| I don’t have access to the full Nature article. Their intro has only a tiny bit of meat on its bones: > 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. |