Tuesday, August 4, 2026

Use the National Denial File to Assess "Claim by Claim" Coding of PLA Codes

 While some PLA codes have high dollar volumes and specific coverage under LCD or NCD, most of the 600 PLA codes do not have policies - so they are under "claim by claim" review, as CMS says.  Let's cal lit CbC.

I think that CbC review usually means either (A) autopay or (B) autodenial, with relatively few codes getting true manual review of actual medical records.

Let's test it with the Denials Database (here).

I filtered 2024 for codes ending in "U" - PLA codes.

This gave about 3400 lines downloaded into XLS.  (Be sure to convert from csv to xls soon.)

Column i (eye) is submitted claims, column L is denied claims.  I mean a new row "S" which is denied/submitted.

Because CMS shows 0 denied by "*", the percent denied when zero comes ot as "value!" rather than "0".

ZERO DENIALS

About 2700 lines had 0 denied (suggesting autopay).

I deleted claims with 0-10 claims (CMS had already deleted all of those that were not 0,)

This left 541 lines. About 83 lines had >100 claims, up to 629.

 Total services were 31,017 and total payments were $13M, average $419.

10-20% DENIALS

About 132 lines had 10-20% denials.

Total services were 75,660 and total payments were $27M, average $363.

45-55% DENIALS

Only 25 lines had 45-55% denials.

Total services were 2961 and total payments were $452,000, average $153.

80-90% DENIALS

Only 20 lines had 80-90% denials.

Total services for these persistent hopeful labs were 5015 and payments $130,000, at $26.

100% DENIALS

Here, we pop up to about 245 lines with 100% denials.

Total services for these scalawags were 22,730

Actually, they're not scalawags, because about 80% of these submitted with the GZ modifier, which expects "denial." In the other groups, nearly nobody used GZ modifier.

We have no payments, but charges were $35M, so under normal circumstances, payments might have been in the $15M range.  If so, implied average payment per service for the 22,000 services might have been $650 each.  (But again, these were 100% denials, no payments).

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100% Denials - Deeper Dive - What Codes?

Here's a look at some of the highest-volume services in the 0 payment, 100% denial filter.

Some may have been submissions of known-to-deny claims, such as for appeal rights.

Others likely represent tiny percentage of all claims for large test,where the code may have had many payable lines, but here we see one odd line with 0% payments.  





01106T - exclusive or inclusive algorithm text

 

1106T  Oncology (breast), augmentative algorithmic analysis of histomorphologic features in digitized hematoxylin and eosin stained slides from formalin-fixed paraffin-embedded (FFPE) breast cancer tissue, algorithm-derived parameters reported as 5-year and 10-year risks of distant metastasis, including digitization of glass microscope slides and hematoxylin and eosin staining of tissue sections, when performed.


This CPT code is silent as to whether the algorithm EXCLUDES a test which uses both histomorphology and cliniccal features like "tumor size".   Does it imply only "histomorphologic" algorithms be coded here, or can algorithms with also clinical features be aloud on the groounds they are not excluded and merely silent.   PS _ i read the question as difficult and perhaps splitting experts.


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CHAT GPT


I agree this is a difficult coding interpretation. It is exactly the sort of question that reasonable CPT experts could disagree about because the descriptor does not expressly answer it.

Here's how I would analyze it.

The descriptor

"Augmentative algorithmic analysis of histomorphologic features in digitized H&E slides..."

The object of the algorithm is stated to be histomorphologic features. It does not say:

  • histomorphologic and clinical features,

  • multivariable prediction model,

  • clinicopathologic model,

  • or "using all available patient information."

That tends to favor a narrow reading.

But it does not say "only"

On the other hand, CPT descriptors often describe the principal work without attempting to enumerate every internal computational step.

Suppose an algorithm:

  • extracts hundreds of image-derived features from H&E,

  • then combines them with

    • age,

    • tumor size,

    • nodal status,

    • ER status,

before generating the final risk score.

Nothing in the descriptor explicitly says this is prohibited.

Arguments for inclusion

Someone favoring use of 1106T would probably argue:

  • The service remains an algorithmic analysis of histomorphologic features.

