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Scientists use machine learning models to help identify long COVID patients

CHAPEL HILL, NC – Clinical scientists used machine learning (ML) models to explore de-identified electronic health record (EHR) data in the National…

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CHAPEL HILL, NC – Clinical scientists used machine learning (ML) models to explore de-identified electronic health record (EHR) data in the National COVID Cohort Collaborative (N3C), a National Institutes of Health-funded national clinical database, to help discern characteristics of people with long-COVID and factors that may help identify such patients using data from medical records.

Credit: NIAID

CHAPEL HILL, NC – Clinical scientists used machine learning (ML) models to explore de-identified electronic health record (EHR) data in the National COVID Cohort Collaborative (N3C), a National Institutes of Health-funded national clinical database, to help discern characteristics of people with long-COVID and factors that may help identify such patients using data from medical records.

The findings, published in The Lancet Digital Health, have the potential to improve clinical research on long COVID and inform a more standardized care regimen for the condition.

“Characterizing, diagnosing, treating and caring for long-COVID patients has proven to be a challenge due to the list of characteristic symptoms continuously evolving over time,” said first author Emily R. Pfaff, PhD, assistant professor in the Division of Endocrinology and Metabolism at the UNC School of Medicine. “We needed to gain a better understanding of the complexities of long-COVID, and for that it made sense to take advantage of modern data analysis tools and a unique big data resource like N3C, where many features of long COVID are represented.”

Sponsored by the National Institutes of Health’s National Center for Advancing Translational Sciences (NCATS), the N3C data enclave currently includes information representing more than 13 million people from 72 sites nationwide, including nearly 5 million COVID-19-positive cases. The resource enables rapid research on emerging questions about COVID-19 vaccines, therapies, risk factors and health outcomes.

This new research is part of the National Institutes of Health’s Researching COVID to Enhance Recovery (RECOVER) initiative, which has been recruiting thousands of participants nationwide in order to answer critical research questions about the syndrome to accurately identify who has long-COVID, risk factors for long-COVID, and potential interventions and treatments.

Using the N3C, researchers developed XGBoost machine learning (ML) models to understand patient characteristics and better identify potential long-COVID patients.

Researchers examined demographics, healthcare utilization, diagnoses, and medications for 97,995 adult COVID-19 patients. They used these features on nearly 600 long-COVID patients from three long-COVID specialty clinics to train and test three ML models, which focused on identifying potential long COVID patients in three groups:: among all COVID-19 patients, among patients hospitalized with COVID-19, and among patients who had COVID-19 but were not hospitalized.

The models proved to be accurate in identifying potential long-COVID patients, achieving areas under the receiver operator characteristic curve, a measure of accuracy used by machine learning researchers, of  0.91 (all patients); 0.90 (hospitalized); and 0.85 (non-hospitalized). Patients flagged by the models can be interpreted as “patients warranting care at a long-COVID specialty clinic.” Applying the model to the larger N3C cohort can also achieve the urgent goal of identifying long-COVID patients for clinical trials.

The models also showed many important features that differentiate potential long-COVID patients from non-long-COVID patients. They focused on patients with a positive COVID diagnosis who were at least 90 days out from their acute infection. Features more commonly identified among potential long COVID patients include post-COVID respiratory symptoms and associated treatments, non-respiratory symptoms widely reported as part of long COVID (such as sleep disorders, anxiety, malaise, chest pain, and constipation), pre-existing risk factors for greater acute COVID severity (such as chronic pulmonary disease, diabetes, and chronic kidney disease), and proxies for hospitalization, suggesting greater severity of acute covid. The study also points out that it is plausible that long-COVID will not ultimately have a single definition, and may be better described as a set of related conditions with their own symptoms, trajectories, and treatments.

“These results speak to the powerful impact of real-world clinical data and the potential capabilities of N3C to help better understand and find solutions for significant public health problems such as long COVID,” said NCATS Acting Director Joni Rutter, PhD.

Josh Fessel, MD, PhD, senior clinical advisor at NCATS and a scientific program lead in RECOVER, added, “Once you’re able to determine who has long COVID in a large database of people, you can begin to ask questions about those people. Was there something different about those people before they developed long COVID? Did they have certain risk factors? Was there something about how they were treated during acute COVID that might have increased or decreased their risk for long COVID?”

The study included how electronic health record (EHR) data is skewed toward patients who make more use of healthcare systems. Pfaff says that it is essential to acknowledge whose data is less likely to be represented – uninsured patients, patients with limited access to or ability to pay for care, or patients seeking care at small practices or community hospitals with limited data exchange capabilities.

