Medical dilemma leaves patients without treatment for pain – 03/09/2024 – Balance and Health

How much does it hurt?

It seems like one of the simplest questions in medicine and health. But in reality, this can be a very difficult question to answer objectively.

Imagine a doctor who has two patients making faces and using similar words to describe their pain. Can the doctor be sure they are experiencing the same level of pain?

What if one of them has a habit of underestimating their suffering? What if one of them has felt pain for a long time and is used to it? What if the doctor has certain prejudices that cause him to believe one patient more than another?

Pain is an enemy that is difficult to fight and measure — and, therefore, difficult to treat.

Pain can be an important sign of distress and failure to investigate it can miss the opportunity to save a life. Or it could be something much smaller.

Even though it is a universal experience, pain remains a great mystery — and especially so is the task of determining how much pain someone is feeling.

“We understand pain very poorly,” says computer scientist Emma Pierson, from Stanford University in the United States, a pain researcher.

“Particularly, the fact that human doctors are often stumped to figure out why a patient feels pain indicates that our current medical understanding of pain is very weak,” she says.

The current gold standard of pain analysis relies on patients’ own report of how they feel. In several places, a numerical scale is used (in which 0 is no pain and 10 is the most severe pain) or a system of smiling faces.

“The first step in adequately treating pain is to measure it accurately, and this is the challenge,” says Carl Saab, leader of the pain research team at the Cleveland Clinic, in Ohio, in the United States.

“Currently the standard of care is based on the ‘smiling faces’ that permeate emergency rooms.” For Saab, this system can be confusing for patients and especially problematic for treating children and non-communicative patients.

The second issue is to believe in the patient’s assessment. One study concluded that there is a widespread notion that people tend to exaggerate the level of pain they are experiencing, although little evidence suggests that this exaggeration is common.

Without an objective way to measure pain, there is room for bias to influence doctors’ decisions.

“Grief has a particularly large impact on underserved populations, and their pain often goes ignored,” says Pierson.

Unfortunately, false beliefs about pain are common among doctors.

In 2016, a study concluded that 50% of white medical students and resident physicians in the United States held false and very dangerous ideas about black people and their experiences of pain.

Another study found that nearly half of medical students heard negative comments about black patients from their older classmates, and the level of racial bias among these students increased significantly in their first four years of medical training.

This prejudice dates back to historical attempts to justify slavery, such as false claims that black people had thicker skin and different nerve endings.

Now, black patients in the United States are 40% less likely to have their pain treated than white patients. And Hispanic patients are 25% less likely than white patients to have their pain treated.

Racial discrimination is not the only form of prejudice that influences pain treatment. There is also the bias towards “hysterical women”, which is still well known in medicine, particularly in relation to pain.

An analysis of 77 separate studies revealed that terms such as “sensitive” and “complaining” are most often applied to women’s reports of pain.

A study carried out with 981 people concluded that women who arrive at the emergency room with pain are less likely to receive pain medication and need to wait 33% longer to receive treatment than men.

Furthermore, when men and women reported similar levels of pain, the men were prescribed stronger medications for treatment.

Social expectations about “normal behavior” for men and women are the cause of these patterns, according to Anke Samulowitz, who researches gender bias at the University of Gothenburg in Sweden.

For her, these prejudices generate “clinically unjustified differences in the way men and women are treated by doctors.”

Samulowitz points out that there are sometimes real reasons why men and women receive different treatments for a specific health issue.

“Differences associated with hormones and genes can sometimes cause variations, for example, in pain medication,” according to her. “But not all distinctions observed in the treatment of men and women with pain can be explained by biological issues.”

The advancement of technology

Could new technologies help provide a way to overcome prejudice and bias around pain in medicine?

Several innovations are being developed to fill this gap by offering an objective “reading” of the severity of a person’s pain. These technologies depend on finding “biomarkers” of pain — measurable biological variables correlated with that experience.

