> comprising 703,782 adult patients across 172 inpatient and 1,062 outpatient sites. Our model predicts 55.8% of all inpatient episodes of acute kidney injury, and 90.2% of all acute kidney injuries that required subsequent administration of dialysis, with a lead time of up to 48 h and a ratio of 2 false alerts for every true alert.
1. Studies have shown that normal, healthy people regularly suffer AKI just walking around - and resolve spontaneously. Most commonly this is transient dehydration. Catching 50% of these is so simple an amateur nurse can do it. Unimpressive.
2. Requiring HD is generally something that occurs over days (still technically an AKI), and it’s almost never a surprise. Catching 90% of these isn’t anything either.
3. A “lead time of up to 2 days” is a lot different than the linked site’s “lead time of 2 days” suggestion. A lead time of “up to” 2 days including cases trending towards dialysis is very unimpressive.
I realize stripped of clinical context this sounds like they pulled something off, but AKI is usually not a meaningful problem, and is one of the easiest things to catch, and basically always gets caught. This algo, at least relative to my experience in my (semi-prestigious, regionally semi-known) institution, underperforms what I would expect out of a fourth year med student / bright third year med student / experienced nurse.
If this wasn’t attached to an AI buzzword, I can’t imagine it being publishable or noteworthy.
A better system (perhaps not even using much AI, whatever that is, and just use basic rules and ML techniques) would be helping physicians monitor and come up with treatment suggestions and outcome prwdictions for a specific patient based on their particular vitals/history. E.g provide a suggestion to physician that heart failure causing aki could be treated by crtp pacemaker bc cf rate is within a range for this particular patient - so they should talk to cardiologist/surgeon immediately.
