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

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.

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

10.2196/preprints.20974 ◽

2020 ◽

Author(s):

Yong Li

Keyword(s):

Logistic Regression ◽

Hospitalized Patients ◽

Diagnostic Model ◽

Validation Data ◽

Data Set ◽

St Segment Elevation ◽

Acute Stemi ◽

St Segment ◽

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. CLINICALTRIAL ChiCTR.org ChiCTR1900027578; http://www.chictr.org.cn/showprojen.aspx?proj=45926

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

JMIR Medical Informatics ◽

10.2196/20974 ◽

2020 ◽

Vol 8 (8) ◽

pp. e20974 ◽

Cited By ~ 1

Author(s):

Yong Li

Keyword(s):

Logistic Regression ◽

Hospitalized Patients ◽

Diagnostic Model ◽

Validation Data ◽

Data Set ◽

St Segment Elevation ◽

Acute Stemi ◽

St Segment ◽

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 Patients with Acute ST-Segment Elevation Myocardial Infarction: Algorithm Development and Validation (Preprint)

10.2196/preprints.25644 ◽

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 ◽

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.

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

10.2196/preprints.22614 ◽

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.

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

10.2196/preprints.23052 ◽

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.

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

10.1101/2020.05.28.20115501 ◽

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 temporal external validation of a simple risk score tool for prediction of outcomes after severe head injury based on admission characteristics from level-1 trauma centre of India using retrospectively collected data

BMJ Open ◽

10.1136/bmjopen-2020-040778 ◽

2021 ◽

Vol 11 (1) ◽

pp. e040778

Author(s):

Vineet Kumar Kamal ◽

Ravindra Mohan Pandey ◽

Deepak Agrawal

Keyword(s):

Hospital Mortality ◽

External Validation ◽

Trauma Centre ◽

Unfavourable Outcome ◽

Motor Score ◽

Validation Data ◽

Data Set ◽

Development Data ◽

Level 1 ◽

Pupillary Reactivity

ObjectiveTo develop and validate a simple risk scores chart to estimate the probability of poor outcomes in patients with severe head injury (HI).DesignRetrospective.SettingLevel-1, government-funded trauma centre, India.ParticipantsPatients with severe HI admitted to the neurosurgery intensive care unit during 19 May 2010–31 December 2011 (n=946) for the model development and further, data from same centre with same inclusion criteria from 1 January 2012 to 31 July 2012 (n=284) for the external validation of the model.Outcome(s)In-hospital mortality and unfavourable outcome at 6 months.ResultsA total of 39.5% and 70.7% had in-hospital mortality and unfavourable outcome, respectively, in the development data set. The multivariable logistic regression analysis of routinely collected admission characteristics revealed that for in-hospital mortality, age (51–60, >60 years), motor score (1, 2, 4), pupillary reactivity (none), presence of hypotension, basal cistern effaced, traumatic subarachnoid haemorrhage/intraventricular haematoma and for unfavourable outcome, age (41–50, 51–60, >60 years), motor score (1–4), pupillary reactivity (none, one), unequal limb movement, presence of hypotension were the independent predictors as its 95% confidence interval (CI) of odds ratio (OR)_did not contain one. The discriminative ability (area under the receiver operating characteristic curve (95% CI)) of the score chart for in-hospital mortality and 6 months outcome was excellent in the development data set (0.890 (0.867 to 912) and 0.894 (0.869 to 0.918), respectively), internal validation data set using bootstrap resampling method (0.889 (0.867 to 909) and 0.893 (0.867 to 0.915), respectively) and external validation data set (0.871 (0.825 to 916) and 0.887 (0.842 to 0.932), respectively). Calibration showed good agreement between observed outcome rates and predicted risks in development and external validation data set (p>0.05).ConclusionFor clinical decision making, we can use of these score charts in predicting outcomes in new patients with severe HI in India and similar settings.

