DeepSOFA: A Continuous Acuity Score for Critically III Patients using Clinically Interpretable Deep Learning
DeepSOFA: A Continuous Acuity Score for Critically III Patients using Clinically Interpretable Deep Learning
复制标题
DOI：
10.1038/s41598-019-38491-0
复制
发表时间：
2019-02-12
影响因子：
4.6
通讯作者：
Rashidi, Parisa
中科院分区：
文献类型：
--
作者：
Shickel, Benjamin;Loftus, Tyler J.;Rashidi, Parisa
评估病情严重程度和预测危重患者住院死亡率的传统方法需要使用静态可变阈值进行耗时且容易出错的计算。这些方法没有利用新出现的流式电子健康记录数据的可用性，也没有捕获对时间敏感的个人生理模式，这是重症监护病房的一项关键任务。我们提出了一种新的敏感度评分框架(DeepSOFA)，该框架利用时间测量和可解释的深度学习模型来评估ICU住院期间任何时间点的疾病严重性。我们比较了使用相同模型输入的DeepSOFA和SOFA(序贯器官衰竭评估)基线模型，发现在ICU入院期间的任何时间点，DeepSOFA对住院死亡率的预测都要准确得多。在公共数据库中开发并在单个机构队列中验证的DeepSOFA模型在整个ICU住院期间的平均AUC为0.90(95%可信区间0.90-0.91)，而基线SOFA模型的平均AUC为0.79(95%可信区间0.79-0.80)和0.85(95%可信区间0.85-0.86)。深度模型非常适合在意外不良事件发生之前识别需要救命干预的ICU患者，并为患者、提供者和家庭之间关于护理目标和最佳资源利用的共同决策过程提供信息。
Traditional methods for assessing illness severity and predicting in-hospital mortality among critically ill patients require time-consuming, error-prone calculations using static variable thresholds. These methods do not capitalize on the emerging availability of streaming electronic health record data or capture time-sensitive individual physiological patterns, a critical task in the intensive care unit. We propose a novel acuity score framework (DeepSOFA) that leverages temporal measurements and interpretable deep learning models to assess illness severity at any point during an ICU stay. We compare DeepSOFA with SOFA (Sequential Organ Failure Assessment) baseline models using the same model inputs and find that at any point during an ICU admission, DeepSOFA yields significantly more accurate predictions of in-hospital mortality. A DeepSOFA model developed in a public database and validated in a single institutional cohort had a mean AUC for the entire ICU stay of 0.90 (95% CI 0.90-0.91) compared with baseline SOFA models with mean AUC 0.79 (95% CI 0.79-0.80) and 0.85 (95% CI 0.85-0.86). Deep models are well-suited to identify ICU patients in need of life-saving interventions prior to the occurrence of an unexpected adverse event and inform shared decision-making processes among patients, providers, and families regarding goals of care and optimal resource utilization.