[00:00:03] Speaker A: Welcome to your Cases on Hold, a JBJS podcast hosted by Antonia Chen and Andrew Stonefield.
[00:00:10] Speaker B: Here we discuss the science of each issue of JBJS with an additional dose of entertainment and pop culture.
[00:00:17] Speaker A: Take us with you in the gym, on the commute, or most certainly whenever your case is on.
Welcome back everyone to your cases on hold Episode 87 we're in the middle of summer. It's August 5th. If you're listening, on the day we drop for a 6th of August issue of the journal Bone and Joint Surgery. We are covering the best and the rest everything in the Journal this month or this issue for the first half of the month. There's, there's a lot to get through, there's a lot of good stuff and I think we have some really interesting things to talk about from both philosophical, scientific and hopefully entertainment aspect as well. As always, what you're hearing in this podcast are my opinions and those of my co hosts and not those of the editorial board, the Board of trustees, the broader editorial leadership at the family of journals that is now JB js.
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As always for the uninitiated, I'm Andrew Schoenfeld, Deputy Editor for methods at JVGs and I have with me Antonia Chen.
[00:02:16] Speaker B: Executive Editor at Davy js.
[00:02:18] Speaker A: All right, so let's talk about what's in the top of the pile. We start off with a multi center study of intertrochanteric and per trochanteric fragility fractures. Spanning fixation mitigates the risk of peri implant fractures. This is by Murphy and colleagues with an infographic and 30 days free. Then we have the T4L1 hip axis objectifies the Rasuli classification using continuous measures by Hills and colleagues. Then we have PGE 2ameliorates aging, aggravated rotator cuff muscle atrophy by Shu and colleagues. Is followed by in the Stillness of the Spine, not in the Stillness of the Night by Chen. Permanently Free.
A photo voice study on Life after Traumatic Brachial Pleasure Plexus Injury. There is somebody out there who knows what you're going through by Faust and colleagues.
Then we have the opinion AI will not save us. Technological Authoritarianism, the Electronic Medical Record and the Erosion of the Practice of Medicine by Sharkey. That seriously could be like a PhD thesis, I think. The Ethics of Operating on a Patient with a Hip Fracture in Hospice Care by Mercer.
That brings us into our headlines. At this point, per the usual, I will kick off with risk of early periprosthetic tibial fracture after Medial Unicompartmental knee Arthroplasty with Cemented versus cementless fixation. A nationwide cohort study by Risagar and colleagues. This does come with a visual summary for our visually inclined learners.
So this was an interesting study. As it says in the subtitle, it is a nationwide cohort study.
This study encompassed the Danish. A Danish population registry from multiple nationwide registries. So the data from these registries is prospectively collected by clinicians, or what the authors call medical secretaries, which I thought was interesting. That's a little antiquated terminology there.
[00:04:28] Speaker B: Medical secretaries.
[00:04:30] Speaker A: Yeah, Mrs. Testmacher came in to mind. But, you know, I don't know. They're like, Mrs. Testmacher, can you record that? This one had a periprosthetic fracture.
Anyway, so this went from 1997 to 2022 and was accessed in 2022 and then analyzed in 2024. I don't know what the two year delay was on that front. I think maybe they. They did everything in 2024, but they only had data up to 2022. I'm not sure. Anyway, they start with 1997 and they go basically for 25 years of data collection, which is pretty lengthy. And obviously the surgical techniques are changing over time. On that front, they basically are using hazard ratios, looking for the risk of the early fracture calculated for covariates using a fine and gray competing risk analysis.
And then because they're concerned about secular trends and covariate effects, which I think is very prescient and reasonable, they perform sensitivity analyses looking at cumulative proportions, one including only those patients from 2010 to 2022, and then another where they do matching for covariates while accounting for competing risks.
Now, you know, I don't necessarily love that approach. We've talked about where you really ideally want to have. And I Think this is going to come up later too. I think from a philosophical standpoint, you want to have one question, one analytic approach, and that should really mean one cohort. Not like for this question we did this one and then we have this other one that we do for this other question.
