Redundant nerve roots in lumbar spinal stenosis: inter- and intra-rater reliability of an MRI-based classification.

PURPOSE: Patients with central lumbar spinal stenosis (LSS) have a longer symptom history, more severe stenosis, and worse postoperative outcomes, when redundant nerve roots (RNRs) are evident in the preoperative MRI. The objective was to test the inter- and intra-rater reliability of an MRI-based classification for RNR. METHODS: This is a retrospective reliability study. A neuroradiologist, an orthopedic surgeon, a neurosurgeon, and three orthopedic surgeons in-training classified RNR on 126 preoperative MRIs of patients with LSS admitted for microsurgical decompression. On sagittal and axial T2-weighted images, the following four categories were classified: allocation (A) of the key stenotic level, shape (S), extension (E), and direction (D) of the RNR. A second read with cases ordered differently was performed 4 weeks later. Fleiss and Cohen's kappa procedures were used to determine reliability. RESULTS: The allocation, shape, extension, and direction (ASED) classification showed moderate to almost perfect inter-rater reliability, with kappa values (95% CI) of 0.86 (0.83, 0.90), 0.62 (0.57, 0.66), 0.56 (0.51, 0.60), and 0.66 (0.63, 0.70) for allocation, shape, extension, and direction, respectively. Intra-rater reliability was almost perfect, with kappa values of 0.90 (0.88, 0.92), 0.86 (0.84, 0.88), and 0.84 (0.81, 0.87) for shape, extension, and direction, respectively. Intra-rater kappa values were similar for junior and senior raters. Kappa values for inter-rater reliability were similar between the first and second reads (p = 0.06) among junior raters and improved among senior raters (p = 0.008). CONCLUSIONS: The MRI-based classification of RNR showed moderate-to-almost perfect inter-rater and almost perfect intra-rater reliability.

Patient demographics and MRI-based measurements predict redundant nerve roots in lumbar spinal stenosis: a retrospective database cohort comparison.

BACKGROUND: Up to 40% of patients diagnosed with lumbar spinal stenosis (LSS) show evidence of redundant nerve roots (RNR) of the cauda equina on their magnetic resonance images (MRI). The etiology of RNR is still unclear. Preoperative evidence of RNR is associated with a worse postsurgical outcome. Consequently, potential predictors of RNR could have a prognostic value. The aim was to test whether patient demographics and MRI-based measurements can predict RNR in LSS patients. METHODS: In a retrospective database-based cohort study the preoperative data of 300 patients, 150 with (RNR+) and 150 without (RNR-) evidence of RNR on their MRI were analyzed. Three independent researchers performed the MRI reads. Potential predictors were age, gender, body height (BH), length of lumbar spine (LLS), segmental length of lumbar spine (SLLS), lumbar spine alignment deviation (LSAD), relative LLS (rLLS), relative SLLS (rSLLS), number of stenotic levels (LSS-level), and grade of LSS severity (LLS-grade, increasing from A to D). Binomial logistic regression models were performed. RESULTS: RNR+ patients were 2.6 years older (p = 0.01). Weak RNR+ predictors were a two-years age increase (OR 1.06; p = 0.02), 3 cm BH decrease (OR 1.09; p = 0.01) and a 5 mm SLLS decrease (OR 1.34; p < 0.001). Strong RNR+ predictors were a 1% rLLS decrease (OR 2.17; p < 0.001), LSS-level ≥ 2 (OR 2.59; p = 0.001), LLS-grade C (OR 5.86; p = 0.02) and LLS-grade D (OR 18.4; p < 0.001). The mean rSLLS of RNR+ patients was 0.6% shorter (p < 0.001; 95% C.I. 0.4 to 0.8) indicating a disproportionate shorter lumbar spine. CONCLUSIONS: We identified LSS severity grade and LSS levels as the strongest predictors of RNR. In addition to previous studies, we conclude that a shortened lumbar spine by degeneration is involved in the development of RNR.

A retrospective analysis of bone mineral status in patients requiring spinal surgery.

BACKGROUND: Impaired bone quality is associated with poor outcome of spinal surgery. The aim of the study was to assess the bone mineral status of patients scheduled to undergo spinal surgery and to report frequencies of bone mineral disorders. METHODS: We retrospectively analyzed the bone mineral status of 144 patients requiring spinal surgery including bone mineral density by dual-energy X-ray absorptiometry (DXA) as well as laboratory data with serum levels of 25-hydroxyvitamin D (25-OH-D), parathyroid hormone, calcium, bone specific alkaline phosphate, osteocalcin, and gastrin. High-resolution peripheral quantitative computed tomography (HR-pQCT) was additionally performed in a subgroup of 67 patients with T-Score below - 1.5 or history of vertebral fracture. RESULTS: Among 144 patients, 126 patients (87.5%) were older than 60 years. Mean age was 70.1 years. 42 patients (29.1%) had suffered from a vertebral compression fracture. 12 previously undiagnosed vertebral deformities were detected in 12 patients by vertebral fracture assessment (VFA). Osteoporosis was present in 39 patients (27.1%) and osteopenia in 63 patients (43.8%). Only 16 patients (11.1%) had received anti-osteoporotic therapy, while 54 patients (37.5%) had an indication for specific anti-osteoporotic therapy but had not received it yet. The majority of patients had inadequate vitamin D status (73.6%) and 34.7% of patients showed secondary hyperparathyroidism as a sign for a significant disturbed calcium homeostasis. In a subgroup of 67 patients, severe vertebral deformities were associated with stronger deficits in bone microarchitecture at the distal radius compared to the distal tibia. CONCLUSIONS: This study shows that bone metabolism disorders are highly prevalent in elderly patients scheduled for spinal surgery. Vertebral deformities are associated with a predominant deterioration of bone microstructure at the distal radius. As impaired bone quality can compromise surgical outcome, we strongly recommend an evaluation of bone mineral status prior to operation and anti-osteoporotic therapy if necessary.

EnzymeML-a data exchange format for biocatalysis and enzymology.

EnzymeML is an XML-based data exchange format that supports the comprehensive documentation of enzymatic data by describing reaction conditions, time courses of substrate and product concentrations, the kinetic model, and the estimated kinetic constants. EnzymeML is based on the Systems Biology Markup Language, which was extended by implementing the STRENDA Guidelines. An EnzymeML document serves as a container to transfer data between experimental platforms, modeling tools, and databases. EnzymeML supports the scientific community by introducing a standardized data exchange format to make enzymatic data findable, accessible, interoperable, and reusable according to the FAIR data principles. An application programming interface in Python supports the integration of software tools for data acquisition, data analysis, and publication. The feasibility of a seamless data flow using EnzymeML is demonstrated by creating an EnzymeML document from a structured spreadsheet or from a STRENDA DB database entry, by kinetic modeling using the modeling platform COPASI, and by uploading to the enzymatic reaction kinetics database SABIO-RK.