Predicting the survivals and favorable neurologic outcomes after targeted temperature management by artificial neural networks

Journal of the Formosan Medical Association

卷:121

Cheng-Hsueh Wu¹  Chen-Chih Chung¹  Yu-san Chien²  Hung-Wen Chiu³  Chien-Hua Huang⁴  Chen-Hsu Wang⁴  Lung Chan⁵  Wei-Ting Chiu⁶  Chih-Hsin Hsu⁶ 

[1] Department of Neurology, School of Medicine, College of Medicine, Taipei Medical University, Taiwan;

[2] Division of Critical Care Medicine, Department of Emergency and Critical Care Medicine, Shuang Ho Hospital, Taipei Medical University, Taipei, Taiwan;

[3] Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei, Taiwan;

[4] Taipei Neuroscience Institute, Taipei Medical University, Taiwan;

[5] Department of Emergency Medicine, National Taiwan University Medical College and Hospital, Taipei, Taiwan;

[6] Department of Neurology, Shuang Ho Hospital, Taipei Medical University, Taiwan;

关键词: Artificial neural network;    Cardiac arrest;    Outcome;    Prediction;    Targeted temperature management;   

DOI  :  

来源: DOAJ

【 摘 要 】

Background: To identify the outcome-associated predictors and develop predictive models for patients receiving targeted temperature management (TTM) by artificial neural network (ANN). Methods: The derived cohort consisted of 580 patients with cardiac arrest and ROSC treated with TTM between January 2014 and August 2019. We evaluated the predictive value of parameters associated with survival and favorable neurologic outcome. ANN were applied for developing outcome prediction models. The generalizability of the models was assessed through 5-fold cross-validation. The performance of the models was assessed according to the accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve (AUC). Results: The parameters associated with survival were age, duration of cardiopulmonary resuscitation, history of diabetes mellitus (DM), heart failure, end-stage renal disease (ESRD), systolic blood pressure (BP), diastolic BP, body temperature, motor response after ROSC, emergent coronary angiography or percutaneous coronary intervention (PCI), and the cooling methods. The parameters associated with the favorable neurologic outcomes were age, sex, DM, chronic obstructive pulmonary disease, ESRD, stroke, pre-arrest cerebral-performance category, BP, body temperature, motor response after ROSC, emergent coronary angiography or PCI, and cooling methods. After adequate training, ANN Model 1 to predict survival achieved an AUC of 0.80. Accuracy, sensitivity, and specificity were 75.9%, 71.6%, and 79.3%, respectively. ANN Model 4 to predict the favorable neurologic outcome achieved an AUC of 0.87, with accuracy, sensitivity, and specificity of 86.7%, 77.7%, and 88.0%, respectively. Conclusion: The ANN-based models achieved good performance to predict the survival and favorable neurologic outcomes after TTM. The models proposed have clinical value to assist in decision-making.

【 授权许可】

Unknown