EWS V1, and the deployed model Can-EWS V2, respectively. Can-EWS V2 outperformed other approaches for specificities, positive predictive values, negative predictive values, and the number of false alarms per day at the same sensitivity level on the held-out test data. Conclusions: We have developed and validated a deep learning-based EWS for cancer patients using the original values and differences between consecutive measurements of basic vital signs. The Can-EWS has acceptable discriminatory power and sensitivity, with extremely decreased false alarms compared with MEWS.
背景：因治疗相关或癌症特异性并发症入院的癌症患者短期内病情恶化的风险较高。当识别出病情正在恶化或存在恶化风险的患者时，会启动快速反应系统（RRS）。本研究旨在利用生命体征的差值，为癌症患者开发一种基于深度学习的早期预警评分（EWS），即Can-EWS。方法：对2016年至2020年间入住普通病房的所有肿瘤患者进行了一项回顾性队列研究。数据分为训练集（2016年1月至2019年12月）和保留测试集（2020年1月至2020年12月）。主要结局是临床恶化，定义为院内心脏骤停（IHCA）和意外转入重症监护病房（ICU）的复合事件。结果：在研究期间，共有19,739名癌症患者入住普通病房并符合本研究条件。其中894例发生临床恶化。IHCA和意外转入ICU的发生率分别为每1000例入院患者中1.77例和43.45例。我们开发了两个模型：Can-EWS V1使用原始五个输入变量的输入向量，而Can-EWS V2使用10个变量的输入向量（包括额外的五个差值变量）。Can-EWS V2在临床恶化预测上的交叉验证性能（AUROC为0.946；95%置信区间[CI]为0.943–0.948）高于5分MEWS（AUROC为0.589；95% CI为0.587–0.560；p < 0.001）和Can-EWS V1（AUROC为0.927；95% CI为0.924–0.931）。作为一项虚拟预后研究，我们在保留测试数据上进行了额外验证。5分MEWS、Can-EWS V1和已部署模型Can-EWS V2的AUROC和95% CI分别为0.588（95% CI, 0.588–0.589）、0.890（95% CI, 0.888–0.891）和0.898（95% CI, 0.897–0.899）。在保留测试数据上，在相同灵敏度水平下，Can-EWS V2在特异性、阳性预测值、阴性预测值以及每日误报次数方面均优于其他方法。结论：我们利用基本生命体征的原始值和连续测量值之间的差异，为癌症患者开发并验证了一种基于深度学习的EWS。与MEWS相比，Can-EWS具有可接受的区分能力和灵敏度，同时误报次数显著减少。
肿瘤(癌症)患者之家