Development and External Validation of Diagnostic Model for Coronary Microvascular Obstruction: Algorithm Development and Validation (Preprint)

2020 ◽  
Author(s):  
Yong Li ◽  
Shuzheng Lyu
Keyword(s):  
Logistic Regression ◽  
Regression Analysis ◽  
Logistic Regression Analysis ◽  
Microvascular Obstruction ◽  
External Validation ◽  
Diagnostic Model ◽  
Total Occlusion ◽  
Validation Data ◽  
Data Set ◽  
Acute Stemi

BACKGROUND Prevention of coronary microvascular obstruction /no-reflow phenomenon(CMVO/NR) is a crucial step in improving prognosis of patients with acute ST segment elevation myocardial infarction (STEMI )during primary percutaneous coronary intervention (PPCI). OBJECTIVE The objective of our study was to develop and externally validate a diagnostic model of CMVO/NR in patients with acute STEMI underwent PPCI. 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 1232 acute STEMI patients who were consecutively treated with PPCI from November 2007 to December 2013. External validation: Totally 1301 acute STEMI patients who were treated with PPCI from January 2014 to June 2018. Outcomes: CMVO/NR during PPCI. We used logistic regression analysis to analyze the risk factors of CMVO/NR in the development data set. We developed a diagnostic model of CMVO/NR 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 A total of 147 out of 1,232 participants (11.9%) presented CMVO/NR in the development dataset.The strongest predictors of CMVO/NR were age, periprocedural bradycardia, using thrombus aspiration devices during procedure and total occlusion of culprit vessel. Logistic regression analysis showed that the differences between two group with and without CMVO/NR in age( odds ratios (OR)1.031; 95% confidence interval(CI), 1.015 ~1.048 ; P <.001), periprocedural bradycardia (OR 2.151;95% CI,1.472~ 3.143 ; P <.001) , total occlusion of the culprit vessel (OR 1.842;95% CI, 1.095~ 3.1 ; P =.021) , and using thrombus aspirationdevices during procedure (OR 1.631; 95% CI, 1.029~ 2.584 ; P =.037).We developed a diagnostic model of CMVO/NR. The area under the receiver operating characteristic curve (AUC) was .6833±.023. We constructed a nomogram. CMVO/NR occurred in 120 out of 1,301 participants (9.2%) in the validation data set. The AUC was .6547±.025. 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:2,533. CONCLUSIONS We developed and externally validated a diagnostic model of CMVO/NR during PPCI. CLINICALTRIAL We registered this study with WHO International Clinical Trials Registry Platform on 16 May 2019. Registration number: ChiCTR1900023213. http://www.chictr.org.cn/edit.aspx?pid=39057&htm=4.

Diagnostic Model of in-Hospital Mortality in Patients with Acute ST-Segment Elevation Myocardial Infarction: Algorithm Development and Validation (Preprint)

2020 ◽  
Author(s):  
Yong Li
Keyword(s):  
Logistic Regression ◽  
Regression Analysis ◽  
Hospital Mortality ◽  
Logistic Regression Analysis ◽  
Diagnostic Model ◽  
Validation Data ◽  
Data Set ◽  
St Segment Elevation ◽  
Acute Stemi ◽  
Development Data

BACKGROUND Coronary heart disease, including ST-segment elevation myocardial infarction (STEMI), remains the main cause of death. OBJECTIVE The objective of our research was to develop and externally validate a diagnostic model of in-hospital mortality in acute STEMI patients. METHODS We performed multiple logistic regression analysis on a cohort of hospitalized acute STEMI patients. Participants: From January 2002 to December 2011, a total of 2,183 inpatients with acute STEMI were admitted for development.The external validation data set of this model comes from 7,485 hospitalized patients with acute STEMI from January 2012 to August 2019.We used logistic regression analysis to analyze the risk factors of in-hospital mortality in the development data set.We developed a diagnostic model of in-hospital mortality and constructed a nomogram. We evaluated the predictive performance of the diagnostic model in the validation data set by examining the measures of discrimination, calibration, and decision curve analysis (DCA). RESULTS In the development data set, 61 of the 2,183 participants (2.8%) experienced in-hospital mortality. The strongest predictors of in-hospital mortality were advanced age and high Killip classification. Logistic regression analysis showed the difference between the two groups with and without in-hospital mortality (odds ratio [OR] 1.058, 95% CI 1.029-1.088; P <.001), Killip III (OR 8.249, 95% CI 3.502-19.433; P <.001) and Killip IV (OR 39.234, 95% CI 18.178-84.679; P <.001). We had developed a diagnostic model of in-hospital mortality. The area under the receiver operating characteristic curve (AUC) was 0.9126 (SD 0.0166, 95% CI 0.88015-0.94504). We constructed a nomogram based on age and Killip classification. In-hospital mortality occurred in 127 of 7,485 participants(1.7%) in the validation data set. The AUC was 0 .9305(SD 0.0113, 95% CI 0. 90827-0. 95264). CONCLUSIONS We had developed and externally validated a diagnostic model of in-hospital mortality in acute STEMI patients. It was found that the discrimination, calibration and DCA of this model were satisfactory. CLINICALTRIAL ChiCTR.org ChiCTR1900027129; http://www.chictr.org.cn/edit.aspx?pid=44888&htm=4.

