People already do need it! In the US alone, about a half million people are on dialysis. There are many health and functional consequences to both intermittent and peritoneal dialysis; they are not exactly benign treatments.
The major benefit to implanting it under the skin, as we do with pacemakers, is that doing without permanent holes or tubes through the skin reduces infection risk.
Consider also the danger of having something dangling from your body that is powered by your arterial blood pressure (from a major artery, as the kidney is). A trip and fall could be instantly fatal.
The video evidence of the L-form switching is no doubt very interesting, but a more accurate headline would be “One cause of resistance to certain antibiotics in UTIs identified.” Or the original article’s title, which is “Possible role of L-form switching in recurrent urinary tract infection”; see https://www.nature.com/articles/s41467-019-12359-3
I would love to see data on how common this phenomenon is in various populations of UTI patients (elderly, young, inpatient, outpatient, etc), given different prior exposures to antibiotics; for now it looks like 30 patients were assessed.
There are obviously many different causes of resistance previously identified, going all the way back to penicillinase enzymes inactivating penicillin. As often happens with lay summaries, this makes it sound a little too much like the cause of all antibiotic resistance has been found.
Well, for starters, it's hard to get 200 people who might be eligible for life-preserving surgery to volunteer to possibly get a sham procedure for the exclusive benefit of othewars; not to mention build a team of surgeons, hospitals, etc. willing to do the trial; and someone to pay for millions of dollars in treatment and administrative costs. Clinical trials involving surgery, especially with sham procedures as a proper control, are exceedingly rare in the US for these reasons. (this is discussed in the OP itself.)
I found the Statistical Learning self paced course on Stanford's site to be a great formal intro to ML algorithms implemented in R, and it is taught by the inimitable Hastie and Tibshirani: http://statlearning.class.stanford.edu
So what good does increasing US medical school graduation rates do? OK, it would displace some IMGs/FMGs from residency positions, but it doesn't ultimately create more doctors. You can't be licensed to practice independently in the US unless you enter a residency, take the USMLE Step 3 after intern year, and typically you also take a specialty board exam at the end of residency.
Thanks for replying! I'll certainly be looking forward to the publication.
>about 10% of people who come in to the cardiology clinic experiencing symptoms are diagnosed with an abnormal heart rhythm
OK, but I'd be more careful about staying apples to apples in your comparisons; your app is about asymptomatic AFib. So how many of those people going to the cardiology clinic had undiagnosed AFib; for how many of those would a new diagnosis of AFib have changed the plan of care; etc. Kind of like robbiep was saying, I would be interested in actual added value from the larger perspective.
Totally appreciate your point about perfect being the enemy of the good. The danger is that these semi-medical wearables currently straddle a strange zone between medical and consumer use. The inevitable marketing strategy is to co-opt the positive reputation of medical products while acknowledging none of the pitfalls of consumer products. Most of the screening methods you bring up are used by a doctor on symptomatic patients with a suggestive history, and only as a partial component of clinical judgement. The way Cardiogram seems to make the most money, on the other hand, is to sell the product to asymptomatic, casual users. (Furthermore, CHA2DS2-Vasc costs 30 seconds of talking or reading a medical record, not $700 in Apple products.) So you're inevitably running up against some doubts among physicians [0].
And finally, I agree that more machine learning practitioners should join medical research. I hope the field works to set more reasonable expectations, however, as in: ML will solve very specific subtasks in clinical reasoning (as in the diabetic retinopathy study [1]). Instead, the headlines usually ratchet that up to "AI will replace radiology/cardiology/$specialty in X years." That tends to hurt the people currently in the trenches, since their contribution in bringing about practical, incremental change is diminished. The top answer of this Quora thread [2] has a good discussion of the many dimensions of the problem.
We need to see the full, published study and its methods (particularly around recruitment and exclusion criteria) before we can judge it properly. Until then, the presented statistics about accuracy, sensitivity, and specificity potentially bear no relation to real world usage, if the cohort and data quality were tightly controlled, as you'd expect for an initial study involving the makers of the algorithm. A few other thoughts:
1. Even at 98% sensitivity and 90% specificity [0], which I don't think would hold up with real world usage in casual, healthy users, if AFib has a prevalence of roughly 2-3% [1] then by a quick back of the envelope calculation a positive test result is still 5× more likely to be a false positive than a true positive. With those odds, I don't think many cardiologists are going to answer the phone. You'd still need an EKG to diagnose AFib.
2. There is huge variance among people's real world use of wearable sensors, and also among the quality of the sensors. (Imagine people that wear the watch looser, sweat more, have different skin, move it around a lot, etc.) You'd likely need to do an open, third-party validation study of the accuracy of the sensors in the Apple Watch before you can expect doctors to use the data. My understanding is that the Apple Watch sensors are actually pretty good compared to other wearable sensors, but I don't know of any rigorous study of that compares them to an EKG.
