Monday, March 21, 2022

Opioid Equianalgesic Tables are Broken

by Drew Rosielle (@drosielle)

I am proposing we do away with equianalgesic table (EAT) as a tool to inform clinical decisions about opioid rotations/conversions. Fundamentally, EATs create too many problems, and there are simpler and safer ways to teach clinicians how to convert between different opioids.

Part 1: New Data Can't Fix the EAT

A couple HPM fellows every year ask me which table do I prefer to use—the old EAT or the new one? By the old one, they refer to the table most of us used or were at least deeply familiar with for the last 10-20 years. By the new one, they mean the one created by Dr. Mary Lynn McPherson, PharmD in her landmark book Demystifying Opioid Conversions, 2nd Ed. If you haven’t read the book, please do, it’s really one of the best things ever written about opioid conversions. My disagreement with the book, which is admittedly a big one, is that the book promulgates the use of EATs, and my entire argument today is really in some ways inspired by the conversations I’ve had with my fellows and others as we grapple with the new EAT in her book. (From here, I’ll refer to the tables as ‘Classic’ and ‘DOC2’.)

Fundamentally, my argument is EATs themselves are intrinsically flawed: they ‘force errors’ in a way that’s entirely unnecessary and avoidable if one just doesn’t use EATs as clinical decision aides (I’ll elaborate on that statement below). Dr. McPherson’s curating and teaching new, reliable clinical data is critically important, yet better data in a flawed model like the EAT creates as many problems as it solves. I feel a little awkward putting all this out like this. Dr. McPherson is one of my heroes. The DOC2 EAT crystallized concerns I’d had over the years, and the followinghas come from conversations about EATs prompted by me and my colleagues grappling with a new table. The new DOC2 EAT is clearly better in important ways, but I also think recapitulates all the problems intrinsic to EATs, so much so we collectively should consider doing away with them.

The 2010 table is the Classic EAT, the 2018 table is the DOC2 EAT. You can see it’s changed throughout, but the big changes are the around hydromorphone, with the biggest changechanging the20:1 PO morphine:IV hydromorphone ratio to a 12.5:1 ratio.

The big changes in the DOC2 EAT are based on this study by Dr. Akhila Reddy and colleagues looking at real life data about converting hospitalized cancer patients from IV hydromorphone to PO morphine, PO hydromorphone, or PO oxycodone. Reddy et al found that the average IV HM to other drug ratios were 1:2.5 for oral hydromorphone, 1:11 for oral morphine, and 1:8 for oral oxycodone (meaning in this population, a patient on 10 mg/24 hours of IV HM, on average would end up on 25 mg PO HM / 110 mg PO MS / 80 mg PO oxycodone per 24 hours). Importantly these doses are lower than what the Classic EAT would predict, and for PO morphine and hydromorphone, a lot lower (Classic EAT promotes a 1:5 ratio for IV HM:PO HM; 1:20 ratio for IV HM:PO MS; and 1:10 ratio for IV HM:PO oxycodone)!

To the best of my knowledge, the DOC2 EAT exists largely to better account for these ratios going from IV hydromorphone, considering that Reddy et al is undoubtedly the best/biggest real-patient study looking at this very relevant real-life clinical question. (It also tweaked the IV to PO morphine ratio in a small manner, going from 1:3 to 1:2.5. Probably small enough not to matter for patient care, but enough to make for more challenging math, but that is a small quibble).

I want to observe that I think the Reddy et al study is a landmark study, and I think it provides the best evidence we have of ‘real-world’ “equianalgesia” when going from IV HM to those other opioids. I absolutely think we should all be modeling our clinical practice of opioid conversions on these data as best we can!

The problem is, however, when you try to ‘map’ these data onto the rigidity of an EAT, in which every ‘relationship’ between opioid doses/routes is fixed and bidirectional, you encounter all sorts of problems, ALL OF WHICH exist due to fact that you are using an equianalgesic table in the first place. What I mean is that all the problems that are introduced by creating a new EAT from this study, merely exist because we, as a professional community, have decided that EATs are the best format for presenting information to guide opioid conversions. The fixed and bidirectional nature of EATs itself creates the problem, and my argument is that all this will go away if we discarded EATs entirely as teaching tools/clinical decision aides for opioid switching.

