Coronavirus : publications scientifiques, cartes, statistiques, essais cliniques etc.

[Nick de Cusa]

la basisine ou CD147 ?

 

[Lancelot]

J'ai hésité à mettre ça sur le thread de Raoult mais, outre le texte en soi, pas mal de références sont données concernant les essais de différents traitements : https://www.mediterranee-infection.com/actualite-du-traitement/

De toute évidence ils préparent le terrain pour les résultats de Discovery qui ne seront (par construction diront certains) sans doute pas flatteurs pour la chloroquine.

[Sekonda]

 

 

[Mathieu_D]

Pas révolutionnaire le modèle.

[Rübezahl]

il y a 43 minutes, Mathieu_D a dit :

Pas révolutionnaire le modèle.

... nettement plus que tes posts.

[Mathieu_D]

10 minutes ago, Rübezahl said:

... nettement plus que tes posts.

Non mais tu vois bien que les variables sont Âge, Sexe, Obésité, Diabète sur cet arbre en tout cas. (Il y a "White" aussi mais j'avais crû comprendre que les afro-américains sont plus obèses en moyenne donc bon)

 

J'étais tout frétillant de curiosité, c'est un peu mon métier ce genre de choses, et j'ai été un peu déçu. Je pensais qu'il y avait des inputs plus spécifiques type allèle de ACE2. (Ce qui aurait été très difficile à collecter comme donnée.)

 

Bref je ne remets pas en cause la qualité du truc, j'espérais trop sans doute.

[fryer]

il y a 25 minutes, Rübezahl a dit :

... nettement plus que tes posts.

 

Nécessaire comme remarque ?

[Lameador]

2 minutes ago, fryer said:

 

Nécessaire comme remarque ?

 

Cela a conduit @Mathieu_D a fournir un deuxième post bien plus intéressant.

[fryer]

il y a 7 minutes, Lameador a dit :

 

Cela a conduit @Mathieu_D a fournir un deuxième post bien plus intéressant.

 

Et ?

[Rübezahl]

Si tu veux monter une mayo, ce sera sans moi hein.

[Largo Winch]

 

[Noob]

2 hours ago, Mathieu_D said:

Non mais tu vois bien que les variables sont Âge, Sexe, Obésité, Diabète sur cet arbre en tout cas. (Il y a "White" aussi mais j'avais crû comprendre que les afro-américains sont plus obèses en moyenne donc bon)

 

J'étais tout frétillant de curiosité, c'est un peu mon métier ce genre de choses, et j'ai été un peu déçu. Je pensais qu'il y avait des inputs plus spécifiques type allèle de ACE2. (Ce qui aurait été très difficile à collecter comme donnée.)

 

Bref je ne remets pas en cause la qualité du truc, j'espérais trop sans doute.

Pareil, après je ne n'ai pas l'impression que d'autres données plus détaillées existent en ce moment. C'est juste un résumé des probabilités que l'on connait et il manque une branche concernant le fait d'avoir un bronchopneumapthie chronique obstructive.

[Lancelot]

Tiens j'avais loupé ça : https://www.medrxiv.org/content/10.1101/2020.04.10.20060699v1

 

Quote

No evidence of clinical efficacy of hydroxychloroquine in patients hospitalized for COVID-19 infection with oxygen requirement: results of a study using routinely collected data to emulate a target trial

 

We used data collected from routine care of all adults in 4 French hospitals with documented SARS-CoV-2 pneumonia and requiring oxygen ≥ 2 L/min to emulate a target trial aimed at assessing the effectiveness of HCQ at 600 mg/day. The composite primary endpoint was transfer to intensive care unit (ICU) within 7 days from inclusion and/or death from any cause. Analyses were adjusted for confounding factors by inverse probability of treatment weighting. Results This study included 181 patients with SARS-CoV-2 pneumonia; 84 received HCQ within 48 hours of admission (HCQ group) and 97 did not (no-HCQ group). Initial severity was well balanced between the groups. In the weighted analysis, 20.2% patients in the HCQ group were transferred to the ICU or died within 7 days vs 22.1% in the no-HCQ group (16 vs 21 events, relative risk [RR] 0.91, 95% CI 0.47-1.80). In the HCQ group, 2.8% of the patients died within 7 days vs 4.6% in the no-HCQ group (3 vs 4 events, RR 0.61, 95% CI 0.13-2.89), and 27.4% and 24.1%, respectively, developed acute respiratory distress syndrome within 7 days (24 vs 23 events, RR 1.14, 95% CI 0.65-2.00). Eight patients receiving HCQ (9.5%) experienced electrocardiogram modifications requiring HCQ discontinuation. Interpretation These results do not support the use of HCQ in patients hospitalised for documented SARS-CoV-2-positive hypoxic pneumonia.

