New algorithm gives trauma survivors a "PTSD risk score"

Ninety percent of the patients it predicted would develop PTSD did.
Subscribe to Freethink on Substack for free
Get our favorite new stories right to your inbox every week

Every year, emergency departments (EDs) in the U.S. treat about 30 million people for traumatic injuries.

By definition, these injuries are both sudden and severe — injuries caused by guns, car wrecks, etc. — and within a year of experiencing them, between 10% and 15% of survivors will develop post-traumatic stress disorder (PTSD).

Now, researchers have developed an algorithm that can predict which traumatic injury patients are at highest risk of developing PTSD — and in need of preventative treatment — before they even leave the ED.

Developing PTSD

The algorithm, detailed in the journal Nature Medicine, generates a single PTSD risk score for trauma patients based on 70 clinical data points associated with future development of the disorder (blood pressure, stress hormone levels, etc.) and a healthcare worker’s brief assessment.

“We selected measures that are routinely collected in the ED and logged in the electronic medical record, plus answers to a few short questions about the psychological stress response,” lead author Katharina Schultebraucks, an assistant professor of behavioral and cognitive sciences at Columbia University, said in a press release.

“The idea was to create a tool that would be universally available and would add little burden to ED personnel,” she added.

The researchers used data from 377 adult trauma survivors in Atlanta to develop the algorithm. They then used it to produce PTSD risk scores for 221 adult trauma survivors in New York City.

Ninety percent of the patients the algorithm categorized as being at risk of developing PTSD did, in fact, develop long-lasting symptoms within a year.

Of the patients who didn’t develop symptoms, the algorithm only categorized 5% as at risk — a relatively low false positive rate.

Path to Clinical Use

Though impressive, the algorithm does have its limitations.

For one, it missed quite a few future PTSD sufferers during the NYC testing: 29% of patients it categorized as “low risk” or “no risk” ended up developing PTSD within a year.

The earlier we can treat those at risk, the better the likely outcomes.


Katharina Schultebraucks

Furthermore, some of the clinical data points it analyzes are collected through blood testing — that means patients who don’t have blood drawn during their ED visit wouldn’t be able to benefit from it.

The algorithm will also need to be validated in tests with larger sample sizes before it could be deployed — but if it reaches that stage, there’s no telling how many people might benefit.

“For many trauma patients, the ED visit is often their sole contact with the health care system,” Schultebraucks said.

“The time immediately after a traumatic injury is a critical window for identifying people at risk for PTSD and arranging appropriate follow-up treatment,” she continued. “The earlier we can treat those at risk, the better the likely outcomes.”

We’d love to hear from you! If you have a comment about this article or if you have a tip for a future Freethink story, please email us at tips@freethink.com.

Subscribe to Freethink on Substack for free
Get our favorite new stories right to your inbox every week
Related
A personal assistant for everyone: The promise of ambient AI
We’re leaving the app era and entering the age of ambient AI: intelligent help that’s always on, but never in the way.
Gen Z: We must resist the temptation to cheat on everything
Adopting the “cheat on everything” mentality — treating thinking as a burden AI can eliminate — is not only wrong, it’s dangerous.
There are no new ideas in AI — only new datasets
Our next AI breakthrough will come when we unlock a source of data we’ve either overlooked or never fully harnessed.
Albert Einstein said automation caused the Great Depression. It didn’t.
Einstein blamed automation for the widespread unemployment of the Great Depression, but his reasoning was based on a false premise.
Has AI made “learn to code” obsolete?
Freethink talks to the creator of the world’s most popular AI coding assistant to find out whether learning to code is still worthwhile.
Up Next
Depression Treatment
Exit mobile version