Dempeus sobre espatlles de gegants

Sid Mukherjee: On A.I., Longevity and Being A Digital Human

En aquest link a veritats fonamentals (ground truths) sobre longevitat, IA i els humans digitals https://open.substack.com/pub/erictopol/p/sid-mukherjee-on-ai-longevity-and?r=284kv6&utm_campaign=post&utm_medium=web trobareu una magnífica entrevista d'Eric Topol a Sid Mukherjee. Molt interessant i profund, tres quarts d'hora de podcast per a reflexionar.

Atenció al minut 35, i a tot el que diu després. En parlaré dilluns.

Al link hi trobareu també la transcripció que la podeu passar pel traductor si voleu.

Revisant l'assistència sanitària basada en el valor

Building on value-based health care: Towards a health system perspective

En una entrevista que li van fer a l'Eric Topol, li preguntaven que en pensava de l'assistència sanitària basada en valor, i va dir que era una broma, posem-ho en context:

It’s a joke, value-based care. Basically, we have one-third of the healthcare, but $3.6 trillion is waste—low-value care. We need to stop that. That’s part of why it’s so costly. And so this whole idea of value-based care doesn’t even get to it. There’s a long list of hundreds of things that each of the professional societies have called out as being shouldn’t be done anymore. And we’re doing it every day, you know, thousands, hundreds of thousands of times, every day and week in this country. We have to get rid of the waste and inappropriate and unnecessary care and we haven’t done anything to do that here of note.

Certament, l'ús del terme value-based se n'ha anat de les mans. En Peter Smith et al. tracten de posar ordre al terme i diuen:

We therefore define health system value to be the contribution of the health system to societal wellbeing. The health system is expected to contribute to wellbeing in a number of respects, which are often expressed as a set of objectives for the health system. There is a core cluster of objectives, developed from the World Health Report 2000 , that has secured widespread acceptance amongst policy-makers as reflecting their central priorities: health improvement, responsiveness, financial protection, efficiency and equity. It is these strategic goals that should reflect the health system’s concept of value.

 i aquestes són les dimensions

Em sembla bé, però no afegeix massa al que ja coneixem de l'OCDE relatiu a Health Systems Performance Assesment.

Hi ha motius per al cribratge genètic poblacional?

 Ten Years of Incidental, Secondary, and Actionable Findings

Population genomic screening for three common hereditary conditions : a cost-effectiveness analysis.

El cribratge genètic poblacional s'ha estat realitzant des de fa anys en alguns entorns, molt pocs. Fa cinc anys ja vaig explicar el cas de Geisinger, que ara pertany a Kaiser Permanente. Aquí hi trobareu totes les entrades d'aquest blog sobre proves genètiques.

Els resultats en diferents entorns diuen que entre el 3 i el 5% de la població tenen alguns gens amb variants patogèniques sobre els que a data d'avui podem actuar per capgirar el seu impacte. És el que en diuen "actionable". Aquesta és la taula:

Han seqüenciat el genoma o exoma i han informat només d'un nombre determinat de gens, entre 32 i 76. I això ve d'un document de posició que explica quants gens són "actionables" i a data d'avui són 83. L'origen cal buscar-lo als Genome Wide Association Studies, quan encara hi havia confiança en que trobar un gen que provoca una malaltia permetria afrontar-la i després es va veure que això passava en molts poc casos (ara sabem que paga la pena informar només de 83, els 20.000 ).

Al NEJM ens expliquen un estudi que:

A unique aspect of this study was the reporting of life-span data for persons who carried a pathogenic or likely pathogenic variant as compared with those who did not, in an analysis involving data from 27,546 persons who had died by 2020 and life-span estimates for other persons who were at least 65 years of age. Key findings included an observed shortening in median life span among persons who carried these variants (86 years vs. 87 years for those without). Harboring such a variant in a cancer gene was associated with the largest difference in life span (3 years). By the age of 65 years, 10% of the participants with pathogenic or likely pathogenic variants in cancer genes had died. In contrast, in the group of participants without such a variant, 10% had died by 73 years of age. 

Quedem-nos amb la idea, trobar aquesta variant patogènica en un gen suposava una diferència de 10 anys de vida.  Al món hi ha dos països que fan cribratge per BRCA2, Israel (població Ashkenazy) i Islàndia. No n'hi ha enlloc més per ara. Ara bé, què costaria fer un cribratge poblacional d'això? Quin seria el cost-efectivitat?