  • Clinical variables merely calibrate or refine the prediction.

  • The dominant source of information remains the digital pathology algorithm.

  • CPT descriptors generally do not police internal mathematical architecture unless they specifically do so.

This is actually common in laboratory medicine. Many genomic algorithms incorporate age or sex without changing the CPT code.

Arguments against inclusion

The opposing argument is also strong.

One could say:

The descriptor intentionally identifies the analyte as histomorphologic features.

If the reported risk is materially generated from a clinicopathologic model rather than image analysis alone, then the reported result is no longer simply an algorithmic analysis of histomorphology.

The service has become a different category of test.

That argument gains force because the CPT Editorial Panel has recently become much more concerned about distinguishing:

  • laboratory measurements,

  • algorithmic analyses,

  • software-only analyses,

  • multicomponent predictive models.

Analogies

This resembles other CPT interpretation problems.

For example:

  • A genomic assay does not stop being a genomic assay merely because age is incorporated into the report.

  • Conversely, if an algorithm combines genomic data with imaging, laboratory values, and EHR variables into a single integrated prediction, many CPT experts would hesitate to call it solely a genomic test.

Where that line falls is often judgment rather than explicit CPT instruction.

My reading

If I were asked to interpret the descriptor without additional AMA guidance, I would probably adopt this principle:

The algorithm may incorporate limited ancillary clinical variables, provided the essential analytical work remains the extraction and interpretation of histomorphologic features from the digital slides.

However,

if the clinical variables materially contribute to the prediction such that the result is better characterized as a clinicopathologic prediction model than as histomorphologic analysis, then the fit with 1106T becomes questionable.

Confidence

I would characterize this as roughly:

  • 40%: descriptor should be read narrowly—image-derived features only.

  • 60%: limited clinical covariates are permissible if the algorithm remains fundamentally a histomorphology algorithm.

The real answer would likely depend less on the wording of the descriptor itself than on the CPT Editorial Panel's intent, which might be reflected in:

  • the original Category III application,

  • CPT Assistant commentary,

  • Editorial Panel discussion minutes,

  • or future coding guidance.

So I think your instinct is right: this is not a question with an obvious textual answer, and it is one that could legitimately split experienced CPT coding experts.


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CLAUDE

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Short answer: experts are likely to differ — but I'd put it 70/30 against, and the strongest evidence is sitting in the same tab.

The decisive point isn't abstract canon-of-construction reasoning; it's internal to the code family. The bladder codes released alongside 1106T read "augmentative algorithmic analysis of histomorphologic features in digitized slides from FFPE bladder cancer tissue (high-grade, non-muscle invasive), with clinicopathologic variables entered by the qualified health care professional." The Panel demonstrably had the vocabulary, understood the distinction, and deployed it where it applied. Its absence from 1106T is therefore not innocent silence — it is an omission made against a live, adjacent alternative that the drafters were staring at the same afternoon. That's about as clean an expressio unius setup as CPT ever gives you.

The second-order support is the placement of "when performed." The descriptor flags optionality exactly once, and it attaches to digitization and H&E staining — the workflow envelope — not to the analytic inputs. The drafters showed you where they meant to leave slack, and it isn't in the algorithm.

Third, note that "tumor size" almost certainly isn't a histomorphologic feature under the Panel's own working vocabulary. Grade, mitotic index, tubule formation, stromal TIL density — those are histomorphologic. Size and nodal status are precisely what the bladder descriptor calls clinicopathologic. The taxonomy is right there in the family.

Now the case for the permissive reading, which is not frivolous. The descriptor lacks any restrictive limiter — no "solely," no "alone," no "without." CPT descriptors are definitions, not exclusion lists, and elsewhere in the code set additional work not named in the descriptor doesn't disqualify the code; it's just uncompensated. A multimodal breast test genuinely does everything 1106T recites: digitizes the slide, runs an augmentative algorithm over histomorphologic features, and reports 5- and 10-year distant-metastasis risk. On that view the clinical variables are a refinement, not a different service. And the alternative — 88399 or 81599 unlisted — is commercially close to a death sentence, which supplies considerable motivated-reasoning pressure toward "not excluded, therefore allowed."

