Obstructive sleep apnea (OSA) may affect as many as 37% of adults in North, Central and South America, according to a review of epidemiological studies presented June 9 at Sleep 2019, the annual meeting of the Associated Professional Sleep Societies in San Antonio, Texas.
The finding was no surprise to the review’s senior author, Dr. Adam Benjafield of ResMed, a manufacturer of medical breathing devices in San Diego, California. In an email to Reuters Health, he pointed to the rising rate of obesity, a risk factor in OSA, and recent changes in scoring rules from the American Academy of Sleep Medicine (AASM).
“This research highlights that there is a large burden of disease that may not be widely appreciated and speaks to the need to leverage new technology and efficient clinical pathways to diagnose and treat these patients,” Dr. Benjafield said.
The prevalence of OSA has not been definitively assessed, and this research may be a first attempt to stitch together findings from published data. Dr. Benjafield and colleagues interviewed the authors of existing studies and created an algorithm to match countries in the Americas that do not have epidemiological studies to those that do.
Based on AASM 2012 criteria and using “a somewhat conservative approach,” the team estimated that 170 million adults in the 40 countries of the Americas may suffer from OSA.
As many as 81 million adults in the hemisphere may have moderate to severe OSA, representing nearly 18% of the adult population. The United States, Brazil and Colombia have the highest numbers of OSA diagnoses.
“This is an important study as the prevalence of OSA in many countries is not known,” said Dr. James Rowley of Wayne State University in Detroit, Michigan, who was not involved in the research.
“Determining the prevalence of OSA requires the performance of sleep studies in a large representative group of the population. This can be time and labor consuming preventing accurate estimation of disease prevalence,” added Rowley, who is president of the AASM Foundation.
The findings are basd on a subset of worldwide data that showed nearly 1 billion people, or one in seven, have OSA. ResMed representatives presented those results at the American Thoracic Society’s annual meeting in 2018.
“Given the high burden of co-morbidities associated with OSA, including excessive daytime sleepiness and cardiovascular disease, this study indicates that OSA is a significant public health burden across the Americas. Physicians and health care leaders will need to take data into account when planning health care programs that focus on obesity and cardiovascular health,” Dr. Rowley said.
SOURCE: aasm.org Sleep 2019 conference, June 9, 2019.
Artificial intelligence is often hailed as a great catalyst of medical innovation, a way to find cures to diseases that have confounded doctors and make health care more efficient, personalized, and accessible.
But what if it turns out to be poison?
Jonathan Zittrain, a Harvard Law School professor, posed that question during a conference in Boston Tuesday that examined the use of AI to accelerate the delivery of precision medicine to the masses. He used an alarming metaphor to explain his concerns:
“I think of machine learning kind of as asbestos,” he said. “It turns out that it’s all over the place, even though at no point did you explicitly install it, and it has possibly some latent bad effects that you might regret later, after it’s already too hard to get it all out.”
In health care, Zittrain said, AI is particularly problematic because of how easily it can be duped into reaching false conclusions. As an example, he showed an image of a cat that a Google algorithm had correctly categorized as a tabby cat. On the next slide was a nearly identical picture of the cat, with only a few pixels changed, and Google was 100 percent positive that the image on the screen was guacamole.
“This is a frontline system … installed across the world for image recognition, and it can be tricked that easily,” Zittrain said. “OK, so now let’s put this in the world of medicine: How do you feel when the [algorithm] spits out with 100 percent confidence that guacamole is what you need to cure what ails you?”
He was part of a panel that explored the pitfalls of applying AI in medicine and the many ethical, political, and scientific questions that must be addressed to ensure its safety and effectiveness. Here’s a look at the key points discussed during the event at Harvard Medical School.
Data from wearables can’t be de-identified
Algorithms have shown an ability to analyze vast amounts of data from wearables to flag the onset of health problems such as irregular heart rhythms or tremors that could indicate the onset of Parkinson’s disease.
But wearable data are not the same as numbers on a spreadsheet; they can’t be easily anonymized, said Andy Coravos, chief executive of Elektra Labs, a company seeking to identify biomarkers in digital data to improve clinical trials.
