Perspective and caveats
The figures of one in five and one in four found by the CDC researchers don’t surprise David Putrino, PT, PhD, director of rehabilitation innovation for Mount Sinai Health System in New York and director of its Abilities Research Center, which cares for long-COVID patients.
“Those numbers are high and it’s alarming,” he said. “But we’ve been sounding the alarm for quite some time, and we’ve been assuming that about one in five end up with long COVID.”
He does see a limitation to the CDC research – that some symptoms could have emerged later, and some in the control group could have had an undiagnosed COVID infection and gone on to develop long COVID.
As for machine learning, “this is something we need to approach with caution,” Dr. Putrino said. “There are a lot of variables we don’t understand about long COVID,’’ and that could result in spurious conclusions.
“Although I am supportive of this work going on, I am saying, ‘Scrutinize the tools with a grain of salt.’ Electronic records, Dr. Putrino points out, include information that the doctors enter, not what the patient says.
Dr. Pfaff responds: “It is entirely appropriate to approach both machine learning and EHR data with relevant caveats in mind. There are many clinical factors that are not recorded in the EHR, and the EHR is not representative of all persons with long COVID.” Those data can only reflect those who seek care for a condition, a natural limitation.
When it comes to algorithms, they are limited by data they have access to, such as the electronic health records in this research. However, the immense size and diversity in the data used “does allow us to make some assertations with much more confidence than if we were using data from a single or small number of health care systems,” she said.
A version of this article first appeared on Medscape.com.