“Electronic Health Records (EHRs) only have information for people who go to the doctor,” said Pfaff, who is also Co-Director of the NC TraCS Informatics and Data Science (IDSci) Program. “They also have more information on people who go to the doctor a lot. So, people who don’t have good access to care or people who don’t go to the doctor, we’re just not going to have information about them. So this is a caveat that I offer with every EHR based study that I do. We need to recognize who’s not in the dataset.”

The N3C team continues to refine its models as more real-world data emerges. Their longitudinal data for COVID-19 patients can provide a comprehensive foundation for the development of ML models to identify potential long-COVID patients. As larger cohorts of long-COVID patients are established, future work will include research to identify subtypes of long-COVID, making the condition easier to study and treat.

“Depending on where the research leads, we may find that patients with different presentations of long COVID are different enough to warrant different treatments entirely,” said Pfaff. “So, it’s important for us to determine if long COVID is one disease, or a constellation of related conditions that are also related to having had acute COVID-19.”

With the help of this big data approach, efficient study recruitment efforts can become available to deepen the understanding and complexities of long-COVID. Beyond identifying cohorts for research studies, understanding and validating the relationship between long-COVID and social determinants of health and demographics, comorbidities, and treatment implications will only improve the algorithm in these models as more evidence emerges.

“Research studies, particularly clinical trials, are one of our best tools for gaining understanding of long COVID — its presentation, risk factors, and potential treatments,” said Pfaff. “For the best chance at success, studies need large and diverse groups of participants who qualify, which aren’t easy to find. Using algorithms like the one we’ve created on large clinical datasets can narrow down vast numbers of patients to those who could qualify for a long COVID trial, potentially giving researchers a head start on recruitment, making trials more efficient, and hopefully getting to findings faster.”

This study was funded by NCATS and NIH through the RECOVER Initiative.

About the National Center for Advancing Translational Sciences (NCATS): NCATS conducts and supports research on the science and operation of translation — the process by which interventions to improve health are developed and implemented — to allow more treatments to get to more patients more quickly. For more information about how NCATS helps shorten the journey from scientific observation to clinical intervention, visit https://ncats.nih.gov.


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Homes listed for sale in early June sell for $7,700 more

New Zillow research suggests the spring home shopping season may see a second wave this summer if mortgage rates fall
The post Homes listed for sale in…

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  • A Zillow analysis of 2023 home sales finds homes listed in the first two weeks of June sold for 2.3% more. 
  • The best time to list a home for sale is a month later than it was in 2019, likely driven by mortgage rates.
  • The best time to list can be as early as the second half of February in San Francisco, and as late as the first half of July in New York and Philadelphia. 

Spring home sellers looking to maximize their sale price may want to wait it out and list their home for sale in the first half of June. A new Zillow® analysis of 2023 sales found that homes listed in the first two weeks of June sold for 2.3% more, a $7,700 boost on a typical U.S. home.  

The best time to list consistently had been early May in the years leading up to the pandemic. The shift to June suggests mortgage rates are strongly influencing demand on top of the usual seasonality that brings buyers to the market in the spring. This home-shopping season is poised to follow a similar pattern as that in 2023, with the potential for a second wave if the Federal Reserve lowers interest rates midyear or later. 

The 2.3% sale price premium registered last June followed the first spring in more than 15 years with mortgage rates over 6% on a 30-year fixed-rate loan. The high rates put home buyers on the back foot, and as rates continued upward through May, they were still reassessing and less likely to bid boldly. In June, however, rates pulled back a little from 6.79% to 6.67%, which likely presented an opportunity for determined buyers heading into summer. More buyers understood their market position and could afford to transact, boosting competition and sale prices.

The old logic was that sellers could earn a premium by listing in late spring, when search activity hit its peak. Now, with persistently low inventory, mortgage rate fluctuations make their own seasonality. First-time home buyers who are on the edge of qualifying for a home loan may dip in and out of the market, depending on what’s happening with rates. It is almost certain the Federal Reserve will push back any interest-rate cuts to mid-2024 at the earliest. If mortgage rates follow, that could bring another surge of buyers later this year.

Mortgage rates have been impacting affordability and sale prices since they began rising rapidly two years ago. In 2022, sellers nationwide saw the highest sale premium when they listed their home in late March, right before rates barreled past 5% and continued climbing. 

Zillow’s research finds the best time to list can vary widely by metropolitan area. In 2023, it was as early as the second half of February in San Francisco, and as late as the first half of July in New York. Thirty of the top 35 largest metro areas saw for-sale listings command the highest sale prices between May and early July last year. 