“How many people here think you could de-identify your genome?” she asked. “Probably not, because your genome is unique to you. It’s the same with most of the biospecimens coming off a lot of wearables and sensors — I am uniquely identifiable with 30 seconds of walk data.”
But if algorithms are the new drugs, she said, shouldn’t they be regulated with the same rigor?
“If you think about digital therapeutics, they all have a certain mechanism of action,” she said. “Is there an argument, with what we’ve learned in health care, to look at [digital treatments] in the same way we look at drugs?”
It is a question that will be answered by entrepreneurs until and unless it is taken up by regulators.
Bias isn’t just in people. It’s in the data they keep
AI is often discussed as a tool for eliminating bias in health care by helping doctors to standardize the way they care for patients. If a computer could provide objective advice on the best treatments for patients, then variations in care would diminish, and everyone would get the most effective care.
But Kadija Ferryman, a fellow at the Data & Society Research Institute in New York, said AI is just as likely to perpetuate bias as it is to eliminate it. That’s because bias is embedded in the data being fed to algorithms, whose outputs could be skewed as a result.
She cited an article in The Atlantic magazine that highlighted an algorithm used to identify skin cancer that was less effective in people with darker skin. In mental health care, data kept in electronic medical records has been shown to be infused with bias toward women and people of color.
The inequity in the data doesn’t just translate to unequal treatment, it can lead to ineffective care, said Ferryman, who is leading a research study on fairness in the application of precision medicine.
“Using AI has the potential to advance medical insights through the collection and analysis of large volumes and types of health data,” she said. “However, we must keep our focus on the potential for these technologies to exacerbate and extend unfair outcomes.”
Confusing correlation and causation
AI is excellent at finding correlations within data that are difficult for humans to detect, a skill that can be used to hone in on the causes of disease and help to develop more effective medicines.
But Zittrain, the Harvard law professor, devoted much of his talk to spurious correlations that AI has been known to surface. He noted one such correlation between the number of suicides by hanging or strangulation in North Carolina and the number of lawyers in the state.
In another example, the shape of a graph of opium production by year in Afghanistan correlated almost exactly with a silhouette of Mount Everest. The point, he said, is that a correlation is just a correlation — not a cause. And AI is not so great at distinguishing between the two.
That means it could advise you to take certain medicines, or change your diet, in order to remedy a medical problem based on associations that, in fact, have nothing to do with causing the medical problem in question.
It will take human logic and collaboration, Zittrain said, to reach meaningful conclusions.
“One hopes that various academic departments could use these associations to set agendas for research and say, ‘Cool, what’s going on here?’” Zittrain said. “Another future is one in which everybody in each department is just running a different machine learning model that spits out answers specific to their zone.”
A new type of treatment for osteoarthritis, currently in canine clinical trials, shows promise for eventual use in humans.
The treatment, developed by Cornell University biomedical engineers, is a synthetic version of a naturally occurring joint lubricant that binds to the surface of cartilage in joints and acts as a cushion during high-impact activities, such as running.
“When the production of that specific lubricant goes down, it creates higher contact between the surfaces of the joint and, over time, it leads to osteoarthritis,” said David Putnam, a professor in the College of Engineering with appointments in the Meinig School of Biomedical Engineering and the Smith School of Chemical and Biomolecular Engineering.
The study focuses on a naturally occurring joint lubricant called lubricin, the production of which declines following traumatic injuries to a joint, such as a ligament tear in a knee.
The knee is lubricated in two ways—hydrodynamic mode and boundary mode.
Hydrodynamic mode lubrication occurs when the joint is moving fast and there isn’t a strong force pushing down on it. In this mode, joints are lubricated by compounds like hyaluronic acid (HA) that are thick and gooey, like car oil. There are numerous HA products on the market, approved by the Food and Drug Administration, for treating hydrodynamic mode lubrication disorders.
But HA is ineffective when strong forces are pushing down on the joint, such as those that occur during running or jumping. In these instances, thick gooey HA squirts out from between the cartilage surfaces, and boundary mode lubrication is necessary. Under these forces, lubricin binds to the surface of the cartilage. It contains sugars that hold on to water, to cushion hard forces on the knee.
In the paper, the researchers describe a synthetic polymer they developed that mimics the function of lubricin and is much easier to produce. “We are in clinical trials, with dogs that have osteoarthritis, with our collaborators at Cornell’s College of Veterinary Medicine,” Putnam said.
