When doctors couldn’t stop her daughter’s seizures, this mom earned a PhD and found a treatment herself.
Savannah Salazar (left) and her mother, Tracy Dixon-Salazaar, who earned a PhD in neurobiology in the quest for a treatment of her daughter's seizure disorder.
Twenty-eight years ago, Tracy Dixon-Salazaar woke to the sound of her daughter, two-year-old Savannah, in the midst of a medical emergency.
“I entered [Savannah’s room] to see her tiny little body jerking about violently in her bed,” Tracy said in an interview. “I thought she was choking.” When she and her husband frantically called 911, the paramedic told them it was likely that Savannah had had a seizure—a term neither Tracy nor her husband had ever heard before.
Over the next several years, Savannah’s seizures continued and worsened. By age five Savannah was having seizures dozens of times each day, and her parents noticed significant developmental delays. Savannah was unable to use the restroom and functioned more like a toddler than a five-year-old.
Doctors were mystified: Tracy and her husband had no family history of seizures, and there was no event—such as an injury or infection—that could have caused them. Doctors were also confused as to why Savannah’s seizures were happening so frequently despite trying different seizure medications.
Doctors eventually diagnosed Savannah with Lennox-Gaustaut Syndrome, or LGS, an epilepsy disorder with no cure and a poor prognosis. People with LGS are often resistant to several kinds of anti-seizure medications, and often suffer from developmental delays and behavioral problems. People with LGS also have a higher chance of injury as well as a higher chance of sudden unexpected death (SUDEP) due to the frequent seizures. In about 70 percent of cases, LGS has an identifiable cause such as a brain injury or genetic syndrome. In about 30 percent of cases, however, the cause is unknown.
Watching her daughter struggle through repeated seizures was devastating to Tracy and the rest of the family.
“This disease, it comes into your life. It’s uninvited. It’s unannounced and it takes over every aspect of your daily life,” said Tracy in an interview with Today.com. “Plus it’s attacking the thing that is most precious to you—your kid.”
Desperate to find some answers, Tracy began combing the medical literature for information about epilepsy and LGS. She enrolled in college courses to better understand the papers she was reading.
“Ironically, I thought I needed to go to college to take English classes to understand these papers—but soon learned it wasn’t English classes I needed, It was science,” Tracy said. When she took her first college science course, Tracy says, she “fell in love with the subject.”
Tracy was now a caregiver to Savannah, who continued to have hundreds of seizures a month, as well as a full-time student, studying late into the night and while her kids were at school, using classwork as “an outlet for the pain.”
“I couldn’t help my daughter,” Tracy said. “Studying was something I could do.”
Twelve years later, Tracy had earned a PhD in neurobiology.
After her post-doctoral training, Tracy started working at a lab that explored the genetics of epilepsy. Savannah’s doctors hadn’t found a genetic cause for her seizures, so Tracy decided to sequence her genome again to check for other abnormalities—and what she found was life-changing.
Tracy discovered that Savannah had a calcium channel mutation, meaning that too much calcium was passing through Savannah’s neural pathways, leading to seizures. The information made sense to Tracy: Anti-seizure medications often leech calcium from a person’s bones. When doctors had prescribed Savannah calcium supplements in the past to counteract these effects, her seizures had gotten worse every time she took the medication. Tracy took her discovery to Savannah’s doctor, who agreed to prescribe her a calcium blocker.
The change in Savannah was almost immediate.
Within two weeks, Savannah’s seizures had decreased by 95 percent. Once on a daily seven-drug regimen, she was soon weaned to just four, and then three. Amazingly, Tracy started to notice changes in Savannah’s personality and development, too.
“She just exploded in her personality and her talking and her walking and her potty training and oh my gosh she is just so sassy,” Tracy said in an interview.
Since starting the calcium blocker eleven years ago, Savannah has continued to make enormous strides. Though still unable to read or write, Savannah enjoys puzzles and social media. She’s “obsessed” with boys, says Tracy. And while Tracy suspects she’ll never be able to live independently, she and her daughter can now share more “normal” moments—something she never anticipated at the start of Savannah’s journey with LGS. While preparing for an event, Savannah helped Tracy get ready.
“We picked out a dress and it was the first time in our lives that we did something normal as a mother and a daughter,” she said. “It was pretty cool.”
Artificial Intelligence Needs Doctors As Much As They Need It
In this futuristic medical concept, a doctor assesses a patient with robust machine assistance.
