Development and Validation of a Novel Risk Score for In‐Hospital Major Bleeding in Acute Myocardial Infarction:—The SWEDEHEART Score

Simonsson, Moa; Winell, Henric; Olsson, Henrik; Szummer, Karolina; Alfredsson, Joakim; Hall, Marlous; Dondo, Tatendashe B; Gale, Chris P; Jernberg, Tomas

doi:10.1161/jaha.119.012157

Cited by 25 publications

(31 citation statements)

References 43 publications

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“…Previous studies demonstrated that the prevalence of history of bleeding increased the risk of bleeding events. 24 25 26 In the previous all-comers study, the prevalence of history of bleeding was approximately 6%, 26 whereas in the present study, a prevalence of only 0.6% was observed. This is a hypothesis-generated study using a database of a randomized controlled trial and bleeding events were site-reported.…”

Section: Discussioncontrasting

confidence: 70%

Comparative Assessment of Predictive Performance of PRECISE-DAPT, CRUSADE, and ACUITY Scores in Risk Stratifying 30-Day Bleeding Events

et al. 2020

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Abstract Background The utility of the PRECISE-DAPT score in predicting short-term major bleeding, either alone, or in comparison with the CRUSADE and ACUITY scores, has not been investigated. This analysis compared the predictive performances of the three bleeding scores in stratifying the risk of 30-day major bleeding postpercutaneous coronary intervention in patients with dual-antiplatelet therapy. Methods In this post hoc subanalysis of the GLOBAL LEADERS trial, the primary safety objective (bleeding according to the Bleeding Academic Research Consortium [BARC] criteria [type 3 or 5]) was assessed at 30 days according to the three scores in the overall population, and in patients with acute (ACS) and chronic coronary syndrome (CCS). Results In a total of 15,968 patients, we calculated all three scores in 14,709 (92.1%). Irrespective of clinical presentation, the PRECISE-DAPT (c-statistics: 0.648, 0.653, and 0.641, respectively), CRUSADE (c-statistics: 0.641, 0.639, and 0.644, respectively), and ACUITY (c-statistics: 0.633, 0.638, and 0.623, respectively) scores were no significant between-score differences in discriminatory performance for BARC 3 or 5 bleeding up to 30 days, and similarly the PRECISE-DAPT score had a comparable discriminative capacity according to the integrated discrimination improvement when compared with the other scores. In ACS, the CRUSADE score had a poor calibration ability (Hosmer–Lemeshow goodness-of-fit [GOF] chi-square = 15.561, p = 0.049), whereas in CCS, the PRECISE-DAPT score had poor calibration (GOF chi-square = 15.758, p = 0.046). Conclusion The PRECISE-DAPT score might be clinically useful in the overall population and ACS patients for the prediction of short-term major bleeding considering its discriminative and calibration abilities.

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Section: Discussioncontrasting

confidence: 70%

Comparative Assessment of Predictive Performance of PRECISE-DAPT, CRUSADE, and ACUITY Scores in Risk Stratifying 30-Day Bleeding Events

et al. 2020

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“…Previous studies constructed nomograms for AMI, but these nomograms only focused on the short-term mortality of patients and the occurrence of complications, such as bleeding and acute kidney injury. 22,23 To the best of our knowledge, this study was the first to establish a nomogram for predicting the long-term survival of patients with AMI. In our prediction model, the predictors integrated into the model were easy to obtain and calculate, including vital signs, such as RR, and routine laboratory tests, such as BUN.…”

Section: Discussionmentioning

confidence: 96%

Development and Validation of Nomogram to Predict Long-Term Prognosis of Critically Ill Patients with Acute Myocardial Infarction

Tang¹,

Chen²,

Zha³

et al. 2021

IJGM

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Purpose: Acute myocardial infarction (AMI) is a common cardiovascular disease with a poor prognosis. The aim of this study was to construct a nomogram for predicting the longterm survival of critically ill patients with AMI. This nomogram will help in assessing disease severity, guiding treatment, and improving prognosis. Patients and Methods: The clinical data of patients with AMI were extracted from the MIMIC-III v1.4 database. Cox proportional hazards models were adopted to identify independent prognostic factors. A nomogram for predicting the long-term survival of these patients was developed on the basis of the results of multifactor analysis. The discriminative ability and accuracy of the multifactor analysis were evaluated according to concordance index (C-index) and calibration curves. Results: A total of 1202 patients were included in the analysis. The patients were randomly divided into a training set (n = 841) and a validation set (n = 361). Multivariate analysis revealed that age, blood urea nitrogen, respiratory rate, hemoglobin, pneumonia, cardiogenic shock, dialysis, and mechanical ventilation, all of which were incorporated into the nomogram, were independent predictive factors of AMI. Moreover, the nomogram exhibited favorable performance in predicting the 4-year survival of patients with AMI. The training set and the validation set had a C-index of 0.789 (95% confidence interval [CI]: 0.765-0.813) and 0.762 (95% CI: 0.725-0.799), respectively. Conclusion:The nomogram constructed herein can accurately predict the long-term survival of critically ill patients with AMI.

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“…This was also a form of internal validation because their cohorts were randomly created [ 9 ]. Moa Simonsson et al [ 6 ] used 97,597 patients to develop a model to predict in-hospital major bleeding in acute myocardial infarction. The internal and temporal validity of the model were assessed; the temporal validity of the score was assessed using internal-external cross-validation [ 6 ].…”

Section: Discussionmentioning

confidence: 99%

“…Alexander et al [ 9 ] developed a model to predict in-hospital major bleeding in acute myocardial infarction, but their models were only internally validated. Moa Simonsson et al [ 6 ] developed a model to predict in-hospital major bleeding in acute myocardial infarction, and the internal and temporal validity of the model was assessed. The aim of our study was to develop and externally validate a diagnostic model of in-hospital bleeding in patients with acute STEMI.…”

Section: Introductionmentioning

confidence: 99%

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

2020

JMIR Med Inform

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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

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Development and Validation of a Novel Risk Score for In‐Hospital Major Bleeding in Acute Myocardial Infarction:—The SWEDEHEART Score

Cited by 25 publications

References 43 publications

Comparative Assessment of Predictive Performance of PRECISE-DAPT, CRUSADE, and ACUITY Scores in Risk Stratifying 30-Day Bleeding Events

Comparative Assessment of Predictive Performance of PRECISE-DAPT, CRUSADE, and ACUITY Scores in Risk Stratifying 30-Day Bleeding Events

Development and Validation of Nomogram to Predict Long-Term Prognosis of Critically Ill Patients with Acute Myocardial Infarction

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

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