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."
Catching colds may help protect kids from Covid
A new study shows that the immune system's response to colds can help prepare it to defend against COVID-19 - but only in the very young.
A common cold virus causes the immune system to produce T cells that also provide protection against SARS-CoV-2, according to new research. The study, published last month in PNAS, shows that this effect is most pronounced in young children. The finding may help explain why most young people who have been exposed to the cold-causing coronavirus have not developed serious cases of COVID-19.
One curiosity stood out in the early days of the COVID-19 pandemic – why were so few kids getting sick. Generally young children and the elderly are the most vulnerable to disease outbreaks, particularly viral infections, either because their immune systems are not fully developed or they are starting to fail.
But solid information on the new infection was so scarce that many public health officials acted on the precautionary principle, assumed a worst-case scenario, and applied the broadest, most restrictive policies to all people to try to contain the coronavirus SARS-CoV-2.
One early thought was that lockdowns worked and kids (ages 6 months to 17 years) simply were not being exposed to the virus. So it was a shock when data started to come in showing that well over half of them carried antibodies to the virus, indicating exposure without getting sick. That trend grew over time and the latest tracking data from the CDC shows that 96.3 percent of kids in the U.S. now carry those antibodies.
Antibodies are relatively quick and easy to measure, but some scientists are exploring whether the reactions of T cells could serve as a more useful measure of immune protection.
But that couldn't be the whole story because antibody protection fades, sometimes as early as a month after exposure and usually within a year. Additionally, SARS-CoV-2 has been spewing out waves of different variants that were more resistant to antibodies generated by their predecessors. The resistance was so significant that over time the FDA withdrew its emergency use authorization for a handful of monoclonal antibodies with earlier approval to treat the infection because they no longer worked.
Antibodies got most of the attention early on because they are part of the first line response of the immune system. Antibodies can bind to viruses and neutralize them, preventing infection. They are relatively quick and easy to measure and even manufacture, but as SARS-CoV-2 showed us, often viruses can quickly evolve to become more resistant to them. Some scientists are exploring whether the reactions of T cells could serve as a more useful measure of immune protection.
Kids, colds and T cells
T cells are part of the immune system that deals with cells once they have become infected. But working with T cells is much more difficult, takes longer, and is more expensive than working with antibodies. So studies often lags behind on this part of the immune system.
A group of researchers led by Annika Karlsson at the Karolinska Institute in Sweden focuses on T cells targeting virus-infected cells and, unsurprisingly, saw that they can play a role in SARS-CoV-2 infection. Other labs have shown that vaccination and natural exposure to the virus generates different patterns of T cell responses.
The Swedes also looked at another member of the coronavirus family, OC43, which circulates widely and is one of several causes of the common cold. The molecular structure of OC43 is similar to its more deadly cousin SARS-CoV-2. Sometimes a T cell response to one virus can produce a cross-reactive response to a similar protein structure in another virus, meaning that T cells will identify and respond to the two viruses in much the same way. Karlsson looked to see if T cells for OC43 from a wide age range of patients were cross-reactive to SARS-CoV-2.
And that is what they found, as reported in the PNAS study last month; there was cross-reactive activity, but it depended on a person’s age. A subset of a certain type of T cells, called mCD4+,, that recognized various protein parts of the cold-causing virus, OC43, expressed on the surface of an infected cell – also recognized those same protein parts from SARS-CoV-2. The T cell response was lower than that generated by natural exposure to SARS-CoV-2, but it was functional and thus could help limit the severity of COVID-19.
“One of the most politicized aspects of our pandemic response was not accepting that children are so much less at risk for severe disease with COVID-19,” because usually young children are among the most vulnerable to pathogens, says Monica Gandhi, professor of medicine at the University of California San Francisco.
“The cross-reactivity peaked at age six when more than half the people tested have a cross-reactive immune response,” says Karlsson, though their sample is too small to say if this finding applies more broadly across the population. The vast majority of children as young as two years had OC43-specific mCD4+ T cell responses. In adulthood, the functionality of both the OC43-specific and the cross-reactive T cells wane significantly, especially with advanced age.
