Han B. Kim, Hieu Nguyen, Qingchu Jin, Sharmila Tamby, Tatiana Gelaf Romer, Eric Sung, Ran Liu, Joseph Greenstein, Jose I. Suarez, Christian Storm, Raimond Winsl… 展开作者
Han B. Kim, Hieu Nguyen, Qingchu Jin, Sharmila Tamby, Tatiana Gelaf Romer, Eric Sung, Ran Liu, Joseph Greenstein, Jose I. Suarez, Christian Storm, Raimond Winslow, Robert D. Stevens
备注：
51 pages, 7 figures, 4 supplementary figures
发表信息：
Anaesthesia Critical Care & Pain Medicine 41.1 (2022): 101015
心脏骤停（CA）患者复苏后面临神经功能障碍和死亡的高风险，但缺乏准确可靠的诊断方法。本研究的目的是建立计算模型，通过利用进入重症监护室（ICU）后早期可用的高维患者数据来预测CA后的结果。我们假设模型性能可以通过整合生理时间序列（PTS）数据和训练机器学习（ML）分类器来增强。我们比较了三种模型，分别集成了从电子健康记录（EHR）中提取的特征、从ICU入院后前24小时收集的PTS（PTS 24）中提取的特征以及集成PTS 24和EHR的模型。关注的结局是ICU出院时的生存率和神经学结局。对于生存率（AUC分别为0.85、0.80和0.68）和神经学结局（AUC分别为0.87、0.83和0.78）的预测，联合EHR-PTS 24模型比单独使用EHR或PTS 24的模型具有更高的区分度（受试者工作特征曲线下面积[AUC]）。最佳ML分类器在生存期（AUC 0.85 vs 0.70）和神经学结局预测（AUC 0.87 vs 0.75）方面的区分度高于参考逻辑回归模型（APACHE III）。特征分析揭示了以前未知的因素与CA后恢复。结果证明了ML模型用于CA后预测建模的有效性，并表明在复苏后非常早期记录的PTS编码短期结果概率。
英文摘要：
Patients resuscitated from cardiac arrest (CA) face a high risk of neurological disability and death, however pragmatic methods are lacking for accurate and reliable prognostication. The aim of this study was to build computational models to predict post-CA outcome by leveraging high-dimensional patient data available early after admission to the intensive care unit (ICU). We hypothesized that model performance could be enhanced by integrating physiological time series (PTS) data and by training machine learning (ML) classifiers. We compared three models integrating features extracted from the electronic health records (EHR) alone, features derived from PTS collected in the first 24hrs after ICU admission (PTS24), and models integrating PTS24 and EHR. Outcomes of interest were survival and neurological outcome at ICU discharge. Combined EHR-PTS24 models had higher discrimination (area under the receiver operating characteristic curve [AUC]) than models which used either EHR or PTS24 alone, for the prediction of survival (AUC 0.85, 0.80 and 0.68 respectively) and neurological outcome (0.87, 0.83 and 0.78). The best ML classifier achieved higher discrimination than the reference logistic regression model (APACHE III) for survival (AUC 0.85 vs 0.70) and neurological outcome prediction (AUC 0.87 vs 0.75). Feature analysis revealed previously unknown factors to be associated with post-CA recovery. Results attest to the effectiveness of ML models for post-CA predictive modeling and suggest that PTS recorded in very early phase after resuscitation encode short-term outcome probabilities.