The rebuttal is that for algorithmic codes the algorithm is the service. In surgery, an extra maneuver is incidental to a described procedure; here, changing the input set changes the intellectual content of the reported output. That's the organizing logic of MAAA coding and Appendix O generally: test identity is defined by what goes in, not merely what comes out. Two tests reporting the same endpoint from different input sets are two services.

Where this actually gets resolved, though, is not textual. Under MolDX the Z-code/DEX registration does the discriminating work, and a contractor can perfectly well pay 1106T-plus-Z-code for a multimodal test without ever adjudicating the descriptor question — the code becomes a container and the registry becomes the definition. So "correct coding" and "what gets paid" can diverge here for years without anyone forcing the issue. Meanwhile the AMA's own answer, if you put it to CPT Network, would almost certainly be: the descriptor describes analysis of histomorphologic features; if your test requires entry of clinical variables, file a code change application — and here is the bladder code proving we'll write one for you.

Which is the real tell. The permissive reading isn't wrong because it's illogical; it's weak because the Panel has already shown, in the same release, exactly how it says the thing 1106T doesn't say.

WAIT

I agree with you, but I think the question is subtlely different. it is not whether the drafters had a habit of including those variables, when they were aware of them. The question is whether this code can be used with a est that includes tumor size as an (unstated0 input. Perhaps we could think of a lot of unstated inputs necessary to run a test or do a procedure.


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You're right, and the reframing is the better question. Drafting habit tells you what the Panel would have written; it doesn't tell you whether an unnamed input is disqualifying. And you're correct that every code is silent about a great many things: 81519 doesn't mention reference genes, a colonoscopy code doesn't mention insufflation, 88360 doesn't mention that someone had to pick the field. Silence about inputs is the normal condition of CPT, not an anomaly demanding explanation.

So the real test isn't stated-vs-unstated. It's enabling versus constitutive — and I'd put three axes under that.

First, does varying it change the reported number? Hold the slide fixed and change the input. Scanner model, stain lot, nuclei count, tumor area annotation, QC thresholds — the answer doesn't move, or moves as noise. Tumor size in a Cox-type model moves the 10-year risk materially and monotonically. An input that is patient-specific, independently prognostic, not derivable from the slide, and additive in the model is a determinant of the answer, not a means of obtaining it. That's the category the descriptor is defining when it names "histomorphologic features" as the object of analysis.

Second, gating versus weighting. This is where a lot of the intuitive force of your point lives. A lab needs ER and HER2 status to know the patient is even eligible for the test; it needs specimen adequacy, fixation time, block selection. Those are unstated inputs genuinely necessary to run the test, and nobody thinks they belong in a descriptor. But they gate — they don't weight. They select who gets the analysis; they don't enter the computation. Tumor size as a model term weights.

Third — and this is the strongest argument for your permissive reading, better than anything I gave you last round — upstream input versus downstream calibration. The descriptor demands output "reported as 5-year and 10-year risks of distant metastasis." Not a score. Not a stratum. Absolute, time-indexed risk. You cannot get absolute risk out of morphology alone; you need a baseline hazard, and a baseline hazard implies a population anchoring that in practice usually means age, at minimum, and often nodal and endocrine-therapy context. The descriptor's own output specification arguably presupposes clinical inputs. If a test's algorithmic analysis is purely histomorphologic and clinical variables are applied only afterward to convert a morphologic score into an absolute risk, an expert could say — quite defensibly — that the algorithmic analysis of histomorphologic features is exactly what the code describes, and the calibration step is the unremarkable arithmetic of turning a hazard ratio into a percentage.

That's a real carve-out, and it's narrower than "tumor size is fine because nobody excluded it." A test that runs a multivariable model with size as a coefficient sits outside; a test whose morphologic classifier is calibrated to absolute risk using age plausibly sits inside.

