April 8, 2020

Commercial fishing industry in free fall as restaurants close, consumers hunker down and vessels tie up https://a.msn.com/r/2/BB12jBMH?m=en-us&a=0

From the front lines of the COVID-19 War

March 25, 2020

30 March 2019

This is from the front lines. Read carefully and look up the words you do not understand. There are a few things I take away from this number 1 if you think rubber gloves and a mask will save you from getting it you are deadly wrong.
I am an ER MD in New Orleans. Class of 98. Every one of my colleagues have now seen several hundred Covid 19 patients and this is what I think I know.

1. Clinical course is predictable. 2-11 days after exposure (day 5 on average) flu like symptoms start. Common are fever, headache, dry cough, myalgias(back pain), nausea without vomiting, abdominal discomfort with some diarrhea, loss of smell, anorexia, fatigue.

2. Day 5 of symptoms- increased SOB, and bilateral viral pneumonia from direct viral damage to lung parenchyma.

3. Day 10 – Cytokine storm leading to acute ARDS and multiorgan failure. You can literally watch it happen in a matter of hours.

4. 81% mild symptoms, 14% severe symptoms requiring hospitalization, 5% critical.

5. Patient presentation is varied. Patients are coming in hypoxic (even 75%) without dyspnea. I have seen Covid patients present with encephalopathy, renal failure from dehydration, DKA. I have seen the bilateral interstitial pneumonia on the xray of the asymptomatic shoulder dislocation or on the CT’s of the (respiratory) asymptomatic polytrauma patient. Essentially if they are in my ER, they have it. Seen three positive flu swabs in 2 weeks and all three had Covid 19 as well. Somehow this ***** has told all other disease processes to get out of town.

6. China reported 15% cardiac involvement. I have seen covid 19 patients present with myocarditis, pericarditis, new onset CHF and new onset atrial fibrillation. I still order a troponin, but no cardiologist will treat no matter what the number in a suspected Covid 19 patient. Even our non covid 19 STEMIs at all of our facilities are getting TPA in the ED and rescue PCI at 60 minutes only if TPA fails.

7. DiagnosticCXR- bilateral interstitial pneumonia (anecdotally starts most often in the RLL so bilateral on CXR is not required). The hypoxia does not correlate with the CXR findings. Their lungs do not sound bad. Keep your stethoscope in your pocket and evaluate with your eyes and pulse ox.

8. Labs- WBC low, Lymphocytes low, platelets lower then their normal, Procalcitonin normal in 95%
CRP and Ferritin elevated most often. CPK, D-Dimer, LDH, Alk Phos/AST/ALT commonly elevated.

9. Notice D-Dimer- I would be very careful about CT PE these patients for their hypoxia. The patients receiving IV contrast are going into renal failure and on the vent sooner.

10. Basically, if you have a bilateral pneumonia with normal to low WBC, lymphopenia, normal procalcitonin, elevated CRP and ferritin- you have covid-19 and do not need a nasal swab to tell you that.

11. A ratio of absolute neutrophil count to absolute lymphocyte count greater than 3.5 may be the highest predictor of poor outcome. the UK is automatically intubating these patients for expected outcomes regardless of their clinical presentation.

12. An elevated Interleukin-6 (IL6) is an indicator of their cytokine storm. If this is elevated watch these patients closely with both eyes. Other factors that appear to be predictive of poor outcomes are thrombocytopenia and LFTs 5x upper limit of normal.

13. Disposition – I had never discharged multifocal pneumonia before. Now I personally do it 12-15 times a shift. 2 weeks ago we were admitting anyone who needed supplemental oxygen. Now we are discharging with oxygen if the patient is comfortable and oxygenating above 92% on nasal cannula. We have contracted with a company that sends a paramedic to their home twice daily to check on them and record a pulse ox. We know many of these patients will bounce back but if it saves a bed for a day we have accomplished something. Obviously we are fearful some won’t make it back.
We are a small community hospital. Our 22 bed ICU and now a 4 bed Endoscopy suite are all Covid 19. All of these patients are intubated except one. 75% of our floor beds have been cohorted into covid 19 wards and are full. We are averaging 4 rescue intubations a day on the floor. We now have 9 vented patients in our ER transferred down from the floor after intubation.

14. Luckily we are part of a larger hospital group. Our main teaching hospital repurposed space to open 50 new Covid 19 ICU beds this past Sunday so these numbers are with significant decompression. Today those 50 beds are full. They are opening 30 more by Friday. But even with the “lockdown”, our AI models are expecting a 200-400% increase in covid 19 patients by 4/4/2020.

