The PRISM trial, funded by the National Institute for Health Research (NIHR) and co-ordinated by Birmingham Clinical Trials Unit in collaboration with Tommy’s National Centre for Miscarriage Research, is the largest ever trial of its kind and involved 4,153 pregnant women who presented with early pregnancy bleeding.

The women, being treated at 48 hospitals across the UK and with the average age of 31, were randomly assigned by computer into one of two groups — one group of 2,079 women were given progesterone, while the other group of 2,074 women were given a placebo.

Dr Adam Devall, Senior Clinical Trial Fellow at the University of Birmingham and Manager of Tommy’s National Centre for Miscarriage Research, explained: “Miscarriage is a common complication of pregnancy, affecting one in five women, and vaginal bleeding in early pregnancy is associated with a one in three risk of miscarriage.

“Several small studies have suggested that administering progesterone, a hormone essential for maintaining a pregnancy, may reduce the risk of miscarriage in women presenting with early pregnancy bleeding.

“The PRISM trial was undertaken to answer a very important research question; whether progesterone given to pregnant women with threatened miscarriage would increase the number of babies born after at least 34 weeks of gestation when compared with a placebo.”

While the research did not show statistically strong enough evidence to suggest that progesterone could help all women who are suffering early pregnancy bleeding to go on to have a baby, importantly the results did show the hormone benefitted those who had early pregnancy bleeding and had previously suffered a miscarriage.

The researchers found that there was a 4% increase in the number of babies born to the women in the study who were given progesterone and had previously had one or two miscarriages compared to those given a placebo. (Of the 777 women given progesterone who had previously had one or two miscarriages, 591 (76%) went on to have a live birth, compared with 534 women out of 738 in the placebo group (72%).)

The benefit was even greater for the women who had previous ‘recurrent miscarriages’ (i.e., three or more miscarriages) — with a 15% increase in the live birth rate in the progesterone group compared to the placebo group. (Of the 137 women taking part in the trial who had previously had three or more miscarriages, 98 (72%) went on to have a live birth, compared to 57% (85 out of 148) women in the placebo group who went on to have a baby).

The ground-breaking research was published today [May 8th] in the New England Journal of Medicine.

Arri Coomarasamy, Professor of Gynaecology at the University of Birmingham and Director of Tommy’s National Centre for Miscarriage Research, said: “The role of progesterone in women with early pregnancy bleeding has been studied and debated for about 60 years, however what we have previously lacked is high quality evidence.

“The largest study before the PRISM trial had less than 200 participants; whereas our study had more than 4,000 participants and was of very high quality, which means we can be confident in our findings.

“Our finding that women who are at risk of a miscarriage because of current pregnancy bleeding and a history of a previous miscarriage could benefit from progesterone treatment has huge implications for practice. This treatment could save thousands of babies who may have otherwise been lost to a miscarriage.

“We hope that this evidence will be considered by the National Institute for Health and Care Excellence (NICE) and that it will be used to update national guidelines for women at risk of miscarriage.”

Jane Brewin, Chief Executive of Tommy’s, said: “The results from this study are important for parents who have experienced miscarriage; they now have a robust and effective treatment option which will save many lives and prevent much heartache.

“It gives us confidence to believe that further research will yield more treatments and ultimately make many more miscarriages preventable.”

The study was published today in the New England Journal of Medicine.

In recent years, chronic kidney disease has emerged as a major illness among workers in hot climates. It was first identified in the 1990s by clinicians treating sugar cane workers in Central America. In 2012, it claimed roughly 20,000 lives and has now been identified in California, Florida and in Colorado’s San Luis Valley.

But the exact cause has been hard to determine.

“We looked at all the available literature on the subject and asked, `What do we know today? Where are the gaps?” said the study’s lead author Lee Newman, MD, MA, director of the Center for Health, Work & Environment and professor in the Dept. of Environmental & Occupational Health at the Colorado School of Public Health. “We were hoping to synthesize everything we know so far for a framework for moving forward.”

The disease is more prevalent in sugar cane workers and other agricultural employees including cotton and corn workers, shrimp farmers and miners. It is less common at higher elevations.

Newman and study co-author Richard Johnson, MD, of the University of Colorado School of Medicine, said the disease could be caused by heat, a direct health impact of climate change, as well as pesticides like glyphosate.

“Some pesticides are nephrotoxic, and these could possibly contaminate the water supply,” Johnson said. “Indeed, there are studies showing the epidemic in Sri Lanka is greatest in areas where there are shallow wells in which toxins might become concentrated.”

Sri Lankan farmers exposed to glyphosate showed an increased risk for chronic kidney disease. Still, the levels in wells were very low and studies in Central America turned up little glyphosate. Along with pesticides, the researchers looked at heavy metals as a possible culprit. Lead and cadmium, known to cause kidney injury, have been reported in the soils of Sri Lanka and Central America.

Other potential causes include infectious diseases that can hurt the kidneys such as the hanta virus and leptospirosis, common in sugar cane workers. Genetic factors are also a possibility.

“The common factors are heat exposure and heavy labor,” Newman said.

Heat stress and persistent dehydration can cause kidney damage.

The disease is moving into the U.S. with agricultural workers in Florida, California’s Central Valley and Colorado’s San Luis Valley reporting incidents of the illness.

“This is not the usual kidney disease we see in the U.S.,” Newman said. “It is not caused by high blood pressure or diabetes. The usual suspects are not the cause.”

Johnson pointed out that CU Anschutz has two major efforts underway that are investigating worker health and the impact of climate change on disease.