L'Eric Topol ressenya un estudi on per cancer d'ovari i mama, Lynch sindrome i colesterolemia familiar seria cost-efectiu per:

adults younger than 40 years if the genomic test was at a relatively low cost of $250 or for people 50 years old at $166 per test (summary for patients). We are now at that level whereby whole genome sequencing has dropped to the $200 threshold, and targeted sequencing or arrays can be done at substantially lower costs. It doesn’t appear that we can continue to use cost now as the reason to not obtain information that can be lifesaving.

 Ens cal molta més informació i anàlisi d'una qüestió tant complexa. Ens trobem davant d'una situació nova que fa molt pocs anys no ens hauríem imaginat. Cal estar atents al cribratge genètic poblacional, i també a què farem quan trobem una variant patogènica "actionable", perquè si no hi ha acció (que vol dir accés a tractament cost-efectiu), llavors, per què cal trobar? Alimentaríem encara més la genòmica recreativa, que a hores d'ara interessa econòmicament moltíssim a alguns.

La petjada de la COVID

 Determinants of the onset and prognosis of the post-COVID-19 condition: a 2-year prospective observational cohort study

Trobo que no s'en parla prou de la petjada que la COVID ha deixat a alguns dels nostres cossos. L'Eric Topol se n'ha preocupat força i n'ha fet aquest resum:

I on diu Spain podeu considerar que diu Catalunya, perquè es refereix a aquest article. I la troballa després de 2 anys d'estudiar la població amb Post-Covid condition PCC, és aquesta:

The study included 548 individuals, 341 with PCC, followed for a median of 23 months (IQR 16.5–23.5), and 207 subjects fully recovered. In the model with the best fit, subjects who were male and had tertiary studies were less likely to develop PCC, whereas a history of headache, or presence of tachycardia, fatigue, neurocognitive and neurosensitive complaints and dyspnea at COVID-19 diagnosis predicted the development of PCC. The cluster analysis revealed the presence of three symptom clusters with an additive number of symptoms. Only 26 subjects (7.6%) recovered from PCC during follow-up; almost all of them (n = 24) belonged to the less symptomatic cluster A, dominated mainly by fatigue. Recovery from PCC was more likely in subjects who were male, required ICU admission, or had cardiovascular comorbidities, hyporexia and/or smell/taste alterations during acute COVID-19. Subjects presenting with muscle pain, impaired attention, dyspnea, or tachycardia, conversely, were less likely to recover from PCC.

Table 4Characteristics of the post-COVID-19 condition clusters.

	Cluster A	Cluster B	Cluster C
N (%)	139 (40.8)	152 (44.6)	50 (14.2)
Age, years, median (IQR)	50 (42–57)	47 (38–56)	45 (39–51)
Sex, female, N (%)	73 (52.5)	120 (78.9)	45 (90)
Hospitalization, N (%)	65 (46.8)	52 (34.2)	13 (26)
Intensive care, N (%)	10 (7.2)	5 (3.3)	1 (2)
Comorbidities, N (%)
Allergy	31 (22.3)	56 (36.8)	20 (40)
Obesity	32 (23.0)	39 (25.7)	13 (26.0)
Dyslipidemia	30 (21.6)	43 (28.3)	9 (18.0)
Hypertension	34 (24.5)	25 (16.4)	8 (16.0)
Lung disease	21 (15.1)	31 (20.4)	6 (12.0)
Persistent symptoms, N (%)
Fatigue	100 (71.9)	139 (91.4)	48 (96.0)
Neurocognitive complaints	62 (44.6)	128 (84.2)	46 (92.0)
Dyspnea	45 (32.4)	131 (86.2)	44 (88.0)
Headache	41 (29.5)	103 (67.8)	44 (88)
Myalgia	31 (22.3)	98 (64.5)	33 (66)
Arthralgia	39 (28.1)	92 (60.5)	47 (94)
Chest pain	31 (22.3)	76 (50.0)	45 (90)
Tachycardia	19 (13.7)	83 (54.6)	39 (78.0)
Cough	18 (12.9)	64 (42.1)	19 (38.0)
Neurosensitive symptoms	31 (22.3)	66 (43.4)	40 (80.0)
Diarrhea	14 (10.1)	59 (38.8)	26 (52.0)
Low grade fever	13 (9.35)	36 (23.7)	21 (42.0)
Smell alterations	34 (24.5)	38 (25)	30 (60)
Dermatological alterations	24 (17.3)	36 (23.7)	43 (86)
Dysphagia	9 (6.47)	18 (11.8)	27 (54)
Dysphonia	10 (7.19)	29 (19.1)	19 (38)
Recovery from PCC, N (%)	24 (17.3)	1 (0.7)	1 (2.0)

I la petjada després de dos anys persisteix encara en bona part de la població amb PCC. I moltes coses que encara no sabem... 

