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

Authors

Zana Haxhija, Department of Clinical Sciences, Anesthesia and Intensive Care, Lund University, Skane University Hospital, Malmo, Sweden. zana.haxhija@med.lu.se.
David B. Seder, Department of Critical Care Services, Maine Medical Center, Portland Maine, USA.
Teresa L. May, Department of Critical Care Services, Maine Medical Center, Portland Maine, USA.
Christian Hassager, Department of Cardiology, Rigshospitalet, University of Copenhagen, Copenhagen, Denmark.
Hans Friberg, Department of Clinical Sciences, Anesthesia and Intensive Care, Lund University, Skane University Hospital, Malmo, Sweden.
Gisela Lilja, Department of Clinical sciences, Neurology, Lund University, Skane University Hospital, Lund, Sweden.
Ameldina Ceric, Department of Clinical Sciences, Anesthesia and Intensive Care, Lund University, Skane University Hospital, Malmo, Sweden.
Niklas Nielsen, Department of Clinical Sciences, Anesthesia and Intensive Care, Lund University, Helsingborg Hospital, Helsingborg, Sweden.
Josef Dankiewicz, Department of Clinical Sciences, Cardiology, Lund University, Skane University Hospital, Lund, Sweden.

Document Type

Article

Publication Date

6-20-2023

Institution/Department

Cardiology; Critical Care Medicine

Journal Title

BMC cardiovascular disorders

MeSH Headings

Humans; Out-of-Hospital Cardiac Arrest (diagnosis, therapy, etiology); Retrospective Studies; Cardiopulmonary Resuscitation (adverse effects); Coronary Artery Disease (complications); ST Elevation Myocardial Infarction (diagnosis, therapy, complications)

Abstract

BACKGROUND: The 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. METHODS: We 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. RESULTS: Among 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). CONCLUSIONS: The 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.

First Page

311

Recommended Citation

Haxhija Z, Seder DB, May TL, et al. 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 Cardiovasc Disord. 2023;23(1):311. Published 2023 Jun 20. doi:10.1186/s12872-023-03334-4