Improving the staging of neck injuries using a new index, the Neck Functional Holistic Analysis Score: Clustering approach to determine degrees of impairment

Paravertebral Muscle Mechanical Properties and Spinal Range of Motion in Patients with Acute Neck or Low Back Pain: A Case-Control Study

Our aims were to identify potential differences in muscle mechanical properties (MMPs) of cervical and lumbar tissues and in spinal range of motion (ROM) between patients with acute low back pain (LBP) or acute neck pain (NP) and healthy controls, and to identify if ROMs and MMPs are able to identify subjects among the three groups. Clinical variables (pain, disability, fear of movement, kinesiophobia, quality of life), MMPs and ROMs were obtained in 33 subjects with acute LBP, 33 with acute NP, and 33 healthy control subjects. Between-groups differences and explanatory models to discriminate groups depending on MMPs and ROMs were calculated. The results showed that cervical tone was higher in patients with acute NP than in controls, while cervical decrement was higher in both spinal pain groups. Patients with acute NP showed reduced cervical flexion when compared to acute LBP and control groups, and also cervical rotation, but just against controls. Furthermore, lumbar flexion was reduced in patients with acute LBP when compared to those with acute NP. Cervical decrement was able to discriminate spinal pain individuals from controls in a multinominal regression (R²: Cox–Snell estimation = 0.533; Nagelkerke estimation = 0.600). Lumbar flexion differentiated patients with acute LBP and controls, whereas cervical flexion differentiated patients with acute NP and controls. This study supports a tendency of the affectation of other spinal regions when only one is affected.

Artificial intelligence prediction of the effect of rehabilitation in whiplash associated disorder

The active cervical range of motion (aROM) is assessed by clinicians to inform their decision-making. Even with the ability of neck motion to discriminate injured from non-injured subjects, the mechanisms to explain recovery or persistence of WAD remain unclear. There are few studies of ROM examinations with precision tools using kinematics as predictive factors of recovery rate. The present paper will evaluate the performance of an artificial neural network (ANN) using kinematic variables to predict the overall change of aROM after a period of rehabilitation in WAD patients. To achieve this goal the neck kinematics of a cohort of 1082 WAD patients (55.1% females), with mean age 37.68 (SD 12.88) years old, from across Spain were used. Prediction variables were the kinematics recorded by the EBI^® 5 in routine biomechanical assessments of these patients. These include normalized ROM, speed to peak and ROM coefficient of variation. The improvement of aROM was represented by the Neck Functional Holistic Analysis Score (NFHAS). A supervised multi-layer feed-forward ANN was created to predict the change in NFHAS. The selected architecture of the ANN showed a mean squared error of 308.07–272.75 confidence interval for a 95% in the Monte Carlo cross validation. The performance of the ANN was tested with a subsample of patients not used in the training. This comparison resulted in a medium correlation with R = 0.5. The trained neural network to predict the expected difference in NFHAS between baseline and follow up showed modest results. While the overall performance is moderately correlated, the error of this prediction is still too large to use the method in clinical practice. The addition of other clinically relevant factors could further improve prediction performance.