I think it's cleaner when you look at the fact that this is evolving over time. And obviously both the cementless technology and the cemented technology are, are not available throughout the entire cohort. The time period of the COHORT you have 9,700 cemented unicompartmental knee arthroplasty, and then with a follow up of nine years, and then there's 12,380 cementless ones with a follow up of just three years.
So it's a lot of potential patients.
Why not just go with the years when the cementless were done? And clearly at some point people just moved way more towards doing the cementless ones than the cemented ones, at least in this Danish context.
So, you know, they are trying to address some of these secular trends with the sensitivity testing. But my thought is, first off, what if you just limited it to the time window where you could do cemented or cementless, and then you do something like a causal inference test, propensity score matching, propensity score weighting, that would account for the confounding by indication.
And I think that approach would provide a greater degree of evidence that one could be more confident and invested in than what we're seeing here. Because there's still the issue of everyone, it seems. You know, obviously I don't work in Denmark and I don't work in this particular specialty of orthopedics, but it seems to me that if you have 9,700 with a mean follow up of nine years, and then 12,000 of the other kind with just the mean follow up of three years, that people are really moving toward preferentially doing the, the uncertainted ones unless they feel like they have to in the cemented ones. And that's just really, you know, there's clear concern for confounding by indication there. And their approach, I would say, doesn't really get at that issue.
Their analytic approach doesn't take into account that issue. Now, they do provide some very interesting and useful data, I think, useful for informing practice going forward. And you know, that includes basically that when they're looking at the four month cumulative proportions of the periprosthetic fracture, it's 0.2% and after cemented and 0.7% after cementless so, you know, pretty low overall. But they did some subgroup analyses looking at, you know, trying to define a higher at risk population and that they identified as female patients over the age of 70 with a BMI greater than equal to 40 and or a height of less than 160 centimeters. And that represented 3% of all the fractures in the group. Well, that represented 3% of all the procedures and then it was 4.5% of all fractures.
So that's obviously a higher risk group. And their conclusion is basically the majority of the fractures occurred within four months and they felt that these were surgery related fractures.
The female patients over 70 who undergo cementless have an increased risk of fracture in combination with low height and or high BMI with a risk of fracture, basically 4.5% within four months. They're saying that in patients with these characteristics, bone conserving, tibial preparation and consideration of using cement may be appropriate essentially.
So, you know, I thought that was interesting. Obviously height and BMI are going to be related because height factors into bmi. So a lower height just makes you more sensitive to the weight side of the BMI and creates a situation where a little bit more weight probably goes a longer way towards elevating bmi.
I was a little bit surprised, you know, because you think that in some respects there's going to be an issue with bone density related phenomena which oftentimes you'd see in patients with lower BMI in certain contexts. But again, this is the Danish population and I don't know that these numbers would hold up in say the Texas population or the Mississippi population or in any sort of environment outside of Denmark. A Nordic situation maybe, or a population of patients that's very similar to, you know, those in, in Denmark.
So do you do a lot of these?
[00:10:53] Speaker B: So what I struggle with this is you harped on one very, very important point. Cemented and cementless came in two different ways. Cementless is more popular or sorry, is only available recently. So I completely agree that though you should have definitely only compared patients within that timeframe for both cemented and cementless are being used. The other problem with cementless is I've never done a cementless unis because they haven't been approved in the US until recently.
Now I take that back. There was one that was on the market and it failed catastrophically. And so you don't know which implants are being used. And that's the other hard part. When you say cemented versus cementless unis in the total knee population, I would say it's probably more common to have cemented a cementless, although you would argue that sometimes the cementless isn't the same. And so here you're saying this is cemented versus cementless uni. You don't know what the implants are. And that actually makes a difference in these cases because not knowing the implants can actually imply whether or not some patients do better or not do better. And so, you know, is it failure because of the implant or is it failure because of technique or the failure because of the fixation? And it doesn't actually clarify that with these larger database studies because we don't have the information. I would say like, you know, registry that should have the implant information, things like that should be able to tease this out. I would vote that that would be more important to add to this group.
So, you know, I take it with a grain of salt everything that you're saying is very true in here in that, you know, do we want to say that the fracture risk is worse in cementless versus cemented? You're not comparing apple to apple, you're comparing apples to oranges. So a lot of confounding factors.