The media loves to hype concerns about artificial intelligence: What if machines become super-intelligent and self-aware? How will humanity compete and survive? But artificial intelligence today is a far cry from a robot takeover. "AI" is a catch-all term that often refers to machine training or machine learning: There is an abundance of data, vastly more than the human mind can assimilate, being tagged, captured and stored. This systematic data processing requires methodologies that can put it in usable form and formats. While these new developments stoke fear in some corners, the ability to predict outcomes is generally seen as a good thing, as it can mitigate risks and even save lives.
We, collectively, want AI even though it is seldom expressed this way.
The prospects and attempts toward artificial intelligence has been with us for decades. Only recently have the underlying technologies and infrastructure--including computer processing, storage, networking speed and advanced software platforms--become omnipresent. These technological advances enabled the implementation of data mining concepts and the subsequent advantages that were not feasible just a decade ago.
AI is fantastical by vision, evolutionary by experience, and disruptive upon reflection. In the world of health care, AI is already transforming research and clinical practice. We, collectively, want AI even though it is seldom expressed this way. What we, the patient population, patient advocates and caregivers, agree on and want is: (1) timely, precise and inexpensive diagnoses of our ailments, injuries and disorders; (2) timely, personalized, highly effective and efficient courses of therapies; and (3) expedited recovery with minimum deficits, complications and recurrence.
"Artificial intelligence and machine learning will impact healthcare as profoundly as the discovery of the microscope."
Implicitly, we all are saying that we want our healthcare systems and clinicians to accomplish truly inhuman feats: to incorporate all sources of structured data (such as published statistics and reports) and unstructured data (including news articles, conversational analysis by care givers, nuances of similar cases, talks at professional societies); to analyze the data sourced and uncover patterns, reveal side effects, define probable success and outcomes; and to present the best personalized course of treatment for the patient that addresses the ailment and mitigates associated risks. It is hard to argue against any of this.
In a recent published interview, Keith J. Dreyer, executive director of the Massachusetts General Hospital and Brigham and Women's Hospital Center for Clinical Data Science, says that "artificial intelligence and machine learning will impact healthcare as profoundly as the discovery of the microscope."
But as AI helps physicians in profound ways, like detecting subtle lesions on scans or distinguishing the symptoms of a stroke from a brain tumor, we humans can't get too complacent. Evolving AI platforms will provide more sophisticated sets of "tools" to address both mundane and complex medical challenges, albeit with humans very much in the mix and routinely at the helm.
Humans do not appear endangered to be replaced anytime soon.
Human beings are capable of a level of nuance and contextual understanding of complex medical scenarios and, consequently, do not appear endangered to be replaced anytime soon. These platforms will do some heavy lifting for sure and provide considerable assistance across the healthcare industry. But human involvement is crucial, as we are best at adaptive learning, cognition, ensuring accuracy of the data, and continually providing feedback to improve the machine learning components of the AI platforms that the health industry will increasingly rely upon.
The human/machine interface is not binary; there is no line in the sand. It is fuzzy and evolutionary, a synchronicity that we all will surely witness and experience. In the future, it may be possible that all recorded knowledge, including genetic, genomic and laboratory data, from structured and unstructured sources, can be at the fingertips of your clinician, and then factored into diagnosing your condition and prescribing your course of treatment. This is precision and personalized medicine on a grand scale applied at the micro level--you!
But none of this will diminish the importance of doctors, nurses and all assortment of care providers. Though they all will undoubtedly become more effective with such awesome AI assistance, their job will always be to heal you with compassion, wisdom, and kindness, for the essence of humanity cannot be automated.
This Revolutionary Medical Breakthrough Is Not a Treatment or a Cure
A doctor examines a patient to determine the cause of her illness.
What is a disease? This seemingly abstract and theoretical question is actually among the most practical questions in all of biomedicine. How patients are diagnosed, treated, managed and excused from various social and moral obligations hinges on the answer that is given. So do issues of how research is done and health care paid for. The question is also becoming one of the most problematic issues that those in health care will face in the next decade.
"The revolution in our understanding of the human genome, molecular biology, and genetics is creating a huge--if little acknowledged--shift in the understanding of what a disease is."
That is because the current conception of disease is undergoing a revolutionary change, fueled by progress in genetics and molecular biology. The consequences of this shift in the definition of disease promise to be as impactful as any other advance in biomedicine has ever been, which is admittedly saying a lot for what is in essence a conceptual change rather than one based on an empirical scientific advance.
