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.”
The Death Predictor: A Helpful New Tool or an Ethical Morass?
A senior in hospice care.
Whenever Eric Karl Oermann has to tell a patient about a terrible prognosis, their first question is always: "how long do I have?" Oermann would like to offer a precise answer, to provide some certainty and help guide treatment. But although he's one of the country's foremost experts in medical artificial intelligence, Oermann is still dependent on a computer algorithm that's often wrong.
Doctors are notoriously terrible at guessing how long their patients will live.
Artificial intelligence, now often called deep learning or neural networks, has radically transformed language and image processing. It's allowed computers to play chess better than the world's grand masters and outwit the best Jeopardy players. But it still can't precisely tell a doctor how long a patient has left – or how to help that person live longer.
Someday, researchers predict, computers will be able to watch a video of a patient to determine their health status. Doctors will no longer have to spend hours inputting data into medical records. And computers will do a better job than specialists at identifying tiny tumors, impending crises, and, yes, figuring out how long the patient has to live. Oermann, a neurosurgeon at Mount Sinai, says all that technology will allow doctors to spend more time doing what they do best: talking with their patients. "I want to see more deep learning and computers in a clinical setting," he says, "so there can be more human interaction." But those days are still at least three to five years off, Oermann and other researchers say.
Doctors are notoriously terrible at guessing how long their patients will live, says Nigam Shah, an associate professor at Stanford University and assistant director of the school's Center for Biomedical Informatics Research. Doctors don't want to believe that their patient – whom they've come to like – will die. "Doctors over-estimate survival many-fold," Shah says. "How do you go into work, in say, oncology, and not be delusionally optimistic? You have to be."
But patients near the end of life will get better treatment – and even live longer – if they are overseen by hospice or palliative care, research shows. So, instead of relying on human bias to select those whose lives are nearing their end, Shah and his colleagues showed that they could use a deep learning algorithm based on medical records to flag incoming patients with a life expectancy of three months to a year. They use that data to indicate who might need palliative care. Then, the palliative care team can reach out to treating physicians proactively, instead of relying on their referrals or taking the time to read extensive medical charts.
But, although the system works well, Shah isn't yet sure if such indicators actually get the appropriate patients into palliative care. He's recently partnered with a palliative care doctor to run a gold-standard clinical trial to test whether patients who are flagged by this algorithm are indeed a better match for palliative care.
"What is effective from a health system perspective might not be effective from a treating physician's perspective and might not be effective from the patient's perspective," Shah notes. "I don't have a good way to guess everybody's reaction without actually studying it." Whether palliative care is appropriate, for instance, depends on more than just the patient's health status. "If the patient's not ready, the family's not ready and the doctor's not ready, then you're just banging your head against the wall," Shah says. "Given limited capacity, it's a waste of resources" to put that person in palliative care.
The algorithm isn't perfect, but "on balance, it leads to better decisions more often."
Alexander Smith and Sei Lee, both palliative care doctors, work together at the University of California, San Francisco, to develop predictions for patients who come to the hospital with a complicated prognosis or a history of decline. Their algorithm, they say, helps decide if this patient's problems – which might include diabetes, heart disease, a slow-growing cancer, and memory issues – make them eligible for hospice. The algorithm isn't perfect, they both agree, but "on balance, it leads to better decisions more often," Smith says.
Bethany Percha, an assistant professor at Mount Sinai, says that an algorithm may tell doctors that their patient is trending downward, but it doesn't do anything to change that trajectory. "Even if you can predict something, what can you do about it?" Algorithms may be able to offer treatment suggestions – but not what specific actions will alter a patient's future, says Percha, also the chief technology officer of Precise Health Enterprise, a product development group within Mount Sinai. And the algorithms remain challenging to develop. Electronic medical records may be great at her hospital, but if the patient dies at a different one, her system won't know. If she wants to be certain a patient has died, she has to merge social security records of death with her system's medical records – a time-consuming and cumbersome process.
An algorithm that learns from biased data will be biased, Shah says. Patients who are poor or African American historically have had worse health outcomes. If researchers train an algorithm on data that includes those biases, they get baked into the algorithms, which can then lead to a self-fulfilling prophesy. Smith and Lee say they've taken race out of their algorithms to avoid this bias.
Age is even trickier. There's no question that someone's risk of illness and death goes up with age. But an 85-year-old who breaks a hip running a marathon should probably be treated very differently than an 85-year-old who breaks a hip trying to get out of a chair in a dementia care unit. That's why the doctor can never be taken out of the equation, Shah says. Human judgment will always be required in medical care and an algorithm should never be followed blindly, he says.
Experts say that the flaws in artificial intelligence algorithms shouldn't prevent people from using them – carefully.
Researchers are also concerned that their algorithms will be used to ration care, or that insurance companies will use their data to justify a rate increase. If an algorithm predicts a patient is going to end up back in the hospital soon, "who's benefitting from knowing a patient is going to be readmitted? Probably the insurance company," Percha says.
Still, Percha and others say, the flaws in artificial intelligence algorithms shouldn't prevent people from using them – carefully. "These are new and exciting tools that have a lot of potential uses. We need to be conscious about how to use them going forward, but it doesn't mean we shouldn't go down this road," she says. "I think the potential benefits outweigh the risks, especially because we've barely scratched the surface of what big data can do right now."