Good to see this data. I would, I want to know more information about the implant specifically to understand it.
[00:12:36] Speaker A: All right, let's, let's see what you have to say about your headline. Differences in Orthopedic Surgeon Merit Based Incentive Payment System Performance Demographics and Patient Populations Based on Patient Social Risk by Hole and colleagues.
[00:12:55] Speaker B: So this is a study out of Mayo, Arizona looking at how orthopedic surgeons with this merit based incentive payment system. So I'm about to use a lot of Alphabet soup here, but hopefully we can break it down to give some more information.
So the first thing is cms, which is the Centers for Medicare and Medicaid Services developed this merit based incentive payment system which is a mips. And they haven't gotten multiple different changes. And the purpose they put for why they did this MIPS program is to hopefully promote care for those patients who are at high social risk.
So the MIPS was implemented under the Medicare Access and chip, which is the Children's Health Insurance Program reauthorization act of 2015. This is called Macra.
So in 2015 they developed the Macra and the Macra says we are going to incentivize people to take care of sicker patients. Under this MIPS program. They consolidated higher quality programs and wanted to promote value based care over volume based care. The idea is that you pay fee for service. You get paid if you do more of something as opposed to doing high quality of something.
So the MIPS provides physicians with positive or negative payment adjustments based on an overall performance score across four domains. They include quality, promoting, interoperability, improvement activities, and cost. But it's hard to integrate these health care quality measures and reimbursement models that truly account for all patient complexities and social determinants of health. Now, in non orthopedic studies, they've shown that lower MIPS scores for physicians who treat a higher proportion of medically and socially complex patient populations.
The lower score, though, means you don't get the same incentives. And that's a problem, obviously, if you're taking care of really, really complex patients.
So Medicare introduced something called the complex Patient bonus. And this aimed to adjust a physician's total MIPS score on the basis of the medical and social complexity of the patient population. The complex patient bonus was implemented to adjust for these differences, and it wanted to disincentivize providers from selecting against complex patients. And how do you define a highly social complex patient is those who are dual eligible for Medicare and Medicaid. And previous studies have shown that these specific patients who are dually eligible for both typically have worse outcomes, belong to minority populations, and potentially require more resources.
But there were no prior studies that evaluated the social complexity of an orthopedic surgeon's caseload and how it affected their MIPS score and the likelihood of receiving a positive payment adjustment. So the purpose of the study was to evaluate how orthopedic surgeon MIPS scores, demographics, patient care practice characteristics, and patient populations varied on the basis of patient social risk in 2017 compared to 2021. So they obviously use CMS data. And why did they choose those two years?
2017 was the first year that MIPS performance scores were published, and 2021 was the most recent year that MIPS performance scores were published. So we're in 2025, so they're cleared a little bit behind when it comes to that.
Now, what they use is the Distressed Communities Index, or the DCI as a measure of the economic well being of US Communities and neighborhoods. And it was assessed in combination with each surgeon's practice zip code to look at various components of neighborhood distress and poverty. The practice zip code for each clinician, which was based on billing, was derived from CMS data sets. And it also includes the primary practice location for the physician along with each provider's NPI number. And then the surgeon address was used to divide surgeons geographic into nine regions based on the U.S. census guidelines.
So what does it mean to get an adjustment? The CMS adjustments included a negative adjustment, a neutral adjustment or a positive adjustment or an exceptional performance bonus for the top performers. Surgeons were stratified into five social risk quintiles on the base of their dual eligible caseload. Dual eligibility again was used a proxy for social risk. So the highest quintile was the highest social risk. And again they looked at all the demographic, patient location characteristics, patient data and MIPS performance for 2017 versus 2021. And they compared differences using Chi squared, student T test and Wilcoxon signed ranked test and multivariable logistic regressions.
What did they find?
In 2017 there were 18,645 orthopedic surgeons and in 2021 there were 17,327 orthopedic surgeons. So in 2017, surgeons with the caseload at the highest compared to the lowest social risk had lower MIPS performance scores. So if you did more complex patients, you had lower MIPS scores and the highest surgeons with the highest social risk caseloads were more often to receive a negative adjustment and were less likely to receive an exceptional performance basis. A bonus.
