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.

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 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.

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 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

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 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 ◽  
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.

scholarly journals Prognostic value of the neutrophil-to-lymphocyte ratio in acute organophosphorus pesticide poisoning

2021 ◽  
Vol 16 (1) ◽  
pp. 703-710
Author(s):  
Yuhang Mu ◽  
Boqi Hu ◽  
Nan Gao ◽  
Li Pang
Keyword(s):  
Logistic Regression ◽  
Regression Analysis ◽  
Hospital Mortality ◽  
Logistic Regression Analysis ◽  
Poor Prognosis ◽  
Prognostic Value ◽  
Organophosphorus Pesticide ◽  
Neutrophil To Lymphocyte Ratio ◽  
Pesticide Poisoning ◽  
Lymphocyte Ratio

Abstract This study investigates the ability of blood neutrophil-to-lymphocyte ratio (NLR) to predict acute organophosphorus pesticide poisoning (AOPP). Clinical data of 385 patients with AOPP were obtained within 24 h of admission, and NLR values were calculated based on neutrophil and lymphocyte counts. The patients were divided into two groups – good and poor – based on prognosis. Poor prognosis included in-hospital death and severe poisoning. The factors affecting prognosis were analyzed by logistic regression analysis, and the prognostic value of NLR was evaluated using the area under the receiver operating characteristic curve (AUC). Univariate logistic regression analysis showed that NLR levels, serum cholinesterase, and creatinine levels were good predictors of AOPP. Multivariate logistic regression analysis showed that high NLR was an independent risk factor for severe poisoning (adjusted odds ratio [AOR], 1.13; 95% CI, 1.10–1.17; p < 0.05) and in-hospital mortality (AOR, 1.07; 95% CI, 1.03–1.11; p < 0.05). NLR values >13 and >17 had a moderate ability to predict severe poisoning and in-hospital mortality, respectively (AUC of 0.782 [95% CI, 0.74–0.824] and 0.714 [95% CI, 0.626–0.803], respectively). Our results show that high NLR at admission is an independent indicator of poor prognosis in AOPP and can be used to optimize treatment and manage patients.

Abstract 1847: Prognostic Impact Of Previous Percutaneous Coronary Interventions On Coronary Artery Bypass Graft Surgery: A Multicentric Analysis FromThe German Federal State North-Rhine-Westphalia

Circulation ◽  
2007 ◽  
Vol 116 (suppl_16) ◽  
Author(s):  
Parwis Massoudy ◽  
Matthias Thielmann ◽  
Nils Lehmann ◽  
Anja Marr ◽  
Georg Kleikamp ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Regression Analysis ◽  
Coronary Artery ◽  
Hospital Mortality ◽  
Logistic Regression Analysis ◽  
Artery Bypass ◽  
Federal State ◽  
Multivariable Logistic Regression Analysis ◽  
Percutaneous Coronary ◽  
German Federal State

Background: We have previously shown that multiple prior percutaneous coronary intervention (PCI) procedures adversely affect outcome after subsequent coronary artery bypass grafting (CABG). We were now interested to investigate this effect on a multicentric basis. Methods: Eight cardiac surgical centers from the German Federal State of North-Rhine-Westphalia provided outcome data of 37140 consecutive patients having undergone isolated first-time CABG between 01/2000 and 12/2005. Twenty-two patient characteristics and outcome variables, which are part of a collection of data claimed by the national medical quality-control commission, were retrieved from the individual databases. Three groups of patients were analyzed for overall in-hospital mortality and major adverse cardiac events (MACE): Patients without a previous PCI procedure, patients with 1 previous PCI procedure and patients with ≥2 previous PCI procedures before surgery. Unadjusted univariable and risk-adjusted multivariable logistic regression analysis were applied. Computed propensity-score matching was performed based on 15 patient major risk factors to correct for and minimize selection bias. Results: A total of 10.3% of patients had 1 previous PCI procedure, and 3.7% of patients had ≥2 previous PCI procedures. Risk-adjusted multivariable logistic regression analysis of ≥2 previous PCI significantly correlated with in-hospital mortality (odds ratio [OR], 2.0; confidence interval [CI], 1.4–3.0; P <0.0005) and MACE (OR, 1.5; CI, 1.2–1.9; P <0.0013). After propensity score matching, conditional logistic regression analysis confirmed the results of adjusted analysis. A history of ≥2 previous PCI procedures was significantly associated with in-hospital mortality (OR, 1.9; CI, 1.3–2.7; P =0.0016) and MACE (OR, 1.5; CI, 1.2–1.9; P =0.0019). Conclusions: This large multicentric trial supports earlier results of our single-center analysis, multiple previous PCI procedures significantly increased the event of in-hospital mortality and MACE after subsequent CABG.

scholarly journals Real-world clopidogrel utilization in acute coronary syndromes: patients selection and outcomes in a single-center experience

2017 ◽  
Vol 11 (12) ◽  
pp. 323-331 ◽  
Author(s):  
Diego Castini ◽  
Simone Persampieri ◽  
Sara Cazzaniga ◽  
Giulia Ferrante ◽  
Marco Centola ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Regression Analysis ◽  
Adverse Events ◽  
Hospital Mortality ◽  
Logistic Regression Analysis ◽  
Left Ventricular ◽  
Hospital Death ◽  
Multivariable Logistic Regression Analysis ◽  
Killip Class ◽  
Female Sex

Background: With this study, we sought to identify patient characteristics associated with clopidogrel prescription and its relationship with in-hospital adverse events in an unselected cohort of ACSs patients. Materials and Methods: We studied all consecutive patients admitted at our institution for ACSs from 2012 to 2014. Patients were divided into two groups based on clopidogrel or novel P2Y12 inhibitors (prasugrel or ticagrelor) prescription and the relationship between clopidogrel use and patient clinical characteristics and in-hospital adverse events was evaluated using logistic regression analysis. Results: The population median age was 68 years (57–77 year) and clopidogrel was prescribed in 230 patients (46%). Patients characteristics associated with clopidogrel prescription were older age, female sex, non-ST-elevation ACS diagnosis, the presence of diabetes mellitus and anemia, worse renal and left ventricular functions and a higher Killip class. Patients on clopidogrel demonstrated a significantly higher incidence of in-hospital mortality (4.8%) than prasugrel and ticagrelor-treated patients (0.4%), while a nonstatistically significant trend emerged considering bleeding events. However, on multivariable logistic regression analysis female sex, the presence of anemia and Killip class were the only variables independently associated with in-hospital death. Conclusion: Patients treated with clopidogrel showed a higher in-hospital mortality. However, clinical variables associated with its use identify a population at high risk for adverse events and this seems to play a major role for the higher in-hospital mortality observed in clopidogrel-treated patients.

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