“Once we finalize the efficacy study in dogs, we will be in a very good position to market the material for veterinary osteoarthritis treatment,” Putnam said. From there, the human market for a lubricin substitute should follow, just as HA has been made available for human use, mainly in knees.
More information: Zhexun Sun et al, Boundary mode lubrication of articular cartilage with a biomimetic diblock copolymer, Proceedings of the National Academy of Sciences (2019). DOI: 10.1073/pnas.1900716116
Three research papers published by The BMJ today examine smoking and efforts to deal with it, and highlight the importance of continued investment in international tobacco control, particularly in low and middle income countries.
The first study by Professor Steven J. Hoffman and colleagues examines how patterns in international cigarette consumption have changed since 1970.
Using data from 71 countries, representing over 95% of global cigarette consumption and 85% of the world’s population, it shows cigarette consumption fell in most countries over the past three decades but trends in country specific consumption were highly variable.
For example, China consumed 2.5 million metric tons (MMT) of cigarettes in 2013, more than Russia (0.36 MMT), the United States (0.28 MMT), Indonesia (0.28 MMT), Japan (0.20 MMT), and the next 35 highest consuming countries combined.
The US and Japan achieved reductions of more than 0.1 MMT from a decade earlier, whereas Russian consumption plateaued, and Chinese and Indonesian consumption increased by 0.75 MMT and 0.1 MMT, respectively.
The authors say the findings “underscore the need for more robust processes in data reporting, ideally built into international legal instruments or other mandated processes.”
Using this data, the second study by Professor Steven J. Hoffman and colleagues found no significant change in the rate at which global cigarette consumption had been decreasing after adoption of the World Health Organization’s Framework Convention on Tobacco Control (FCTC) – an international treaty adopted in 2003 that aims to reduce harmful tobacco consumption.
After 2003, high income and European countries showed a decrease in annual consumption by more than 1000 cigarettes per adult, whereas low and middle income and Asian countries showed an increased annual consumption by more than 500 cigarettes per adult.
Although causal associations cannot be stated with certainty, the quasi-experimental designs used in the study provide robust evidence of shifting patterns in global cigarette consumption, which the researchers say “should motivate greater implementation of proven tobacco control policies” and “encourage more assertive responses to tobacco industry activities.”
The third study examined differences in vaping and smoking among adolescents in Canada, England, and the United States using online surveys of 16 to 19 year olds in 2017 and 2018.
Prevalence of vaping (past 30 days, past week, and 15 days or more in the past month) increased among 16 to 19 year olds in Canada and the US, and smoking also increased among Canadian adolescents, while little change was seen in England.
The use of JUUL (a nicotine salt based electronic cigarette) increased in all countries, particularly the US and Canada.
Despite some study limitations, the authors say that vaping among adolescents increased in Canada and the US “in parallel with the rise of nicotine salt based vaping products and the introduction of more permissive vaping regulations in Canada.”
Fewer changes were seen among adolescents in England, where there are stronger marketing restrictions and maximum nicotine limits, they conclude.
“Taken together, these new studies emphasise the value of comparative research for tobacco control across different countries,” writes Professor Linda Bauld at the University of Edinburgh in a linked editorial. “They also warn against complacency in our attempts to address smoking, now and in the future.”
She argues that continued investment in international tobacco control is more important than ever, particularly in low and middle income countries with limited capacity to combat industry attempts to delay or derail public health policies.
In an accompanying feature, Beijing-based reporters Flynn Murphy and Gabriel Crossley argue that state owned tobacco companies in China and Japan are at odds with their countries’ commitments to reduce the immense toll of disease and death caused by tobacco.
But in a linked editorial, US and Canadian researchers, Joanna Cohen and Kelley Lee, say regardless of ownership, the answer lies in comprehensive regulation “that makes public health and not economic interests the top priority.”
Brain imaging showing loss in serotonin function as Parkinson’s disease progresses. Red/yellow areas show that serotonin function reduces before movement symptoms develop. Credit: Neurodegeneration Imaging Group, King’s College London
Researchers from King’s College London have uncovered the earliest signs of Parkinson’s disease in the brain, many years before patients show any symptoms. The results, published in The Lancet Neurology, challenge the traditional view of the disease and could potentially lead to screening tools for identifying people at greatest risk.