On multivariable regression, orthopedic surgeons with the caseload at the highest social risk and an increased likelihood of a negative payment adjustment and a decreased likelihood of getting this exceptional performance bonus. Controlling for variables, the same things were found in 2021. There was a difference. So what was the difference here? Orthopedic surgeons with the highest risk patients compared to the lowest risk patients had actually higher MIPS performance scores. Now remember, they implemented that conversion factors are essentially looking at complex patients. Surgeons with the highest social caseloads often received a negative payment adjustment but also more often received an exceptional bonus performance, this was about around 76% versus 56% which is a pretty high percentage difference.
When they did multivariable regression, they adjusted for demographics and geography. There was no significant relationship found between social risk quantile and those receiving a negative payment adjustment or exceptional performance bonus. When they take out those variables, then the differences were not there.
Who were most likely to have the highest social risk patients? The surgeons typically were more often women, more concentrated in the Pacific west, more often had a D.O. degree, more recently graduated from medical school, worked with more partners in their practice, had fewer Medicare patients, performed fewer Medicare services annually, and worked in areas with higher Distressed Communities Index distress scores.
In conclusion, in 2017, if you had MIPS and you based on the MIPS and you were an orthopedic surgeon caring for a high risk social group you had lower MIPS score, but in 2021, this was reversed. Surgeons with the highest social risk group, meaning they took care of higher risk patients, had higher MIPS scores. But it was still more common for orthopedic surgeons caring for populations at the highest social risk to receive a negative payment adjustment compared to those treating populations at the lowest social risk. Even their scores were higher. It doesn't mean that they got a neutral or positive payment or exceptional payment bonus there. And that said, the demographics and practice patterns of those orthopedic surgeons who cared for patients at the highest social risk did seem to remain consistent in the four years time frame that the study was done. So I think it was good to be able to see MIPS and see what they've done over time. I would like to see this carried out further the other way.
[00:20:20] Speaker A: Yeah, I mean, fundamentally, you know, all this comes down to like economists and people who do not have MD degrees. And I mean this is a broad stroke and I understand that, but this is basically my stance. Like health policy thought leaders and economists tend to come up with these kinds of ideas about how we're going to enhance care delivery or incentivize this or incentivize that. But ultimately, at the end of the day, what none of these things ever take into account is human behavior.
And that includes the behavior of the surgeons and the behavior of the patients.
Because no one is compelled to have their care at any one particular location or place. And no surgeon is essentially compelled to do these types of elective interventions. And what this study can't get at because it's just using CMS data, is how much of the rest of that case volume is being made up by other insurance, you know, patients with other types of insurance product or in certain cases, you know, patients who are paying cash or, or whatever it may be. And you know, I think what you're seeing here, part of the signal, particularly around, you know, those with the DO degree, I think that's the marker typically for smaller hospitals and more rural hospitals, particularly or especially in orthopedics, maybe changing a little bit, but I don't think it's changing that much. And it's certainly not changing that much between 2017 and 2020. I mean, that's a four year window, isn't even enough to graduate a residency class. Right. Like why do you think that in like it's basically the same people except for people who are retiring by and large in 2017-2020. So the fact that things haven't changed, why would you expect that there would be like this drastic shift in that time frame. You need a much, much bigger time window to kind of look at these things which, which I, I know that you mentioned, but all of these incentive projects, because it's a zero sum game essentially is it's not based on equity. It's, it's based on high performers are going to get more resources and low performers are going to be penalized to some degree. So I think it's misplaced the, the idea that somehow there should be, you know, everyone should get paid the same for what they're doing is not what the, the program was intended to do. And we can all rationalize why I shouldn't be penalized for whatever it is that I'm doing. Right.
