scholarly journals Development and external validation of a diagnostic model for periprocedural bradycardia during primary percutaneous coronary intervention

2020 ◽  
Author(s):  
Yong Li ◽  
Shuzheng Lyu
Keyword(s):  
Heart Rate ◽  
Percutaneous Coronary Intervention ◽  
Primary Percutaneous Coronary Intervention ◽  
External Validation ◽  
Coronary Intervention ◽  
Validation Dataset ◽  
Diagnostic Model ◽  
Acute Stemi ◽  
Percutaneous Coronary ◽  
Medical University

AbstractBackgroundPeriprocedural 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).ObjectiveTo develop and externally validate a diagnostic model of periprocedural bradycardia..MethodsDesign: Multivariable 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.ResultsTotally 332 (18.2%)patients presented periprocedural bradycardia in the development dataset and 102 (14.2%) patients presented periprocedural bradycardia in the validation 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 0.8384± 0.0122 in the development set. We constructed a nomogram based on predictors of periprocedural bradycardia using the development database. The AUC was 0.8437±0.0203 in the validation set. Discrimination, calibration, and decision curve analysis were satisfactory.ConclusionsWe developed and externally validated a diagnostic model of periprocedural bradycardia during PPCI.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

Development and External Validation of Diagnostic Model for Periprocedural Bradycardia during Primary Percutaneous Coronary Intervention: Algorithm Development and Validation (Preprint)

2020 ◽  
Author(s):  
Yong Li ◽  
Shuzheng Lyu
Keyword(s):  
Heart Rate ◽  
Percutaneous Coronary Intervention ◽  
Logistic Regression ◽  
Primary Percutaneous Coronary Intervention ◽  
External Validation ◽  
Coronary Intervention ◽  
Diagnostic Model ◽  
Acute Stemi ◽  
Percutaneous Coronary ◽  
Medical University

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. CLINICALTRIAL 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.

scholarly journals Development and External Validation of a Diagnostic Model for Intra-Procedural Hypotension during Primary Percutaneous Coronary Intervention

2020 ◽  
Author(s):  
Yong Li
Keyword(s):  
Percutaneous Coronary Intervention ◽  
Primary Percutaneous Coronary Intervention ◽  
External Validation ◽  
Model Development ◽  
Coronary Intervention ◽  
University Hospital ◽  
Validation Dataset ◽  
Diagnostic Model ◽  
Acute Stemi ◽  
Percutaneous Coronary

AbstractBackgroundIntra-procedural hypotension weaken the benefit of primary percutaneous coronary intervention (PPCI) and worsens the prognosis of acute ST elevation myocardial infarction (STEMI) patients.ObjectivesTo develop and externally validate a diagnostic model of intra-procedural hypotension.MethodsDesign:Multivariable logistic regression of a cohort of acute STEMI patients. Setting: Emergency department ward of a university hospital. Participants: Diagnostic model development: Totally 1239 acute STEMI patients who were consecutively treated with PPCI from November 2007 to December 2013. External validation: Totally 1294 acute STEMI patients who were treated with PPCI from January 2014 to June 2018. Outcomes: Intra-procedural hypotension. Intra-procedural hypotension was defined as pre-procedural systolic blood pressure (SBP) was > 90mmHg, intra-procedural SBP less than or equal to 90 mmHg persistent or transient.ResultsTotally 121(9.8%) patients presented intra-procedural hypotension in the development dataset; 123 (9.5%) patients presented intra-procedural hypotension in the validation dataset. The strongest predictors of intra-procedural hypotension were no-reflow, the culprit vessel was not left anterior descending, complete occlusion of culprit vessel, using thrombus aspiration devices during operation, and without history of diabetes. We developed a diagnostic model of intra-procedural hypotension. The area under the receiver operating characteristic (ROC) curve (AUC) was 0.685 ± 0.022, 95% CI = 0.641 ~ 0.729 in the development set. We constructed a nomogram using the development database based on predictors of intra-procedural hypotension. The AUC was 0.718 ±0.022, 95% CI = 0.674 ~ 0.761 in the validation set. Discrimination, calibration, and decision curve analysis were satisfactory.ConclusionsWe developed and externally validated a diagnostic model of intra-procedural hypotension during PPCI. We can use the formula or nomogram to predict intra-procedural hypotension.This study was registered with WHO International Clinical Trials Registry Platform (ICTRP) on 6 September 2019 (registration number:ChiCTR1900025706). http://www.chictr.org.cn/edit.aspx?pid=42913&htm=4.