IA pertot arreu (2)

 Foundation models for generalist medical artificial intelligence

La revista Nature publica aquesta setmana un article de revisió que explica l'estat de situació de la Intel·ligència Artificial mèdica generalitzada GMAI. Fa unes setmanes explicava en una entrada els models multimodals, i ara anem més enllà. El concepte generalista ens condueix cap a models que van més enllà de tasques, i que aprenen del context, modelitzen el llenguatge i introdueixen l'aprenentatge contrastat. Per fer-nos una idea, fins ara tot els prop de 500 models d'IA que ha aprovat la FDA eren orientats a 1 o 2 tasques. El salt cap a la IA generalista suposa un canvi substancial. I això ha passat amb pocs mesos de diferència.

Les tres capacitats clau:

First, adapting a GMAI model to a new task will be as easy as describing the task in plain English (or another language). Models will be able to solve previously unseen problems simply by having new tasks explained to them (dynamic task specification), without needing to be retrained3,5. Second, GMAI models can accept inputs and produce outputs using varying combinations of data modalities (for example, can take in images, text, laboratory results or any combination thereof). This flexible interactivity contrasts with the constraints of more rigid multimodal models, which always use predefined sets of modalities as input and output (for example, must always take in images, text and laboratory results together). Third, GMAI models will formally represent medical knowledge, allowing them to reason through previously unseen tasks and use medically accurate language to explain their outputs.

a, A GMAI model is trained on multiple medical data modalities, through techniques such as self-supervised learning. To enable flexible interactions, data modalities such as images or data from EHRs can be paired with language, either in the form of text or speech data. Next, the GMAI model needs to access various sources of medical knowledge to carry out medical reasoning tasks, unlocking a wealth of capabilities that can be used in downstream applications. The resulting GMAI model then carries out tasks that the user can specify in real time. For this, the GMAI model can retrieve contextual information from sources such as knowledge graphs or databases, leveraging formal medical knowledge to reason about previously unseen tasks. b, The GMAI model builds the foundation for numerous applications across clinical disciplines, each requiring careful validation and regulatory assessment.

Tres aplicacions potencials:

 a, GMAI could enable versatile and self-explanatory bedside decision support. b, Grounded radiology reports are equipped with clickable links for visualizing each finding. c, GMAI has the potential to classify phenomena that were never encountered before during model development. In augmented procedures, a rare outlier finding is explained with step-by-step reasoning by leveraging medical domain knowledge and topographic context. The presented example is inspired by a case report58. Image of the fistula in panel c adapted from ref. 58, 

L'aplicació de la IA a la medicina representa una impuls transformador crucial, i encara no en sabem les conseqüències. L'article detalla alguns dels reptes, cal llegir-lo per fer-nos una idea de cap on va la cosa. 

 PS. Per entendre el context de l'article, Eric Topol al seu blog.

 

Modificar l'entorn obesogènic o medicalitzar l'obesitat?

 WHO European Regional Obesity Report 2022

A new class of drugs for weight loss could end obesity

Ens ho han dit moltes vegades, i l'informe de la OMS insisteix novament en explicar la situació de l'obesitat a Europa:

Overweight and obesity affect almost 60% of adults and nearly one in three children (‎29% of boys and 27% of girls)‎ in the WHO European Region. Recent estimates suggest that overweight and obesity is the fourth most common risk factor for NCDs in the Region, after high blood pressure, dietary risks and tobacco. It is also the leading risk factor for disability, causing 7% of total years lived with disability.

 Segons dades de l’Enquesta de salut de Catalunya (ESCA) 2020 la meitat de la població de 18 a 74 anys té excés de pes (sobrepès o obesitat), 58,8% els homes i 42,4% les dones. El 33,7% té sobrepès (42,0% els homes i 25,2% les dones) i el 17,0% obesitat (sense diferències entre homes i dones). Respecte a la població de 6 a 12 anys, el 35,9% té excés de pes (el 24,2% té sobrepès i l’11,7% té obesitat). 

