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Purpose: To provide a summarized state of the art of the relative efficacy and rapidity of action of pharmacological treatments to prevent imminent osteoporotic fractures. Methods: We reviewed metanalyses (MA) and network metaanalyses (NMA) published during the last 10 years concerning the pharmacological treatment of osteoporosis. We compared the anti-fracture efficacy and the rapidity of action of various agents versus placebo and versus risedronate. Results: All bisphosphonates decrease the incidence of vertebral fractures compared with placebo. Ibandronate is the only one without demonstrated efficacy against non-vertebral and hip fractures. Zoledronate, denosumab and anabolic therapy are associated with a higher fracture risk reduction than oral bisphosphonates. Compared with risedronate, which significantly reduces the rate of hip fractures, zoledronate, denosumab, teriparatide, abaloparatide and romosozumab are more efficient for vertebral fractures but not for non-vertebral or hip fractures reduction. No studies have compared bone anabolic treatments with zoledronate or denosumab. Oral bisphosphonates significantly reduce fracture risk only after more than one year of therapy. A faster reduction of fracture risk is observed with zoledronate and denosumab, or with anabolic agents. For denosumab and anabolic agents, a sequential treatment is required to keep gains after treatment withdrawal. Conclusions: In patients at high risk of imminent fracture, starting therapy with potent antiresorptive agents or with an anabolic agent seems most appropriate to promptly reduce the fracture risk. Available NMA/MA suggest that, compared to zoledronate and denosumab, anabolic agents have a higher efficacy for vertebral fractures but head-to-head studies are lacking.

Summary: Five-year fracture risk prediction from the Fracture Risk Brussels Epidemiological Enquiry (FRISBEE) models was externally tested in 9716 Canadian women and demonstrated good discrimination but consistently overestimated risk. Introduction: Five-year risk prediction models for all fractures, major osteoporotic fractures (MOFs) and central fractures (proximal to forearm and ankle) from the FRISBEE cohort demonstrated good performance in the original derivation cohort. Our aim was to externally validate the FRISBEE-based 5-year prediction models in routine practice. Methods: Using the population-based Manitoba Bone Mineral Density (BMD) registry, we identified women aged 60–85 years undergoing baseline BMD assessment from September 1, 2012 to March 31, 2018. Five-year probabilities of all fractures, MOFs and central fractures were calculated using the FRISBEE prediction models. We identified incident non-traumatic fractures up to 5 years from population-based healthcare data sources. Performance characteristics included area under the receiver operating characteristic curve (AUROC), gradient of risk (hazard ratio [HR] per SD increase and across risk tertiles) from Cox regression analysis, and calibration (ratio 5-year observed cumulative incidence to predicted fracture probability). Results: We included 9716 women (mean age 70.7 + / − SD 5.3 years). During a mean observation time of 2.5 years, all fractures, MOFs and central fractures were identified in 377 (3.9%), 264 (2.7%) and 259 (2.7%) of the women. AUROC showed significant fracture risk stratification with the FRISBEE models (all fractures 0.69 [95%CI 0.67–0.72], MOFs 0.71 [95%CI 0.68–0.74], central fractures 0.72 [95%CI 0.69–0.75]). There was a strong gradient of risk for predicting fracture outcomes per SD increase (HRs from 1.98 to 2.26) and across risk tertiles (HRs for middle vs lowest from 2.25 to 2.41, HRs for highest vs lowest from 4.70 to 6.50). However, risk was overestimated for all fractures (calibration-in-the-large 0.63, calibration slope 0.63), MOF (calibration-in-the-large 0.51, calibration slope 0.57) and central fractures (calibration-in-the-large 0.55, calibration slope 0.60). Conclusions: FRISBEE 5-year prediction models were externally validated to stratify fracture risk similar to the derivation cohort, but would need recalibration for Canada as risk was overestimated. [ABSTRACT FROM AUTHOR]

In addition to their often‐cited role as global service hubs, world cities are also global innovation hubs with their connectivity in knowledge networks crucial for the innovation clusters they harbour. While both aspects of world cities' global connectivity may allow their urban economies to grow, we argue that specialization in one of these two networks may increase economic performance. We provide systematic evidence of this substitution effect by examining the changing positions of 129 world cities in inter‐city innovation and service firm networks (2000–2012). Fixed effects panel analysis of city GDP growth confirm that the two networks are partial substitutes. [ABSTRACT FROM AUTHOR]

