Significant sex‐related disparities persist in presentation and revascularisation times for women with ST‐segment–elevation myocardial infarction (STEMI), Victorian research shows.
Women with STEMI had delays of 28.8 minutes in symptom-to-balloon time and 7.7 minutes in door-to-balloon time compared to men receiving percutaneous coronary intervention (PCI), according to data on 13 451 patients from the Victorian Cardiac Outcomes Registry covering 2013-2016.
Women with STEMI also had a significantly higher 30-day mortality rate (9.3% vs 6.5%) compared to men. No such delays or differences were seen in outcomes for women with non-STEMI, the study showed.
Led by Dr Julia Stehli from the Cardiology Department, The Alfred Hospital, the study investigators said the long delays in symptom-to-door time for women showed the need to continue with media campaigns that encourage women to recognise and act on more atypical MI symptoms.
Delays in door-to-balloon times have previously been linked to older age and a higher burden of comorbidities in women, but these were accounted for in the present study, they noted.
The answer might lie more in the use of protocol-driven care pathways and more focus on ECG interpretation in female patients, they suggested.
“Women have been described as having higher rates of incorrect triage and delay to first ECG.”
“We found that the culprit lesion was more often the right coronary artery in women and the left anterior descending artery in men—perhaps resulting in more subtle ECG changes in women.”
Previous efforts to reduce sex disparities in door to balloon time had successfully used a 4-step protocol focused on cardiac catheterisation lab activation, checklist‐guided early triage, guideline‐directed medical therapy, and a radial first approach in the treatment of STEMI, they wrote
An accompanying commentary said the persistent gaps in STEMI management also showed a need for more education of physicians and medical students in this area
It also suggested a role for artificial intelligence and machine learning to improve STEMI care and eliminating sex disparities.
“Artificial intelligence may be able to use predictive modelling of features of STEMI and provide a standardised approach to care, which can eliminate implicit bias in the care of women,” said author Dr Martha Gulati, a cardiologist at the University of Arizona.
The study is published in the Journal of the American Heart Association.