So I'd revise my verdict this way: experts will still differ, but the disagreement should be about whether the clinical variable is constitutive of the risk estimate or ancillary to producing it — not about whether silence implies permission. Which also happens to be the fight that CPT Assistant or a code change application could actually settle, and the one a lab's coding memo should be arguing on the merits rather than on the absence of the word "solely."

Sunday, August 2, 2026

Tracking Abrupt Changes to Remote Monitoring and its Software

 Here's a linked in post from Kelli Stovall RPh on the pharmacy angle:

https://www.linkedin.com/feed/update/urn:li:activity:7488560277790253056/

But see also connection to Nixon Peabody 6pp memo:

Homepage:

https://www.nixonpeabody.com/insights/alerts/2026/07/22/medicare-proposes-changes-to-remote-patient-monitoring-and-remote-therapeutic-monitoring-services

And as 6pp pdf:

https://www.nixonpeabody.com/-/media/files/alerts/2026/07/medicare_proposes_changes_to_remote_monitoring_services.pdf

Here, memo from ATA on RTM cutbacks:

https://www.americantelemed.org/press-releases/ata-action-sounds-alarm-as-cms-pfs-proposed-rule-threatens-to-reverse-bipartisan-congressional-gains-on-remote-patient-monitoring/


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Don't forget the July 2026 proposals aren't in a vacuum.  

See supposed risks and drawbacks of RTM from my May 2025 blog:

https://www.discoveriesinhealthpolicy.com/2025/05/remote-monitoring-peterson-center-oig.html

Note: I also have some private notes and assisted searches also from earlier in March 2026:

https://www.blogger.com/blog/post/edit/8334321271827217759/6434864259285744275


Handy Citations on Screening - Not Saving Costs

 Here's a tag at Linked In from Jeff Levin-Scherz, around August 1, 2026:

https://www.linkedin.com/posts/jlevinscherz_how-should-we-think-about-the-cost-of-cancer-share-7485631887173001216-_KsM/ 


It pulls up a Substack article, July 26, 2026, by Levin-Scherz, "How should we think about the cost of cancer screening?"

https://employercoverage.substack.com/p/how-should-we-think-about-the-cost-d49?triedRedirect=true

Which kicks off, in part, from a Ann Intern Med article,  The Annual Cost of Cancer Screening in the United States, Halpern et al.

https://www.acpjournals.org/doi/epdf/10.7326/M24-0375

Toward the end see a callback to Levin-Scherz on Substack, from July 2024, on "fallacy of cost savings" -

https://employercoverage.substack.com/p/the-fallacy-of-future-cost-savings

See the home page for his Substack here:

https://employercoverage.substack.com/


Tuesday, July 28, 2026

SIDEBAR: Valar Comment on Digital Pathology OPPS Pricing (As Sidebar)

 SIDEBAR to July 28 blog


Yes. The interesting thing about the Valar Labs comment is that it is not attacking CMS’s new SaMS framework at all. It embraces the framework and argues that CMS applied it inconsistently to Valar’s two bladder tests.

What is Valar’s pricing problem?

Valar has three new H&E/AI oncology services:

Valar test2027 CPTCMS proposed APCProposed paymentValar asks
Vesta Bladder Risk Stratify1063T / X568T1542$350.50APC 1509, $750.50
Vesta Bladder BCGPredict1064T / X569T1542$350.50APC 1509, $750.50
Vitara Pancreas ChemoPredict1097T / X614T1509$750.50Keep $750.50

So the practical request is quite simple: more than double payment for each of the two bladder tests, from $350.50 to $750.50. The pancreas test is already where Valar wants it.

Valar thinks CMS has somehow treated the two bladder codes as related/similar procedures and effectively subjected them to a roughly 50% reduction. The letter says this appears to reflect a misunderstanding because the two tests are separately ordered, produce separate reports, and answer different clinical questions: one is prognostic for recurrence/progression and surgical decisions; the other predicts response to BCG.

What rules does Valar say should apply?

Valar's argument is essentially four layers.

First, O1 separate payment. Valar strongly supports CMS's proposed new status indicator O1, with payment specifications like status indicator S: SaMS gets a separate APC payment, rather than being packaged. That is particularly important because these tests are performed after the hospital encounter and their results affect later oncology management.

