External validation of the CREST model to predict early circulatory-etiology death after out-of-hospital cardiac arrest without initial ST-segment elevation myocardial infarction

BMC Cardiovascular Disorders

Research

Christian Hassager¹  Niklas Nielsen²  Hans Friberg³  Ameldina Ceric³  Zana Haxhija⁴  Josef Dankiewicz⁵  Gisela Lilja⁶  David B Seder⁷  Teresa L May⁷ 

[1] Department of Cardiology, Rigshospitalet, University of Copenhagen, Copenhagen, Denmark;Department of Clinical Sciences, Anesthesia and Intensive Care, Lund University, Helsingborg Hospital, Helsingborg, Sweden;Department of Clinical Sciences, Anesthesia and Intensive Care, Lund University, Skane University Hospital, Malmo, Sweden;Department of Clinical Sciences, Anesthesia and Intensive Care, Lund University, Skane University Hospital, Malmo, Sweden;Division of Anesthesia and Intensive Care, Department of Clinical sciences Lund, Lund University, Skane University Hospital, Carl Bertil Laurells gata 9, 205 02, Malmo, Sweden;Department of Clinical Sciences, Cardiology, Lund University, Skane University Hospital, Lund, Sweden;Department of Clinical sciences, Neurology, Lund University, Skane University Hospital, Lund, Sweden;Department of Critical Care Services, Maine Medical Center, Portland Maine, USA;

关键词: Cardiac arrest;    Resuscitation;    Prediction model;   

DOI  :  10.1186/s12872-023-03334-4

 received in 2022-12-04, accepted in 2023-06-06,  发布年份 2023

来源: Springer

【 摘 要 】

BackgroundThe CREST model is a prediction model, quantitating the risk of circulatory-etiology death (CED) after cardiac arrest based on variables available at hospital admission, and intend to guide the triage of comatose patients without ST-segment-elevation myocardial infarction after successful cardiopulmonary resuscitation. This study assessed performance of the CREST model in the Target Temperature Management (TTM) trial cohort.MethodsWe retrospectively analyzed data from resuscitated out-of-hospital cardiac arrest (OHCA) patients in the TTM-trial. Demographics, clinical characteristics, and CREST variables (history of coronary artery disease, initial heart rhythm, initial ejection fraction, shock at admission and ischemic time > 25 min) were assessed in univariate and multivariable analysis. The primary outcome was CED. The discriminatory power of the logistic regression model was assessed using the C-statistic and goodness of fit was tested according to Hosmer-Lemeshow.ResultsAmong 329 patients eligible for final analysis, 71 (22%) had CED. History of ischemic heart disease, previous arrhythmia, older age, initial non-shockable rhythm, shock at admission, ischemic time > 25 min and severe left ventricular dysfunction were variables associated with CED in univariate analysis. CREST variables were entered into a logistic regression model and the area under the curve for the model was 0.73 with adequate calibration according to Hosmer-Lemeshow test (p = 0.602).ConclusionsThe CREST model had good validity and a discrimination capability for predicting circulatory-etiology death after resuscitation from cardiac arrest without ST-segment elevation myocardial infarction. Application of this model could help to triage high-risk patients for transfer to specialized cardiac centers.

【 授权许可】

CC BY   
© The Author(s) 2023

【 图 表 】

Fig. 1

Fig. 1

41116_2023_37_Article_IEq189.gif