scholarly journals Diagnostic Model for In-Hospital Bleeding in Patients with Acute ST-Segment Elevation Myocardial Infarction: Algorithm Development and Validation (Preprint)

2020 ◽  
Author(s):  
Yong Li
Keyword(s):  
Logistic Regression ◽  
Hospitalized Patients ◽  
Diagnostic Model ◽  
Validation Data ◽  
Data Set ◽  
St Segment Elevation ◽  
Acute Stemi ◽  
Segment Elevation Myocardial Infarction ◽  
St Segment ◽  
Development Data

BACKGROUND Bleeding complications in patients with acute ST-segment elevation myocardial infarction (STEMI) have been associated with increased risk of subsequent adverse consequences. OBJECTIVE The objective of our study was to develop and externally validate a diagnostic model of in-hospital bleeding. METHODS We performed multivariate logistic regression of a cohort for hospitalized patients with acute STEMI in the emergency department of a university hospital. Participants: The model development data set was obtained from 4262 hospitalized patients with acute STEMI from January 2002 to December 2013. A set of 6015 hospitalized patients with acute STEMI from January 2014 to August 2019 were used for external validation. We used logistic regression analysis to analyze the risk factors of in-hospital bleeding in the development data set. We developed a diagnostic model of in-hospital bleeding 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 In-hospital bleeding occurred in 112 of 4262 participants (2.6%) in the development data set. The strongest predictors of in-hospital bleeding were advanced age and high Killip classification. Logistic regression analysis showed differences between the groups with and without in-hospital bleeding in age (odds ratio [OR] 1.047, 95% CI 1.029-1.066; <i>P</i>&lt;.001), Killip III (OR 3.265, 95% CI 2.008-5.31; <i>P</i>&lt;.001), and Killip IV (OR 5.133, 95% CI 3.196-8.242; <i>P</i>&lt;.001). We developed a diagnostic model of in-hospital bleeding. The area under the receiver operating characteristic curve (AUC) was 0.777 (SD 0.021, 95% CI 0.73576-0.81823). We constructed a nomogram based on age and Killip classification. In-hospital bleeding occurred in 117 of 6015 participants (1.9%) in the validation data set. The AUC was 0.7234 (SD 0.0252, 95% CI 0.67392-0.77289). CONCLUSIONS We developed and externally validated a diagnostic model of in-hospital bleeding in patients with acute STEMI. The discrimination, calibration, and DCA of the model were found to be satisfactory. CLINICALTRIAL ChiCTR.org ChiCTR1900027578; http://www.chictr.org.cn/showprojen.aspx?proj=45926

scholarly journals Development and External Validation of a Diagnostic Model for Coronary Microvascular Obstruction

2020 ◽  
Author(s):  
Yong Li ◽  
Shuzheng Lyu
Keyword(s):  
Microvascular Obstruction ◽  
Characteristic Curve ◽  
External Validation ◽  
Model Development ◽  
University Hospital ◽  
Validation Dataset ◽  
Diagnostic Model ◽  
Total Occlusion ◽  
St Segment Elevation ◽  
Acute Stemi