Parkinson’s disease is the second most common neurodegenerative disorder, after Alzheimer’s disease. The disease is characterised by movement and cognitive problems but is known to become established in the brain a long time before patients are diagnosed. Studying the crucial early stages of the disease, when treatment could potentially slow its progress, is a huge challenge.
The new study, funded by the Lily Safra Foundation, provides the first evidence of a central role for the brain chemical serotonin in the very earliest stages of Parkinson’s. The results suggest changes to the serotonin system could act as a key early warning signal for the disease.
Chief investigator Professor Marios Politis, Lily Safra Professor of Neurology & Neuroimaging at the Institute of Psychiatry, Psychology & Neuroscience (IoPPN), says: ‘Parkinson’s disease has traditionally been thought of as occurring due to damage in the dopamine system, but we show that changes to the serotonin system come first, occurring many years before patients begin to show symptoms. Our results suggest that early detection of changes in the serotonin system could open doors to the development of new therapies to slow, and ultimately prevent, progression of Parkinson’s disease.’
Brain imaging showing loss in serotonin function as Parkinson’s disease progresses. Blue/black areas show that serotonin function reduces before movement symptoms develop. Credit: Neurodegeneration Imaging Group, King’s College London
People with Parkinson’s disease have build-ups of the protein α-synuclein in the brain. While there is no clear cause for most people, a minority of cases are caused by genetic mutations. People with mutations in the α-synuclein (SNCA) gene are extremely rare but are almost certain to develop Parkinson’s disease during their lifetime, making them ideal for studying the train of biological events that leads to Parkinson’s disease.
The SNCA genetic mutation originates in villages in the northern Peloponnese in Greece and can also be found in people who migrated to nearby regions in Italy. Over two years, the researchers identified 14 people with the SNCA gene mutation from Greece and Italy and flew them to London for brain imaging and clinical assessments. Half of the participants had not begun to show any symptoms of Parkinson’s.
Data from the 14 people with SNCA gene mutations were compared with 65 patients with non-genetic Parkinson’s disease and 25 healthy volunteers. The researchers found that the serotonin system starts to malfunction in people with Parkinson’s well before symptoms affecting movement occur, and before the first changes in the dopamine system.
First author Heather Wilson, from the IoPPN, says: ‘We found that serotonin function was an excellent marker for how advanced Parkinson’s disease has become. Crucially, we found detectable changes to the serotonin system among patients who were not yet diagnosed. Therefore, brain imaging of the serotonin system could become a valuable tool to detect individuals at risk for Parkinson’s disease, monitor their progression and help with the development of new treatments.’
Brain imaging was carried out using PET scans which are expensive and difficult to carry out. The researchers say further work is required to develop the scanning techniques in order to make them more affordable and straightforward for use as screening tools.
Lab tests play a key role in clinical decision-making. More than 4000 lab tests are available, and an estimated 70% of clinical decisions are based on their results. Correct interpretation is critical.
The pharmacologic effects of drugs can change the results of lab tests; for example, levothyroxine increases thyroid levels, or lisinopril may increase potassium levels. But these changes do not involve interference with the lab test; that is, the result is accurate. True drug-lab test interactions are the result of a drug altering the test specimen, or direct interference from the drug itself reacting with the test reagents.[1]
Despite their importance, surprisingly little is known about drug interactions with lab tests. Most information has been published as case reports about specific drugs. The US Food and Drug Administration (FDA) requires that drug-lab test interactions be described in the Warnings and Precautions section of the prescribing information; however, there are no industry requirements for drug-lab test interactions during drug development.
A review of the prescribing information for 10 commonly used prescription medications shows that eight made no mention of interactions with lab tests, one specifically stated “none known,” and one described an interaction (Table).