Second, New Technology APCs are the right interim home. Valar accepts CMS's premise that ordinary clinical APCs don't fit algorithm-driven services very well. New Technology APCs are therefore a reasonable bridge while CMS collects claims/cost information and develops a permanent SaMS methodology. That tracks CMS's own stated transitional approach.

Third, comparability should drive the APC assignment. Valar doesn't have an established CLFS price for these brand-new Category III codes. So it says CMS should look to similar H&E AI oncology services, particularly 0220U, 0376U, 0414U, 0418U, 0512U and 0513U—all of which CMS proposes to put in APC 1509 at $750.50. It gives especially strong weight to new Category III codes 1106T/X623T and 1107T/X624T, whose descriptors are almost twins of Valar's bladder codes except for tumor site and clinical endpoint; CMS also put those at $750.50.

Fourth, don't apply a multiple-procedure-type discount to the two bladder tests. This is a particularly good argument under CMS's own proposed architecture. CMS defines O1 as “separate APC payment.” Valar's point is that 1063T and 1064T are independently useful tests rather than two components of one procedure. CMS itself proposes O1 as the separately payable SaMS indicator.

There is also an older packaging argument: Valar cites CMS's prior rationale for separately paying some cancer algorithm tests because they are relatively disconnected from the encounter where the specimen was obtained and inform subsequent treatment.

And yes—the tables are unusually useful

Pages 5–7 are probably the most reusable part of the comment. Valar essentially builds a mini-reference table for H&E computational pathology reimbursement.

For each service it supplies:

code/test/company → specimen → workflow/resources → algorithm methodology → proposed APC/SI/payment.

It covers six existing PLA H&E-AI services:

0220U PreciseDx Breast; 0376U ArteraAI Prostate; 0414U LungOI; 0418U PreciseDx Breast Biopsy; 0512U Tempus p-MSI; and 0513U Tempus p-Prostate.

Then it adds Valar's three tests and the two very similar new Category III breast/prostate codes 1106T and 1107T. That's an 11-row comparison table, and it is much richer than CMS's table because Valar has supplied the operational details—accessioning, QC, pathologist review, WSI digitization, image transfer/storage, GPU inference, report generation, etc.

For digital-pathology work, that table is quite valuable independent of Valar's lobbying position.

Does Valar call out CMS's strange Table 62?

Surprisingly, essentially no.

CMS's actual Table 62 contains exactly 10 codes, not eleven. CMS says it selected them according to a purportedly simple rule: if the descriptor contained no laboratory method and only algorithmic analysis, CMS classified it as a SaMS laboratory analysis.

But the table itself is rather obviously troublesome. Among the ten are:

  • 0511U, whose descriptor actually says “tumor cell culture in 3d microenvironment”—rather hard to characterize as having no laboratory method.

  • 0208U, which expressly describes mRNA gene-expression analysis of 108 genes from an FNA specimen.

  • 81416, which is sequence analysis of a comparator exome—again, rather conspicuously a sequencing code rather than merely an algorithm running on previously generated data.

That seems internally inconsistent with CMS's own sentence immediately preceding the table: no laboratory methods in the descriptor; only algorithmic analysis. CMS also inexplicably puts 81416 in APC 1575, $10,001–$15,000, while the genuine H&E-AI codes cluster at APC 1509.

Valar does not take CMS to task for any of that. Nor does it point out the rule text's erroneous reference to 42 CFR 493.21 in defining the underlying CLIA concept. Instead, Valar very sensibly cherry-picks the part of Table 62 that helps its case—the H&E AI comparators—and leaves the rest alone.

Its only mild comment about Table 62 is that Valar assumes its own codes weren't in the published Table 62 because the final Category III CPT numbers weren't yet available when CMS prepared the proposed rule.

So I would characterize the comment this way:

Valar accepts CMS's SaMS theory, accepts O1, accepts New Technology APCs, and even uses CMS's Table 62 as precedent. Its dispute is narrowly about horizontal equity: if substantially similar H&E-AI oncology tests get APC 1509/$750.50, Valar's two bladder tests should too. It does not challenge the much more fundamental oddities in CMS's selection of the ten “lab SaMS” codes.

