Adherence to dosing schedule of denosumab therapy for osteoporosis during COVID-19 lockdown: an electronic medical record and pharmacy claims database study from Asia.

COVID-19 lockdowns have impacted management of chronic diseases such as osteoporosis. Adherence to the 6-monthly dosing schedule of denosumab, the parenteral anti-osteoporosis medication most often used in Singapore, was significantly reduced during the lockdown period compared to that during pre-COVID-19 times. Patients managed by endocrinologists were more likely to be adherent. PURPOSE: No study thus far has quantified actual adherence rates to anti-osteoporosis therapy with denosumab during COVID-19 or explored factors associated with it. We aimed to estimate the adherence rates to denosumab in Singaporean men and women during COVID-19 lockdown and to compare it with those during the pre-COVID-19 period. METHOD: We conducted this retrospective, electronic medical records, and pharmacy claims database study at Singapore General Hospital, the largest hospital in the country. Patients initiated on subcutaneous denosumab between August 2019 and December 2019 and were thus scheduled to receive the second dose during the COVID-19 first-wave period from February 2020 to June 2020 (lockdown group) were analyzed, as were patients initiated anytime on denosumab between September 2011 and December 2018 (pre-COVID-19 group). Data extracted from the hospital's electronic prescription platform and patients' pharmacy purchase records were matched. Adherence was defined as being punctual (with an allowable delay of up to 4 weeks) with the second dose scheduled 6 months from the 1st dose. A sensitivity analysis with an allowable delay up to 8 weeks was also performed. We compared the adherence rates between the two periods and explored factors associated with adherence. RESULTS: A total of 768 and 1458 patients respectively during the lockdown and pre-COVID-19 periods were analyzed. The mean adherence rate during lockdown was 63.9%. The odds of being adherent during lockdown were higher if patients were managed by endocrinologists as opposed to those by other specialists [OR 2.516 (95% CI: 1.836-3.448); p < 0.001]. Adherence rates during the pre-COVID-19 period was 75.4%. Overall, the odds of being adherent to denosumab was significantly lower during lockdown than that during the pre-COVID-19 period [OR 0.525 (95% CI 0.430-0.640); p < 0.001], and odds of being adherent were higher if patients were managed by endocrinologists than if they were managed by other specialists (OR 1.765 (95% CI: 1.444-2.158; p < 0.001). CONCLUSION: Adherence to denosumab was significantly lower during COVID-19 lockdown than the pre-COVID-19 period. The odds of being adherent were higher in patients managed by endocrinologists. Whether healthcare providers from certain specialties spend more time counselling and educating patients about the importance of adherence to osteoporosis medications needs to be explored further.

Addressing bone quality and bone density after renal transplantation: A prospective evaluation of the evolution of trabecular bone score and bone mineral density over the first 5 years following renal transplantation in Asian patients.

The evolution of trabecular bone score (TBS) and bone mineral density (BMD) over the first 5 years after renal transplantation was prospectively evaluated in 164 patients. Dual energy X-ray absorptiometry (DXA) scans were performed at 0, 6, 12, 24, and 60 months. Cumulative steroid dose, serum 25(OH)D, calcium, parathyroid hormone, and total ALP levels at these time points were checked. Incident fractures were identified from X-rays/vertebral fracture assessments. Mean (SD) age, TBS, and lumbar spine BMD at baseline were 47.11 (9.53), 1.424 (0.097), and 0.935 (0.183) gm/cm2 , respectively. Baseline TBS was lower in tertiary 1.38 (0.07) vs secondary hyperparathyroidism 1.43 (0.01) vs post-parathyroidectomy 1.46 (0.11); P = .035. Trabecular bone score and BMD significantly decreased from baseline->6 months, changes after that at consecutive time points were non-significant. 11% had incident fractures during the follow-up period, majority being metatarsal with no vertebral or hip fractures noted. This first prospective evaluation of TBS and BMD evolution at multiple time points over 5 years suggest that microarchitectural and bone density deteriorations post-renal transplantation stabilize after 6 months. Stabilization of these parameters could partially account for the absence of major fractures noted in this Asian population. Possible genetic and ethnic differences in fracture risk between Asian and Caucasian renal transplant patients have to be explored through large population-based studies.

Application of artificial intelligence in the assessment of thyroid eye disease (TED) - a scoping review.

Background: There is emerging evidence which suggests the utility of artificial intelligence (AI) in the diagnostic assessment and pre-treatment evaluation of thyroid eye disease (TED). This scoping review aims to (1) identify the extent of the available evidence (2) provide an in-depth analysis of AI research methodology of the studies included in the review (3) Identify knowledge gaps pertaining to research in this area. Methods: This review was performed according to the 2020 Preferred Reporting Items for Systematic Reviews and Meta-Analyses statement (PRISMA). We quantify the diagnostic accuracy of AI models in the field of TED assessment and appraise the quality of these studies using the modified QUADAS-2 tool. Results: A total of 13 studies were included in this review. The most common AI models used in these studies are convolutional neural networks (CNN). The majority of the studies compared algorithm performance against healthcare professionals. The overall risk of bias and applicability using the modified Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2) tool led to most of the studies being classified as low risk, although higher deficiency was noted in the risk of bias in flow and timing. Conclusions: While the results of the review showed high diagnostic accuracy of the AI models in identifying features of TED relevant to disease assessment, deficiencies in study design causing study bias and compromising study applicability were noted. Moving forward, limitations and challenges inherent to machine learning should be addressed with improved standardized guidance around study design, reporting, and legislative framework.