[...]

We report a comparative study that uses real-world data collected from routine care to assess the efficacy and safety of HCQ in a population of 181 patients hospitalised for COVID-19 hypoxemic pneumonia. We found that HCQ treatment at 600 mg/day added to standard of care was not associated with a reduction of admissions to ICUs or death 7 days after hospital admission, compared to standard of care alone. The rate of ARDS did not decrease either. Our population of patients hospitalised because they required oxygen is very similar to that reported by others, and the percentage of patients transferred to the ICU was similar to that reported in a Chinese cohort of 138 patients hospitalised for COVID-19 pneumonia.16 The clinical features of patients included were also consistent with other reports, with a predominance of men and of patients with cardiovascular comorbidities and/or obesity.16,17Almost all patients had bilateral pneumonia, and 75% moderate or severe lung infiltrate.18 The patients in this study did not receive any other drug, in particular, potential confounders such as anti-viral and anti-inflammatory treatments, including steroids, before ICU admission.

 The timing of antiviral treatment initiation may be critical in reducing SARS-Cov-2 viral load.19 Accordingly, in the recent lopinavir–ritonavir trial, a post-hoc subgroup analysis suggested that lopinavir–ritonavir could have a clinical benefit if started earlier than 12 days after the onset of symptoms.20 This was the case here, because (1) patients had a median time from symptom onset to inclusion of 7 days, (2) they were treated with HCQ as soon as possible (i.e. in the 48 hours after hospital admission), and (3) we found that viral RNA for SARS-CoV-2 was detectable for all patients at inclusion, showing active viral shedding. Previous reports21 indicate that HCQ should have been expected to show some antiviral efficacy. We did not check subsequent SARS-Cov-2 PCR in this study and therefore cannot reach a conclusion about its potential efficacy for decreasing viral shedding. Although this may appear to be a limitation, we used robust clinical outcomes here, i.e., death and ICU admission, which are substantially more clinically relevant.

COVID-19 pneumonia progression in the second week of illness is associated with a so-called “cytokine storm”,17,22 which is thought to be responsible for the clinical worsening of many patients. Most of the patients included in this study had an inflammatory syndrome defined by C-reactive-protein higher than 40 mg/l, which suggests that a cytokine storm syndrome had already begun.23 Drugs decreasing virus shedding may therefore be inadequate at this stage; this is why many anti-inflammatory drugs are currently being tested, such as tocilizumab, corticosteroids, and others. Despite the immunomodulatory properties of HCQ, which include regulation of the production of pro-inflammatory cytokines such as IL-2, IL-1, IL-6 and TNFα,24 and endosomal inhibition of toll-like receptors, which play a major role in innate immune response,25 this treatment showed no effectiveness in this specific population. Finally, HCQ blocks the KCNH2-encoded hERG/Kv11.1 potassium channel and can potentially prolong the QTc, with potential severe consequences such as sudden cardiac death and cardiac arrhythmia.26 Besides QTc prolongations, we observed 2 other significant cardiologic events in this study, and the French national drug agency has reported 3 deaths potentially related to HCQ since its promotion to the public as a potential treatment for COVID-19. Although HCQ was considered safe in the context of SLE, these adverse events may be explained by the use of high-dose HCQ in elderly patients with renal impairment and frequent drug interactions. Accordingly, the negative clinical results of this study argue against the widespread use of HCQ in patients with COVID-19 pneumonia.

Our study has several limitations. First, although our aim was to emulate a target trial and we used robust statistical techniques for adjustment, treatment was not randomly assigned and potential unmeasured confounders may bias our results. Second, four potentially important prognostic variables could not be balanced in the PS model because none or only one patient in the HCQ group presented with these variables. Accordingly, caution is required in the interpretation of results, especially for overall mortality where only a limited number of events were observed. Third, we did not take a centre effect into account in the PS model because the number of patients treated with HCQ in centres was unbalanced (some centres treated all patients with HCQ, while others did not).

