Lal Amos, Li Guangxi, Cubro Edin, Chalmers Sarah, Li Heyi, Herasevich Vitaly, Dong Yue, Pickering Brian W, Kilickaya Oguz, Gajic Ognjen
Department of Medicine, Division of Pulmonary and Critical Care Medicine, Multidisciplinary Epidemiology and Translational Research in Intensive Care Group, Mayo Clinic, Rochester, MN.
Department of Anesthesiology and Perioperative Medicine, Division of Critical Care, Multidisciplinary Epidemiology and Translational Research in Intensive Care Group, Mayo Clinic, Rochester, MN.
Crit Care Explor. 2020 Nov 16;2(11):e0249. doi: 10.1097/CCE.0000000000000249. eCollection 2020 Nov.
To develop and verify a digital twin model of critically ill patient using the causal artificial intelligence approach to predict the response to specific treatment during the first 24 hours of sepsis.
Directed acyclic graphs were used to define explicitly the causal relationship among organ systems and specific treatments used. A hybrid approach of agent-based modeling, discrete-event simulation, and Bayesian network was used to simulate treatment effect across multiple stages and interactions of major organ systems (cardiovascular, neurologic, renal, respiratory, gastrointestinal, inflammatory, and hematology). Organ systems were visualized using relevant clinical markers. The application was iteratively revised and debugged by clinical experts and engineers. Agreement statistics was used to test the performance of the model by comparing the observed patient response versus the expected response (primary and secondary) predicted by digital twin.
Medical ICU of a large quaternary- care academic medical center in the United States.
Adult (> 18 year yr old), medical ICU patients were included in the study.
No additional interventions were made beyond the standard of care for this study.
During the verification phase, model performance was prospectively tested on 145 observations in a convenience sample of 29 patients. Median age was 60 years (54-66 d) with a median Sequential Organ Failure Assessment score of 9.5 (interquartile range, 5.0-14.0). The most common source of sepsis was pneumonia, followed by hepatobiliary. The observations were made during the first 24 hours of the ICU admission with one-step interventions, comparing the output in the digital twin with the real patient response. The agreement between the observed versus and the expected response ranged from fair (kappa coefficient of 0.41) for primary response to good (kappa coefficient of 0.65) for secondary response to the intervention. The most common error detected was coding error in 50 observations (35%), followed by expert rule error in 29 observations (20%) and timing error in seven observations (5%).
We confirmed the feasibility of development and prospective testing of causal artificial intelligence model to predict the response to treatment in early stages of critical illness. The availability of qualitative and quantitative data and a relatively short turnaround time makes the ICU an ideal environment for development and testing of digital twin patient models. An accurate digital twin model will allow the effect of an intervention to be tested in a virtual environment prior to use on real patients.
使用因果人工智能方法开发并验证危重症患者的数字孪生模型，以预测脓毒症最初24小时内对特定治疗的反应。
使用有向无环图明确界定器官系统与所用特定治疗之间的因果关系。采用基于智能体建模、离散事件模拟和贝叶斯网络的混合方法，模拟主要器官系统（心血管、神经、肾脏、呼吸、胃肠道、炎症和血液学）在多个阶段和相互作用中的治疗效果。使用相关临床指标对器官系统进行可视化。临床专家和工程师对该应用进行了反复修订和调试。通过比较观察到的患者反应与数字孪生预测的预期反应（主要和次要），使用一致性统计来测试模型的性能。
美国一家大型四级医疗学术医学中心的医学重症监护病房。
纳入研究的为成年（>18岁）医学重症监护病房患者。
本研究除标准治疗外未采取额外干预措施。
在验证阶段，对29例患者的便利样本中的145次观察进行了模型性能的前瞻性测试。中位年龄为60岁（54 - 66岁），序贯器官衰竭评估评分中位数为9.5（四分位间距，5.0 - 14.0）。脓毒症最常见的来源是肺炎，其次是肝胆疾病。观察在重症监护病房入院的最初24小时内进行，采用一步干预措施，将数字孪生中的输出与真实患者反应进行比较。观察到的反应与预期反应之间的一致性，主要反应为中等（kappa系数为0.41），对干预的次要反应为良好（kappa系数为0.65）。检测到的最常见错误是50次观察（35%）中的编码错误，其次是29次观察（20%）中的专家规则错误和7次观察（5%）中的时间错误。
我们证实了开发并前瞻性测试因果人工智能模型以预测危重症早期治疗反应的可行性。定性和定量数据的可用性以及相对较短的周转时间使重症监护病房成为开发和测试数字孪生患者模型的理想环境。准确的数字孪生模型将允许在对真实患者使用之前在虚拟环境中测试干预效果。