15. Treatment – Supportive.
Worldwide 86% of covid 19 patients that go on a vent die. Seattle reporting 70%. Our hospital has had 5 deaths and one patient who was extubated. Extubation happens on day 10 per the Chinese and day 11 per Seattle.

16. Plaquenil which has weak ACE2 blockade doesn’t appear to be a savior of any kind in our patient population. Theoretically, it may have some prophylactic properties but so far it is difficult to see the benefit to our hospitalized patients, but we are using it and the studies will tell. With Plaquenil’s potential QT prolongation and liver toxic effects (both particularly problematic in covid 19 patients), I am not longer selectively prescribing this medication as I stated on a previous post.

17. We are also using Azithromycin, but are intermittently running out of IV.
Do not give these patient’s standard sepsis fluid resuscitation. Be very judicious with the fluids as it hastens their respiratory decompensation. Outside the DKA and renal failure dehydration, leave them dry.

18. Proning vented patients significantly helps oxygenation. Even self proning the ones on nasal cannula helps.
Vent settings- Usual ARDS stuff, low volume, permissive hypercapnia, etc. Except for Peep of 5 will not do. Start at 14 and you may go up to 25 if needed.
Do not use Bipap- it does not work well and is a significant exposure risk with high levels of aerosolized virus to you and your staff. Even after a cough or sneeze this virus can aerosolize up to 3 hours.
The same goes for nebulizer treatments. Use MDI. you can give 8-10 puffs at one time of an albuterol MDI. Use only if wheezing which isn’t often with covid 19. If you have to give a nebulizer must be in a negative pressure room; and if you can, instruct the patient on how to start it after you leave the room.

19. Do not use steroids, it makes this worse. Push out to your urgent cares to stop their usual practice of steroid shots for their URI/bronchitis. We are currently out of Versed, Fentanyl, and intermittently Propofol. Get the dosing of Precedex and Nimbex back in your heads.

20. One of my colleagues who is a 31 yo old female who graduated residency last may with no health problems and normal BMI is out with the symptoms and an SaO2 of 92%. She will be the first of many.

21. I PPE best I have. I do wear a MaxAir PAPR the entire shift. I do not take it off to eat or drink during the shift. I undress in the garage and go straight to the shower. My wife and kids fled to her parents outside Hattiesburg. The stress and exposure at work coupled with the isolation at home is trying. But everyone is going through something right now.

Everyone is scared; patients and employees. But we are the leaders of that emergency room. Be nice to your nurses and staff. Show by example how to tackle this crisis head on.

Good luck to us all.

Chromebook 101: how to use Android apps on your Chromebook – The Verge

March 10, 2020

With Google discontinuing support for the Google Chrome Store apps (see:  https://chromeunboxed.com/google-announces-timeline-for-the-end-of-chrome-apps-on-chromebooks/ ) over the next year, it is probably a good idea to get familiar with Google Play Store (i.e. Android) apps.

https://www.theverge.com/2019/10/9/20903281/chromebook-os-android-apps-how-to-use

If you use your Chromebook as primarily a web browser, and run Android apps on your Android Phone then this change probably has little impact on you.  Especially considerinng that support for Chrome browser extensions will continue to be supported.

Full Stack Data Science. Next wave in incorporating AI into the corporation.

October 22, 2019

https://www.forbes.com/sites/cognitiveworld/2019/09/11/the-full-stack-data-scientist-myth-unicorn-or-new-normal/#1eb0d4f32c60

I like the concept of “Full Stack Data Science”, especially the way the author depicts it in the included graphic.

One thing I would like to point out is the recognition that the process is really a circle (as depicted) and not a spiral, or line.  What I mean by that, is the path does not close between what can be perceived as the beginning “Business goal” and the end “Use, monitor and optimize”.

The results of applying Data Science to business problems not only helps solve these problems, but actually changes the motivators that drive the seeking of solutions in the first place.  Business goals are usually held up as the ends with the lowest dependency gradient of any component of any complex enterprise architecture.  While this may be true at any point in time, the dependency is not zero.  Business goals themselves change over time and not just in response to changing economic, societal or environmental factors.  The technology used to meet these goals does itself drive changes to the business goals.

A party, whether person or organization, tends to do what it is capable of doing.  Technology gives it more activities to undertake and things to produce and consume, which then feedback to the goals that motivate it.

I think this article is one of the best I’ve seen in making that point.

 

Machine Learning and Database Reverse Engineering

October 13, 2019

Artificial intelligence (AI) is based on the assumption that programming a computer using a feedback loop can improve the accuracy of its results.  Changing the values of the variables, called “parameters”, used in the execution of the code, in the right way, can influence future executions of the code.  These future executions are then expected to produce results that are closer to a desired result than previous executions.  If this happens the AI is said to have “learned”.

