Department of Molecular and Clinical Medicine, Institute of Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden; Department of Anesthesiology and Intensive Care Medicine, Sahlgrenska University Hospital, Mölndal, Sweden.
Mount Sinai Heart, Icahn School of Medicine at Mount Sinai Health System, New York, NY, USA.
EBioMedicine. 2023 Mar;89:104464. doi: 10.1016/j.ebiom.2023.104464. Epub 2023 Feb 9.
A prediction model that estimates survival and neurological outcome in out-of-hospital cardiac arrest patients has the potential to improve clinical management in emergency rooms.
We used the Swedish Registry for Cardiopulmonary Resuscitation to study all out-of-hospital cardiac arrest (OHCA) cases in Sweden from 2010 to 2020. We had 393 candidate predictors describing the circumstances at cardiac arrest, critical time intervals, patient demographics, initial presentation, spatiotemporal data, socioeconomic status, medications, and comorbidities before arrest. To develop, evaluate and test an array of prediction models, we created stratified (on the outcome measure) random samples of our study population. We created a training set (60% of data), evaluation set (20% of data), and test set (20% of data). We assessed the 30-day survival and cerebral performance category (CPC) score at discharge using several machine learning frameworks with hyperparameter tuning. Parsimonious models with the top 1 to 20 strongest predictors were tested. We calibrated the decision threshold to assess the cut-off yielding 95% sensitivity for survival. The final model was deployed as a web application.
We included 55,615 cases of OHCA. Initial presentation, prehospital interventions, and critical time intervals variables were the most important. At a sensitivity of 95%, specificity was 89%, positive predictive value 52%, and negative predictive value 99% in test data to predict 30-day survival. The area under the receiver characteristic curve was 0.97 in test data using all 393 predictors or only the ten most important predictors. The final model showed excellent calibration. The web application allowed for near-instantaneous survival calculations.
Thirty-day survival and neurological outcome in OHCA can rapidly and reliably be estimated during ongoing cardiopulmonary resuscitation in the emergency room using a machine learning model incorporating widely available variables.
Swedish Research Council (2019-02019); Swedish state under the agreement between the Swedish government, and the county councils (ALFGBG-971482); The Wallenberg Centre for Molecular and Translational Medicine.
一种能够预测院外心脏骤停患者生存和神经功能结局的预测模型，有可能改善急诊科的临床管理。
我们使用瑞典心肺复苏注册中心研究了 2010 年至 2020 年期间瑞典所有院外心脏骤停（OHCA）病例。我们有 393 个候选预测因子，描述了心脏骤停时的情况、关键时间间隔、患者人口统计学、初始表现、时空数据、社会经济地位、药物和发病前的合并症。为了开发、评估和测试一系列预测模型，我们对研究人群进行了分层（基于结局测量）随机抽样。我们创建了训练集（数据的 60%）、评估集（数据的 20%）和测试集（数据的 20%）。我们使用几种带有超参数调整的机器学习框架评估了 30 天生存率和出院时的脑功能分类（CPC）评分。测试了具有前 1 到 20 个最强预测因子的简约模型。我们对决策阈值进行了校准，以评估产生 95%生存率的截断值。最终模型被部署为一个网络应用程序。
我们纳入了 55615 例 OHCA 病例。初始表现、院前干预和关键时间间隔变量是最重要的。在测试数据中，以 95%的灵敏度预测 30 天生存率，特异性为 89%，阳性预测值为 52%，阴性预测值为 99%。使用所有 393 个预测因子或仅使用前 10 个最重要的预测因子，测试数据中受试者工作特征曲线下面积为 0.97。最终模型显示出良好的校准度。该网络应用程序允许在心肺复苏期间即时计算生存率。
使用一种机器学习模型，结合广泛可用的变量，可以在急诊科进行的心肺复苏过程中，快速可靠地估计 OHCA 的 30 天生存率和神经功能结局。
瑞典研究委员会（2019-2021 年）；瑞典政府与县议会之间的协议（ALFGBG-971482）；沃伦伯格分子和转化医学中心。
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