Development and External Validation of Diagnostic Model for Intra-Procedural Hypotension during Primary Percutaneous Coronary Intervention: Algorithm Development and Validation (Preprint)

2020 ◽  
Author(s):  
Yong Li
Keyword(s):  
Percutaneous Coronary Intervention ◽  
Primary Percutaneous Coronary Intervention ◽  
External Validation ◽  
Model Development ◽  
Coronary Intervention ◽  
University Hospital ◽  
Diagnostic Model ◽  
Validation Data ◽  
Acute Stemi ◽  
Percutaneous Coronary

BACKGROUND Intra-procedural hypotension weaken the benefit of primary percutaneous coronary intervention (PPCI) and worsens the prognosis of acute ST elevation myocardial infarction ( STEMI ) patients. OBJECTIVE The objective of our study was to develop and externally validate a diagnostic model of intra-procedural hypotension. METHODS Design:Multivariable logistic regression of a cohort of acute STEMI patients. Setting: Emergency department ward of a university hospital. Participants: Diagno The objective of our study was to develop and externally validate a diagnostic model of intra-procedural hypotension. stic model development: A total of 1239 acute STEMI patients who were consecutively treated with PPCI from November 2007 to December 2013. External validation: A total of 1294 acute STEMI patients who were treated with PPCI from January 2014 to June 2018. Outcomes: Intra-procedural hypotension. Intra-procedural hypotension was defined as pre-procedural systolic blood pressure (SBP) was > 90mmHg, intra-procedural SBP less than or equal to 90 mmHg persistent or transient. RESULTS Intra-procedural hypotension occurred in121 out of 1,239 participants (9.8%) in the development data set.The strongest predictors of intra-procedural hypotension were no-reflow(odds ratios (OR) 1.911; 95% confidence interval(CI), 1.177～3.102 ; P =.009), the culprit vessel was left anterior descending(OR.488;95% CI, .326～.732 ; P =.001), complete occlusion of culprit vessel(OR4.351;95% CI, 2.076～9.12 ; P<.001), using thrombus aspiration devices during operation(OR 1.793;95% CI, 1.058～3.039 ; P =.03) ,and history of diabetes (OR .589;95% CI, .353~.983 ; P =.042). We developed a diagnostic model of intra-procedural hypotension. The area under the receiver operating characteristic (ROC) curve (AUC)was .685 ± .022, 95% CI= .641 ~ .729 in the development set. We constructed a nomogram using the development database based on predictors of intra-procedural hypotension. Intra-procedural hypotension occurred in 123 out of 1,294 participants (9.5%)patients in the validation data set.The AUC was .718 ±.022, 95% CI= .674 ~ .761 in the validation set . Discrimination, calibration, and decision curve analysis were satisfactory. Date of approved by ethic committee: 2 September 2019. Date of data collection start: 10 September 2019. Numbers recruited as of submission of the manuscript:2,533. CONCLUSIONS We developed and externally validated a diagnostic model of intra-procedural hypotension during PPCI . We can use the formula or nomogram to predict intra-procedural hypotension. CLINICALTRIAL This study was registered with WHO International Clinical Trials Registry Platform (ICTRP) on 6 September 2019 (registration number:ChiCTR1900025706). http://www.chictr.org.cn/edit.aspx?pid=42913&htm=4.

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