An algorithmic approach to reducing unexplained pain disparities in underserved populations.
Aged
Algorithms
Deep Learning
Female
Health Status Disparities
Humans
Male
Middle Aged
Osteoarthritis, Knee
/ diagnostic imaging
Pain
/ physiopathology
Pain Measurement
Race Factors
/ statistics & numerical data
Severity of Illness Index
Socioeconomic Factors
Vulnerable Populations
/ statistics & numerical data
Journal
Nature medicine
ISSN: 1546-170X
Titre abrégé: Nat Med
Pays: United States
ID NLM: 9502015
Informations de publication
Date de publication:
01 2021
01 2021
Historique:
received:
20
03
2020
accepted:
24
11
2020
entrez:
14
1
2021
pubmed:
15
1
2021
medline:
23
1
2021
Statut:
ppublish
Résumé
Underserved populations experience higher levels of pain. These disparities persist even after controlling for the objective severity of diseases like osteoarthritis, as graded by human physicians using medical images, raising the possibility that underserved patients' pain stems from factors external to the knee, such as stress. Here we use a deep learning approach to measure the severity of osteoarthritis, by using knee X-rays to predict patients' experienced pain. We show that this approach dramatically reduces unexplained racial disparities in pain. Relative to standard measures of severity graded by radiologists, which accounted for only 9% (95% confidence interval (CI), 3-16%) of racial disparities in pain, algorithmic predictions accounted for 43% of disparities, or 4.7× more (95% CI, 3.2-11.8×), with similar results for lower-income and less-educated patients. This suggests that much of underserved patients' pain stems from factors within the knee not reflected in standard radiographic measures of severity. We show that the algorithm's ability to reduce unexplained disparities is rooted in the racial and socioeconomic diversity of the training set. Because algorithmic severity measures better capture underserved patients' pain, and severity measures influence treatment decisions, algorithmic predictions could potentially redress disparities in access to treatments like arthroplasty.
Identifiants
pubmed: 33442014
doi: 10.1038/s41591-020-01192-7
pii: 10.1038/s41591-020-01192-7
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Research Support, U.S. Gov't, Non-P.H.S.
Langues
eng
Sous-ensembles de citation
IM
Pagination
136-140Subventions
Organisme : US Social Security Administration
ID : RDR18000003
Pays : International
Commentaires et corrections
Type : CommentIn
Type : CommentIn
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