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Jay Lusk, MD, MBA @jayblusk.bsky.social · May 24

Proud to share my latest paper in @bmj.com www.bmj.com/content/389/.... Takeaways: Dementia incidence ⬇️, prevalence ⬆️, and dementia is unequally distributed. In an opinion article, I argue we need to redouble our efforts to manage multimorbidity across the life course: www.bmj.com/content/389/...

Incidence and prevalence of dementia among US Medicare beneficiaries, 2015-21: population based study

Objective To determine the incidence and prevalence of dementia in a nationally representative cohort of US Medicare beneficiaries, stratified by important subgroups. Design Population based study. ...

www.bmj.com

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Reposted by Jay Lusk, MD, MBA

Parham Eshtehardi MD @cardioobdoc.bsky.social · Dec 16

Smoking cigarettes is associated with developing both subtypes of HF, independent from baseline lung function.
Jackson Heart Study

#CardioSky #MedSky #JAHA
@robmentz.bsky.social @dranulala.bsky.social @anastasiasmihaili.bsky.social
@ahajournals.bsky.social
www.ahajournals.org/doi/10.1161/...

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Jay Lusk, MD, MBA @jayblusk.bsky.social · Dec 2

Really essential to have patient perspectives centered in research around DBS. Very nice paper highlighting the complexity and nuance of patient experiences in this space.

Michael Okun @michaelokun.bsky.social · Dec 2

Are there changes in personal characteristics following deep brain stimulation (DBS) in Parkinson's disease? I love that many responses did not reflect 'prototypical traits or behaviors reflected in standard measures, but instead reflect patient-specific values.'

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Jay Lusk, MD, MBA @jayblusk.bsky.social · Dec 2

Excellent pragmatic trial out in Lancet showing that a traditional Chinese medicine did not improve ICH outcomes. Shows how important trials are for TCM/herbal interventions. Contrast with positive trial of tongxinluo published in JAMA last year. #MedSky #StrokeSky www.thelancet.com/journals/lan...

Screenshot of title page of the lancet paper reporting a trial of the traditional Chinese medicine Zhongfeng Xingnao.

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Jay Lusk, MD, MBA @jayblusk.bsky.social · Nov 29

Really interesting pragmatic, factorial trial of feedback to PCPs on antibiotic prescribing. Adds to literature from other settings (e.g. alcohol use among college students) that this type of comparison-based intervention can be powerful to drive behavior change. www.bmj.com/content/385/...

Mailed feedback to primary care physicians on antibiotic prescribing for patients aged 65 years and older: pragmatic, factorial randomised controlled trial

Objectives To evaluate whether providing family physicians with feedback on their antibiotic prescribing compared with that of their peers reduces antibiotic prescriptions. To also identify effects on...

www.bmj.com

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Reposted by Jay Lusk, MD, MBA

Michael D. Green @michaeldgreen.com · Nov 27

Maybe instead grants, early career investigators will need to support their work through affiliate links and corporate brand deals in our manuscripts.

“Acknowledgements: We thank Peloton for supporting our work. Use the code ‘Green’ for 10% off a yearly subscription of workouts!”🧪🛟 #MedSky

Dr Kathleen Millen @neurodevkathy.bsky.social · Nov 24

#NIH #NINDS paylines are at 8% for fiscal 2025. Tough year is due to increased research costs and federal budget uncertainty for FY 2025. Director's message here. Worth reading. www.ninds.nih.gov/news-events/...

Featured Director's Message

www.ninds.nih.gov

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Reposted by Jay Lusk, MD, MBA

Michael D. Green @michaeldgreen.com · Nov 25

Now out in #AHAJournals Most studies focus on the association between socioeconomic disadvantage and 30-day Heart Failure readmissions, our work focuses on the cumulative impact on patient outcomes after diagnosis. #CardioSky #MedSky #HF @ahajournals.bsky.social www.ahajournals.org/doi/abs/10.1...

Title and author list for the article. The title is "association between socioeconomic disadvantage and risks of early and recurrent admissions among patients with newly diagnosed heart failure".

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Reposted by Jay Lusk, MD, MBA

Tim Riffe @timriffe1.bsky.social · Nov 25

New paper on how to calculate a multistate death distribution! What's that, you ask? Usually, we plot a death distribution by age at death. But age at death is the sum of our time spent in different states. If we split time between being healthy or not, then the distribution is multistate (1/3)

a multistate death distribution. This plot has healthy years in x, unhealthy years in y, and x+y (descending diagonals) is age at death. We display the death distribution as filled contours. The health measure in question is ADLs, which has a high mortality penalty, so the distribution mostly hugs the lower axis. state expectancies and total life expectancy converge on a point. SHARE data, females, ADL, 2015-17.

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Jay Lusk, MD, MBA @jayblusk.bsky.social · Nov 24

Also a reason we need nationwide state medical licensing reform. The cross-state forgery in this case would have been much more difficult to achieve with uniform, nationwide licensing of physicians and physicians assistants. Plus this would limit abusers crossing state lines to avoid discipline.