“Young Blood” Transfusions Are Not Ready For Primetime – Yet
A young woman donates blood.
The world of dementia research erupted into cheers when news of the first real victory in a clinical trial against Alzheimer's Disease in over a decade was revealed last October.
By connecting the circulatory systems of a young and an old mouse, the regenerative potential of the young mouse decreased, and the old mouse became healthier.
Alzheimer's treatments have been famously difficult to develop; 99 percent of the 200-plus such clinical trials since 2000 have utterly failed. Even the few slight successes have failed to produce what is called 'disease modifying' agents that really help people with the disease. This makes the success, by the midsize Spanish pharma company Grifols, worthy of special attention.
However, the specifics of the Grifols treatment, a process called plasmapheresis, are atypical for another reason - they did not give patients a small molecule or an elaborate gene therapy, but rather simply the most common component of normal human blood plasma, a protein called albumin. A large portion of the patients' normal plasma was removed, and then a sterile solution of albumin was infused back into them to keep their overall blood volume relatively constant.
So why does replacing Alzheimer's patients' plasma with albumin seem to help their brains? One theory is that the action is direct. Alzheimer's patients have low levels of serum albumin, which is needed to clear out the plaques of amyloid that slowly build up in the brain. Supplementing those patients with extra albumin boosts their ability to clear the plaques and improves brain health. However, there is also evidence suggesting that the problem may be something present in the plasma of the sick person and pulling their plasma out and replacing it with a filler, like an albumin solution, may be what creates the purported benefit.
This scientific question is the tip of an iceberg that goes far beyond Alzheimer's Disease and albumin, to a debate that has been waged on the pages of scientific journals about the secrets of using young, healthy blood to extend youth and health.
This debate started long before the Grifols data was released, in 2014 when a group of researchers at Stanford found that by connecting the circulatory systems of a young and an old mouse, the regenerative potential of the young mouse decreased, and the old mouse became healthier. There was something either present in young blood that allowed tissues to regenerate, or something present in old blood that prevented regeneration. Whatever the biological reason, the effects in the experiment were extraordinary, providing a startling boost in health in the older mouse.
After the initial findings, multiple research groups got to work trying to identify the "active factor" of regeneration (or the inhibitor of that regeneration). They soon uncovered a variety of compounds such as insulin-like growth factor 1 (IGF1), CCL11, and GDF11, but none seemed to provide all the answers researchers were hoping for, with a number of high-profile retractions based on unsound experimental practices, or inconclusive data.
Years of research later, the simplest conclusion is that the story of plasma regeneration is not simple - there isn't a switch in our blood we can flip to turn back our biological clocks. That said, these hypotheses are far from dead, and many researchers continue to explore the possibility of using the rejuvenating ability of youthful plasma to treat a variety of diseases of aging.
But the bold claims of improved vigor thanks to young blood are so far unsupported by clinical evidence.
The data remain intriguing because of the astounding results from the conjoined circulatory system experiments. The current surge in interest in studying the biology of aging is likely to produce a new crop of interesting results in the next few years. Both CCL11 and GDF11 are being researched as potential drug targets by two startups, Alkahest and Elevian, respectively.
Without clarity on a single active factor driving rejuvenation, it's tempting to try a simpler approach: taking actual blood plasma provided by young people and infusing it into elderly subjects. This is what at least one startup company, Ambrosia, is now offering in five commercial clinics across the U.S. -- for $8,000 a liter.
By using whole plasma, the idea is to sidestep our ignorance, reaping the benefits of young plasma transfusion without knowing exactly what the active factors are that make the treatment work in mice. This space has attracted both established players in the plasmapheresis field – Alkahest and Grifols have teamed up to test fractions of whole plasma in Alzheimer's and Parkinson's – but also direct-to-consumer operations like Ambrosia that just want to offer patients access to treatments without regulatory oversight.
But the bold claims of improved vigor thanks to young blood are so far unsupported by clinical evidence. We simply haven't performed trials to test whether dosing a mostly healthy person with plasma can slow down aging, at least not yet. There is some evidence that plasma replacement works in mice, yes, but those experiments are all done in very different systems than what a human receiving young plasma might experience. To date, I have not seen any plasma transfusion clinic doing young blood plasmapheresis propose a clinical trial that is anything more than a shallow advertisement for their procedures.
The efforts I have seen to perform prophylactic plasmapheresis will fail to impact societal health. Without clearly defined endpoints and proper clinical trials, we won't know whether the procedure really lowers the risk of disease or helps with conditions of aging. So even if their hypothesis is correct, the lack of strong evidence to fall back on means that the procedure will never spread beyond the fringe groups willing to take the risk. If their hypothesis is wrong, then people are paying a huge amount of money for false hope, just as they do, sadly, at the phony stem cell clinics that started popping up all through the 2000s when stem cell hype was at its peak.
Until then, prophylactic plasma transfusions will be the domain of the optimistic and the gullible.
The real progress in the field will be made slowly, using carefully defined products either directly isolated from blood or targeting a bloodborne factor, just as the serious pharma and biotech players are doing already.
The field will progress in stages, first creating and carefully testing treatments for well-defined diseases, and only then will it progress to large-scale clinical trials in relatively healthy people to look for the prevention of disease. Most of us will choose to wait for this second stage of trials before undergoing any new treatments. Until then, prophylactic plasma transfusions will be the domain of the optimistic and the gullible.