And in the process, Valar has inadvertently given everybody else a very nice little comparative inventory of the emerging H&E-AI CPT/PLA universe and its proposed Medicare pricing.


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Valar's argument is logical and well-crafted.   Yet, it may represent the very problem CMS alluded to in the original OPPS pages about CLFS and digital.  CMS may worry we image a $10 H&E slide once, and every ten seconds run a new algorithm in memory - $750, $1500, $2250, $3000, etc.

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Yes. Valar is a very well-written illustration of the exact economic problem CMS says it is trying to solve.

CMS says rather candidly that OPPS was built to pay for things whose costs are largely material resources, whereas SaMS may derive its value from proprietary algorithms and “scalable, non-material costs.” CMS then adds that the cost-based system may provide limited transparency and “may not effectively constrain pricing.” It separately flags subscription, license, and per-use or “per-click” fees as raising program-integrity concerns.

Now put Valar into that framework.

A patient already has an FFPE block. You make an H&E slide; perhaps the slide costs tens of dollars. You digitize it once. Thereafter, the same WSI data file can potentially support:

  • Risk Stratify → $750.50

  • BCGPredict → another $750.50

  • conceivably some future biomarker/prediction algorithm → another $750.50

  • and so forth.

The marginal physical resource cost of the second and third analyses may be dramatically lower than the first. I would not assert that they literally take only seconds without Valar-specific evidence—there may be QC, data handling, pathologist oversight, computing, report production, etc.—but economically the important point survives: the cost of running another trained algorithm against an already-created digital image is unlikely to resemble the resource structure of repeating a $750 physical medical procedure.

And Valar's particular request almost highlights the issue. It says its two bladder tests are genuinely different clinical services, which is perfectly plausible: one predicts recurrence/progression and one predicts BCG response. Therefore, Valar says, pay each one separately at $750.50.

That is a sound coding/clinical-distinctness argument, but it does not answer the CMS policymaker's payment-economics question:

If two different answers are generated from the same slide and substantially the same digital infrastructure, should Medicare pay 2 × $750 merely because there are two separately coded outputs?

Indeed, CMS seems to have anticipated almost exactly this problem. Its proposed O1 status indicator would initially work like status indicator S—separate payment without a multiple-procedure reduction. But CMS specifically asks whether SaMS should instead get a status indicator functioning like T, so that multiple-procedure discounting would apply, expressly mentioning program-integrity concerns.

That makes Valar's situation a nearly textbook example:

Valar's position:
“These are two separate clinically meaningful tests, therefore don't discount either one.”

CMS's problem:
“Fine—but if both are software analyses of the same previously created image, paying the full standalone price every time may wildly overpay the incremental resource cost and create an incentive to proliferate separately coded algorithms.”

There is an even deeper problem. A 50% multiple-procedure reduction is itself borrowed from a world of procedures and may be economically arbitrary for software. If the first algorithm costs $750 and the incremental computational/reporting cost of the second is, hypothetically, $30 or $100, then paying the second one $375 still doesn't solve the problem. Conversely, some software may entail significant licensing, human review, cloud infrastructure, validation and ongoing development costs that make a pure marginal-compute-cost price too low.

So CMS really needs something more sophisticated than either:

$750 + $750 + $750, or
$750 + $375 + $375.

The long-term answer could be some combination of a base payment plus incremental-analysis payment, episode bundling, multiple-SaMS packaging, capped payment per underlying dataset/image, or a valuation methodology that recognizes both fixed intellectual-property costs and very low marginal production costs.

And this is why I think the Valar comment is unusually instructive. Valar may be entirely right under the payment rules CMS has proposed, while simultaneously demonstrating why CMS believes those rules cannot be the permanent answer. The company's excellent tables actually make the policy problem more visible: once H&E becomes a reusable digital substrate, an unlimited number of separately coded algorithms can potentially sit on top of it.

That, much more than whether this bladder algorithm deserves $350 or $750, is the elephant in CMS's SaMS discussion.