And I think that's fundamentally where these sort of demonstration projects and implement, you know, these, you know, we're going to do this and it's going to lead to improved care and this, this and this. But then you get the characteristics of which are, you know, they're not called out here, but it's things that you brought up, lemon dropping, cherry picking. The, you know, the high performing centers, they have the ability to select out who they want and then they say no, we don't, we can't help you, I'm sorry. And then that person has to go back to a different facility, a lower performing facility, a less excellent facility, and have the surgery there. And then that just compounds that. You know, this, this is a Venn diagram of the intersection of which you get the volume outcome issue, you get the surgeon experience issue. And you're seeing that here where these folks are more likely to have surgery with a recently graduated from medical school. That just means someone with less experience. Right. Less cases under their belt, less adept. Again, broad strokes, okay. We're talking about the average individual, not are there going to be. Is there ecological fallacy in the points that I'm making? Absolutely. But that's somebody who's probably going to be slower. Right. And all of those things.
[00:24:16] Speaker B: Less volume, maybe? Things like that. Exactly.
[00:24:19] Speaker A: Less volume, less experience, less capable of dealing with intraoperative surprises. Less familiar, less. All the, everything is less.
And then you get these calls for correction and that just basically defaults to the status quo ante, which is pay everyone the same with you.
[00:24:39] Speaker B: It's a very interesting model there.
[00:24:41] Speaker A: So I'm generally against like people who are not clinicians tinkering with the, the delivery of healthcare. That's. So you know that, that would solve that problem.
[00:24:53] Speaker B: That would help. I completely agree all right, Boots on the ground means a lot.
[00:24:58] Speaker A: The non clinicians tinkering in healthcare delivery is definitely on hold. Is now that your case on hold featurette? Is this going on hold? So risk of revision and patient reported outcomes following primary UKR performed using computer navigation or patient specific instrumentation analysis of national joint registry data by Farhan Alani and colleagues. It's 30 days free, so you don't have to take it from me and Antonia, you can draw your own conclusions when you read it yourself.
[00:25:33] Speaker B: I just want to point out that I'm very impressed that you chose 2 arthroplasty Related articles for your article.
[00:25:40] Speaker A: So, you know, from a few episodes ago, we really established that, you know, I'm kind of like a total joint guru after spending years working side by side with you in our shared, you know, clinic.
[00:25:55] Speaker B: So I learned spine, you learn joints. It was a good combination.
[00:25:58] Speaker A: I feel very comfortable not doing them, but conversating in the joint arthroplasty research space.
So this was a cool study. I thought it occurred in England, which I love. I love England. It basically was done in the njr, the National Health Service PROM program and the Office for National Statistics data sets. Three sources to analyze data on unicompartmental knee arthroplasties performed obviously in England. I don't know if that just includes England proper and not Wales, but essentially we'll say in England for right now.
So this obviously is very powerful data because they kind of have comprehensive surveillance and the primary analyses are focused on all cause revision following unit compartmental knee replacement performed using computer navigation or, or patient specific instrumentation compared with conventional techniques. And the approach relied on propensity scores derived through logistic regression model.
And so they have.
I just touched on this two papers ago in my headline. I don't love it when you have like we use this approach for this question and that approach for that question. Especially when you're talking about the same population of patients. I think it's better you're going to use a propensity score approach. You do the propensity score, you have the cohort of patients and then you work within that cohort. Not we do one propensity score for this question, we do another propensity score for that question. I think that's kind of very similar to just sort of multiple comparisons. You're just running all of these models, different models to see what pops out and that's what you're going to sort of build your narrative around. But for revision outcomes, they used age, sex, ASA score, Operation, funding, hospital setting, year of surgery, approach, fixation, caseload and usage.
And then in the model for proms, they say they were very similar to revision, but fixation was removed and preoperative OKs and EQ5D were included. That's important because those could be proxies, I think, for disease severity. So in the revision outcomes, I don't see that they really effectively have a way to account for, for disease severity. And that could really come into play because, you know, I can imagine, correct me if I'm wrong, but I can imagine that you'd want a computer navigation or, you know, maybe you'd want potentially to have custom approach, patient specific instrumentation when the case that you're dealing with is less than, you know, sort of normal, it's kind of outside the bounds of normal. Otherwise you'd go with like the conventional.
[00:28:50] Speaker B: Approach, unless you're still typically right. But some people use it for every single case, but they can be selectively used, which are typically harder cases, more deformity, etc. Etc.
