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

Abstract

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 a diagnostic model for cardiometabolic-based chronic disease : results from the China health and retirement longitudinal study (CHARLS)

“…Outcome of interest was CMBCD. The absence or presence of CMBCD was decided blinded to the predictor variables and based on the survey record [ 11 , 13 ].…”

“…We selected 12 predictor according to the results of baseline descriptive statistics and clinical relevance [ 11 ]. The potential candidate variables were biological (age, sex, pain, sleep duration, and general health status) and social (housework ability, smoking, alcohol consumption, location of residential address, exercise tolerance, marital status, and education level) determinants of health.…”

“…Our sample and the number of events exceed all approaches for determining samples sizes and therefore are expected to provide estimates that are very robust [ 11 , 13 ]. To ensure reliability of data, we excluded individuals who had missing information on key predictors: age, general health status, housework ability, pain, location of residential address, smoking, and exercise tolerance [ 11 , 13 ].…”

“…We used univariable and multivariable logistic regression models to identify the correlates of CMBCD [ 11 , 13 ]. We entered all variables of Table 1 into the univariable logistic regression [ 11 ].…”

Diagnostic Model of in-Hospital Mortality in Patients with Acute ST-Segment Elevation Myocardial Infarction Used Artificial Intelligence Methods

“…[10] 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). [8,10] Discrimination was the ability of the diagnostic model to differentiate between patient who with and without in-hospital mortality. This measure was quantified by calculating the area under the receiver operating characteristic (ROC) curve (AUC).…”