Table. Drug-Lab Test Interactions Listed in Prescribing Information for 10 Commonly Used Prescription Medications
DRUG
DRUG-LAB TEST INTERACTIONS LISTED
Albuterol inhaler
No
Amlodipine
None known
Atorvastatin
No
Levothyroxine
No
Lisinopril
No
Losartan
No
Metformin
No
Metoprolol
No
Omeprazole
Yes
Simvastatin
No
A recent view of the prescribing information for 1368 prescription drugs found that 134 (9.8%) included information about a specific lab test interaction, 31 (2.3%) stated that the drug did not interfere with lab tests, and four stated that there was no available information.[1]
Lab tests that rely on immunoassay can be skewed by interfering antibodies, causing potentially misleading results at a rate of 0.4%-4%.[2] For example, spironolactone may interfere with some immunoassays for serum digoxinmeasurement.[3]
The most common examples of drug-lab test interactions are with urine specimens, because drugs may interfere with the assays for the chemical components in urine. For example, cephalosporins may alter urine glucose and ketone tests. Such products as Clinitest rely on reducing substances in urine to convert cupric sulfate to cuprous oxide, causing color changes depending on the amount of reducing substances present. Some cephalosporins can also reduce copper and cause false-positive results, leading to infrequent clinical use of copper reduction tests today with the development of more specific testing methods.[1]
Urine screening for illicit drug use, which relies on immunoassay screening, is hampered by both unintentional and deliberate drug-lab test interactions. Interacting drugs can cause false-positive and false-negative results. For example, labetalol and ranitidine can cause a false-positive result for amphetamines, and rifampin can cause a false-positive result for opioids. False-positive results usually undergo more sensitive and specific testing.[4]
Antibacterial agents are the drug class most frequently implicated in drug-lab test interactions.[1] For example, daptomycin and telavancin can falsely prolong or elevate prothrombin time and the international normalized ratio. Daptomycin interacts with the recombinant prothrombin reagent, and telavancin interacts with phospholipid surfaces of the testing materials and interferes with the ability of the coagulation complexes to assemble for measurement.[5,6]
Psychotropic drugs and contrast media are also reported to be frequent causes of drug-lab test interactions.[1]
Even less information about lab test interference is available for over-the-counter drugs and herbal products, which have less detailed labelling requirements.
For example, the fact that acetaminophen can interfere with some continuous glucose monitors (CGMs) (particularly older versions) and cause falsely high glucose readings—a potentially serious interaction—does not appear on the package labelling for acetaminophen. CGMs monitor glucose in the interstitial fluid and convert this reading to a blood glucose level. CGMs measure glucose by converting glucose into an electronic signal, using glucose oxidase, which converts glucose to hydrogen peroxide. The hydroxyl group on acetaminophen is also oxidized at the CGM electrode, causing an error.
Other drugs, including lisinopril, albuterol, and atenolol, as well as red wine, have also been reported to cause falsely high CGM readings.[7]
The herbal products Asian ginseng (Panax ginseng) and Siberian ginseng(Eleutherococcus senticosus) have been reported to interfere with some digoxin assays.[8]
Clinical Takeaways
Because drug-lab test interactions may occur more frequently than currently recognized,[1,9] what can clinicians do?
First, be wary. Because the FDA does not require drug-lab test interactions during new drug development, the absence of a drug-lab test interaction in the prescribing information does not preclude the existence of a yet-unreported interaction. In the case of over-the-counter and herbal medicines, even known interactions may not appear on package labelling. A complete list of all medications the patient is receiving or has recently received is essential in anticipating a potential drug-lab test interaction.
Second, remain vigilant. For test results that do not correspond with the clinical picture, clinicians and laboratory staff should obtain confirmatory tests by an alternate assay method if available.
Finally, investigations into many of the known drug-lab test interactions are a result of case reports. Publication of confirmed or suspected drug-lab test interactions can improve this understudied facet of healthcare. Reporting interactions to the FDA through MedWatch is also recommended.
Almost 500,000 Americans die each year from cardiac arrest, when the heart suddenly stops beating.
People experiencing cardiac arrest will suddenly become unresponsive and either stop breathing or gasp for air, a sign known as agonal breathing. Immediate CPR can double or triple someone’s chance of survival, but that requires a bystander to be present.
Cardiac arrests often occur outside of the hospital and in the privacy of someone’s home. Recent research suggests that one of the most common locations for an out-of-hospital cardiac arrest is in a patient’s bedroom, where no one is likely around or awake to respond and provide care.