“Considering that the mortality rate in children is the lowest from ages five to nine, and higher in younger children, our results imply that cross-reactive mCD4+ T cells may have a role in the control of SARS-CoV-2 infection in children,” the authors wrote in their paper.
“One of the most politicized aspects of our pandemic response was not accepting that children are so much less at risk for severe disease with COVID-19,” because usually young children are among the most vulnerable to pathogens, says Monica Gandhi, professor of medicine at the University of California San Francisco and author of the book, Endemic: A Post-Pandemic Playbook, to be released by the Mayo Clinic Press this summer. The immune response of kids to SARS-CoV-2 stood our expectations on their head. “We just haven't seen this before, so knowing the mechanism of protection is really important.”
Why the T cell immune response can fade with age is largely unknown. With some viruses such as measles, a single vaccination or infection generates life-long protection. But respiratory tract infections, like SARS-CoV-2, cause a localized infection - specific to certain organs - and that response tends to be shorter lived than systemic infections that affect the entire body. Karlsson suspects the elderly might be exposed to these localized types of viruses less often. Also, frequent continued exposure to a virus that results in reactivation of the memory T cell pool might eventually result in “a kind of immunosenescence or immune exhaustion that is associated with aging,” Karlsson says. https://leaps.org/scientists-just-started-testing-a-new-class-of-drugs-to-slow-and-even-reverse-aging/particle-3 This fading protection is why older people need to be repeatedly vaccinated against SARS-CoV-2.
Policy implications
Following the numbers on COVID-19 infections and severity over the last three years have shown us that healthy young people without risk factors are not likely to develop serious disease. This latest study points to a mechanism that helps explain why. But the inertia of existing policies remains. How should we adjust policy recommendations based on what we know today?
The World Health Organization (WHO) updated their COVID-19 vaccination guidance on March 28. It calls for a focus on vaccinating and boosting those at risk for developing serious disease. The guidance basically shrugged its shoulders when it came to healthy children and young adults receiving vaccinations and boosters against COVID-19. It said the priority should be to administer the “traditional essential vaccines for children,” such as those that protect against measles, rubella, and mumps.
“As an immunologist and a mother, I think that catching a cold or two when you are a kid and otherwise healthy is not that bad for you. Children have a much lower risk of becoming severely ill with SARS-CoV-2,” says Karlsson. She has followed public health guidance in Sweden, which means that her young children have not been vaccinated, but being older, she has received the vaccine and boosters. Gandhi and her children have been vaccinated, but they do not plan on additional boosters.
The WHO got it right in “concentrating on what matters,” which is getting traditional childhood immunizations back on track after their dramatic decline over the last three years, says Gandhi. Nor is there a need for masking in schools, according to a study from the Catalonia region of Spain. It found “no difference in masking and spread in schools,” particularly since tracking data indicate that nearly all young people have been exposed to SARS-CoV-2.
Both researchers lament that public discussion has overemphasized the quickly fading antibody part of the immune response to SARS-CoV-2 compared with the more durable T cell component. They say developing an efficient measure of T cell response for doctors to use in the clinic would help to monitor immunity in people at risk for severe cases of COVID-19 compared with the current method of toting up potential risk factors.
In this week's Friday Five: The eyes are the windows to the soul - and biological aging?
Plus, what bean genes mean for health and the planet, a breathing practice that could lower levels of tau proteins in the brain, AI beats humans at assessing heart health, and the benefits of "nature prescriptions"
The Friday Five covers five stories in research that you may have missed this week. There are plenty of controversies and troubling ethical issues in science – and we get into many of them in our online magazine – but this news roundup focuses on new scientific theories and progress to give you a therapeutic dose of inspiration headed into the weekend.
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Here are the stories covered this week:
- The eyes are the windows to the soul - and biological aging?
- What bean genes mean for health and the planet
- This breathing practice could lower levels of tau proteins
- AI beats humans at assessing heart health
- Should you get a nature prescription?