[00:29:03] Speaker A: So for the person who's going to use it, no matter what, they're always using patient specific instrumentation or they, they don't know how to do the total joint without the computer guidance. Like if the computer breaks that, canceling the case for the people who are doing the conventional technique, they're only going to go to something else when there's a call for it.
And that's something unique about the patient. Right.
[00:29:27] Speaker B: Right.
[00:29:28] Speaker A: Now, if everyone, if all the surgeons who are doing just conventional are totally different, and we know that isn't true, but just to just gain it out, so to speak, if we're talking about the surgeons who do the computer, patient specific or conventional are all mutually exclusive from each other, then really you're just talking about examining the performance of these surgeons themselves rather than it devolves from a causal inference study. But we know that's not the case. I mean, by and large there's obviously overlap, but there is concern for confounding by indication and there is concern in that case for disease severity, which is going to be the primary driver around confounding by indication.
They then go into Cox proportional models that were used and then they say they have fixed effects for surgical technique, sex, age, year, surgeon caseload. So they're doing this layered analysis on top of their propensity score matching, which I think raises concerns for some things being too specific or overfit. And then they have, for the PROM analyses, the NHS digital case mix adjustment methodology was utilized. So you Know, it's very layered and that just introduces some degrees of opacity which makes it hard to unpack. And you can't always objectively decide, you know, whether or not you're going to invest in this or not based on whether or not the methodology was sound because it's sort of, there's a certain amount of concern about like, there's a lot of like window dressing and ignore the man behind the green curtain. Or is it all just, you know, very thoughtful, applied and erudite? I certainly can't judge. I don't think others, you know, can judge.
I will say that, that they do engage in what we have talked about as kind of a Harry Potter paradigm in the past, where they sort of have these, you know, basically they're looking at the propensity score matching as a magic spell. And you cast your propensity score matching spell and then you don't have to worry about confounding by indication. And they say that in the limitations where they say the potential for confounding by indication was mitigated by the use of propensity score statistical methods. But was it really? I'm not sure that it was. I just went through my concerns around that approach. So it's nice to just have that as a way to disarm any critiques from your reviewers or an editor. And they say confounded by indication, you say propensity score matching and then you've cast the spell that counteracts their critique. But that just because you say it doesn't mean that it is so.
And I don't think that based on my look, my first look at this, that they've really satisfied for me that their propensity score approach is really getting at the confounding by indication. So let's, let's go into their results.
So the hazard ratio for all cause revision was 1.13 essentially with computer navigation and 0.81 with patient specific instrumentation, neither of which was significant.
But the signal there, of course, is that there's a 13% increase in the hazard of revision. If you had a larger sample and they are very close, the lower bound is at 0.91. So if you had a larger sample and the findings hold, you're probably going to find that there's a higher risk of all cause revision with the computer navigation. Now granted, you know, this is already a very large sample, so. But still, and I think that that's probably just the signal that the risk at baseline is not the same across these, these patients. I think there's probably a higher risk at baseline, certainly among the surgeons who would normally just use a conventional technique.
And that's why there's also this. Computer navigation was associated with an increased risk of all cause revision.
It's not the use of the computer. I don't think based on other studies that we've seen in other areas, the computer is not causing you to need to do the revisions more.
[00:33:27] Speaker B: I mean if you.
I'm just thinking of like a robot, you know, I mean like you are doing this like the matrix or someone plugging into you and saying you better do this because it's going to mess you up so you can do more of it. No, I completely agree. And we have a common phrase for that. Right. Garbage in, garbage out, what we put in as the information. It's not that the robot or the navigation is telling you, oh, do this and then you mess it up. Right. We're putting those inputs in. So there's something about either trusting it or not having a feel of how to do it correctly or putting in the wrong inputs. But it's not that the computer is clearly just executing what you tell it to do.
[00:34:02] Speaker A: Exactly. I mean, if it was perfectly calibrated and everything is perfect, there should be no error. Everything that occurs in any kind of use of any kind of machine, from simple machines, simple pulley systems, to the most complex computer navigation you can have is going to be human error.
[00:34:21] Speaker B: Yep, agreed.
