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Go Vegetarian to Avoid COVID? Making Dialysis Centers Work Better

TTHealthWatch is a weekly podcast from Texas Tech. In it, Elizabeth Tracey, director of electronic media for Johns Hopkins Medicine in Baltimore, and Rick Lange, MD, president of the Texas Tech University Health Sciences Center in El Paso, look at the top medical stories of the week.

This week’s topics include a proteomic approach to cancer screening, vegetarian diet and COVID risk, performance of dialysis centers, and medical errors in hospitalized patients.

Program notes:

0:40 Incentivizing dialysis centers

1:40 Over 1,000 dialysis centers

2:40 How to adjust for neighborhood?

3:41 Comprehensive approach needed

4:00 Vegetarian diet and COVID risk

5:03 Importance of diet and disease

6:00 Immunity and foods

6:12 Diagnostic errors in hospitalized patients

7:12 Problems assessing the patient

8:18 Novel proteomics-based cancer screening

9:18 10 proteins with high accuracy

10:18 Different in men and women

11:18 Most useful biomarkers the low concentration ones

12:05 End

Transcript:

Elizabeth: Does being a vegetarian help you avoid COVID infection?

Rick: How often do we see diagnostic errors in hospitalized patients who die or are transferred to the ICU?

Elizabeth: Looking at proteomics to look for multiple cancers.

Rick: And looking at social risk and how it affects dialysis facility performance.

Elizabeth: That’s what we’re talking about this week on TTHealthWatch, your weekly look at the medical headlines from Texas Tech University Health Sciences Center in El Paso. I’m Elizabeth Tracey, a Baltimore-based medical journalist.

Rick: And I’m Rick Lange, president of Texas Tech University Health Sciences Center in El Paso, where I’m also dean of the Paul L. Foster School of Medicine.

Elizabeth: Rick, the one that you served up from JAMA about what happens in these dialysis facilities is one that really speaks to my heart having had multiple patients who have had circumstances that have been a little questionable surrounding dialysis. Are you okay with it if we start with that one?

Rick: Yeah. No, I think it’s great, Elizabeth. It’s an attempt to try to improve performance in dialysis centers by using incentives. How effective is it? We know that individuals that are on Medicaid in low-income situations, certain races, their outcome oftentimes at dialysis centers isn’t quite as good. Things that we’re trying to push people towards — that is, doing home dialysis, it’s less likely that they’re able to participate.

But what CMS has tried to do was to compose a model that would reward facilities that have really good outcomes, take money away from centers that have a poor outcome. They thought by doing this, by the way, that they could actually drive and improve outcomes. By the way, they did that in about 30% of dialysis studies.

What this study did was they looked to see how effective that was. They analyzed almost 126,000 patients at over 1,000 dialysis centers. They noticed that about 50% of them had no social risk — that is, they weren’t African American and they didn’t live in a low-income area. Then about 22% had two or more of these risk factors.

If you had two or more of these risk factors, you were much more likely to have money taken away from you than if you had none. In fact, if you had none you were more likely to have an incentive provided. Well, that’s just the opposite of what we’re trying to do. What we want to do is we want to try to take those centers and actually contribute more money to get a better outcome.

Now, in fairness, they tried to adjust these for individual patient-centered differences, but what they discovered is there are things that happen in the neighborhood that aren’t captured by things like this: education status, transportation, crime, and access. If you actually incorporated these things, that also added some predictive value. It may mean that we need to adjust our incentives not only based upon the individual, but also a neighborhood or societal things as well.

Elizabeth: Help me to construct that model. What would it look like to try to adjust for the neighborhood and for social factors that surround the center?

Rick: If you’re in a neighborhood that has a high crime rate, doesn’t have transportation, doesn’t have healthy food, doesn’t have a transport center nearby, it’s not surprising that you’re going to have a worse outcome. We want to take those centers where they’re going to and actually provide them additional monies or additional resources, so they can improve the outcome. We don’t want to take money away from them. We actually want to incentivize them.

One of the indexes you can use is what’s called an area deprivation index (ADI). It looks at an individual neighborhood to say, “Is that neighborhood deprived of things that will provide good outcomes for the patient?” We can use that to help adjust for these things.

Elizabeth: Remember, last week we talked about this comprehensive approach to pre-pregnancy, pregnancy, and post-pregnancy with regard to early childhood outcomes. What this study says to me is that this comprehensive approach to health, which is, of course, one of those “duh” conclusions — I’m really good at restating the obvious — is really the important thing here.

Rick: Yes. We’re trying to make sure that all individuals have the same quality of care and some individuals, some neighborhoods, just need more attention to get there. This is a societal problem. If we don’t address this, it costs us more as a society.

Elizabeth: Since we’re talking about cost of health care, let’s turn to the BMJ Nutrition, Prevention & Health. This is an examination that came from Brazil, of vegetarian and plant-based diets and their association with COVID-19 infection. It’s observational, 702 participants, where sociodemographic characteristics, dietary information, and COVID-19 outcomes were collected between March and July of 2022.

When they took a closer look at these folks, their omnivorous group comprised 424 people and their plant-based group 278. They adjusted for all kinds of confounders — BMI, physical activity, preexisting medical conditions — and found that the plant-based and vegetarian group had a 39% reduced incidence of COVID-19 infection compared with the omnivorous group. Gosh, this vegetarian and plant-based thing is something we should probably be looking at more closely from a societal and policy perspective.