“This disease is not confined to the southern hemisphere, it is coming here and in fact it is already here,” Johnson said. “It is my opinion that climate change plays a role in this epidemic.”

Those impacted are mostly agricultural workers, the people who grow and harvest the world’s food.

“They are the people who feed the planet,” Johnson said. “If climate change continues like this who is going to feed us?”

Newman and Johnson believe the epidemic is caused by a combination of heat and some kind of toxin and they recognize the need to take preventative action immediately. That means ensuring workers get adequate breaks, drink enough fluids and spend time in the shade. It also means maintaining a clean water supply, free of chemicals toxic to the kidneys.

“When clinicians detect clusters of patients with chronic kidney disease who work for the same employer or in similar jobs,” the authors said, “they should contact occupational health and safety and public health professionals to promote investigations of workplace conditions.”

The system, called Marko, transmits a low-power radio-frequency (RF) signal into an environment. The signal will return to the system with certain changes if it has bounced off a moving human. Novel algorithms then analyze those changed reflections and associate them with specific individuals.

The system then traces each individual’s movement around a digital floor plan. Matching these movement patterns with other data can provide insights about how people interact with each other and the environment.

In a paper being presented at the Conference on Human Factors in Computing Systems this week, the researchers describe the system and its real-world use in six locations: two assisted living facilities, three apartments inhabited by couples, and one townhouse with four residents. The case studies demonstrated the system’s ability to distinguish individuals based solely on wireless signals — and revealed some useful behavioral patterns.

In one assisted living facility, with permission from the patient’s family and caregivers, the researchers monitored a patient with dementia who would often become agitated for unknown reasons. Over a month, they measured the patient’s increased pacing between areas of their unit — a known sign of agitation. By matching increased pacing with the visitor log, they determined the patient was agitated more during the days following family visits. This shows Marko can provide a new, passive way to track functional health profiles of patients at home, the researchers say.

“These are interesting bits we discovered through data,” says first author Chen-Yu Hsu, a PhD student in the Computer Science and Artificial Intelligence Laboratory (CSAIL). “We live in a sea of wireless signals, and the way we move and walk around changes these reflections. We developed the system that listens to those reflections … to better understand people’s behavior and health.”

The research is led by Dina Katabi, the Andrew and Erna Viterbi Professor of Electrical Engineering and Computer Science and director of the MIT Center for Wireless Networks and Mobile Computing (Wireless@MIT). Joining Katabi and Hsu on the paper are CSAIL graduate students Mingmin Zhao and Guang-He Lee and alumnus Rumen Hristov SM ’16.

Predicting “tracklets” and identities

When deployed in a home, Marko shoots out an RF signal. When the signal rebounds, it creates a type of heat map cut into vertical and horizontal “frames,” which indicates where people are in a three-dimensional space. People appear as bright blobs on the map. Vertical frames capture the person’s height and build, while horizontal frames determine their general location. As individuals walk, the system analyzes the RF frames — about 30 per second — to generate short trajectories, called tracklets.

A convolutional neural network — a machine-learning model commonly used for image processing — uses those tracklets to separate reflections by certain individuals. For each individual it senses, the system creates two “filtering masks,” which are small circles around the individual. These masks basically filter out all signals outside the circle, which locks in the individual’s trajectory and height as they move. Combining all this information — height, build, and movement — the network associates specific RF reflections with specific individuals.

But to tag identities to those anonymous blobs, the system must first be “trained.” For a few days, individuals wear low-powered accelerometer sensors, which can be used to label the reflected radio signals with their respective identities. When deployed in training, Marko first generates users’ tracklets, as it does in practice. Then, an algorithm correlates certain acceleration features with motion features. When users walk, for instance, the acceleration oscillates with steps, but becomes a flat line when they stop. The algorithm finds the best match between the acceleration data and tracklet, and labels that tracklet with the user’s identity. In doing so, Marko learns which reflected signals correlate to specific identities.

The sensors never have to be charged, and, after training, the individuals don’t need to wear them again. In home deployments, Marko was able to tag the identities of individuals in new homes with between 85 and 95 percent accuracy.

Striking a good (data-collection) balance

The researchers hope health care facilities will use Marko to passively monitor, say, how patients interact with family and caregivers, and whether patients receive medications on time. In an assisted living facility, for instance, the researchers noted specific times a nurse would walk to a medicine cabinet in a patient’s room and then to the patient’s bed. That indicated that the nurse had, at those specific times, administered the patient’s medication.

The system may also replace questionnaires and diaries currently used by psychologists or behavioral scientists to capture data on their study subjects’ family dynamics, daily schedules, or sleeping patterns, among other behaviors. Those traditional recording methods can be inaccurate, contain bias, and aren’t well-suited for long-term studies, where people may have to recall what they did days or weeks ago. Some researchers have started equipping people with wearable sensors to monitor movement and biometrics. But elderly patients, especially, often forget to wear or charge them. “The motivation here is to design better tools for researchers,” Hsu says.

Why not just install cameras? For starters, this would require someone watching and manually recording all necessary information. Marko, on the other hand, automatically tags behavioral patterns — such as motion, sleep, and interaction — to specific areas, days, and times.

Also, video is just more invasive, Hsu adds: “Most people aren’t that comfortable with being filmed all the time, especially in their own home. Using radio signals to do all this work strikes a good balance between getting some level of helpful information, but not making people feel uncomfortable.”

Katabi and her students also plan to combine Marko with their prior work on inferring breathing and heart rate from the surrounding radio signals. Marko will then be used to associate those biometrics with the corresponding individuals. It could also track people’s walking speeds, which is a good indicator of functional health in elderly patients.