Amb totes aquestes dades a la mà, què fem? Doncs, la primera opció segur que passa per menjar millor i fer exercici, responsabilitat sobretot individual. L'altra és canviant l'entorn obesogènic, responsabilitat sobretot col·lectiva. Ho he explicat i ens ho han explicat en moltes ocasions. Ara bé la novetat, és que des de fa uns mesos han arribat nous medicaments, es tracta de la tirzetapida i la semaglutida. El Economist hi dedica un especial aquesta setmana, i l'Eric Topol en va parlar amb molt de detall a finals d'any. El meu suggeriment és que hi feu una ullada per tal d'il·lustrar-vos d'un canvi que ve, i que resumeixen amb un titular a l'editorial que diu: Menja, injecta't, torna-hi. És a dir suggereix saltar-se precisament la prescripció inicial a la que feia referència i aprimar-se mitjançant injeccions de tirzetapida o semaglutida. D'això en podríem dir la medicalització de l'obesitat. Els preus al mes als USA són 1.300$ i 900$ respectivament. Per tant, la prescripció de l'editorial de l'Economist no sortirà barata d'entrada, i alhora té alguns efectes indesitjats. El resultat final podria ser que la falta de l'acció individual i col·lectiva, la paguem col·lectivament tots plegats, si el tractament acaba finançat amb diner públic.

Seguirem atents per veure com evoluciona a Europa. Tot plegat hauria de fer reaccionar als governs per tal de modificar l'entorn, la política alimentària i l'accés a determinats greixos. Un metge citat a l'article diu: "l'obesitat és una resposta fisiològica al que ha esdevingut un entorn patològic".  I jo hi afegiria "un entorn patològic on els governs han estat capturats per la indústria alimentària"

PD. A es diari de Menorca: Y si el medicamento más "potente" del mundo es el ejercicio físico?

Coïnt olives, Van Gogh

PD. Gran 60 minutes sobre ChatGPT

Human genomics vs. clinical genomics

 Today my suggestion is to read the post by Eric Topol with the same title. 

It begins with this statement:

We’re now well over 20 years since the first human genome was sequenced, but with few exceptions the massive amount of data that has been generated has not been transformed to routine patient care.

So, why?

Covid-19 testing landscape

COVID-19 diagnostics in context

This is the best summary of current supply of diagnostic tests for Covid-19:

COVID-19 tests can be grouped as nucleic acid, serological, antigen, and ancillary tests, all of which play distinct roles in hospital, point-of-care, or large-scale population testing.
Table 1 summarizes the existing and emerging tests, current at the time of writing (May 2020). A continuously updated version of this table is available at https://csb.mgh.harvard.edu/covid

Eric Topol says:

There are now *88* @US_FDA  cleared (by EUA) #COVID19 tests so far. Their false negative rates range from 10-48% (by post-release reports).
Might be better to have less tests, more accuracy, with faster turnaround

I agree.

Table 1 Performance comparison of different test types.
Throughput is determined by process type and assay time. In general, automated plate-based assays have higher daily throughputs. Hashtag (#) indicates example systems that have received FDA emergency use authorization (FDA-EUA). See https://csb.mgh.harvard.edu/covid to access continuously updated information. PCR, polymerase chain reaction; PCR-POC, PCR–point-of-care; ddPCR, digital droplet PCR; NEAR, nicking endonuclease amplification reaction; RCA, rolling circle amplification; SHERLOCK, specific high-sensitivity enzymatic reporter; DETECTR, DNA endonuclease-targeted CRISPR transreporter; NGS, next-generation sequencing; μNMR, micro–nuclear magnetic resonance; LFA, lateral flow assay; ELISA, enzyme-linked immunosorbent assay; CLIA, chemiluminescence immunoassay; EIA, enzyme immunoassay; ECLIA, electrochemiluminescence immunoassay; ECS, electrochemical sensing; VAT, viral antigen assay; IFM, immunofluorescence microscopy; WB, Western blot.

