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. 2023 Jun 20;23(1):311.
doi: 10.1186/s12872-023-03334-4.

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

Zana Haxhija  1   2 , David B Seder  3 , Teresa L May  3 , Christian Hassager  4 , Hans Friberg  5 , Gisela Lilja  6 , Ameldina Ceric  5 , Niklas Nielsen  7 , Josef Dankiewicz  8
Affiliations

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

Zana Haxhija et al. BMC Cardiovasc Disord. .

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.

Keywords: Cardiac arrest; Prediction model; Resuscitation.

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Conflict of interest statement

The authors declare no competing interests.

Figures

Fig. 1
Fig. 1
Flow diagram for patient selection
Fig. 2
Fig. 2
Receiver operating characteristic curve (ROC curve) for the CREST-model The CREST model had a good discrimination capability for predicting circulatory-etiology death, with an area under the curve of 0.73 (confidence interval [CI], 0.66–0.79). The optimal cut-off point was at a CREST score of 2
Fig. 3
Fig. 3
Observed versus predicted incidence of CED according to incremental increases of CREST-variables (CREST score)
See this image and copyright information in PMC

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