“Without biomarkers, we will not be able to adequately diagnose and treat pain,” explains Saab. “We will not be able to predict the likelihood that someone with acute back injuries will acquire treatment-resistant chronic pain, and we will not be able to objectively monitor the response to innovative therapies in clinical trials.”

There are several possible biomarkers. Researchers from Indiana, in the United States, have developed a blood test to identify when a very specific set of genes involved in the body’s reaction to pain is activated. The levels of these biomarkers could indicate not only that someone has pain, but its intensity.

Brain activity could be another useful biomarker.

While at Brown University in the United States, Saab and his team devised a technique that measures the ebb and flow of a type of brain activity known as theta waves. The team concluded that these waves increase during pain.

Saab also found that administration of painkillers reduces theta activity to normal levels.

Since then, the team’s work has been independently reproduced by other laboratories. But Saab says he believes pain assessment based on theta waves will be another method of measuring pain, not a replacement for current methods.

“We will never be able to know for sure how someone feels, whether in terms of pain or another mental state,” says Saab.

“The patient’s verbal report should always remain the ‘basic truth’ for the pain. I imagine this is used as an adjunctive diagnosis, especially in cases where verbal reports are unreliable, such as children, adults with altered mental status, and patients non-communicative.”

Saab makes a distinction between acute pain, which acts as an alarm (“and in this case, we shouldn’t ignore it”) and chronic pain.

Sometimes a closer look at the injury or condition causing the pain can help make treatments better and more reasonable.

The Kellgren and Lawrence classification, first proposed in 1957, examines the severity of physical changes to the knees caused by osteoarthritis.

But one of the criticisms of this system is the fact that patients from low-income or minority groups often experience more intense pain due to this condition. This brings a double whammy to these individuals.

“As these intensity measures have a strong influence on [decidir] who will have their knee operated on, underserved groups may be sub-referred for surgery”, says Pierson.

Pierson and his colleagues at Stanford University have developed a new algorithm that can analyze this question.

“We use a deep learning technique to look for additional pain-relevant elements in the knee X-ray that the doctor may not be seeing that could explain more severe pain in vulnerable patients, training a deep learning algorithm to predict the pain from knee X-rays”, explains the researcher.

“So you can imagine, basically, using this algorithm to help better define surgeries, signaling to the doctor, ‘You said this patient has no physical injuries to the knee, but here’s an indication on the X-ray that it might If there is, don’t you want to take another look?'”

The algorithm will still take some time to reach the real world, according to Pierson. There are challenges to overcome that are common across the field of artificial intelligence (AI) in medicine, such as developing and training humans and algorithms to work well together.

But she’s excited to see that her algorithm finds signals in the knee that predict pain and can help reduce the problem. For Pierson, this work highlights the potential of AI to reduce bias in healthcare.

“I am often drawn to issues where medical knowledge is clearly inadequate and especially harms populations historically ignored by medicine, such as racial minorities and women,” she comments.

But Pierson notes that algorithms like his won’t solve all knee osteoarthritis problems.

“It’s not that our algorithm does some fantastic magical job of predicting pain,” she explains. “But we’re comparing it to basic knowledge of pain, which is very poor, and an assessment of intensity that was developed decades ago in predominantly white British populations, and it’s not that difficult to improve based on those starting points.”

Anke Samulowitz highlights that using technology to reduce prejudice can also bring its own difficulties. There is, for example, the issue of bias in the application of technology.

“About a fifth of the general population is affected by moderate to severe pain,” she explains. “Most of these people seek medical treatment in primary care. Will they all have brain imaging pain measurement or will there be bias in the selection?”

“Research shows that men are prescribed more somatic exams than women, while more women are referred to psychologists. There is a risk of gender bias in determining who will have objective pain measurement.”

Despite the challenges ahead, Saab believes there is a thirst for change in the field of pain. “Doctors are saying, ‘look, we can’t base our clinical workflow on this, that’s not how we should practice medicine.'”

“When you have a high temperature, you use a thermometer. When you have high blood pressure, you look at the concentrations in your blood. In this case, people come in in pain and we show them smiling faces.”

The original text was published here.