Type	Target	Virus	Assay time	Process type	FDA-EUA	Examples
PCR	Viral RNA	SARS-CoV-2	2–8 hours; >12 hours	Plate	56	#Roche, #LabCorp, #BioMerieux, #Qiagen, #Perkin-Elmer, #Becton Dickinson, #Luminex, #Thermo Fisher, others
PCR-POC	Viral RNA	SARS-CoV-2	<1 hour="" td="">	Cartridge	2	#Cepheid, #Mesa, Credo
ddPCR	Viral RNA	SARS-CoV-2	2–4 hours	Manual	1	#BioRAD
NEAR	Viral RNA	SARS-CoV-2	15 min	Cartridge	1	#Abbott
OMEGA	Viral RNA	SARS-CoV-2	1 hour	Plate	1	#Atila BioSystems
RCA	Viral RNA	SARS-CoV	2 hours		0
SHERLOCK	Viral RNA	SARS-CoV-2	1.5 hours	Kit	1	#Sherlock Biosciences (CAS13a)
DETECTR	Viral RNA	SARS-CoV-2	1 hour	Kit	0	Mammoth Biosciences (CAS12a)
NGS	Viral RNA	SARS-CoV-2	Days		1	#IDbyDNA, Vision, Illumina
μNMR	Viral RNA	SARS-CoV-2	2 hours	Cartridge	0	T2 Biosystems
LFA	IgG, IgM	SARS-CoV-2	15 min	Cartridge	3	#Cellex, #Sugentech, #ChemBio, Innovita
ELISA	IgG, IgM	SARS-CoV-2	2–4 hours	Plate	4	#Mount Sinai, #Ortho-Clinical (2), #EUROIMMUN US Inc., BioRAD, Snibe, Zhejiang orient, Creative Dx
CLIA	IgG, IgM	SARS-CoV-2	30 min	Cartridge	2	#Abbott, #DiaSorin
EIA	IgG, IgM	SARS-CoV-2	2 hours	Plate	1	#BioRAD
MIA	IgG, IgM	SARS-CoV-2		Plate	1	#Wadsworth Center
ECLIA	IgG, IgM	SARS-CoV-2	20 min	Plate	1	#Roche
ECS	IgG, cytokine	SARS-CoV-2	1 hour	Cartridge	0	Accure Health
VAT	Viral antigen	SARS-CoV-2	20 min	Cartridge	1	#Quidel, Sona NT, RayBiotech, SD Biosensors, Bioeasy
Microarrays	Ig epitopes	SARS-CoV-2	1.5 hours	Plate	0	RayBiotech, PEPperPRINT
IFM	Viral protein	SARS-CoV	3 hours	Manual	0
WB	IgG, IgM; viral protein	SARS-CoV	4 hours	Manual	0

Tackling COVID-19 beyond testing

How We Can Tackle the COVID-19 Crisis Beyond Testing

If you wear a smartwatch or fitness tracker, you can play a role in monitoring the spread of COVID-19 and other viral diseases like the flu. In this Front Row lecture, Eric Topol, MD, and Jennifer Radin, PhD, discuss how they’re calling on the public to share data from wearable devices for a study that’s helping scientists flag the early onset of contagious respiratory illnesses. By harnessing this key data—including heart rates, sleep and activity levels—from hundreds of thousands of individuals, they seek to improve real-time disease surveillance.

Books of the year

The Economist
Financial Times
Prospect (economics)

Top ten books of 2019 by Eric Topol

Improving population health (2)

References of my speech

Carreras, Marc, et al. "Morbilidad y estado de salud autopercibido, dos aproximaciones diferentes al estado de salud." Gaceta Sanitaria (2019).

Postman, Neil. Amusing ourselves to death: Public discourse in the age of show business. Penguin, 2006.

Lakoff, George. The political mind: why you can't understand 21st-century politics with an 18th-century brain. Penguin, 2008.

Barer, Morris L., et al., eds. An Undisciplined Economist: Robert G. Evans on Health Economics, Health Care Policy, and Population Health. McGill-Queen's Press-MQUP, 2016.

 Pere Ibern, Jordi Calsina. Més enllà de la separació de funcions: organitzacions sanitàries integrades. Fulls econòmics del sistema sanitari, Nº 35, 2001, págs. 17-20

Harris, Jeffrey E. "The internal organization of hospitals: some economic implications." The Bell Journal of Economics (1977): 467-482.

Freidson, Eliot. Professionalism, the third logic: On the practice of knowledge. University of Chicago press, 2001.

Topol, Eric. The patient will see you now: the future of medicine is in your hands. Basic Books, 2015.

Kindig, David, and Greg Stoddart. "What is population health?." American journal of public health 93.3 (2003): 380-383.

Mateu i Serra, Antoni. "Salud en Todas las Políticas e intersectorialidad en la promoción de la salud: el Plan Interdepartamental de Salud Pública (PINSAP) de Cataluña." Medicina Clínica 145.Extr. 1 (2015): 34-37.

Verschuuren, Marieke, and Hans Van Oers, eds. Population Health Monitoring: Climbing the Information Pyramid. Springer, 2018.

Hansen, Pelle Guldborg. "BASIC: Behavioural Insights Toolkit and Ethical Guidelines for Policy Makers." (2018).

Kahneman, Daniel. Thinking, fast and slow. Macmillan, 2011.

Courtwright, David T. The Age of Addiction: How Bad Habits Became Big Business. Harvard University Press, 2019.

Kindig, David A. Purchasing population health: paying for results. University of Michigan Press, 1997.

Baciu, Alina, and Joe Alper, eds. Financing Population Health Improvement: Workshop Summary. National Academies Press, 2015.