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
(2023) In BMC Cardiovascular Disorders 23(1).- 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... (More)
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.
(Less)
- author
- Haxhija, Zana LU ; Seder, David B. ; May, Teresa L. ; Hassager, Christian ; Friberg, Hans LU ; Lilja, Gisela LU ; Ceric, Ameldina LU ; Nielsen, Niklas LU and Dankiewicz, Josef LU
- organization
- publishing date
- 2023
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- Cardiac arrest, Prediction model, Resuscitation
- in
- BMC Cardiovascular Disorders
- volume
- 23
- issue
- 1
- article number
- 311
- publisher
- BioMed Central (BMC)
- external identifiers
-
- pmid:37340361
- scopus:85163215307
- ISSN
- 1471-2261
- DOI
- 10.1186/s12872-023-03334-4
- language
- English
- LU publication?
- yes
- id
- bda30612-3087-43e8-a9f9-9ed1c6cb1d96
- date added to LUP
- 2023-08-25 15:19:29
- date last changed
- 2024-04-20 01:53:45
@article{bda30612-3087-43e8-a9f9-9ed1c6cb1d96, abstract = {{<p>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.</p>}}, author = {{Haxhija, Zana and Seder, David B. and May, Teresa L. and Hassager, Christian and Friberg, Hans and Lilja, Gisela and Ceric, Ameldina and Nielsen, Niklas and Dankiewicz, Josef}}, issn = {{1471-2261}}, keywords = {{Cardiac arrest; Prediction model; Resuscitation}}, language = {{eng}}, number = {{1}}, publisher = {{BioMed Central (BMC)}}, series = {{BMC Cardiovascular Disorders}}, title = {{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}}, url = {{http://dx.doi.org/10.1186/s12872-023-03334-4}}, doi = {{10.1186/s12872-023-03334-4}}, volume = {{23}}, year = {{2023}}, }