3. Obviously, this is only for AFib. AFib is a sweet corner case in terms of extrapolating from heart rate to arrhythmia, because it's a rapid & irregular rhythm that probably contains some subpatterns in beats that are hard for humans to appreciate. As others—including Cardiogram themselves [2]—have pointed out previously, many serious arrhythmias are not possible to detect with only an optical heart rate sensor.
As of right now, the USB-C hub situation is actually pretty dire, as in the only all-in-one solutions are $100+, or not yet released [1] [2]. My past experience with non-powered USB hubs has taught me they're pretty hit-or-miss with a new device, so I don't see how cramming audio, video, and pass-thru power into them is going to help that situation.
See the first five minutes of this video [3], which actually involves Apple's "Digital AV Multiport" adapter, to see the near-term experience of "dongle hell."
The upgrade from floppy to optical only, or optical to bigger hard drives and SSDs and fast WiFi, are leaps in capability that don't even compare to the marginal gain of USB-C over USB3/Thunderbolt 2... especially when that "upgrade" is paired with the number of ports on an entry-level MBP dropping from 7 to 2 (not counting audio, which thankfully survived).
With only 2 ports, you need a ridiculous number of big dongles or hubs to get serious work done, and any pro user with >1 external HD, display, or gigabit wired is rightfully wondering what the hell Apple was thinking.
Get them while they're still in stock. That's all I can say, it's what I just did. I cannot stand the new microwave-keypad feel of the new keyboard, and the lack of useful ports and price hike is just the extra slap in the face for me to wake up. This is probably going to be the last Mac laptop I buy.
I love this brief history of the Ruby ecosystem and its community, maybe the Trump parody I've enjoyed most. Maybe, the best.
Incidentally, I suppose the first 5 minutes of fighting with a three-way dongle and a poor connection, having to restart the talk, etc. is just a little preview of what every 2016 MBP owner will soon have the pleasure of experiencing themselves.
Well, if you want to become a doctor, at least in the US, knowing some biology will certainly help you on the first step of licensing exams... :-)
But the general point does hold that yes, a high level of math or CS training is advantageous for anyone moving into a career in the life sciences, since it appears that that's where much of the foreseeable growth (in careers and research funding) seems to be.
It's also been said by many that it's easier to learn some biology after training rigorously in CS/math, rather than the other way around. Dudley Herschbach (a Nobel-prize winning chemist) once said to me that his one piece of advice for young researchers would be simply, "Learn as much math as you can."
Hmm, well as a counterpoint to your point about the FDA, fidaxomicin was approved in 2011 for general use against C. difficile colitis, because it showed certain outcomes that compared favorably against the current standard of care (oral vancomycin) [1]. The reason it isn't used more often is probably because it is one of the most expensive antibiotics available. Antibiotics aren't typically approved only as "last resort"; it remains at the discretion of the physician to jump straight to the big guns before drug susceptibility test results are available (which is part of the problem).
Teixobactin is cool, but if it's only active against gram positives, it's never going to work against most of the bacteria listed in the article: E. coli, Salmonella, Klebsiella, N. gonorrhoeae, etc. Most of the terrible new drug resistance genes are showing up in gram negatives.
Sure, new methods of finding antibiotics are in the works, although the article you link has plenty of experts recommending caution about their potential. The bigger point is that in 2016 there is a looooooong road from antibiotic "candidate" to FDA-approved drug. That road involves decades of trials and costs billions of dollars per approved drug.
The larger problem is that there is little if any incentive for pharma companies to invest in antibiotics compared to traditional blockbuster drugs that are supposed to be taken chronically (and therefore have better ROI). It's the same reason little R&D goes into making new vaccines. It doesn't matter how many candidates are found if they can't make it to market in a timely fashion (the point of the CDC bar graph), and this is what the "slow catastrophe" really is. It is not that scientists will never figure out new ways to kill bacteria.
Whoa, let's not put words in my mouth here. First of all, I said nothing about denying people access to their medical data. Once the tests are done, yes, it's the patient's data (and in the US, HIPAA concurs). We're not in disagreement there.
Secondly, there may be all kinds of other uses for glucose tests that one could research, but consumers running tests on themselves in an uncontrolled manner is not research. I would never say that no other uses will ever be discovered, but let's do that scientifically, please. My specific issue was with how diabetic glucose self-testing was used as rhetorical evidence that more blood tests help people, while failing to note that those tests are done to dose (potentially dangerous, fast-acting) medications, not to "keep tabs" on anybody's diabetes in a diagnostic sense, as was implied by the omission.
You say below that "people are coming around on glucose in the same way that we now understand that the cardio signal [...] are predictive of an enormous number of physiological and psychological phenomena." That's a lovely hypothesis, but please tell me who these people are, and please show me the evidence of the predictive value.
Until then, the Credentialed Professionals are perfectly justified in shrugging their shoulders at post-prandial glucose data from healthy patients (who, contrarily, will demand that needless and dangerous follow-up procedures are ordered for them), and the companies selling consumers these tests will not be helping anybody become healthier. I could go on, but this comment sums up the societal effects better than I could, even referencing your "ideal" of the ECG for screening. https://news.ycombinator.com/item?id=11694341