Cardiogenic Shock, the Residual SYNTAX Score, and Prognosis: Corroborative "Real-World" Data

Sex-Specific Differences in Potent P2Y12 Inhibitor Use in British Cardiovascular Intervention Society Registry STEMI Patients

BACKGROUND

Sex-based outcome differences for women with ST-segment-elevation myocardial infarction (STEMI) have not been adequately addressed, and the role played by differences in prescription of potent P2Y12 inhibitors (P-P2Y12) is not well defined. This study explores the hypothesis that disparities in P-P2Y12 (prasugrel or ticagrelor) use may play a role in outcome disparities for women with STEMI.

METHODS

Data from British Cardiovascular Intervention Society national percutaneous coronary intervention database were analyzed, and 168 818 STEMI patients treated with primary percutaneous coronary intervention from 2010 to 2020 were included.

RESULTS

Among the included women (43 131; 25.54%) and men (125 687; 74.45%), P-P2Y12 inhibitors were prescribed less often to women (51.71%) than men (55.18%; P<0.001). Women were more likely to die in hospital than men (adjusted odds ratio, 1.213 [95% CI, 1.141-1.290]). Unadjusted mortality was higher among women treated with clopidogrel (7.57%), than P-P2Y12-treated women (5.39%), men treated with clopidogrel (4.60%), and P-P2Y12-treated men (3.61%; P<0.001). The strongest independent predictor of P-P2Y12 prescription was radial access (adjusted odds ratio, 2.368 [95% CI, 2.312-2.425]), used in 67.93% of women and 74.38% of men (P<0.001). Two risk adjustment models were used. Women were less likely to receive a P-P2Y12 (adjusted odds ratio, 0.957 [95% CI, 0.935-0.979]) with risk adjustment for baseline characteristics alone, when procedural factors including radial access were included in the model differences were not significant (adjusted odds ratio, 1.015 [95% CI, 0.991-1.039]).

CONCLUSIONS

Women were less likely to be prescribed prasugrel or ticagrelor, were less likely to have radial access, and had a higher mortality when being treated for STEMI. Improving rates of P-P2Y12 use and radial access may decrease outcome disparities for women with STEMI.

Using Text Content From Coronary Catheterization Reports to Predict 5-Year Mortality Among Patients Undergoing Coronary Angiography: A Deep Learning Approach

Background

Current predictive models for patients undergoing coronary angiography have complex parameters which limit their clinical application. Coronary catheterization reports that describe coronary lesions and the corresponding interventions provide information of the severity of the coronary artery disease and the completeness of the revascularization. This information is relevant for predicting patient prognosis. However, no predictive model has been constructed using the text content from coronary catheterization reports before.

Objective

To develop a deep learning model using text content from coronary catheterization reports to predict 5-year all-cause mortality and 5-year cardiovascular mortality for patients undergoing coronary angiography and to compare the performance of the model to the established clinical scores.

Method

This retrospective cohort study was conducted between January 1, 2006, and December 31, 2015. Patients admitted for coronary angiography were enrolled and followed up until August 2019. The main outcomes were 5-year all-cause mortality and 5-year cardiovascular mortality. In total, 11,576 coronary catheterization reports were collected. BioBERT (bidirectional encoder representations from transformers for biomedical text mining), which is a BERT-based model in the biomedical domain, was utilized to construct the model. The area under the receiver operating characteristic curve (AUC) was used to assess model performance. We also compared our results to the residual SYNTAX (SYNergy between PCI with TAXUS and Cardiac Surgery) score.

Results

The dataset was divided into the training (60%), validation (20%), and test (20%) sets. The mean age of the patients in each dataset was 65.5 ± 12.1, 65.4 ± 11.2, and 65.6 ± 11.2 years, respectively. A total of 1,411 (12.2%) patients died, and 664 (5.8%) patients died of cardiovascular causes within 5 years after coronary angiography. The best of our models had an AUC of 0.822 (95% CI, 0.790–0.855) for 5-year all-cause mortality, and an AUC of 0.858 (95% CI, 0.816–0.900) for 5-year cardiovascular mortality. We randomly selected 300 patients who underwent percutaneous coronary intervention (PCI), and our model outperformed the residual SYNTAX score in predicting 5-year all-cause mortality (AUC, 0.867 [95% CI, 0.813–0.921] vs. 0.590 [95% CI, 0.503–0.684]) and 5-year cardiovascular mortality (AUC, 0.880 [95% CI, 0.873–0.925] vs. 0.649 [95% CI, 0.535–0.764]), respectively, after PCI among these patients.

Conclusions

We developed a predictive model using text content from coronary catheterization reports to predict the 5-year mortality in patients undergoing coronary angiography. Since interventional cardiologists routinely write reports after procedures, our model can be easily implemented into the clinical setting.