Stepwise Fine and Gray: Subject-Specific Variable Selection Shows When Hemodynamic Data Improves Prognostication of Comatose Post-Cardiac Arrest Patients
Heinz College of Information Systems and Public Policy Carnegie Mellon University（卡内基梅隆大学海因兹信息系统与公共政策学院）；Department of Emergency Medicine University of Pittsburgh（匹兹堡大学急诊医学系）
提出一种逐步动态竞争风险模型，通过扩展Fine and Gray模型并引入神经网络，自动确定何时利用时变血流动力学数据，从而改善心脏骤停后昏迷患者的预后预测。
Continuous Cardiac Arrest Prediction in ICU using PPG Foundation Model
Nell Hodgson Woodruff School of Nursing, Emory University（埃默里大学内尔·霍奇森·伍德拉夫护理学院）；Nihon Kohden Digital Health Solutions, Inc（光电数字健康解决方案公司）；University of California, San Francisco（加州大学旧金山分校）；Children’s Healthcare of Atlanta（亚特兰大儿童医疗保健系统）
Accelerometry-based classification of circulatory states during out-of-hospital cardiac arrest
Institute of Mathematics and Scientific Computing, University of Graz（格拉茨大学数学与科学计算研究所）；BioTechMed-Graz（格拉茨生物科技医学中心）；Medical University of Graz（格拉茨医科大学）；University Hospital Schleswig-Holstein（石勒苏益格-荷尔斯泰因大学医院）；City of Münster Fire Department（明斯特市消防局）；University Hospital Münster（明斯特大学医院）；University of Namibia（纳米比亚大学）
A Bayesian Learning Approach for Drone Coverage Network: A Case Study on Cardiac Arrest in Scotland
School of Mathematics, Statistics and Physics（数学、统计与物理学院）；Newcastle University（新卡茨大学）；Civil and Environmental Engineering（土木与环境工程）；Centre for Maritime Research（海事研究与实验中心）；Mechanical and Aerospace Engineering（机械与航空航天工程）
FairFML: Fair Federated Machine Learning with a Case Study on Reducing Gender Disparities in Cardiac Arrest Outcome Prediction
Duke-NUS Medical School（杜克-新加坡国立大学医学院）；Duke University（杜克大学）；Kyoto University（京都大学）；Singapore Health Services（新加坡保健服务）；Singapore General Hospital（新加坡中央医院）；NUS Artificial Intelligence Institute（新加坡国立大学人工智能研究所）；National University of Singapore（新加坡国立大学）
Distributionally Robust Transfer Learning with Structurally Missing Covariates, with Application to Cross-National Cardiac Arrest Prediction
Centre for Biomedical Data Science, Duke-NUS Medical School, Singapore（生物医学数据科学中心，杜克-国家大学医学院，新加坡）；Duke-NUS AI + Medical Sciences Initiative, Duke-NUS Medical School, Singapore（杜克-国家大学医学院AI+医学科学倡议，新加坡）；Department of Biostatistics and Bioinformatics, Duke University, Durham, NC, USA（生物统计学与生物信息学系，杜克大学，北卡罗来纳州达勒姆，美国）；Duke Clinical Research Institute, Durham, NC, USA（杜克临床研究学院，北卡罗来纳州达勒姆，美国）；Emergency Medicine Department, National University Hospital, Singapore（急诊医学部，国立大学医院，新加坡）；Department of Sport and Medical Science, Faculty of Physical Education, Kokushikan University, Tokyo, Japan（体育与医学科学系，体育学院，立命馆大学，东京，日本）；Graduate School of Emergency Medical System, Kokushikan University, Tokyo, Japan（急救医疗系统研究生院，立命馆大学，东京，日本）；Department of Emergency Medicine, Seoul National University College of Medicine, Seoul, Republic of Korea（急诊医学系，首尔国立大学医学院，首尔，韩国）；Center for Emergency Medicine, Bach Mai Hospital, Hanoi, Vietnam（急救医学中心，巴赫梅医院，河内，越南）；Center for Critical Care Medicine, Bach Mai Hospital, Hanoi, Vietnam（重症医学中心，巴赫梅医院，河内，越南）；Health Services Research Centre, Singapore Health Services, Singapore（卫生服务研究中心，新加坡卫生服务，新加坡）；Department of Emergency Medicine, Singapore General Hospital, Singapore（急诊医学部，新加坡中央医院，新加坡）；Pre-hospital & Emergency Research Centre, Health Services Research and Population Health, Duke-NUS Medical School, Singapore（院前与急诊研究中心，卫生服务研究与人口健康，杜克-国家大学医学院，新加坡）
Uncertainty-Driven Anomaly Detection for Psychotic Relapse Using Smartwatches: Forecasting and Multi-Task Learning Fusion
Robotics Institute, Athena Research and Innovation Center, Greece（阿提卡研究与创新中心机器人研究所，希腊）；School of ECE, National Technical University of Athens, Greece（雅典技术大学电子工程学院，希腊）；HERON - Hellenic Robotics Center of Excellence, Athens, Greece（希腊卓越机器人中心，雅典，希腊）
PMB-NN: Physiology-Centred Hybrid AI for Personalized Hemodynamic Monitoring from Photoplethysmography
Department of Biomedical Signals and Systems, University of Twente（乌得勒支理工大学生物医学信号与系统系）；Department of Cardiovascular and Respiratory Physiology, University of Twente（乌得勒支理工大学心血管与呼吸生理学系）；Department of Intensive Care, University Medical Center Utrecht（乌得勒支大学医学中心重症医学科）