AbstractBackgroundPrevention of coronary microvascular obstruction /no-reflow phenomenon(CMVO/NR) is a crucial step in improving prognosis of patients with acute ST segment elevation myocardial infarction (STEMI)during primary percutaneous coronary intervention (PPCI). We wanted to develop and externally validate a diagnostic model of CMVO/NR in patients with acute STEMI underwent PPCI.MethodsDesign: Multivariable logistic regression of a cohort of acute STEMI patients. Setting: Emergency department ward of a university hospital. Participants: Diagnostic model development: Totally 1232 acute STEMI patients who were consecutively treated with PPCI from November 2007 to December 2013. External validation: Totally 1301 acute STEMI patients who were treated with PPCI from January 2014 to June 2018. Outcomes: CMVO/NR during PPCI.Results147(11.9%)patients presented CMVO/NR in the development dataset and 120(9.2%) patients presented CMVO/NR in the validation dataset. The strongest predictors of CMVO/NR were age, periprocedural bradycardia, using thrombus aspiration devices during procedure and total occlusion of culprit vessel. We developed a diagnostic model of CMVO/NR.The area under the receiver operating characteristic curve (AUC) was 0.6833 in the development set.We constructed a nomogram using the development database.The AUC was 0.6547 in the validation set. Discrimination, calibration, and decision curve analysis were satisfactory.ConclusionsWe developed and externally validated a diagnostic model of CMVO/NR during PPCI.We registered this study with WHO International Clinical Trials Registry Platform on 16 May 2019. Registration number: ChiCTR1900023213. http://www.chictr.org.cn/edit.aspx?pid=39057&htm=4.

scholarly journals Diagnostic Model for In-Hospital Bleeding in Patients with Acute ST-Segment Elevation Myocardial Infarction: Algorithm Development and Validation

10.2196/20974 ◽  
2020 ◽  
Vol 8 (8) ◽  
pp. e20974 ◽  
Author(s):  
Yong Li
Keyword(s):  
Logistic Regression ◽  
Hospitalized Patients ◽  
Diagnostic Model ◽  
Validation Data ◽  
Data Set ◽  
St Segment Elevation ◽  
Acute Stemi ◽  
Segment Elevation Myocardial Infarction ◽  
St Segment ◽  
Development Data

Background Bleeding complications in patients with acute ST-segment elevation myocardial infarction (STEMI) have been associated with increased risk of subsequent adverse consequences. Objective The objective of our study was to develop and externally validate a diagnostic model of in-hospital bleeding. Methods We performed multivariate logistic regression of a cohort for hospitalized patients with acute STEMI in the emergency department of a university hospital. Participants: The model development data set was obtained from 4262 hospitalized patients with acute STEMI from January 2002 to December 2013. A set of 6015 hospitalized patients with acute STEMI from January 2014 to August 2019 were used for external validation. We used logistic regression analysis to analyze the risk factors of in-hospital bleeding in the development data set. We developed a diagnostic model of in-hospital bleeding 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 In-hospital bleeding occurred in 112 of 4262 participants (2.6%) in the development data set. The strongest predictors of in-hospital bleeding were advanced age and high Killip classification. Logistic regression analysis showed differences between the groups with and without in-hospital bleeding in age (odds ratio [OR] 1.047, 95% CI 1.029-1.066; P<.001), Killip III (OR 3.265, 95% CI 2.008-5.31; P<.001), and Killip IV (OR 5.133, 95% CI 3.196-8.242; P<.001). We developed a diagnostic model of in-hospital bleeding. The area under the receiver operating characteristic curve (AUC) was 0.777 (SD 0.021, 95% CI 0.73576-0.81823). We constructed a nomogram based on age and Killip classification. In-hospital bleeding occurred in 117 of 6015 participants (1.9%) in the validation data set. The AUC was 0.7234 (SD 0.0252, 95% CI 0.67392-0.77289). Conclusions We developed and externally validated a diagnostic model of in-hospital bleeding in patients with acute STEMI. The discrimination, calibration, and DCA of the model were found to be satisfactory. Trial Registration ChiCTR.org ChiCTR1900027578; http://www.chictr.org.cn/showprojen.aspx?proj=45926

Diagnostic Model of in-Hospital Mortality in Patient with Acute ST-Segment Elevation Myocardial Infarction: Algorithm Development and Validation (Preprint)

2020 ◽  
Author(s):  
Yong Li
Keyword(s):  
Logistic Regression ◽  
Hospital Mortality ◽  
Hospitalized Patients ◽  
Diagnostic Model ◽  
Validation Data ◽  
Data Set ◽  
St Segment Elevation ◽  
Acute Stemi ◽  
St Segment ◽  
Development Data