 

En gros ils font du data-mining sur les données d'hôpitaux français pour simuler un test clinique entre pas d'hydroxychloroquine dans les premières 48h après hospitalisation vs 600mg/jour pour 181 patients (en majorité) au stade "cytokine storm". Ils ne trouvent pas de différence significative en termes d'admission en soins intensifs, de nombre de décès et d'ARDS. Ils trouvent environ 10% de patients qui ont dû arrêter l'hydroxychloroquine pour cause de dérèglements cardiaux.

Côté critique ils n'ont pas de données sur la diminution de la charge virale et ils remarquent qu'à ce stade relativement avancé le virus n'est peut-être déjà plus le problème (ce qui justifierait des tests préventifs et des traitements plus précoces au delà du débat sur l'hydroxychloroquine). Ils ont une grosse marge de manoeuvre pour faire du data massaging comparé à un vrai essai clinique. Dans les limitations ils donnent comme exemple le fait qu'ils ne prennent pas en compte l'effet de l'hopital alors que les hopitaux sont plus ou moins bons, plus ou moins saturés et qu'ils ont tendance à être consistants sur le traitement (un hopital donné va prescrire tendanciellement de l'hydroxychloroquine à tout le monde ou à personne).

[Fagotto]

Le buzz actuel c'est le remdesivir:

 

https://www.boursorama.com/bourse/actualites/gilead-donnees-favorables-pour-les-essais-preliminaires-d-un-traitement-du-covid-19-943d4ef0cca1bfbf867d6380a7c08972

 

(AOF) - La biotech américaine Gilead est attendue en forte hausse à Wall Street, son traitement contre le Covid-10, le remdesivir, aurait donné de bons résultats, selon des essais préliminaires. Selon la publication médicale Stat, un groupe de 125 patients traités à Chicago aurait enregistré une récupération rapide de la fièvre et des symptômes respiratoires. Dans un courriel adressé à Reuters, l'Université de Chicago a indiqué que "les données partielles d'un essai clinique en cours sont par définition incomplètes et ne doivent jamais être utilisées pour tirer des conclusions".

[Nick de Cusa]

l'approche suedoise. Remarquable (il reconnait une grave erreur de n'avoir pas protege les seniors assez tot). NB: entre nous, imaginez ca + optimiser pour de bon la sante metabolique

 

 

 

 

[Nick de Cusa]

mortalite selon analyse serologique, Stanford University 

 

https://twitter.com/DrAseemMalhotra/status/1251417325103992832

[Newbie]

14 hours ago, Nick de Cusa said:

mortalite selon analyse serologique, Stanford University 

 

https://twitter.com/DrAseemMalhotra/status/1251417325103992832

 

Il y a quelques doutes quant à cette étude et l'ordre de grandeur selon lequel le nombre de cas réels serait 50-80x supérieurs aux cas testés à cet endroit: 

Apparemment 10-15x serait plus réaliste:

Si on part du principe que 3% de la population a été infectée, ça nous donnerait un CFR de 1,5% si mon calcul est bon (Belgique 5500 morts/330000 infectés). Ca serait une bonne nouvelle je trouve 

Par contre, on serait encore loin de l'immunité globale (si tant est qu'il y a bien immunité après infection et en supposant que les cas de réinfection/réactivation observés ne soient que marginaux en nombre et/ou en gravité).

[h16]

Il y a 10 heures, Newbie a dit :

Si on part du principe que 3% de la population a été infectée, ça nous donnerait un CFR de 1,5% si mon calcul est bon (Belgique 5500 morts/330000 infectés). Ca serait une bonne nouvelle je trouve 

Par contre, on serait encore loin de l'immunité globale (si tant est qu'il y a bien immunité après infection et en supposant que les cas de réinfection/réactivation observés ne soient que marginaux en nombre et/ou en gravité).

Ca ferait de l'ordre de 1% en France (2M de contaminés, 20K morts env.) ; si on veut 60% de contaminés pour atteindre une immunité de groupe, ça nous donne 39M de contaminés et 390.000 morts tout de même   

 

Maintenant, je pense que les 2M de contaminés français ne sont pas homogènes (surtout des populations de grandes villes) ce qui fait que le CFR est probablement inférieur ; on peut retenir 0.5% (~Corée) soit moitié moins.

[Nick de Cusa]

ca se precise chaque jour un peu plus 

 

https://twitter.com/FatEmperor/status/1251852168413528064

[Mathieu_D]

Ouais mais bon le taux de mortalité global ça ne veut rien dire tant la maladie frappe fort dans certaines catégories et rien dans d'autres.

