By Stuart Long, CEO, InfoBionic.
With a recent study revealing a substantial rise in heart attacks among younger women, developers of cardiac medical technology are seeking to help reverse this trend. The study, published in the journal Circulation, examined the incidence of heart attacks in women ages 35 to 55, finding: From 1995 through 1999, this population accounted for 21 percent of reported heart attacks among all women; and from 2010 through 2014, that number increased to 31 percent of reported heart attacks among women.
That’s an increase of nearly a third in just 10 to 15 years – a huge jump in epidemiology terms for a cardiovascular condition that is not known to be communicable. It’s especially disconcerting because the incidence of heart attacks among men in the same age group barely budged during that time.
Heart disease in women often goes undetected or un-diagnosed. Then, when a cardiac event finally strikes, it is much more lethal:
- Women are significantly more likely than men to die of their first heart attack.
- Women who survive a first heart attack are twice as likely as men to experience a second heart attack within six years.
- Younger women have worse heart attack outcomes than men of a similar age.
- Ages younger than 45 is a known predictor of 30-day survival for men, but not for women.
Gender differences in cardiac events
There are a number of reasons for these poor outcomes. Because the symptoms of a heart attack are different for women than men, women often attribute their symptoms something far less serious. But even when younger women experiencing heart attack symptoms do seek medical treatment, doctors themselves have struggled to get to an accurate and timely diagnosis.
Women are less likely to experience dramatic chest pains. Instead, they are more likely to experience fatigue, nausea, dizziness/lightheadedness and vomiting – symptoms that can cause doctors to mistakenly diagnose women with other conditions, thus delaying treatment.
The difficulty is compounded by the fact that spontaneous coronary arterial dissection (SCAD) – a form of heart attack that overwhelmingly affects women – is not as highly correlated with known heart attack precursors such as diabetes and hypertension. Unlike myocardial infarction, SCAD often strikes healthy women, with an average onset age of about 42 – with reported cases as young as 14.
These relatively healthy and active women are not thought of as high risk for a heart attack when they present, and so are even less likely to receive important diagnostic procedures like CT angiograms, coronary calcium scans and remote cardiac monitoring.
According to a study from the University of Leeds, women who had a final diagnosis of ST-elevation myocardial infarction (STEMI) were initially misdiagnosed 59 percent more often than men, while women who had a final diagnosis of non-ST elevation myocardial infarction (NSTEMI) were initially misdiagnosed 41 percent than men.
Early misdiagnosis predictably led to substantially higher mortality among young women experiencing their first heart attack. Even when women do seek treatment, and get a cardiac event diagnosis, they are less likely to receive the same treatments that men do:
- A study published in the International Journal of Cardiology showed that both younger and older women with acute coronary syndrome are less likely to receive reperfusion.
- Women with blocked coronary arteries were 34 percent less likely than men to receive stents or bypass surgery.
- Women were 24 percent less likely to be prescribed statins.
- Women with known heart issues were also 16 percent less likely to be put on an aspirin regimen.
Medical technology advancements
Fortunately, new technology can play an important role in the fight to track and diagnose cardiac events earlier and faster. Advances in data storage and management, deep learning, artificial intelligence and secure telecommunications are helping to make cardiac monitoring and diagnosis easier, faster, less invasive, more convenient and more economical for patients and doctors alike.
In the past, doctors couldn’t access diagnostic data themselves at any point. They had to wait days until either they received a call or report from the independent diagnostic and testing facility (IDTF) during service or the patient physically returned with the device. Then they had to wait until an IDTF (independent diagnostic testing facility) analyzed the data. They would make a few tiny snippets of data available to the doctor, but 99 percent of all the data – including critical onset and offset data – was lost. Cardiologists had no reliable way of mapping activities and symptoms to telemetry data. Meanwhile, IDTFs were adding days to the diagnostic process – leaving patients vulnerable to cardiac events in the meantime.
But new developments in artificial intelligence and deep learning enable doctors to sift through tens of thousands of individual heartbeats themselves and zero in on critical events nearly instantly, and at any time after the patient has been fitted with the monitor.
This is a major improvement in several ways:
- Doctors can now bypass the third party IDTFs – and hundreds of dollars in added costs per patient.
- Providers no longer need to make time-consuming phone calls to IDTFs call center reps who don’t even know the patient, or waste time waiting for Faxes or emails to arrive with telemetry data.
- Time to diagnosis can be much faster – and in some cases almost immediate.
- No data is lost at the IDTF before transmission to the doctor. The doctor has access to full-disclosure, beat-to-beat data throughout the entire monitoring period, without a break.
- Full disclosure data allows doctors to scroll forward or backward through the patient’s data at any point of the day to view important onset and offset events – right from their own smartphones, or from any computer in the world.
- Doctors can get an automated alert of a cardiac event or dangerous arrhythmia even while the patient is driving home from the appointment. They don’t need to wait for the patient to return with the monitor to upload the data to a third party for analysis.