Let’s look at the IV HM to PO MS ratio in the EATs as an example. In the Classic EAT there is a 1:20 (look at the table: 1.5:30 is a 1:20 ratio). Meaning you’d convert 1 mg of IV hydromorphone to 20 mg of PO morphine, and you’d convert 20 mg of PO morphine to 1 mg of IV HM (before dose-adjusting due to ‘incomplete cross tolerance, patient factors, safety, etc).

Reddy et al gave us good reason to believe that when converting from IV HM to PO MS, that 1:20 is too aggressive: the morphine dose is too high. So, if you change the EAT from 1:20 to 1:12.5 which is what the DOC2 EAT recommends, you get a conversion that’s more in line with current knowledge.

So far, so good! But, now look at what happens when you use the tables to convert from PO MS to IV HM. 100 mg PO morphine in 24 h converts to 5 mg IV HM in 24 h using the Classic EAT ratio of 20. Using the DOC2 EAT ratio of 12.5 you get 8 mg of IV hydromorphone. We’ve all of a sudden made the conversion in the reverse direction far more aggressive than it previously was with the Classic EAT! Is there any data to support being far more aggressive in this direction? No, not that I know of; the data are only about going from IV HM to other opioids, not to IV HM.

So, in making a conversion more conservative/more evidence based in one direction, we’ve made it substantially more aggressive in the opposite direction, and done so without any evidence backing it up.

The same thing happens with IV to PO HM. Reddy et al suggests IV HM going to PO HM is a 1:2.5 ratio which is reflected in the DOC2 EAT (Classic EAT has a 1:5 ratio). However, that makes a user of that table use twice the amount of IV HM when going from PO HM than the Classic EAT suggests. This doubling-of-how-aggressive-we-should-be with that conversion, as far as I can tell, is not based on any data, and I argue it’s no small thing to put out a EAT which encourages doubling the dose of IV hydromorphone compared to prior practice without safety data, but it’s an unavoidable byproduct of the Reddy data being forced into the format of an EAT.

My point here is that using an EAT forces us to do this. There is no way around this in the format of an EAT: the fixed bidirectionality of EATs causes this. So—why do we even use EATs?

The best data we have about going from IV HM to PO oxycodone is that it’s a 1:8 ratio. However, the new DOC2 EAT uses the Classic EAT 1:10 ratio. Why? I’m assuming it’s an effort to keep the DOC2 EAT coherent. Think about what it would look like if you used a 1:8 ratio. In the DOC2 EAT, 2 mg of IV HM would map to 16 mg of PO oxycodone. But that means that 16 mg PO oxycodone would map to 25 mg of PO morphine! What a bleeding mess!

And that’s the point: EATs exist in some sort of fantasy universe in which we have for some reason assumed, based as far as I can tell, on no data whatsoever, that the RATIO of morphine to hydromorphone, has to exist in a fixed and eternal relationship with the relationship between, say, oxycodone and hydromorphone. Based on EATs, if you tweak the relationship between oxycodone and hydromorphone it impacts the potency relationship between morphine and oxycodone! They all are forced to shift together because the whole idea behind an EAT is that every value in every cell of that table is ‘equianalgesic’ with the other values.

Why? I don’t see any data to think this has to be the case, at all. All of this strikes me as an overly simplistic and rationalistic assumption of how incredibly complex things work, that creates all of these issues I’ve been elaborating above when you try to do the right thing, which is what Dr McPherson did when she updated her EAT based on good data! The problem is that the format of the EAT itself undermines any effort to use better data to improve it.

Dear colleagues, we don’t have to keep doing this to ourselves. There is another way, some of you are probably already using different methods anyway, and I’ll propose one in a bit, but first I want to make one more argument against EATs in general: they force people into doing unnecessarily complex math.

Part 2: Complex Math is Complex

EATs encourage people to do unnecessarily complicated math. I’m sure I’m not the only greying palliative clinician who’s realized the last several years that a large number of the fellows and residents I work with don’t actually know how to use an EAT. They instead use opioid conversion smartphone apps or websites. Years ago they didn’t know how to use EATs either for the most part, what’s changed now is that they can avoidlearning how to use them completely due to the software converters. (By the way, every single app/website I’ve looked at base their math on EATs and so have in them all the fixed bidirectional problems I mentioned above).