[Nick de Cusa]

c'est bien la raison pour laquelle la priorite numero 1 est coacher les gens pour minimiser / eliminer leur syndrome metabolique

 

Autre sujet, les contamines asymptomatiques

 

https://twitter.com/EricTopol/status/1251872896080732160

[Nick de Cusa]

Et aussi, deja evoque plus tot et merite reflexion, l'hydroxychloroquine a une efficacite aussi contre le diabete 

 

https://pubmed.ncbi.nlm.nih.gov/32190699/?from_term=Hydroxychloroquine+and+hemoglobin&from_pos=2

[Nick de Cusa]

1/ qu'est-ce que ca fait aux vaisseaux sanguins ? https://www.scmp.com/news/china/science/article/3080750/coronavirus-attacks-lining-blood-vessels-all-over-body-swiss

 

2/ comment le diabete prepare le terrain, https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2903979/

 

PS: j'ai eu quelques discussions maintenant avec des gens a qui je demande, ou en es-tu diabete ? Et qui me repondent, non je n'ai pas de diabete. Et je leur reponds, par quel test sais-tu ca ? Et ils ne savent pas. Les docteurs disent aux gens qu'ils n'ont pas de diabete. J'ai vu au moins un cas ou le ratio tricglycerides/HDL aurait du serieusement declencher plus d'investigations ; mais non

[Nick de Cusa]

 

[Nick de Cusa]

explication 

https://www.bbc.co.uk/sounds/play/p08b07dr

[Freezbee]

Le 19/04/2020 à 00:40, Newbie a dit :

Par contre, on serait encore loin de l'immunité globale (si tant est qu'il y a bien immunité après infection et en supposant que les cas de réinfection/réactivation observés ne soient que marginaux en nombre et/ou en gravité).

 

Le 19/04/2020 à 11:12, h16 a dit :

si on veut 60% de contaminés pour atteindre une immunité de groupe...

 

Hum... au sujet de l'immunité de groupe, il y a un soucis : dans la pratique, elle pourrait être dépassée (overshoot).

 

Citation

Carl T. Bergstrom

@CT_Bergstrom

 

1. An hour ago I posted a thread explaining the concept of herd immunity and the problem of overshoot, whereby an epidemic infects more people that are required for herd immunity.

Despite disclaimers, people interpreted it as an endorsement of the herd immunity approach.

 

2. I think that it is important to talk about the science of epidemic models and how they work, so I am going to repost parts of the thread, but taking even more care to make it clear that allowing #COVID19 to go to herd immunity and beyond would be a disaster.

 

3. Given the uncertainties we face around the degree and duration of of acquired immunity, it is not even clear that one could reach herd immunity for #SARS2CoV.

But even if one could, the cost in lives would be unacceptable.

 

4. The main point in the original thread was that if herd immunity requires 60% of the population to be infected, you can't just let the epidemic go unchecked and expect to suffer "only" 60% infected. This is because of something known as overshoot. Let's take a look.

 

5. Herd immunity—while a disastrous control strategy for this pandemic—is a key concept in epidemiological modeling. This is the state at which enough people are immune that a new outbreak cannot take off—because a new case infects fewer than one downstream cases.

 

6. In an SIR or SEIR model, herd immunity is reached when the effective reproductive number Re drops below one, where Re=R0 * S and S is the fraction of the population that is susceptible to infection. Herd immunity thus arises when the fraction susceptible drops below S=1/R0.

 

7. Assuming that immunity is complete and permanent—a huge assumption for #COVID19—herd immunity is reached once a fraction 1-1/R0 of the population has been infected. So if R0 is 2.5 for #SARSCoV2, herd immunity would require that 60% of the population become infected.

 

8. That's already untenably high.

But it's far worse. When an epidemic sweeps through, it won't stop once 1-1/R0 of the population have been infected.

This is because epidemics have momentum of a sort, and they overshoot the herd immunity threshold before coming to a halt.

 

9. (Reminder: hoping for herd immunity is a bad idea)

It turns out that in a basic SIR or SEIR model, the final size of an epidemic is not p=1-1/R0, but rather p=1-e^R0 p. @joelcmiller has a nice paper about this:

A note on the derivation of epidemic final sizes

 

10. I can illustrate overshoot with a simple SEIR model of the COVID19 epidemic. These models have four types of individuals: susceptible, exposed, infectious, and recovered.

(Researches use more complex models for forecasting. But this model allows us to understand the effect.)

 

11. For those who want to see under the hood, here are the differential equations that describe this model. It is not necessary to understand these to understand what follows.