Zillow also found a wide range in the sale price premiums associated with homes listed during those peak periods. At the hottest time of the year in San Jose, homes sold for 5.5% more, a $88,000 boost on a typical home. Meanwhile, homes in San Antonio sold for 1.9% more during that same time period.  

 

Metropolitan Area Best Time to List Price Premium Dollar Boost
United States First half of June 2.3% $7,700
New York, NY First half of July 2.4% $15,500
Los Angeles, CA First half of May 4.1% $39,300
Chicago, IL First half of June 2.8% $8,800
Dallas, TX First half of June 2.5% $9,200
Houston, TX Second half of April 2.0% $6,200
Washington, DC Second half of June 2.2% $12,700
Philadelphia, PA First half of July 2.4% $8,200
Miami, FL First half of June 2.3% $12,900
Atlanta, GA Second half of June 2.3% $8,700
Boston, MA Second half of May 3.5% $23,600
Phoenix, AZ First half of June 3.2% $14,700
San Francisco, CA Second half of February 4.2% $50,300
Riverside, CA First half of May 2.7% $15,600
Detroit, MI First half of July 3.3% $7,900
Seattle, WA First half of June 4.3% $31,500
Minneapolis, MN Second half of May 3.7% $13,400
San Diego, CA Second half of April 3.1% $29,600
Tampa, FL Second half of June 2.1% $8,000
Denver, CO Second half of May 2.9% $16,900
Baltimore, MD First half of July 2.2% $8,200
St. Louis, MO First half of June 2.9% $7,000
Orlando, FL First half of June 2.2% $8,700
Charlotte, NC Second half of May 3.0% $11,000
San Antonio, TX First half of June 1.9% $5,400
Portland, OR Second half of April 2.6% $14,300
Sacramento, CA First half of June 3.2% $17,900
Pittsburgh, PA Second half of June 2.3% $4,700
Cincinnati, OH Second half of April 2.7% $7,500
Austin, TX Second half of May 2.8% $12,600
Las Vegas, NV First half of June 3.4% $14,600
Kansas City, MO Second half of May 2.5% $7,300
Columbus, OH Second half of June 3.3% $10,400
Indianapolis, IN First half of July 3.0% $8,100
Cleveland, OH First half of July  3.4% $7,400
San Jose, CA First half of June 5.5% $88,400

 

The post Homes listed for sale in early June sell for $7,700 more appeared first on Zillow Research.

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Survey Shows Declining Concerns Among Americans About COVID-19

Survey Shows Declining Concerns Among Americans About COVID-19

A new survey reveals that only 20% of Americans view covid-19 as "a major threat"…

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Survey Shows Declining Concerns Among Americans About COVID-19

A new survey reveals that only 20% of Americans view covid-19 as "a major threat" to the health of the US population - a sharp decline from a high of 67% in July 2020.

(SARMDY/Shutterstock)

What's more, the Pew Research Center survey conducted from Feb. 7 to Feb. 11 showed that just 10% of Americans are concerned that they will  catch the disease and require hospitalization.

"This data represents a low ebb of public concern about the virus that reached its height in the summer and fall of 2020, when as many as two-thirds of Americans viewed COVID-19 as a major threat to public health," reads the report, which was published March 7.

According to the survey, half of the participants understand the significance of researchers and healthcare providers in understanding and treating long COVID - however 27% of participants consider this issue less important, while 22% of Americans are unaware of long COVID.

What's more, while Democrats were far more worried than Republicans in the past, that gap has narrowed significantly.

"In the pandemic’s first year, Democrats were routinely about 40 points more likely than Republicans to view the coronavirus as a major threat to the health of the U.S. population. This gap has waned as overall levels of concern have fallen," reads the report.

More via the Epoch Times;

The survey found that three in ten Democrats under 50 have received an updated COVID-19 vaccine, compared with 66 percent of Democrats ages 65 and older.

Moreover, 66 percent of Democrats ages 65 and older have received the updated COVID-19 vaccine, while only 24 percent of Republicans ages 65 and older have done so.

“This 42-point partisan gap is much wider now than at other points since the start of the outbreak. For instance, in August 2021, 93 percent of older Democrats and 78 percent of older Republicans said they had received all the shots needed to be fully vaccinated (a 15-point gap),” it noted.

COVID-19 No Longer an Emergency

The U.S. Centers for Disease Control and Prevention (CDC) recently issued its updated recommendations for the virus, which no longer require people to stay home for five days after testing positive for COVID-19.