[00:34:23] Speaker A: Or random error, or random error in some way, shape or form.
So their conclusions just have like, kind of like a lot of what have you.
It seems like almost they don't, they don't really want to trust their data because they start talking about it's underpowered. And then the sensitivity analysis showed that computer navigation could worsen implant survival. But it was a small sample size. And then they basically say these, these findings highlight potential signals that warrant further investigation. But you know, their causal inference is not addressed at all. The likelihood that this is all driven by the baseline risk being different in terms of the potential for revision. In the very straightforward simplest case you can imagine it's going to take 45 minutes skin to skin, everything is going to be perfect. And I'm just going to do the conventional approach versus it's a horrible various knee with collapse in the medial compartment and all sorts of osteophytes and who knows, like prior fracture or something where there's loss of vascularity in the skin. Like, should I go on? Like, can I describe a worst case scenario?
The patient has a high asa.
They're also diabetic, poor blood sugar control.
So we're going to do the computer guided or we need to do patient specific instrumentation because of the compromise of one particular compartment.
[00:35:48] Speaker B: Agreed. It's not a perfect science. You can't truly match all that.
[00:35:53] Speaker A: You know, I think all in all, it's an interesting study. It allows us to discuss a variety of concepts, but their conclusions are going on hold.
[00:36:02] Speaker B: I agree with you.
[00:36:03] Speaker A: On hold.
All right, on to honorable mentions. We're running out of time. We're trying to go a little bit faster here. Measurement of value in uncomplicated total knee arthroplasty Patient level and provider level value analyses of a one year episode of care. This is by Klein Smith and colleagues.
So this study, they have an institutional patient reported outcome database that they queried for all patients who underwent total knee from 2020 to 2022. They had a total of 684 patients and they feel that they have identified patient and surgical characteristics that drive costs and patient reported outcomes. And they feel that PLVA can be used to identify what they call bright spots in orthopedic procedures to optimize care delivery. Surgery performed at an ambulatory surgery center and as an outpatient procedure were predictive of lower costs. I don't think there should be any surprise there. Patient specific instrumentation tibial stem extension were predictive of higher costs. Older age and male sex were predictive of less improvement. Increased junior scores from baseline to one year.
Next, we have high failure rates of polyethylene glenoid complex components in stemless anatomic total shoulder arthroplasty for primary and secondary OA by Kraus and colleagues. This was a study graded level 3 evidence 211 patients who underwent primarily stemless anatomic total shoulder using the eclipse humeral component with a cemented pegged all polyethylene glenoid. They have a median postoperative period of 72 months. Overall revision rate was 51%. Reasons for revision were glenide component loosening, periprosthetic humeral fracture, early rotator cuff failure and low braided infection.
So they highlight a high rate of glenoid component loosening as the primary cause of revision in patients with primary and secondary OA undergoing this procedure. It's basically a clinical retrospective of the author's experience with these particular implants.
And then when I saw this one, I was like, wow, we're doing this. Intraneural ganglion cysts arising from the hip joint as rare causes of sciatic neuropathy. A case series of 13 patients treated with hip arthroscopy by park and colleagues. So obviously, as in the title, this is a rote clinical retrospective to evaluate the clinical and radiographic outcomes of hip arthroscopy for the treatment of intraneural ganglion cell cysts involving the sciatic nerve.
[00:38:39] Speaker B: Of course, this is where we connect. We connect with hip spine syndrome in a different way.
[00:38:44] Speaker A: Now, of course, there's prospect for selection, indication, cluster expertise bias, but in these author's hands, in patients with sciatic neuropathy, the treatment showed that arthroscopic hip surgery is not only less invasive and effective, they say more effective, but I would just say in this context it's a level four evidence. We should just say it's effective treatment in their hands for relieving neuropathic pain and neurologic deficits associated with this sciatic nerve condition.
That's all we have for this month.
We will try and do better next month OR well, in two weeks when Dr. Chen has taken over. But hopefully your cases are all green and go in the middle of summer here and everything is moving smoothly or you're on vacation or you're out for a run. But you can trust when these studies come up in here, all our cases are still on hold.