A few months into the fellowship year when I’m working with fellows and one is using an app I’ll ask them to walk me through an opioid conversion calculation. It’s not uncommon for me to realize that they just can’t do it. These are physicians, these are people who are highly educated and know their way around algebra, but they still avoid doing EAT math because it’s clunky, it’s easily confusing, and legitimately easy to make mistakes. This is because they are taught that the way you use EATs to calculate the dose of the new opioid is this:

Holy smokes we make them do cross multiplication!

I’ve learned that cross multiplication makes some people really uncomfortable. And frankly I don’t blame them—it’s easymake mistakes— it’sto multiply when you should divide, etc. I myself, long before my current thoughts about wanting to discard the clinical application of EATs began to solidify, had just completely stopped using cross multiplication in doing these conversions over a decade ago. I have always looked at the table, calculated a conversion factor, and just used that.

For example, looking at the Classic EAT, it suggests 1.5 mg IV hydromorphone is ‘equipotent’ to 30 mg PO MS. That’s a conversion factor of 20: 1.5 x 20 = 30, right? So if a patient was on 10 mg of IV hydromorphone, I’d just multiply that by 20 to get 200 mg of PO morphine, and go from there (adjust the dose down for patient safety reasons etc). I would’t bother doing cross multiplication at all.

This math is much easier to my brain, and also easier to ‘gut check double check’ your math, because you ‘know’ a morphine dose is 20 times the HM dose. With cross-multiplication, those relationships are less obvious, and so easy to invert, especially for novice users, who don’t have an intuitive sense for instance if PO hydromorphone is more or less potent than, say, IV morphine!

Making people to cross-multiplication was a huge pedagogical error. Thankfully, it’s totally unnecessary.

Part 3: Conversion Tables as a Better Tool (?)

All this adds up to my modest proposal: that we do away with EATs entirely as we teach others how to convert between opioids, and replace them with Conversion Tables, that aren’t really tables, just a condensed clinical decision aide in prompting clinicians with easy math and individualized, conservative conversion recommendations that are specific to each conversion. For instance, the conversion tip for going from IV hydromorphone to PO morphine exists independently of the conversion tip for any other conversion including even a different ratio for converting from PO morphine to IV hydromorphone.

I want to acknowledge that I did not invent this idea. I’ll be honest, I’m not sure where in the depths of my memory it came from, but in some ways it’s such an obvious idea I hope when I post this people will comment with examples of opioid rotation decision tools which use the conversion factor approach instead of an EAT! I’m sure it’s out there. If anything is novel about what I’m writing today, it’s mostly in the call to broadly give up on the EAT as a clinical decision tools, not the novelty of using conversion factor tables.

From all this, I’ve mocked up a Conversion Table (pics below of an abbreviated pocket card sized Table and part of the full, annotated Table, with all my explanations and anxious thoughts about the factors, and links to more detailed (but not ready for clinical implementation-see below) versions: Dropbox, Google Drive).

This is one section of the longer, annotated Table:

Benefits and Caveats of Conversion Tables:

• Every single conversion can have its own ratio, and that ratio can evolve/change over time (due to new data or clinical wisdom) easily, without messing up any of the other conversions. You can change the IV HM to PO morphine conversion without that having ripple effects on every other conversion!
• Along those lines, we can have different ratios going from, e.g., IV HM to PO morphine than when we go from PO morphine to IV HM. My argument is that a key reason to do it this way is safety, mainly because if we as a professional community tighten up one conversion it doesn’t force us to ‘liberalize’ its reverse.
• It prompts easier math. Every conversion is just one multiplication or division problem.
• This is true in my opinion of all opioid conversions, regardless of which table you are using: we should increasingly become more and more cautious in all opioid conversions the higher the doses that are involved. I personally think all conversion tables and EATs should have a huge warning on them that says something like “DO NOT USE THESE RATIOS FOR OPIOID DOSES HIGHER THAN 150 MG OF ORAL MORPHINE EQUIVALENTS—CONSULT AN EXPERT” or something like that. (Feel free to argue with me about the 150 mg figure—I don’t myself know what the limit should be other than that number is something that feels good in my gut but that’s all.) I wrote about this in detail in 2019. The bottom line is that I think we should treat all opioid conversion ratios as non-linear at high doses, like we do with methadone at all doses, and that every single guide to opioid conversions needs to SCREAM this.
• Along those lines, I can imagine someone saying about all this, “Look, does it really matter which table we use because, let’s say we’re going from PO to IV hydromorphone, does it really matter if the final answer is 2 or 4 mg!” I would say I am highly sympathetic to that perspective. Look, the reality is, at lower doses, for the vast majority of our patients, it honestly doesn’t make that much of a difference! But, my argument is, the higher the doses involved, the more this stuff really starts making a difference. I am NOT going to lose sleep over if my fellow puts a patient on 2 vs 4 mg of IV hydromorphone a day. I am going to lose sleep over 20 vs 40 mg of IV hydromorphone a day though! This is why 1) I think whatever we put out there for the non-specialist clinician to use needs to be conservative because clinicians will use these at high doses if we put them out there, and 2) we need to signal/emphasize/warn on EATs or Conversion Tables that they are not to be applied at those higher doses! Honestly, I think that’s an even more critical safety practice than fine-tuning the actual conversion ratios.
• Please look through the draft/mock ups of the conversion tables. I don’t mean these to be implemented in clinical settings currently!! I have spent more time arguing with myself about the actual numbers (conversion factors) I put down on paper in those tables than I have writing this cruelly long blog post (you can see some of my comments about my decisions in the Annotated Tables—I’ll let you know that at one point these included conversions around IV fentanyl and I just gave up it’s such a mess). Part of the debate I have with myself is, from a pedagogical standpoint, just how conservative should these numbers be? Should we automatically make them so conservative that we can say that for the ‘average patient’ you don’t need to dose-reduce further? Should we still even be incorporating ideas of incomplete cross tolerance, which, I probably don’t need to tell you, is an incredibly complex, and nuanced thing we inflict upon others. I.e., we show people these tables, tell them to use them, then say, "Oh by the way once you’ve done that you need to make a highly complicated, patient-specific decision about manipulating the final dose ever further, based on these TEN DIFFERENT FACTORS!" I do wonder if we can do better than that.
• I’m not sure what the right answer is here. Certainly, I’m not the one to be deciding this, either! My dream would be that something like this is workshopped and researched with clinical and pedagogical leaders then tested in some way for safety, and I think the ‘best answers’ as to how conservative these numbers should be would come out of that dream process. What I’ve mocked up are things I’d feel comfortable handing a fellow personally, but I just want to be clear to the world I am not proposing my draft tables as anything more than an example of what a hypothetical conversion table could look like in the future. That’s it.

I’m very curious what people think about this. Who’s already moved away from EATs to conversion tables or something similar? What do people think of my draft Tables? I am not an investigator, but if someone who is, and knows how to lead an investigation into validating something like this (not that EATs themselves were ever validated), please ping me. I’d love to chat. I smell a Delphi study!

Post-script, because I sure someone is going to bring this up, and rightfully so: I’m not arguing that we entirely discard EATs for every single application out there, just the clinical/pedagogical ones. They arguably have a role in research and public health—e.g., the ideas in EATs give us a way to calculate OMEs/MEDDs (24 h oral morphine equivalent dose). I think the idea of the OME has its utility at least in research contexts, and we need some way to calculate it, and sure an EAT is an ok way to do that, although I’ll also point out that the OME has been weaponized in the last several years by those who insist, for example, that an OME of 50 mg is some sort of evidence-based hard limit on safe opioid dosing, and the insidious carceral/cops-in-medicine ‘tool’ that is the NarxCare Score.

For more Pallimed posts about opioids.
For more Pallimed posts by Dr. Rosielle click here.

Drew Rosielle, MD is a palliative care physician at the University of Minnesota & M Health Fairview in Minneapolis. He founded Pallimed in 2005. You can occasionally find him on Twitter at @drosielle.

Monday, March 21, 2022 by Drew Rosielle MD ·

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