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Jay Lusk, MD, MBA @jayblusk.bsky.social · Nov 24

Medical licensing can be a frustrating process and there are plenty of bugs in the system, but stories like this really drive home why we need regulation of the medical profession. Turns out the “PA” in this case NEVER attended PA school (she also unsurprisingly didn’t show up to the hearing…)

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Jay Lusk, MD, MBA @jayblusk.bsky.social · Nov 24

State medical board records have some amazing stories. Take this one. PA applies for a license in NC, submits license documentation. States she had changed her name, provides documents. Sounds legit. Licensed approved. Turns out, SHE STOLE ANOTHER PA’s identity?! License summarily suspended.

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Jay Lusk, MD, MBA @jayblusk.bsky.social · Nov 23

New Lancet paper on disparities in life expectancy since 2000. In the “ten americas”, precipitous decline in life expectancy among “America 10” (1.3 million American Indian/Alaska Native residents in the west) stands out. Urgent need for policy interventions #MedSky www.thelancet.com/journals/lan...

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Jay Lusk, MD, MBA @jayblusk.bsky.social · Nov 20

Such complex modeling strategies and assumptions are directly my area of research expertise haha. The blog post is very nice and makes excellent points, but is not really relevant to what I’m trying to say here. Regardless, appreciate your engagement on this topic!

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Jay Lusk, MD, MBA @jayblusk.bsky.social · Nov 20

After all, one can calculate an NNT, but even in a magical world with perfect applicability/generalization, no one knows which is the 1/N patient to benefit. I feel that individual decision making isn’t a question of ARR vs RRR- its a process of updating priors on the basis of patient AND trial data

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Jay Lusk, MD, MBA @jayblusk.bsky.social · Nov 20

The question I am more deeply interested in is whether a trial’s risk reduction (for me, I prefer ARR) generalizes to the clinical setting (and population) in question. For individual decision-making, I think inevitably the decision is often driven by other, non-trial data.

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Jay Lusk, MD, MBA @jayblusk.bsky.social · Nov 20

You make an interesting argument. For me, as a public health physician, I tend to separate individual risk-benefit from population risk-benefit. Across an entire population, HTE (within a trial population) shouldn’t matter (on balance) but for the individual heterogeneous patient of course it does.

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Jay Lusk, MD, MBA @jayblusk.bsky.social · Nov 19

The approach described above almost inevitably overestimates the magnitude of benefit by taking population-level base-rate risk (almost always higher than clinical trials due to survivorship) and comparing it to RCT RRR (higher magnitude of benefit from therapy than seen in gen. pop) (4/4)

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Jay Lusk, MD, MBA @jayblusk.bsky.social · Nov 19

RRR is not “more robust” by any defensible statistical definition. In fact, empirical data from as far back as 1990s shows that physicians over-estimate magnitude of benefit when presented with RRR instead of ARR, hence CONSORT requirement to report both pubmed.ncbi.nlm.nih.gov/1443954/ (3/4)

Measured enthusiasm: does the method of reporting trial results alter perceptions of therapeutic effectiveness? - PubMed

Clinicians' views of drug therapies are affected by the common use of relative risk reductions in both trial reports and advertisements, by end-point emphasis, and, above all, by underuse of summary m...

pubmed.ncbi.nlm.nih.gov

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Jay Lusk, MD, MBA @jayblusk.bsky.social · Nov 19

That counterfactual comparison is only valid from its sample population, which usually is directly and explicitly NON-comparable to the pop used for an absolute risk tool (due to inclusion/exclusion criteria) (2/4)

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Jay Lusk, MD, MBA @jayblusk.bsky.social · Nov 19

I see, thanks for explaining. Have to hard disagree- absolute risk from population-based risk prediction tools should NOT be directly combined with relative risk reductions from clinical trials. RRR from an RCT derives from a counterfactual comparison (1/4)

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Jay Lusk, MD, MBA @jayblusk.bsky.social · Nov 19

Seems to me you are engaging in bad faith argumentation against a strawman that I or the other folks on this thread are interested in withdrawing treatment of hypertension from older adults. But no one in this thread has advocated that (again, I literally said the opposite in my first post).

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Jay Lusk, MD, MBA @jayblusk.bsky.social · Nov 19

Still haven’t answered my question. What RCT data do you use in “shared decision making” if not ARR? I don’t need the lecture on the limitations of NNT, but you seem to be dodging my actual question. Also, who said anything about deprescribing? I have advocated treatment this whole thread.

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Jay Lusk, MD, MBA @jayblusk.bsky.social · Nov 19

My apologies, I was not trying to say that. Trying to understand: NNT is derived directly from key trial data via 1/ARR, so if you aren’t interested in that specific trial data, what data do you use to guide shared decision making? Are you opposed to the general concept of absolute risk reduction?

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Jay Lusk, MD, MBA @jayblusk.bsky.social · Nov 19

Gonna have to disagree with you there. Relying on data about benefits and harms is clearly better medicine than ignoring that data entirely to avoid “dichotomania.” Not sure what alternative you are suggesting other than to reject evidence-based medicine entirely.

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Jay Lusk, MD, MBA @jayblusk.bsky.social · Nov 19

I think you may be misunderstanding me: the tool suggests we SHOULD intensively treat blood pressure in the vast majority of older adults given how rapidly the benefits accrue. To not do so is ageism in my opinion.

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