Bone Microarchitecture Phenotypes Identified in Older Adults Are Associated With Different Levels of Osteoporotic Fracture Risk

Prevalence of osteoporosis is more than 50% in older adults, yet current clinical methods for diagnosis that rely on areal bone mineral density (aBMD) fail to detect most individuals who have a fragility fracture. Bone fragility can manifest in different forms, and a “one-size-fits-all” approach to diagnosis and management of osteoporosis may not be suitable. High-resolution peripheral quantitative computed tomography (HR-pQCT) provides additive information by capturing information about volumetric density and microarchitecture, but interpretation is challenging because of the complex interactions between the numerous properties measured. In this study, we propose that there are common combinations of bone properties, referred to as phenotypes, that are predisposed to different levels of fracture risk. Using HR-pQCT data from a multinational cohort (n = 5873, 71% female) between 40 and 96 years of age, we employed fuzzy c-means clustering, an unsupervised machine-learning method, to identify phenotypes of bone microarchitecture. Three clusters were identified, and using partial correlation analysis of HR-pQCT parameters, we characterized the clusters as low density, low volume, and healthy bone phenotypes. Most males were associated with the healthy bone phenotype, whereas females were more often associated with the low volume or low density bone phenotypes. Each phenotype had a significantly different cumulative hazard of major osteoporotic fracture (MOF) and of any incident osteoporotic fracture (p < 0.05). After adjustment for covariates (cohort, sex, and age), the low density followed by the low volume phenotype had the highest association with MOF (hazard ratio = 2.96 and 2.35, respectively), and significant associations were maintained when additionally adjusted for femoral neck aBMD (hazard ratio = 1.69 and 1.90, respectively). Further, within each phenotype, different imaging biomarkers of fracture were identified. These findings suggest that osteoporotic fracture risk is associated with bone phenotypes that capture key features of bone deterioration that are not distinguishable by aBMD. © 2021 American Society for Bone and Mineral Research (ASBMR).     

Automated classification of depression from structural brain measures across two independent community‐based cohorts

Major depressive disorder (MDD) has been the subject of many neuroimaging case–control classification studies. Although some studies report accuracies ≥80%, most have investigated relatively small samples of clinically‐ascertained, currently symptomatic cases, and did not attempt replication in larger samples. We here first aimed to replicate previously reported classification accuracies in a small, well‐phenotyped community‐based group of current MDD cases with clinical interview‐based diagnoses (from STratifying Resilience and Depression Longitudinally cohort, ‘STRADL’). We performed a set of exploratory predictive classification analyses with measures related to brain morphometry and white matter integrity. We applied three classifier types—SVM, penalised logistic regression or decision tree—either with or without optimisation, and with or without feature selection. We then determined whether similar accuracies could be replicated in a larger independent population‐based sample with self‐reported current depression (UK Biobank cohort). Additional analyses extended to lifetime MDD diagnoses—remitted MDD in STRADL, and lifetime‐experienced MDD in UK Biobank. The highest cross‐validation accuracy (75%) was achieved in the initial current MDD sample with a decision tree classifier and cortical surface area features. The most frequently selected decision tree split variables included surface areas of bilateral caudal anterior cingulate, left lingual gyrus, left superior frontal, right precentral and paracentral regions. High accuracy was not achieved in the larger samples with self‐reported current depression (53.73%), with remitted MDD (57.48%), or with lifetime‐experienced MDD (52.68–60.29%). Our results indicate that high predictive classification accuracies may not immediately translate to larger samples with broader criteria for depression, and may not be robust across different classification approaches.     

Impact of stereotactic body radiation therapy on geriatric assessment and management for older patients with head and neck cancer using G8

PurposeManagement of head and neck cancers (HNC) in older adults is a common but challenging clinical scenario. We assess the impact of Stereotactic Body Radiation Therapy (SBRT) on survival utilizing the Geriatric-8 (G8) questionnaire.Materials and methods171 HNC patients, deemed medically unfit for definitive treatment, were treated with SBRT ± systemic therapy. G8 questionnaires were collected at baseline, at 4–6 weeks, and at 2–3 months post-treatment. Patients were stratified according to their baseline G8 score: <11 as ‘vulnerable’, 11–14 as ‘intermediate’, and >14 as ‘fit’. Overall survival (OS) was assessed through univariate Kaplan Meier analysis. Repeated measures ANOVA was used to determine if baseline characteristics affected G8 score changes.ResultsMedian follow-up was seventeen months. 60% of patients presented with recurrent HNC, 30% with untreated HNC primaries, and 10% with metastatic non-HNC primaries. Median age was 75 years. Median Charlson Comorbidity Index score was 2. 51% of patients were ‘vulnerable’, 37% were ‘intermediate’, and 12% were ‘fit' at baseline, with median survival of 13.2, 24.3, and 41.0 months, respectively (p = .004). Patients who saw a decrease in their follow-up G8 score (n = 69) had significantly lower survival than patients who had stable or increased follow-up G8 scores (n = 102), with median survival of 8.6 vs 36.0 months (p < .001).ConclusionThe G8 questionnaire may be a useful tool in upfront treatment decision-making to predict prognosis and prevent older patients from receiving inappropriate anti-cancer treatment. Decline in follow-up G8 scores may also predict worse survival and aid in goals of care following treatment.     

Stress Testing Versus CT Angiography in Patients With Diabetes and Suspected Coronary Artery Disease

Background

The optimal noninvasive test (NIT) for patients with diabetes and stable symptoms of coronary artery disease (CAD) is unknown.

Tanzania IR Initiative: Training the First Generation of Interventional Radiologists

Despite a population of nearly 60 million, there is currently not a single interventional radiologist in Tanzania. Based on an Interventional Radiology (IR) Readiness Assessment, the key obstacles to establishing IR in Tanzania are the lack of training opportunities and limited availability of disposable equipment. An IR training program was designed and initiated, which relies on US-based volunteer teams of IR physicians, nurses, and technologists to locally train radiology residents, nurses, and technologists. Preliminary results support this strategy for addressing the lack of training opportunities and provide a model for introducing IR to other resource-limited settings.