Rick: We have talked before about the importance of diet in a number of disease entities in terms of reducing inflammation and reducing high blood pressure. I’m going to put a little bit of a cautionary note to this. First of all, do I think that eating healthy is good? Yes, I do. At best, this is an association. You and I know that these individuals that eat healthy also have other healthy lifestyle behaviors. They are more likely to exercise. They have less weight. They have less comorbidities. I’m wondering whether it’s not that the diet, but we just have a group of individuals that are more likely to wear a mask, or more likely to be isolated, or more likely to wash their hands, or more likely to have other healthy lifestyle things that could account for it.

Elizabeth: There is no question that the vegetarians have a lower BMI, a lower prevalence of overweight, obesity, and metabolic syndrome, and that they exercised more. That probably had some impact on how often they got infected with COVID-19. Then they also make the point about the relationship between immunity and foods, which is something that we seem to be seeing a lot more.

Rick: In the end, Elizabeth, whether we decide there is a causality or association, I think we’re both in agreement that a healthy diet is in fact a healthy diet.

Elizabeth: You got that right. Let’s turn then to JAMA Internal Medicine, this issue of diagnostic errors.

Rick: We’ve known for well over a decade now that diagnostic errors play an important role in patients receiving care in the hospital. This particular study focused on two groups of individuals: those individuals who die in the hospital, or those who are hospitalized and then are transferred to an intensive care unit. They ask a very simple question: “How often do we see diagnostic errors in these individuals?”

To determine the presence, the underlying cause, and actually the harms of diagnostic errors, they did a retrospective study of 29 different academic medical centers. They had two trained clinicians comb the charts to see whether there were any diagnostic errors or not, and if so, did they result in harm?

After examining the records of about 2,400 patients, they discovered that about a fourth of these, 23%, had experienced a diagnostic error. This error was judged to have contributed to harm in about 20% (17.8%).

When they look at the most common diagnostic errors, they fell in primarily two groups: problems assessing the patient — either we didn’t get the right diagnosis or we didn’t establish it quickly enough; or secondly, problems with test ordering and interpretation. We didn’t order the right test, we ordered the test and didn’t look at it, or we ordered the test and didn’t see how it fit in the entire picture at all.

What this study doesn’t tell us is, would the outcome of these patients have been any different? Regardless, this is an area that we still need to address.

Elizabeth: Yeah. This has, of course, emerged as a cause célèbre in lots of arenas and it’s unclear to me exactly how we’re going to get our arms around it, because they seem like fairly variable kinds of errors.

Rick: Yep, and you’re right. There were six or seven different types. I focused on the two that were most common. We’re hopeful that artificial intelligence can help in some ways. Unfortunately, as we talked about a couple of weeks ago, it can actually make the problem worse if the data in and the way you’re analyzing it isn’t particularly helpful.

Are there other things that we can do? We can educate the physician workforce better, make sure we’re not anchoring on a specific diagnosis, and not overburdening the physicians and healthcare providers. A number of different ways to address this.

Elizabeth: Finally, let’s turn to BMJ Oncology and this is a look at a novel proteomics-based plasma test for early detection of multiple cancers in the general population. This is obviously an objective. Wouldn’t it be great to be able to just draw blood and assess somebody for the presence of very early cancers? It’s also helpful in terms of early detection, early treatment, and better outcomes, although that is not a solid-line relationship.

This paper describes this novel proteome-based multicancer screening test. They had 440 participants, healthy and diagnosed with 18 early-stage solid tumors. In this group, they measured more than 3,000 high-abundance and low-abundance proteins in each sample using a number of approaches. They identified a limited set of sex-specific proteins that could detect early-stage cancers and their tissue of origin with high accuracy.

They were able to boil this down to 10 proteins that showed high accuracy for both males and females — in the males, 98%, and in the females, 98% at stage 1, and a specificity of 99%. Their panels were able to identify 93% of cancers among the males and 84% of the cancers among the females. They were able to identify in more than 80% of cases the tissue of origin of the cancer. A lot of pretty impressive results in people they already knew had cancer, and this proteome-based screening test is promising and they say clearly should be followed up.

Rick: Elizabeth, I would agree. It does need follow-up and validation. They’re all patients from the Ukraine. They are all racially or ethnically very similar.

The thing that was fascinating is, they test over 3,000 proteins. They found that 10 specific proteins could identify the presence of cancer, but they were different in men than they were in women. The second thing that was fascinating is they tried to identify was the cancer present, but where was it located, and they had to use over 150 different proteins to do that.

It would be nice to take a blood test and to be able to screen. To be able to do that, it’s got to be very sensitive and very specific. Ultimately, as you mentioned, it needs to improve cancer outcome. The thought is if you can detect it early, you can treat it, get rid of it early and improve outcomes. That may be true in some cancers and may not be true in others.

The other thing I would say is that most of the cancers they detected weren’t as early as they thought. They weren’t stage 1 cancers. Most of them were stage 2 and stage 3. We know as cancers evolve their proteins change, so a lot of work to be done, but I’m glad that people are pursuing this.

Elizabeth: Oh, absolutely. They cite something that I thought was really interesting. They say that nearly 60% of cancer-related deaths are due to cancers for which no screening test exists. I was really unaware of that particular statistic. The other thing I would note about their test, not only the fact that men and women screen very differently, but that their most useful biomarkers for early-stage cancers were those that were present in low concentrations, not the ones that were present in high concentrations, which is also a novel finding.

Rick: It is and what it means is that you’ve got to have very sensitive ways of looking for protein that’s at a very low level. I hope that in 20 or 30 years we’re able to crack this nut.

Elizabeth: I’m hoping it’s not going to be 20 or 30 years. On that note then, that’s a look at this week’s medical headlines from Texas Tech. I’m Elizabeth Tracey.

Rick: And I’m Rick Lange. Y’all listen up and make healthy choices.

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