BACKGROUND Coronary heart disease, including ST-segment elevation myocardial infarction (STEMI), is still the leading cause of death. OBJECTIVE The objective of our study was to develop and externally validate a diagnostic model of in-hospital mortality in the patients with acute STEMI . METHODS We performed multivariate logistic regression of a cohort for hospitalized patients with acute STEMI in the emergency department of a university hospital. Participants: The model development data set was obtained from 2,183 hospitalized patients with acute STEMI from January 2002 to December 2011. A set of 7,485 hospitalized patients with acute STEMI from January 2012 to August 2019 were used for external validation. We used logistic regression analysis to analyze the risk factors of in-hospital mortality in the development data set. We developed a diagnostic model of in-hospital mortality 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 In-hospital mortality occurred in 61 of 2,183 participants (2.8%) in the development data set. The strongest predictors of in-hospital mortality were advanced age and high Killip classification. Logistic regression analysis showed differences between the groups with and without in-hospital mortality in age (odds ratio [OR] 1.058, 95% CI 1.029-1.088; P<.001), Killip III (OR 8.249, 95% CI 3.502-19.433; P<.001), and Killip IV (OR 39.234, 95% CI 18.178-84.679; P<.001). We developed a diagnostic model of in-hospital mortality. The area under the receiver operating characteristic curve (AUC) was 0. 9126 (SD 0. 0166, 95% CI 0. 88015-0. 94504). We constructed a nomogram based on age and Killip classification. In-hospital mortality occurred in 127 of 7,485 participants (1.7%) in the validation data set. The AUC was 0 .9305 (SD 0.0113, 95% CI 0. 90827-0. 95264). CONCLUSIONS We developed and externally validated a diagnostic model of in-hospital mortality in patient with acute STEMI . The discrimination, calibration, and DCA of the model were found to be satisfactory. CLINICALTRIAL Trial Registration: ChiCTR.org ChiCTR1900027129; http://www.chictr.org.cn/edit.aspx?pid=44888&htm=4.

Development and External Validation of Diagnostic Model for in-Hospital Mortality in Patient with Acute ST Elevation Myocardial Infarction: Algorithm Development and Validation (Preprint)

2020 ◽  
Author(s):  
Yong Li
Keyword(s):  
Myocardial Infarction ◽  
Hospital Mortality ◽  
External Validation ◽  
Hospitalized Patients ◽  
Diagnostic Model ◽  
Validation Data ◽  
Data Set ◽  
Acute Stemi ◽  
Development Data ◽  
St Elevation

BACKGROUND Coronary heart disease, including ST elevation myocardial infarction(STEMI), was still the leading cause of mortality. OBJECTIVE The objective of our study was to develop and externally validate a diagnostic model of in-hospital mortality in the patients with acute STEMI . METHODS Design: Multivariable logistic regression of a cohort of hospitalized patients with acute STEMI. Setting: Emergency department ward of a university hospital. Participants: Diagnostic model development: A total of 2,183 hospitalized patients with acute STEMI from January 2002 to December 2011.External validation: A total of 7,485 hospitalized patients with acute STEMI from January 2012 to August 2019. Outcomes: In-hospital mortality. All cause in-hospital mortality was defined as cardiac or non-cardiac death during hospitalization. We used logistic regression analysis to analyze the risk factors of in-hospital mortality in the development data set. We developed a diagnostic model of in-hospital mortality 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 In-hospital mortality occurred in 61out of 2,183 participants (2.8%) in the development data set. The strongest predictors of in-hospital mortality were age and Killip classification. We developed a diagnostic model of in-hospital mortality .The area under the receiver operating characteristic (ROC) curve (AUC) was .9126±.0166, 95% confidence interval(CI)= .88015 ~ .94504 in the development set .We constructed a nomogram based on age and Killip classification. In-hospital mortality occurred in 127 out of 7,485 participants(1.7%) in the validation data set. The AUC was .9305±.0113, 95% CI= .90827 ~ .95264 in the validation set . Discrimination, calibration ,and DCA were satisfactory. Date of approved by ethic committee:25 October 2019. Date of data collection start: 6 November 2019. Numbers recruited as of submission of the manuscript:9,668. CONCLUSIONS Conclusions: We developed and externally validated a diagnostic model of in-hospital mortality in patient with acute STEMI . CLINICALTRIAL We registered this study with WHO International Clinical Trials Registry Platform (ICTRP) (registration number: ChiCTR1900027129; registered date: 1 November 2019). http://www.chictr.org.cn/edit.aspx?pid=44888&htm=4.

scholarly journals Development and external validation of a diagnostic model for in-hospital bleeding in patients with acute ST elevation myocardial infarction

2020 ◽  
Author(s):  
Yong Li
Keyword(s):  
Myocardial Infarction ◽  
External Validation ◽  
Hospitalized Patients ◽  
Bleeding Complications ◽  
Diagnostic Model ◽  
Validation Data ◽  
Data Set ◽  
Acute Stemi ◽  
Increased Risk ◽  
Medical University