For a long time, disease was defined by patient reports of feeling sick. It was not until the twentieth century that a shift occurred away from subjective reports of clusters of symptoms to defining diseases in terms of physiological states. Doctors began to realize that not all symptoms of fever represented the presence of the same disease. Flu got distinguished from malaria. Diseases such as hypertension, osteoporosis, cancer, lipidemia, silent myocardial infarction, retinopathy, blood clots and many others were recognized as not producing any or slight symptoms until suddenly the patient had a stroke or died.
The ability to assess both biology and biochemistry and to predict the consequences of subclinical pathological processes caused a distinction to be made between illness—what a person experiences—and disease—an underlying pathological process with a predictable course. Some conditions, such as Gulf War Syndrome, PTSD, many mental illnesses and fibromyalgia, remain controversial because no underlying pathological process has been found that correlates with them—a landmark criterion for diagnosing disease throughout most of the last century.
"Diseases for which no relationship had ever been posited are being lumped together due to common biochemical causal pathways...that are amenable to the same curative intervention."
The revolution in our understanding of the human genome, molecular biology, and genetics is creating a huge--if little acknowledged--shift in the understanding of what a disease is. A better understanding of the genetic and molecular roots of pathophysiology is leading to the reclassification of many familiar diseases. The test of disease is now not the pathophysiology but the presence of a gene, set of genes or molecular pathway that causes pathophysiology. Just as fever was differentiated into a multitude of diseases in the last century, cancer, cognitive impairment, addiction and many other diseases are being broken or split into many subkinds. And other diseases for which no relationship had ever been posited are being lumped together due to common biochemical causal pathways or the presence of similar dangerous biochemical products that are amenable to the same curative intervention, no matter how disparate the patients' symptoms or organic pathologies might appear.
We used to differentiate ovarian and breast cancers. Now we are thinking of them as outcomes of the same mutations in certain genes in the BRCA regions. They may eventually lump together as BRCA disease.
Other diseases such as familial amyloid polyneuropathy (FAP) which causes polyneuropathy and autonomic dysfunction are being split apart into new types or kinds. The disease is the product of mutations in the transthyretin gene. It was thought to be an autosomal dominant disease with symptomatic onset between 20-40 years of age. However, as genetic testing has improved, it has become clear that FAP's traditional clinical presentation represents a relatively small portion of those with FAP. Many patients with mutations in transthyretin — even mutations commonly seen in traditional FAP patients — do not fit the common clinical presentation. As the mutations begin to be understood, some people that were previously thought to have other polyneuropathies, such as chronic inflammatory demyelinating neuropathy, are now being rediagnosed with newly discovered variants of FAP.
"We are at the start of a major conceptual shift in how we organize the world of disease, and for that matter, health promotion."
Genome-wide association studies are beginning to find many links between diseases not thought to have any connection or association. For example some forms of diabetes, rheumatoid arthritis and thyroid disease may be the products of a small family of genetic mutations.
So why is this shift toward a genetic and molecular diagnostics likely to shake up medicine? One obvious way is that research projects may propose to recruit subjects not according to current standards of disease but on the basis of common genetic mutations or similar errors in biochemical pathways. It won't matter in a future study if subjects in a trial have what today might be termed nicotine addiction or Parkinsonism. If the molecular pathways producing the pathology are the same, then both groups might well wind up in the same trial of a drug.
In addition, what today look like common maladies—pancreatic cancer, severe depression, or acne, for example, could wind up being subdivided into so many highly differentiated versions of these conditions that each must be treated as what we now classify as a rare or ultra-rare disease. Unique biochemical markers or genetic messages may see many diseases broken into a huge number of distinct individual disease entities.
Patients may find that common genetic pathways or multiple effects from a single gene may create new alliances for fund-raising and advocacy. Groups fighting to cure mental and physical illnesses may wind up forgetting about their outward differences in the effort to alter genes or attack common protein markers.
Disease classification appears stable to us—until it isn't. And we are at the start of a major conceptual shift in how we organize the world of disease, and for that matter, health promotion. Classic reductionism, the view that all observable biological phenomena can be explained in terms of underlying chemical and physical principles, may turn out not to be true. But the molecular and genetic revolutions churning through medicine are illustrating that reductionism is going to have an enormous influence on disease classification. That is not a bad thing, but it is something that is going to take a lot to get used to.