Developing a triage predictive model for access to a spinal surgeon using clinical variables and natural language processing of radiology reports.

PURPOSE: To utilize natural language processing (NLP) of MRI reports and various clinical variables to develop a preliminary model predictive of the need for surgery in patients with low back and neck pain. Such a model would be beneficial for informing clinical practice decisions and help reduce the number of unnecessary surgical referrals, streamlining the surgical process. METHODS: A historical cohort study was conducted using de-identified data from patients referred to a spine assessment clinic. Various demographic, clinical, and radiological variables were included as potential predictors. Full-text radiology reports of patients' MRI findings were vectorized using NLP before applying machine learning algorithms to develop models predicting who underwent surgery. Outputs from these models were then entered into a logistic regression model with clinical variables to develop a preliminary model predictive of surgical recommendations. RESULTS: Of the 398 patients assessed, 71 underwent spine surgery. NLP variables were significant predictors in univariate analysis but did not remain in the final logistic regression model. An outcome of receiving surgery was predicted by a primary symptom of low back and leg pain (adjusted odds ratio 2.81), distal pain indicated by a pain diagram (adjusted odds ratio 2.49) and self-reported difficulties walking (adjusted odds ratio 2.73). CONCLUSION: A logistic regression model was created to predict which patients may require spine surgery. Simple clinical variables appeared more predictive than variables created using NLP. However, additional research with more data samples is needed to validate this model and fully evaluate the usefulness of NLP for this task.

Risk Factors for Developing Concurrent Posttraumatic Stress Injury After Work-Related Musculoskeletal Injury: A Case-Control Study.

OBJECTIVE: This study aimed to study risk factors for developing concurrent posttraumatic stress injury (PTSI) among workers experiencing work-related musculoskeletal injury (MSI). METHODS: A case-control study was conducted using workers' compensation data on injured workers undergoing rehabilitation programs for concurrent MSI and PTSI (cases) and MSI only (controls). A variety of measures known at the time of the compensable injury were entered into logistic regression models. RESULTS: Of the 1948 workers included, 215 had concurrent MSI and PTSI. Concurrent MSI and PTSI were predicted by type of accident (adjusted odds ratio [OR], 25.8), experiencing fracture or dislocation fracture or dislocation (adjusted OR, 3.7), being public safety personnel (adjusted OR, 3.1), and lower level of education (adjusted OR, 1.9). CONCLUSIONS: Experiencing a concurrent PTSI diagnosis with MSI after work-related accident and injury appears related to occupation, type of accident, and educational background.