Background: Magnetic resonance imaging (MRI) has become important in the diagnostic work‐up of neurodegenerative diseases. icobrain dm, a CE‐labeled and FDA‐cleared automatic tool for brain volumetry using clinical MRI scans, has shown great potential in differentiating cognitively healthy controls (HC) from Alzheimer’s disease (AD) dementia (ADD) patients in selected research cohorts (Struyfs H, et al. (2020)).This study examined the diagnostic value of icobrain dm for AD in routine clinical practice, and compared it to FreeSurfer, one of the standard tools in neuroimaging research. Method: The Belgian Dementia Council (BeDeCo) initiated a retrospective, multi‐center Belgian study (REMEMBER). Through automated volumetric analyses of whole brain, gray matter, cortical gray matter, frontal, parietal, occipital and temporal cortices, hippocampal volumes and lateral ventricles on real‐world clinical brain MRI T1w (n=820) images across the AD continuum, icobrain dm’s (v.4.4.0) ability to differentiate diagnostic groups in the AD continuum vs. HC was compared to FreeSurfer (v.6.0). Using icobrain dm’s automated volumetric output, we also investigated which combined brain volumes contributed most to establish an AD diagnosis and could differentiate between different stages of the disease. Result: The final study population consisted of subjects from eight Belgian memory clinics, with HC (n=89), subjective cognitive decline (SCD, n=89), mild cognitive impairment (MCI, n=350), and ADD (n=250) (Table 1). icobrain dm outperformed FreeSurfer in processing time (15‐30 minutes vs. 9‐32 hours), robustness (0 failures vs. 67 failures due to complete abortion of analysis during topology corrections) and diagnostic performance (AUC, sensitivity and specificity) in univariate differentiation between HC and ADD patients (Figure 1, Table 2). Stepwise backward regression models showed improved diagnostic accuracy for pairwise group differentiations when combining multiple brain volumes computed by icobrain dm, with highest performance obtained for distinguishing HC from ADD patients (AUC=0.914; Specificity 83.0%; Sensitivity 86.3%) (Table 3). Conclusion: Automated volumetry has a diagnostic value for ADD diagnosis in routine clinical practice. Our findings indicate that icobrain dm’s automated volumetry can improve diagnostic certainty in a real‐world clinical setting with a significantly reduced processing time, robustness and diagnostic performance, as compared to FreeSurfer. [ABSTRACT FROM AUTHOR]

Background: Magnetic resonance imaging (MRI) has become important in the diagnostic work-up of neurodegenerative diseases. icobrain dm, a CE-labeled and FDA-cleared automated brain volumetry software, has shown potential in differentiating cognitively healthy controls (HC) from Alzheimer's disease (AD) dementia (ADD) patients in selected research cohorts.Objective: This study examines the diagnostic value of icobrain dm for AD in routine clinical practice, including a comparison to the widely used FreeSurfer software, and investigates if combined brain volumes contribute to establish an AD diagnosis.Methods: The study population included HC (n = 90), subjective cognitive decline (SCD, n = 93), mild cognitive impairment (MCI, n = 357), and ADD (n = 280) patients. Through automated volumetric analyses of global, cortical, and subcortical brain structures on clinical brain MRI T1w (n = 820) images from a retrospective, multi-center study (REMEMBER), icobrain dm's (v.4.4.0) ability to differentiate disease stages via ROC analysis was compared to FreeSurfer (v.6.0). Stepwise backward regression models were constructed to investigate if combined brain volumes can differentiate between AD stages.Results: icobrain dm outperformed FreeSurfer in processing time (15-30 min versus 9-32 h), robustness (0 versus 67 failures), and diagnostic performance for whole brain, hippocampal volumes, and lateral ventricles between HC and ADD patients. Stepwise backward regression showed improved diagnostic accuracy for pairwise group differentiations, with highest performance obtained for distinguishing HC from ADD (AUC = 0.914; Specificity 83.0%; Sensitivity 86.3%).Conclusion: Automated volumetry has a diagnostic value for ADD diagnosis in routine clinical practice. Our findings indicate that combined brain volumes improve diagnostic accuracy, using real-world imaging data from a clinical setting. [ABSTRACT FROM AUTHOR]