Thursday, July 16, 2026

How AI Reports on a New LCD: 11 Rules

 https://drive.google.com/file/d/1zYZV35vIym9R9Ob5bIjMraC5WsGoyESG/view?usp=sharing


GENERAL RULES:  COMPARE DRAFT AND FINAL LCDS

Please perform the following steeps and analysis. 

Medicare documents by document number are found at:

https://www.cms.gov/medicare-coverage-database/search.aspx

#1 Find LCD L40140, new final version fo public notice (issued in July 2026).

#2 Find LCD DL40140, proposed in Summer 2025.

WRITING

#3 Describe for the reader in a few sentences the purpose of the LCD (orient the reader to the topic, but concisely). 

#4  Compare the body of the document for changes in L40140 compared to DL40140.  There may be few (e.g. spelling, punctuation) or significant changes. If there are significant changes, discuss them in general.  Discuss changes in bibliography.

#5  Now compare the coverage rules of each document.  This is the section “Coverage Indications, Limitations, and/or Medical Necessity”.  Compare Dl40140 old version to L40140 new version.   These changes in coverage rules are the most important to our readers. 

#6 Describe the differences in coverage rules.

#7 Now print the coverage rules in L4140, and then next print your editorial improvement, simplification, logical flow, and improved clarity.  This is a good skill that you have (in past LCDs).

#8 Now address the lengthy exchanges in A60439.  It’s quite unusual to go on for 194 pages, but the constituents (labs) had tens of millioins of dollars to win or lose based on small wording changes.

 #9 Now, having discussed the document and discussed the response to comments, in your closing section, discuss what you can glean as to the medical director’s interests, biases, skills, preferences, and overall approach to policy writing in this area.   The details of the outcome are  highly watched by Wall St and industry both.

 #10  Write 500 word story for blog

#11 Make portrait png report cover


Tuesday, July 14, 2026

Science ALZ Copathology

 Science ALZ Copathology

https://www.science.org/content/article/most-dementia-patients-have-multiple-brain-diseases-how-should-they-be-treated 

Most dementia patients have multiple brain diseases. How should they be treated?

Growing awareness of “copathology” inspires new diagnostic tests and clinical trials

Close-up of dyed brain and other tissue specimen slices, mounted on glass slides and labelled and catalogued.
Analysis of brain tissue from autopsies has revealed most people with dementia have markers of multiple diseases.LEWIS HOUGHTON/Science Source
issue cover image
A version of this story appeared in Science, Vol 392, Issue 6799.Download PDF

About 20 years ago, neuropathologists began to report an inconvenient finding in the autopsied brains of people with dementia: Most have evidence of more than one disease. Studies since have shown the brains of up to half of people diagnosed with Alzheimer’s disease also have a key feature of Parkinson’s disease—deposits of the protein alpha synuclein. At the same time, up to half of Parkinson’s patients who develop dementia have elevated levels of beta amyloid and tau proteins, hallmarks of Alzheimer’s.

Researchers studying neurodegenerative diseases are catching on to the importance of this phenomenon, often called copathology. It complicates current disease classifications, which are tightly linked to their signature proteins. But it also offers clues as to why some dementia patients show faster cognitive decline, and some people on antiamyloid drugs for Alzheimer’s seem to fare worse than others. Copathology “helps explain why symptoms don’t match biomarkers, why trajectories vary so much, and why treatment results are not necessarily what we expect them to be,” neuropathologist Lea Grinberg of the Mayo Clinic told researchers at the Alzheimer’s and Parkinson’s Diseases Conference (AD/PD) in March.

Why the diseases overlap so often remains a mystery, but it’s not a coincidence. “It seems that they stimulate each other,” Grinberg says. Tests now being developed to pick up multiple biomarkers should give a clearer picture of these mixed pathologies in living patients. And an upcoming clinical trial will be the first to take aim at a common dementia copathology, testing the amyloid-clearing Alzheimer’s drug donanemab in people who have both amyloid in their brains and dementia with Lewy bodies—abnormal clumps of alpha synuclein.

