Previously submitted to: Journal of Medical Internet Research (no longer under consideration since Aug 24, 2020)
Date Submitted: Jul 7, 2020
Open Peer Review Period: Jul 7, 2020 - Aug 24, 2020
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Development and External Validation of Diagnostic Model for Periprocedural Bradycardia during Primary Percutaneous Coronary Intervention: Algorithm Development and Validation
ABSTRACT
Background:
Periprocedural bradycardia weaks the benefit of primary percutaneous coronary intervention (PPCI) and has deleterious effects on organ perfusion of patients with acute ST elevation myocardial infarction (STEMI).
Objective:
The objective of our study was to develop and externally validate a diagnostic model of periprocedural bradycardia. .
Methods:
Design: Multivariate logistic regression of a cohort of acute STEMI patients. Setting: Emergency department ward of a university hospital. Participants: Diagnostic model development: Totally 1820 acute STEMI patients who were consecutively treated with PPCI from November 2007 to December 2015 in Beijing Anzhen Hospital, Capital Medical University. External validation: Totally 716 acute STEMI patients who were treated with PPCI from January 2016 to June 2018 in Beijing Anzhen Hospital, Capital Medical University. Outcomes: Periprocedural bradycardia during PPCI. Periprocedural bradycardia was defined as preoperative heart rate ≥ 50 times / min, intraoperative heart rate <50 times / min persistent or transient. We used logistic regression analysis to analyze the risk factors of periprocedural bradycardia in the development data set. We developed a diagnostic model of periprocedural bradycardia and constructed a nomogram.We assessed the predictive performance of the diagnostic model in the validation data sets by examining measures of discrimination, calibration, and decision curve analysis (DCA).
Results:
Periprocedural bradycardia occurred in 332 out of 1,820 participants (18.2%) in the development dataset. The strongest predictors of periprocedural bradycardia were intra-procedural hypotension, the culprit vessel was not left anterior descending (LAD), using thrombus aspiration devices during procedure, sex, history of coronary artery disease, total occlusion of culprit vessel, and no-reflow. We developed a diagnostic model of periprocedural bradycardia.The area under the receiver operating characteristic(ROC) curve(AUC) was was.8384 ±.0122, 95% confidence interval(CI)=.81460~.86225in the development set. We constructed a nomogram based on predictors of periprocedural bradycardia. Periprocedural bradycardia occurred in 102 out of 716 participants (14.2%)in the validation dataset. The AUC was was .8437 ±.0203, 95% CI= .80390 ~ .88357. Discrimination, calibration, and DCA were satisfactory. Date of approved by ethic committee:16 May 2019. Date of data collection start: 1 June 2019. Numbers recruited as of submission of the manuscript:2,536.
Conclusions:
We developed and externally validated a diagnostic model of periprocedural bradycardia during PPCI. Clinical Trial: We registered this study with WHO International Clinical Trials Registry Platform（ICTRP）. Registration number: ChiCTR1900023214. Registered Date :16 May 2019. http://www.chictr.org.cn/edit.aspx?pid=39087&htm=4.
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