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The Composite Severity Score for Lumbar Spine MRI: a Metric of Cumulative Degenerative Disease Predicts Time Spent on Interpretation and Reporting
- Source :
- Journal of Digital Imaging
- Publication Year :
- 2021
- Publisher :
- Springer International Publishing, 2021.
-
Abstract
- Conventional measures of radiologist efficiency, such as the relative value unit, fail to account for variations in the complexity and difficulty of a given study. For lumbar spine MRI (LMRI), an ideal performance metric should account for the global severity of lumbar degenerative disease (LSDD) which may influence reporting time (RT), thereby affecting clinical productivity. This study aims to derive a global LSDD metric and estimate its effect on RT. A 10-year archive of LMRI reports comprising 13,388 exams was reviewed. Objective reporting timestamps were used to calculate RT. A natural language processing (NLP) tool was used to extract radiologist-assigned stenosis severity using a 6-point scale (0 = “normal” to 5 = “severe”) at each lumbar level. The composite severity score (CSS) was calculated as the sum of each of 18 stenosis grades. The predictive values of CSS, sex, age, radiologist identity, and referring service on RT were examined with multiple regression models. The NLP tool accurately classified LSDD in 94.8% of cases in a validation set. The CSS increased with patient age and differed between men and women. In a univariable model, CSS was a significant predictor of mean RT (R2 = 0.38, p p R2 = 0.83, p 25, R2 = 0.15, p = 0.05). Individual radiologist study volume was negatively correlated with mean RT (Pearson’s R = − 0.35, p
- Subjects :
- Male
medicine.medical_specialty
030218 nuclear medicine & medical imaging
03 medical and health sciences
0302 clinical medicine
Degenerative disease
Lumbar
Patient age
Linear regression
Radiologists
medicine
Humans
Radiology, Nuclear Medicine and imaging
Original Paper
Lumbar Vertebrae
Radiological and Ultrasound Technology
business.industry
Lumbar spine MRI
medicine.disease
Magnetic Resonance Imaging
Computer Science Applications
Stenosis
surgical procedures, operative
Female
Radiology
Metric (unit)
business
030217 neurology & neurosurgery
Relative value unit
Subjects
Details
- Language :
- English
- ISSN :
- 1618727X and 08971889
- Volume :
- 34
- Issue :
- 4
- Database :
- OpenAIRE
- Journal :
- Journal of Digital Imaging
- Accession number :
- edsair.doi.dedup.....7c1b09e6b8ffecd4f530e00355c4a88e