“The ultimate goal is to define these diseases based on the biology, and understand the occurrence of these pathologies across the spectrum of diseases,” says Mark Frasier, chief scientist at the Michael J. Fox Foundation for Parkinson’s Research, which is running large studies to track and characterize copathologies. “I do think this is where the field is headed.”

Copathologies increase with advancing age. “Almost no one has only one pathology in the brain when they get to be 80,” says neurologist David Wolk of the University of Pennsylvania. Last year, he and colleagues published clinical criteria for another neurodegenerative disease that often occurs with Alzheimer’s in people over age 85: limbic-predominant age-related TDP-43 encephalopathy (LATE). It is marked by the buildup of TDP-43, a protein also seen in some types of frontotemporal dementia.

But copathologies aren’t limited to the old. One study of familial Alzheimer’s patients who developed dementia in their 40s found that half died with Lewy bodies as well as Alzheimer’s pathology in their brains. And in research presented at this year’s AD/PD meeting, neurologist Tom Tropea of the Institute for Neurodegenerative Disorders showed that people in the early stages of Parkinson’s, who represent a younger patient group, had elevated levels of p-tau217, a blood-based marker of Alzheimer’s pathology. Higher p-tau217 was associated with faster rates of cognitive decline and steeper drops in day-to-day functioning.

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Most cases of copathology are discovered at autopsy or in academic centers that conduct specialized testing, often for research. For example, a spinal fluid test developed by Amprion Diagnostics can detect Parkinson’s, dementia with Lewy bodies, and related diseases. Hopefully, wider use of such testing will lead more people with copathologies to trials of experimental agents, says Amprion’s chief executive, Russ Lebovitz.

Some blood tests in development could give patients a broad snapshot of mixed pathologies. Richard Mayeux, a neurologist at Columbia University, is working on one using extracellular vesicles—protein-laden particles that cells, including neurons, release into the blood. Measuring the payloads of neuron-derived vesicles can reveal the presence of proteins associated with LATE, Alzheimer’s, Parkinson’s, and diseases of the brain’s blood vessels, another important type of copathology.

Last month, Carlos Cruchaga, a human genomicist at Washington University in St. Louis, unveiled an experimental blood test using 15 protein markers that, together, can distinguish among the major dementia brain pathologies and quantify them. To develop the test, Cruchaga used artificial intelligence to analyze blood data from thousands of patients. The panel still needs to be validated in real-world clinical settings, and isn’t able to predict disease in asymptomatic people. But for a person with dementia, “It can tell you, you have 75% Alzheimer’s pathology and 20% Parkinson’s pathology and 5% frontotemporal dementia,” Cruchaga says.

Unfortunately, Wolk says, “We have a pretty limited armamentarium” to treat any single neurodegenerative disease—let alone multiple overlapping diseases. Currently only the antibody drugs for Alzheimer’s can clear a disease protein. It’s uncertain whether giving them to people who may also have dementia with Lewy bodies will be useful, but some patients will opt for them anyway, he says. “Can we really say that it’s not going to help them to at least remove the thing that you can remove?”

The upcoming trial, slated to launch later this year, will recruit people in the early stages of dementia with Lewy bodies—but who also have high amyloid—to see whether donanemab tempers their symptoms. Neurologist Sharon Sha of Stanford University, a lead investigator on the trial, thinks an “interactive effect” between beta amyloid and alpha synuclein speeds patients’ damage and decline. She hopes the trial not only shows benefit, but “helps the field to recognize the importance of being inclusive of copathology in clinical trials.”

Treatment for mixed pathology could one day mean combining drugs—administering treatments developed for Alzheimer’s alongside those for Parkinson’s or frontotemporal dementia, for example. Or, some predict, it could aim at a yet-to-be-discovered common source or pathway in neurons that drives multiple pathologies—making today’s rigid disease categories even less relevant.

“In the future we will not talk about ‘Alzheimer’s disease,’” says Johannes Attems, a neuropathologist at the University of Innsbruck. Rather, “You see a patient and in his brain is so much amyloid beta and so much tau, so much alpha synuclein, so much TDP-43. That’s it,” he says. “And you have something to give him.”