Correlation between body mass index and in-hospital mortality in patients with ST-segment elevation myocardial infarction in Erbil city- Iraq

Diyala Journal of Medicine ◽

10.26505/djm.21015930222 ◽

2021 ◽

Vol 21 (1) ◽

pp. 35-43

Author(s):

Azad Ahmed Abdullah ◽

◽

Salam Naser Zangana

Keyword(s):

Myocardial Infarction ◽

Body Mass Index ◽

Hospital Mortality ◽

Body Mass ◽

Hospital Death ◽

St Segment Elevation ◽

Acute Stemi ◽

St Segment ◽

Significant Difference

Background: Although High body mass index is associated with many cardiovascular diseases including coronary artery disease. Its effect on in-hospital death in patients with acute ST-segment elevation myocardial infarction (STEMI) is still a subject of controversy. Objective: To determine the correlation between body mass index (BMI) and in-hospital mortality in those patients. Patients and Methods: In this cross-sectional study, 180 adult patients with acute STEMI were enrolled and their BMI was measured. The participants were classified according to BMI into three groups as normal, overweight, and obese. A correlation between in-hospital mortality due to STEMI and BMI was evaluated. Results: Of the total participants, 62 (34.4%) were normally weighted, 61(33.8%) were over-weighted, and 57(31.6%) were obese. There was a significant difference (p= <0.001) between the groups concerning troponin I, hs-CRP, GRACE score, and the probability of in-hospital death. There were 16 (8.8%) in-hospital deaths during the study distributed as follows; 1(1.6%) in the normal-weight group, 5(8.1%) in the overweight group, and 10 (17.5%) in the obese group. In-hospital death showed a significant difference (p=0.04) between the study groups. In addition, a significant positive correlation(r=0.9) was found between BMI and in-hospital death. Conclusion: A robust positive correlation was detected between BMI and in-hospital mortality due to acute STEMI. When BMI increases, the number of deaths also increases exponentially. Keywords: Body mass index, ST-segment elevation myocardial infarction, mortality

Development and External Validation of a Diagnostic Model for In-Hospital Mortality in Patient with Acute ST-Segment Elevation Myocardial Infarction

SSRN Electronic Journal ◽

10.2139/ssrn.3723618 ◽

2020 ◽

Author(s):

Yong Li

Keyword(s):

Myocardial Infarction ◽

Hospital Mortality ◽

External Validation ◽

Diagnostic Model ◽

St Segment Elevation ◽

St Segment

Development and External Validation of a Diagnostic Model for in-Hospital Mortality in Patient with Acute ST Elevation Myocardial Infarction

10.1101/2020.05.28.20115485 ◽

2020 ◽

Cited By ~ 1

Author(s):

Yong Li

Keyword(s):

Myocardial Infarction ◽

Hospital Mortality ◽

External Validation ◽

Hospitalized Patients ◽

St Elevation Myocardial Infarction ◽

University Hospital ◽

Validation Dataset ◽

Diagnostic Model ◽

Acute Stemi ◽

St Elevation

AbstractObjectiveTo develop and externally validate a diagnostic model of in-hospital mortality in the population of unselected real-world patients with acute ST elevation myocardial infarction (STEMI).DesignMultivariable logistic regression of a cohort of hospitalized patients with acute STEMI.SettingEmergency department ward of a university hospital.ParticipantsDiagnostic model development: Totally 2183 hospitalized patients with acute STEMI from January 2002 to December 2011. External validation: Totally 7485 hospitalized patients with acute STEMI from January 2012 to August 2019.OutcomesIn-hospital mortality.ResultsTotally 61 (2.8%) patient died in the development dataset and 127(1.7%) patient died in the validation dataset. 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 0.9126+0.0166, 95% confidence ±interval(CI)= 0.88015 ~ 0.94504 in the development set. We constructed a nomograms using the development database based on age and Killip classification. The AUC was 0.9305+0.0113,95% CI= 0.90827 ~ 0.95264 in the validation set. Discrimination, calibration, and decision curve analysis were satisfactory.ConclusionsWe developed and externally validated a strong diagnostic model of in-hospital mortality in patient with acute STEMI.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.