The updated guidance recommends that people who contracted a respiratory virus stay home, and they can resume normal activities when their symptoms improve overall and their fever subsides for 24 hours without medication.

“We still must use the commonsense solutions we know work to protect ourselves and others from serious illness from respiratory viruses, this includes vaccination, treatment, and staying home when we get sick,” CDC director Dr. Mandy Cohen said in a statement.

The CDC said that while the virus remains a threat, it is now less likely to cause severe illness because of widespread immunity and improved tools to prevent and treat the disease.

Importantly, states and countries that have already adjusted recommended isolation times have not seen increased hospitalizations or deaths related to COVID-19,” it stated.

The federal government suspended its free at-home COVID-19 test program on March 8, according to a website set up by the government, following a decrease in COVID-19-related hospitalizations.

According to the CDC, hospitalization rates for COVID-19 and influenza diseases remain “elevated” but are decreasing in some parts of the United States.

Tyler Durden Sun, 03/10/2024 - 22:45

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Rand Paul Teases Senate GOP Leader Run – Musk Says “I Would Support”

Rand Paul Teases Senate GOP Leader Run – Musk Says "I Would Support"

Republican Kentucky Senator Rand Paul on Friday hinted that he may jump…

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Rand Paul Teases Senate GOP Leader Run - Musk Says "I Would Support"

Republican Kentucky Senator Rand Paul on Friday hinted that he may jump into the race to become the next Senate GOP leader, and Elon Musk was quick to support the idea. Republicans must find a successor for periodically malfunctioning Mitch McConnell, who recently announced he'll step down in November, though intending to keep his Senate seat until his term ends in January 2027, when he'd be within weeks of turning 86. 

So far, the announced field consists of two quintessential establishment types: John Cornyn of Texas and John Thune of South Dakota. While John Barrasso's name had been thrown around as one of "The Three Johns" considered top contenders, the Wyoming senator on Tuesday said he'll instead seek the number two slot as party whip. 

Paul used X to tease his potential bid for the position which -- if the GOP takes back the upper chamber in November -- could graduate from Minority Leader to Majority Leader. He started by telling his 5.1 million followers he'd had lots of people asking him about his interest in running...

...then followed up with a poll in which he predictably annihilated Cornyn and Thune, taking a 96% share as of Friday night, with the other two below 2% each. 

Elon Musk was quick to back the idea of Paul as GOP leader, while daring Cornyn and Thune to follow Paul's lead by throwing their names out for consideration by the Twitter-verse X-verse. 

Paul has been a stalwart opponent of security-state mass surveillance, foreign interventionism -- to include shoveling billions of dollars into the proxy war in Ukraine -- and out-of-control spending in general. He demonstrated the latter passion on the Senate floor this week as he ridiculed the latest kick-the-can spending package:   

In February, Paul used Senate rules to force his colleagues into a grueling Super Bowl weekend of votes, as he worked to derail a $95 billion foreign aid bill. "I think we should stay here as long as it takes,” said Paul. “If it takes a week or a month, I’ll force them to stay here to discuss why they think the border of Ukraine is more important than the US border.”

Don't expect a Majority Leader Paul to ditch the filibuster -- he's been a hardy user of the legislative delay tactic. In 2013, he spoke for 13 hours to fight the nomination of John Brennan as CIA director. In 2015, he orated for 10-and-a-half-hours to oppose extension of the Patriot Act

Rand Paul amid his 10 1/2 hour filibuster in 2015

Among the general public, Paul is probably best known as Capitol Hill's chief tormentor of Dr. Anthony Fauci, who was director of the National Institute of Allergy and Infectious Disease during the Covid-19 pandemic. Paul says the evidence indicates the virus emerged from China's Wuhan Institute of Virology. He's accused Fauci and other members of the US government public health apparatus of evading questions about their funding of the Chinese lab's "gain of function" research, which takes natural viruses and morphs them into something more dangerous. Paul has pointedly said that Fauci committed perjury in congressional hearings and that he belongs in jail "without question."   

Musk is neither the only nor the first noteworthy figure to back Paul for party leader. Just hours after McConnell announced his upcoming step-down from leadership, independent 2024 presidential candidate Robert F. Kennedy, Jr voiced his support: 

In a testament to the extent to which the establishment recoils at the libertarian-minded Paul, mainstream media outlets -- which have been quick to report on other developments in the majority leader race -- pretended not to notice that Paul had signaled his interest in the job. More than 24 hours after Paul's test-the-waters tweet-fest began, not a single major outlet had brought it to the attention of their audience. 

That may be his strongest endorsement yet. 

Tyler Durden Sun, 03/10/2024 - 20:25

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