Researchers at the University of Washington have developed a new tool to monitor people for cardiac arrest while they’re asleep without touching them. A new skill for a smart speaker– like Google Home and Amazon Alexa — or smartphone lets the device detect the gasping sound of agonal breathing and call for help. On average, the proof-of-concept tool, which was developed using real agonal breathing instances captured from 911 calls, detected agonal breathing events 97% of the time from up to 20 feet (or 6 meters) away. The findings are published June 19 in npj Digital Medicine.
“A lot of people have smart speakers in their homes, and these devices have amazing capabilities that we can take advantage of,” said co-corresponding author Shyam Gollakota, an associate professor in the UW’s Paul G. Allen School of Computer Science & Engineering. “We envision a contactless system that works by continuously and passively monitoring the bedroom for an agonal breathing event, and alerts anyone nearby to come provide CPR. And then if there’s no response, the device can automatically call 911.”
Agonal breathing is present for about 50% of people who experience cardiac arrests, according to 911 call data, and patients who take agonal breaths often have a better chance of surviving.
“This kind of breathing happens when a patient experiences really low oxygen levels,” said co-corresponding author Dr. Jacob Sunshine, an assistant professor of anesthesiology and pain medicine at the UW School of Medicine. “It’s sort of a guttural gasping noise, and its uniqueness makes it a good audio biomarker to use to identify if someone is experiencing a cardiac arrest.”
The researchers gathered sounds of agonal breathing from real 911 calls to Seattle’s Emergency Medical Services. Because cardiac arrest patients are often unconscious, bystanders recorded the agonal breathing sounds by putting their phones up to the patient’s mouth so that the dispatcher could determine whether the patient needed immediate CPR. The team collected 162 calls between 2009 and 2017 and extracted 2.5 seconds of audio at the start of each agonal breath to come up with a total of 236 clips. The team captured the recordings on different smart devices — an Amazon Alexa, an iPhone 5s and a Samsung Galaxy S4 — and used various machine learning techniques to boost the dataset to 7,316 positive clips.
“We played these examples at different distances to simulate what it would sound like if it the patient was at different places in the bedroom,” said first author Justin Chan, a doctoral student in the Allen School. “We also added different interfering sounds such as sounds of cats and dogs, cars honking, air conditioning, things that you might normally hear in a home.”
For the negative dataset, the team used 83 hours of audio data collected during sleep studies, yielding 7,305 sound samples. These clips contained typical sounds that people make in their sleep, such as snoring or obstructive sleep apnea.
From these datasets, the team used machine learning to create a tool that could detect agonal breathing 97% of the time when the smart device was placed up to 6 meters away from a speaker generating the sounds.
Next the team tested the algorithm to make sure that it wouldn’t accidentally classify a different type of breathing, like snoring, as agonal breathing.
“We don’t want to alert either emergency services or loved ones unnecessarily, so it’s important that we reduce our false positive rate,” Chan said.
For the sleep lab data, the algorithm incorrectly categorized a breathing sound as agonal breathing 0.14% of the time. The false positive rate was about 0.22% for separate audio clips, in which volunteers had recorded themselves while sleeping in their own homes. But when the team had the tool classify something as agonal breathing only when it detected two distinct events at least 10 seconds apart, the false positive rate fell to 0% for both tests.
The team envisions this algorithm could function like an app, or a skill for Alexa that runs passively on a smart speaker or smartphone while people sleep.
“This could run locally on the processors contained in the Alexa. It’s running in real time, so you don’t need to store anything or send anything to the cloud,” Gollakota said.
“Right now, this is a good proof of concept using the 911 calls in the Seattle metropolitan area,” he said. “But we need to get access to more 911 calls related to cardiac arrest so that we can improve the accuracy of the algorithm further and ensure that it generalizes across a larger population.”
The researchers plan to commercialize this technology through a UW spinout, Sound Life Sciences, Inc.
“Cardiac arrests are a very common way for people to die, and right now many of them can go unwitnessed,” Sunshine said. “Part of what makes this technology so compelling is that it could help us catch more patients in time for them to be treated.”
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Dr. Thomas Rea, a professor of general internal medicine at the UW School of Medicine and the medical director of King County Medic One was also a co-author on this paper. This research was funded by the National Science Foundation.
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