Background and aims Multiple adalimumab (ADA) biosimilars are now approved for use in IBD; however, effectiveness and safety data remain scarce. We aimed to investigate long-term outcomes of the adalimumab (ADA) biosimilar SB5 in IBD patients following a switch from the ADA originator (SB5-switch cohort) or after start of SB5 (SB5-start cohort). Methods We performed an observational cohort study in a tertiary IBD referral centre. All IBD patients treated with Humira® underwent an elective switch to SB5. We identified all these patients in a biologic prescription database that prospectively registered all ADA start and stop dates including brand names. Data on IBD phenotype, CRP, drug persistence, ADA drug and antibody levels, and faecal calprotectin were collected. Results 481 patients were treated with SB5, 256 in the SB5-switch cohort (median follow-up: 13.7 months [8.6-15.2]) and 225 in the SB5-start cohort (median follow-up: 8.3 months [4.2-12.8]). 70.8% of the SB5-switch cohort remained on SB5 beyond one year; 90/256 discontinued SB5, mainly due to adverse events (46/90) or secondary loss of response (37/90). In the SB5-start cohort, 81/225 discontinued SB5 resulting in SB5-drug persistence of 60.3% beyond one year. No differences in clinical remission (p=0.53), CRP (p=0.80), faecal calprotectin (p=0.40) and ADA trough levels (p=0.55) were found between baseline, week 26 and week 52 following switch. Injection site pain was the most frequently reported adverse event. Conclusion Switching from ADA originator to SB5 appeared effective and safe in this study with over 12 months of follow-up.

ObjectivesIn this prospective cohort study, we investigated whether patient-specific finite element (FE) models can identify patients at risk of a pathological femoral fracture resulting from metastatic bone disease, and compared these FE predictions with clinical assessments by experienced clinicians.MethodsA total of 39 patients with non-fractured femoral metastatic lesions who were irradiated for pain were included from three radiotherapy institutes. During follow-up, nine pathological fractures occurred in seven patients. Quantitative CT-based FE models were generated for all patients. Femoral failure load was calculated and compared between the fractured and non-fractured femurs. Due to inter-scanner differences, patients were analyzed separately for the three institutes. In addition, the FE-based predictions were compared with fracture risk assessments by experienced clinicians.ResultsIn institute 1, median failure load was significantly lower for patients who sustained a fracture than for patients with no fractures. In institutes 2 and 3, the number of patients with a fracture was too low to make a clear distinction. Fracture locations were well predicted by the FE model when compared with post-fracture radiographs. The FE model was more accurate in identifying patients with a high fracture risk compared with experienced clinicians, with a sensitivity of 89% versus 0% to 33% for clinical assessments. Specificity was 79% for the FE models versus 84% to 95% for clinical assessments.ConclusionFE models can be a valuable tool to improve clinical fracture risk predictions in metastatic bone disease. Future work in a larger patient population should confirm the higher predictive power of FE models compared with current clinical guidelines.Cite this article: F. Eggermont, L. C. Derikx, N. Verdonschot, I. C. M. van der Geest, M. A. A. de Jong, A. Snyers, Y. M. van der Linden, E. Tanck. Can patient-specific finite element models better predict fractures in metastatic bone disease than experienced clinicians? Towards computational modelling in daily clinical practice. Bone Joint Res 2018;7:430–439. DOI: 10.1302/2046-3758.76.BJR-2017-0325.R2.

No abstract

In musculoskeletal modelling, several optimization techniques are used to calculate muscle forces, which strongly influence resultant hip contact forces (HCF). The goal of this study was to calculate muscle forces using four different optimization techniques, i.e., two different static optimization techniques, computed muscle control (CMC) and the physiological inverse approach (PIA). We investigated their subsequent effects on HCFs during gait and sit to stand and found that at the first peak in gait at 15–20% of the gait cycle, CMC calculated the highest HCFs (median 3.9 times peak GRF (pGRF)). When comparing calculated HCFs to experimental HCFs reported in literature, the former were up to 238% larger. Both static optimization techniques produced lower HCFs (median 3.0 and 3.1 pGRF), while PIA included muscle dynamics without an excessive increase in HCF (median 3.2 pGRF). The increased HCFs in CMC were potentially caused by higher muscle forces resulting from co‐contraction of agonists and antagonists around the hip. Alternatively, these higher HCFs may be caused by the slightly poorer tracking of the net joint moment by the muscle moments calculated by CMC. We conclude that the use of different optimization techniques affects calculated HCFs, and static optimization approached experimental values best. © 2014 Orthopaedic Research Society. Published by Wiley Periodicals, Inc. J Orthop Res 33:???–???, 2015.