AbstractBackgroundBleeding complications in patients with acute ST segment elevation myocardial infarction (STEMI) are associated with an increased risk of subsequent adverse consequences. We want to develop and externally validate a diagnostic model of in-hospital bleeding in the population of unselected real-world patients with acute STEMI.MethodsDesign: Multivariable logistic regression of a cohort for hospitalized patients with acute STEMI. Setting: Emergency department ward of a university hospital. Participants: Diagnostic model development: Totally 4262 hospitalized patients with acute STEMI from January 2002 to December 2013 in Beijing Anzhen Hospital, Capital Medical University. External validation: Totally 6015 hospitalized patients with acute STEMI from January 2014 to August 2019 in Beijing Anzhen Hospital, Capital Medical University. Outcomes: All-cause in-hospital bleeding not related to coronary artery bypass graft surgery or catheterization.ResultsIn-hospital bleeding occurred in 2.6% (112/4262) of patients in the development data set (117/6015) of patients in the validation data set. The strongest predictors of in-hospital bleeding were advanced age and high Killip classification. We developed a diagnostic model of in-hospital bleeding. The area under the receiver operating characteristic ROC curve (AUC) was 0.777±0.021, 95% confidence interval(CI) = 0.73576 ~ 0.81823. We constructed a nomograms using the development database based on age, and Killip classification. The AUC was 0.7234±0.0252,95% CI = 0.67392 ~ 0.77289 in the validation data set. Discrimination, calibration, and decision curve analysis were satisfactory.ConclusionsWe developed and externally validated a moderate diagnostic model of in-hospital bleeding in patients with acute STEMI.We registered this study with WHO International Clinical Trials Registry Platform (ICTRP) (registration number: ChiCTR1900027578; registered date: 19 Novmober 2019). http://www.chictr.org.cn/edit.aspx?pid=45926&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.

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.

scholarly journals 182. increasing Odds of Resistance for Subsequent Urinary e. Coli Isolates

2020 ◽  
Vol 7 (Supplement_1) ◽  
pp. S220-S220
Author(s):  
Lauren Frisbie ◽  
Scott Weissman ◽  
Hema Kapoor ◽  
Marisa A D’Angeli ◽  
Ann Salm ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Regression Analysis ◽  
Logistic Regression Analysis ◽  
Bacterial Species ◽  
Healthcare Outcomes ◽  
Data Set ◽  
Susceptibility Data ◽  
Commercial Laboratory ◽  
Resistance Patterns ◽  
Amoxicillin Clavulanate

Abstract Background Annual cumulative antibiograms are routinely used by clinicians to guide selection of empirical antibiotic therapies. CLSI guidelines recommend that these antibiograms to analyze data yearly, include only final, verified results, include bacterial species with &gt; 30 isolates and to include only the first isolate for each species/patient instance per analysis period. Handling multiple isolates from individual patients in cumulative antibiograms is a controversial topic within the antimicrobial stewardship community. Current practice favors removing subsequent isolates, thereby discarding data reflecting impact of selective antibiotic pressure on resistance patterns in recurring urinary tract infection (UTI). In this study we analyzed a five-year data set of deidentified outpatient antibiotic results from a commercial laboratory to determine whether there were significant differences in resistance patterns between first and subsequent isolates from the same patient. Methods The 5-year antibiotic susceptibility data was restricted to urinary Escherichia coli (EC) isolates. Patient occurrence(s) of urinary EC were categorized by frequency: 1st occurrence, 2nd occurrence, 3rd occurrence, and 4th or greater occurrence. A logistic regression analysis using a binary outcome for resistance and independent variable of patient isolate occurrence was run for amoxicillin-clavulanate, ampicillin, ceftriaxone, ciprofloxacin, gentamicin, levofloxacin, nitrofurantoin, and trimethoprim-sulfa. Results From a logistic regression analysis, we estimate that for each occurrence in the data, an isolate’s odds of resistance were higher for every increase in a patient’s number of occurrences in the data for all antibiotics reported with p values &lt; 0.0001. Table 1: Odds ratios (OR) of resistance for each subsequent urinary EC isolate occurrence over 5 years Conclusion Our findings suggest that individuals with higher numbers of urinary EC occurrences have more resistant EC than the first EC occurrence, with effects that vary by antibiotic class. Although traditional antibiograms include only the first occurrence of urinary EC from a single patient, this approach may underestimate levels of reservoir resistance in a community. Such an underestimation likely impacts efficacy of empiric therapeutic choice, healthcare outcomes, and cost. Disclosures All Authors: No reported disclosures

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