(Reminder: Herd immunity is not a viable control strategy for #COVID19)

 

 

12. Let's see what these dynamics look like. Below I show the number exposed and infectious individuals over time, stacked atop one another, for parameters similar to those often used in #COVID19 models.

R0=2.5
Exposed period=average 3 days
Infectious period=average 8 days

 

 

13. Now we visualize fractions of susceptible and recovered individuals as well. This is a stacked chart, so the shaded areas are proportional to the number in each category. Every "recovered" individual is a case. "Recovered" is a misnomer. Not all actually recovered. Some died.

 

 

14. If the epidemic stopped once we reached the herd immunity threshold, the turquoise curve would top out at the dashed line, with 60% of the population having been infected.

That would be bad enough. But it keeps going. Those extra infections are what we call "overshoot."

 

 

15. This overshoot represents a huge number of infections. For the model shown here, it represents nearly 30% of the population and nearly 33% of the total infections. So if letting the epidemic go through to herd immunity is even worse than it might sound.

 

16. Why is this happening? It takes place because an epidemic has something akin to momentum, in the form of people who are already infected when you reach the herd immunity threshold. An epidemic couldn't start anew from that place, but the current one can keep going.

 

17. In fact, for an SIR or SEIR model, you reach herd immunity not when the epidemic is nearly over, but rather *precisely* at the epidemic peak. This makes sense. Before the herd immunity threshold, each case creates >1 new cases, and the epidemic curve keeps increasing.

 

 

18. And that is the take-home point. Herd immunity without vaccination requires a huge number of infections, at least 1-1/R0. But in practice, you get far more people infected than that due to overshoot.

The cost in lives makes this approach untenable.

 

19. As a final note, overshoot is one of those important ideas that seemed to become common knowledge late in development of the field. I first learned about it in a paper by @andreashandel:

What is the best control strategy for multiple infectious disease outbreaks?

We also used it in our work here:

Timing of antimicrobial use influences the evolution of antimicrobial resistance during disease epidemics

 

Missed a minus sign. The final size pi is the solution to

 

 

[ttoinou]

il y a 8 minutes, Freezbee a dit :

Hum... au sujet de l'immunité de groupe, il y a un soucis : dans la pratique, elle pourrait être dépassée (overshoot).

Et oui ce n'est qu'une bête formule mathématique qui ne représente pas la vraie vie comme la plupart des formules de maths qu'on utilise couramment

[Nick de Cusa]

c'est apres 27'  que c'est specifique COVID-19 (mais l'ensemble de la video est geniale) 

 

 

[Tesla]

https://www.timesofisrael.com/the-end-of-exponential-growth-the-decline-in-the-spread-of-coronavirus/?fbclid=IwAR3G75lAnceU6uqTq9bkEaLW9DxdGgu3qXuPsCBKnuRbuQd9eqL8zCMHa0M

 

Citation

Has the Coronavirus continued to spread exponentially?

With careful consideration, the answer is simply negative. The spread of the virus starts at an exponential rate, however, continues to moderate and ultimately fades after 8 weeks or so since its outbreak.

 

Citation

Given that the evidence reveals that the Corona disease declines even without a complete lockdown, it is recommendable to reverse the current policy and remove the lockdown. At the same time, it is advisable to continue with low-cost measures, such as wearing masks, expanding testing for defined populations and prohibiting mass gatherings.

 

[Freezbee]

Une étude de l'Institut Pasteur, analysant la situation en France, vient de paraître :

 

Citation

Estimating the burden of SARS-CoV-2 in France

 

Abstract : France has been heavily affected by the SARS-CoV-2 epidemic and went into lockdown on the 17th March 2020.

 

Using models applied to hospital and death data, we estimate the impact of the lockdown and current population immunity.

 

We find 2.6% of infected individuals are hospitalized and 0.53% die, ranging from 0.001% in those <20y to 8.3% in those >80y.

 

Across all ages, men are more likely to be hospitalized, enter intensive care, and die than women.

 

The lockdown reduced the reproductive number from 3.3 to 0.5 (84% reduction).

 

By 11 May, when interventions are scheduled to be eased, we project 3.7 million (range: 2.3-6.7) people, 5.7% of the population, will have been infected.

 

Population immunity appears insufficient to avoid a second wave if all control measures are released at the end of the lockdown

 

PDF : https://hal-pasteur.archives-ouvertes.fr/pasteur-02548181/document