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. 2023 Mar 21:10:930839.
doi: 10.3389/fcvm.2023.930839. eCollection 2023.

Establishment of a nomogram model for acute chest pain triage in the chest pain center

Na Yan  1 , Ling Wei  1   2 , Zhiwei Li  3 , Yu Song  1   2
Affiliations

Establishment of a nomogram model for acute chest pain triage in the chest pain center

Na Yan et al. Front Cardiovasc Med. .

Abstract

Background: Acute myocardial infarction (AMI) is the leading life-threatening disease in the emergency department (ED), so rapid chest pain triage is important. This study aimed to establish a clinical prediction model for the risk stratification of acute chest pain patients based on the Point-of-care (POC) cardiac troponin (cTn) level and other clinical variables.

Methods: We conducted a post-hoc analysis of the database from 6,019 consecutive patients (excluding prehospital-diagnosed non-cardiac chest pain patients) attending a local chest pain center (CPC) in China between October 2016 and January 2019. The plasma concentration of cardiac troponin I (cTnI) was measured using a POC cTnI (Cardio Triage, Alere) assay. All the eligible patients were randomly divided into training and validation cohorts by a 7:3 ratio. We performed multivariable logistic regression to select variables and build a nomogram based on the significant predictive factors. We evaluated the model's generalization ability of diagnostic accuracy in the validation cohort.

Results: We analyzed data from 5,397 patients that were included in this research. The median turnaround time (TAT) of POC cTnI was 16 min. The model was constructed with 6 variables: ECG ischemia, POC cTnI level, hypotension, chest pain symptom, Killip class, and sex. The area under the ROC curve (AUC) in the training and validation cohorts was 0.924 and 0.894, respectively. The diagnostic performance was superior to the GRACE score (AUC: 0.737).

Conclusion: A practical predictive model was created and could be used for rapid and effective triage of acute chest pain patients in the CPC.

Keywords: acute chest pain triage; acute coronary syndrome; acute myocardial infarction; point-of-care (POC); troponin.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Figure 1
Figure 1
Flowchart for the selection of study participants.
Figure 2
Figure 2
ROC curve of cTnI for AMI diagnosis. (A) Violin plot of point-of-care cTnI concentration levels in different groups of patients; (B) ROC curve of cTnI for AMI diagnosis in all patients; (C) ROC curve of cTnI for AMI diagnosis in different time points. STEMI, ST-segment elevation myocardial infarction; NSTEMI, non-ST-elevation myocardial infarction; UA, unstable angina pectoris; AMI, acute myocardial infarction.
Figure 3
Figure 3
LASSO model profile plots. (A) LASSO coefficient profiles of the 6 variables showing how the size of the coefficients associated with increasing value of the lambda penalty; (B) penalty plot for the LASSO model; color error bars indicate the standard error; (C) ROC curve of the prediction model for AMI diagnosis in the training cohort; (D) ROC curve of the prediction model for AMI diagnosis in the validation cohort.
Figure 4
Figure 4
The diagnostic performance of high sensitivity (hs)-cTnI and GRACE score. (A) Violin plot of hs-cTnI concentration levels in different groups of patients; (B) ROC curve of hs-cTnI for AMI diagnosis in all patients with the cut-off value determined by Youden index; (C) ROC curve of hs-cTnI for AMI diagnosis in all patients with the cut-off value same as POC cTnI; (D) ROC curve of the GRACE score for AMI diagnosis in all patients.
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