Probabilistic models including clinical risk factors with or without bone mineral density (BMD) have been developed to estimate the 5‐ or 10‐year absolute fracture risk. We investigated the performance of the FRAX and Garvan tools in a well‐characterized population‐based cohort of 3560 postmenopausal, volunteer women, aged 60 to 85 years at baseline, included in the Fracture Risk Brussels Epidemiological Enquiry (FRISBEE) cohort, during 5 years of follow‐up. Baseline data were used to calculate the estimated 10‐year risk of hip and major osteoporotic fractures (MOFs) for each participant using FRAX (Belgium). We computed the 5‐year risk according to the Garvan model with BMD. For calibration, the predicted risk of fracture was compared with fracture incidence across a large range of estimated fracture risks. The accuracy of the calculators to predict fractures was assessed using the area under the receiver operating characteristic curves (AUC). The FRAX tool was well calibrated for hip fractures (slope 1.09, p < 0.001; intercept −0.001, p = 0.46), but it consistently underestimated the incidence of major osteoporotic fractures (MOFs) (slope 2.12, p < 0.001; intercept −0.02, p = 0.06). The Garvan tool was well calibrated for "any Garvan" fractures (slope 1.05, p < 0.001; intercept 0.01, p = 0.37) but largely overestimated the observed hip fracture rate (slope 0.32, p < 0.001; intercept 0.006, p = 0.05). The predictive value for hip fractures was better for FRAX (AUC: 0.841, 95% confidence interval [CI] 0.795–0.887) than for Garvan (AUC: 0.769, 95% CI 0.702–0.836, p = 0.01). The Garvan AUC for "any Garvan" fractures was 0.721 (95% CI 0.693–0.749) and FRAX AUC for MOFs was 0.708 (95% CI 0.675–0.741). In conclusion, in our Belgian cohort, FRAX estimated quite well hip fractures but underestimated MOFs, while Garvan overestimated hip fracture risk but showed a good estimation of "any Garvan" fractures. Both models had a good discriminatory value for hip fractures but only a moderate discriminatory ability for MOFs or "any Garvan" fractures. © 2021 The Authors. JBMR Plus published by Wiley Periodicals LLC on behalf of American Society for Bone and Mineral Research. [ABSTRACT FROM AUTHOR]

The article offers information on direct comparison of the 2005 and 2017 criteria for dementia with Lewy bodies. It mentions diagnosis of probable DLB was to be made when a patient had dementia with at least two of three core features, namely recurrent visual hallucinations, fluctuating cognition, and/or spontaneous parkinsonism.

The article describes the case of a woman with Alzheimer's disease (AD) occurring with Lewy body disease (LBD) who developed Takotsubo cardiomyopathy (TC) following an acute episode of anxiety. Topics covered include the patient's medical history, the findings of the physical neurological and laboratory examinations, and patient outcome after treatment with furosemide and paroxetine after diagnosis and levodopa eighteen months after the diagnosis.

Context: Individualized fracture risk may help to select patients requiring a pharmacological treatment for osteoporosis. FRAX and the Garvan fracture risk calculators are the most used tools, although their external validation has shown significant differences in their risk prediction ability., Objective and Methods: Using data from the Fracture Risk Brussels Epidemiological Enquiry study, a cohort of 3560 postmenopausal women aged 60 to 85 years, we aimed to construct original 5-year fracture risk prediction models using validated clinical risk factors (CRFs). Three models of competing risk analysis were developed to predict major osteoporotic fractures (MOFs), all fractures, and central fractures (femoral neck, shoulder, clinical spine, pelvis, ribs, scapula, clavicle, sternum)., Results: Age, a history of fracture, and hip or spine BMD were predictors common to the 3 models. Excessive alcohol intake and the presence of comorbidities were specific additional CRFs for MOFs, a history of fall for all fractures, and rheumatoid arthritis for central fractures. Our models predicted the fracture probability at 5 years with an acceptable accuracy (Brier scores ≤ 0.1) and had a good discrimination power (area under the receiver operating curve of 0.73 for MOFs and 0.72 for central fractures) when internally validated by bootstrap. Three simple nomograms, integrating significant CRFs and the mortality risk, were constructed for different fracture sites. In conclusion, we derived 3 models predicting fractures with an acceptable accuracy, particularly for MOFs and central fractures. The models are based on a limited number of CRFs, and we constructed nomograms for use in clinical practice., (© The Author(s) 2022. Published by Oxford University Press on behalf of the Endocrine Society.)

