Q: Why do we use a stethoscope?
A: For many reasons, and here’s one of them that I will argue is undervalued. And is still at the heart (hint hint) of some ongoing research…
This cohort had quite the notable med regimen: digoxin (84%), furosemide (84%), vasodilators (56%), and milrinone (4%); 39/50 were being evaluated for transplant
A plausible explanation for this observation in chronic (as opposed to acute HF):
https://t.co/UKT9XDf0mc
https://t.co/xWeIhzZ93b
https://t.co/Plaev6fe4q
Substratification showed that the lowest risk group (low NT-proBNP and not in alert status) vs. the highest risk group (high NT-proBNP levels and being in alert status), had a 50-fold increased risk of an HF event (1.00/pt-year vs 0.02/pt-year).
Look how far we’ve come from simply placing a stethoscope on a patient - although this is still not obsolete, it has led to some excellent advances over the years.
https://t.co/6jyS3lhfE5
Next exciting thing on the agenda @PennStHershey - using @nanowearinc\u2019s wearable vest with nano-fiber ECG pads and a very sensitive microphone to quantify S3 heart sounds in the inaudible range, to predict HF exacerbations. pic.twitter.com/kmxpRL6a0Y
— Alexander Hajduczok, MD (@AHajduczok) December 19, 2020
Ali, O., Hajduczok, A.G. & Boehmer, J.P. Remote Physiologic Monitoring for Heart Failure. Curr Cardiol Rep 22, 68 (2020).
https://t.co/1bmFVDU4Ij
More from Health
You gotta think about this one carefully!
Imagine you go to the doctor and get tested for a rare disease (only 1 in 10,000 people get it.)
The test is 99% effective in detecting both sick and healthy people.
Your test comes back positive.
Are you really sick? Explain below 👇
The most complete answer from every reply so far is from Dr. Lena. Thanks for taking the time and going through
You can get the answer using Bayes' theorem, but let's try to come up with it in a different —maybe more intuitive— way.
👇
Here is what we know:
- Out of 10,000 people, 1 is sick
- Out of 100 sick people, 99 test positive
- Out of 100 healthy people, 99 test negative
Assuming 1 million people take the test (including you):
- 100 of them are sick
- 999,900 of them are healthy
👇
Let's now test both groups, starting with the 100 people sick:
▫️ 99 of them will be diagnosed (correctly) as sick (99%)
▫️ 1 of them is going to be diagnosed (incorrectly) as healthy (1%)
👇
Imagine you go to the doctor and get tested for a rare disease (only 1 in 10,000 people get it.)
The test is 99% effective in detecting both sick and healthy people.
Your test comes back positive.
Are you really sick? Explain below 👇
The most complete answer from every reply so far is from Dr. Lena. Thanks for taking the time and going through
Really doesn\u2019t fit well in a tweet. pic.twitter.com/xN0pAyniFS
— Dr. Lena Sugar \U0001f3f3\ufe0f\u200d\U0001f308\U0001f1ea\U0001f1fa\U0001f1ef\U0001f1f5 (@_jvs) February 18, 2021
You can get the answer using Bayes' theorem, but let's try to come up with it in a different —maybe more intuitive— way.
👇
Here is what we know:
- Out of 10,000 people, 1 is sick
- Out of 100 sick people, 99 test positive
- Out of 100 healthy people, 99 test negative
Assuming 1 million people take the test (including you):
- 100 of them are sick
- 999,900 of them are healthy
👇
Let's now test both groups, starting with the 100 people sick:
▫️ 99 of them will be diagnosed (correctly) as sick (99%)
▫️ 1 of them is going to be diagnosed (incorrectly) as healthy (1%)
👇