Machine learning (ML) is a subset of AI.  An ML execution is called an “activation”.  Activations are what “train” the code to get more accurate.  An ML activation is distinctly a two-step process.  In the first step, input data is conceptualized into what are called “features”.  These features are labeled and assigned weights based on assumptions about their relative influence on the output.  The data is then processed by selected algorithms to produce the output.  The output of this first step is then compared to an expected output and a difference is calculated.  This closes out the first step which is often called “forward propagation”.

The second step, called “back propagation” takes the differences between the output of the first step, called “y_hat” and the expected output, called “y” and, using a different but related set of algorithms, determines how the weights of the features should be modified to reduce the difference between y and y_hat.  The activations are repeated until either the user is satisfied with the output, or changing the weights makes no more difference.  The trained and tested model can then be used to do predictions on similar data sets, and hopefully create value for the owning party (either person or organization).

In a sense, ML is a bit like database reverse engineering (DRE).  In DRE we have the data, which is the result of some set of processing rules, which we don’t know[i], that have been applied to that data.  We also have our assumptions of what we think a data model would have to look like to produce such data, and what it would need to look like to increase the value of the data.  We iteratively apply various techniques to try to decipher the data modeling rules, mostly based on data profiling. With each iteration we try to get closer to what we believe the original data model looked like.  As with ML activation we eventually stop, either because we are satisfied or because of resource limitations.

At that point we accept that we have produced a “good enough model” of the existing data.  We then move on to what we are going to do with the data, feeling confident that we have an adequate abstraction of the data model as it exists, how it was arrived at, and what we need to do to improve it.  This is true even if there was never any “formal” modeling process originally.

Let’s look at third normal form (3NF) as an example of a possible rule that might have been applied to the data.  3NF is a rule that all columns of a table must be dependent on the key, or identifier of the table, and nothing else.  If the data shows patterns of single key dependencies we can assume that 3NF was applied in its construction.  The application of the 3NF rule will create certain dependencies between the metadata and the data that represent business rules.

These dependencies are critical to what we need to do to change the data model to more closely fit, and thus be more valuable for, changing organizational expectations.  It is also these dependencies that are discovered through both ML and DRE that enable, respectively, both artificial intelligence and business intelligence (BI).

It has been observed that the difference between AI and BI is that in BI we have the data and the rules, and we try to find the answers.  In AI we have the data and the answers, and we try to find the rules.  Whether results derived from either technology are answers to questions, or rules governing patterns, both AI and BI are tools for increasing the value of data.

These are important goals because attaining them, or at least approaching them, will allow a more efficient use of valuable resources, which in turn will allow a system to be more sustainable, support more consumers of those resources, and produce more value for the owners of the resources.

[i] If we knew what the original data model looked like we would have no need for reverse engineering.

Fortune: Google Claims ‘Quantum Supremacy,’ Marking a Major Milestone in Computing

September 21, 2019

Fortune: Google Claims ‘Quantum Supremacy,’ Marking a Major Milestone in Computing.
https://fortune.com/2019/09/20/google-claims-quantum-supremacy/

Much has been said for a long time about the relative merits of and differences between quality and quantity. One school of thought holds it is not how much you produce but the ‘fit to purpose’ or quality of the output that trumps everything else. The other school holds that large amounts of nearly anything overwhelms subjectively measured attributes simply because enough of anything will ultimately include the best examples of that thing, as well as the non-best examples.

However what is becoming abundantly clear to many people, is that speed cures all ills. Go fast enough and quality and quantity are rendered meaningless as differentiators. Enough speed, for example, allows a party or a thing to just ‘re-do’ until it’s right. Quantity and quality become the same attribute at high enough speeds.

Google Sheets or Microsoft Excel? The differences are disappearing — Quartz

September 19, 2019

https://qz.com/1283203/google-sheets-or-microsoft-excel-the-differences-are-disappearing/

Google Kills Hyper-Threading On Chrome OS In Wake Of Critical Intel Flaw

May 15, 2019

https://chromeunboxed.com/google-kills-hyper-threading-on-chrome-os-in-wake-of-critical-intel-flaw/

Android Authority: 8 years on from the first Chromebooks: Google was right about them

May 11, 2019

Android Authority: 8 years on from the first Chromebooks: Google was right about them.
https://www.androidauthority.com/google-chromebook-launch-984205/

New Feature Coming For Chromebook Extended Displays

April 13, 2019

New Feature Coming For Chromebook Extended Displays

It looks like Display Port and USB-C are required for daisy chaining monitors with Chromebooks.