Patients who sustain a fracture are at greatest risk of recurrent fracture during the next 2 years. We propose three models to identify subjects most at risk of an imminent fracture, according to fracture site (any fracture, major osteoporotic fracture [MOF] or central). They were constructed using data of the prospective Frisbee cohort, which includes 3560 postmenopausal women aged 60 to 85 years who were followed for at least 5 years. A total of 881 subjects had a first incident validated fragility fracture before December 2018. Among these, we validated 130 imminent fractures occurring within the next 2 years; 79 were MOFs, and 88 were central fractures. Clinical risk factors were re-evaluated at the time of the index fracture. Fine and Gray proportional hazard models were derived separately for each group of fractures. The following risk factors were significantly associated with the risk of any imminent fracture: total hip bone mineral density (BMD) (p < 0.001), a fall history (p < 0.001), and comorbidities (p = 0.03). Age (p = 0.05 and p = 0.03, respectively) and a central fracture as the index fracture (p = 0.04 and p = 0.005, respectively) were additional predictors of MOFs and central fractures. The three prediction models are presented as nomograms. The calibration curves and the Brier scores based on bootstrap resampling showed calibration scores of 0.089 for MOF, 0.094 for central fractures, and 0.132 for any fractures. The predictive accuracy of the models expressed as area under the receiver operating characteristic (AUROC) curve (AUC) were 0.74 for central fractures, 0.72 for MOFs, and 0.66 for all fractures, respectively. These AUCs compare well with those of FRAX and Garvan to predict the 5- or 10-year fracture probability. In summary, five predictors (BMD, age, comorbidities, falls, and central fracture as the incident fracture) allow the calculation with a reasonable accuracy of the imminent risk of fracture at different sites (MOF, central fracture, and any fracture) after a recent sentinel fracture. © 2021 American Society for Bone and Mineral Research (ASBMR)., (© 2021 American Society for Bone and Mineral Research (ASBMR).)

Introduction: Hospitalization is associated with acute changes in sarcopenia status in older people, but the influencing factors are not fully understood. Pre-admission care dependency level as a risk factor has not yet been investigated., Objective: Evaluate if pre-admission care dependency level is an independent predictor of sarcopenia changes following hospitalization., Setting and Subjects: Data came from the Sarcopenia 9+ EAMA Project, a European prospective multi-centre study. For this study, 227 hospitalised older people were included from four different hospitals in Belgium, Spain and Poland, between 18 February 2019 and 5 September 2020., Methods: Sarcopenia status at admission and discharge were calculated using a combined score (desirability value) based on muscle mass (calf circumference), strength (grip) and function (walking speed). Ratio of admission to discharge status was the outcome (desirability ratio; 1.00 meaning no difference). Predictor variable was the pre-admission care dependency level, classified into three groups: independent older people living at home, dependent older people living at home and older people living in a care home. Linear regression models were applied, considering potential confounders., Results: Mean desirability ratio for dependent older people living at home ('middle dependent group') was lower (0.89) compared to independent older people (0.98; regression coefficient -0.09 [95% CI -0.16, -0.02]) and care home patients (1.05; -0.16 [95% CI -0.01, -0.31]). Adjusting for potential confounders or using another statistical approach did not affect the main results., Conclusion: Dependent older people living at home were at higher risk of deterioration in sarcopenia status following hospitalization. In-depth studies investigating causes and potential interventions of these findings are needed., (© The Author(s) 2021. Published by Oxford University Press on behalf of the British Geriatrics Society. All rights reserved. For permissions, please email: journals.permissions@oup.com.)

Background: Magnetic resonance imaging (MRI) acquisition/processing techniques assess brain volumes to explore neurodegeneration in Alzheimer's disease (AD)., Objective: We examined the clinical utility of MSmetrix and investigated if automated MRI volumes could discriminate between groups covering the AD continuum and could be used as a predictor for clinical progression., Methods: The Belgian Dementia Council initiated a retrospective, multi-center study and analyzed whole brain (WB), grey matter (GM), white matter (WM), cerebrospinal fluid (CSF), cortical GM (CGM) volumes, and WM hyperintensities (WMH) using MSmetrix in the AD continuum. Baseline (n = 887) and follow-up (FU, n = 95) T1-weighted brain MRIs and time-linked neuropsychological data were available., Results: The cohort consisted of cognitively healthy controls (HC, n = 93), subjective cognitive decline (n = 102), mild cognitive impairment (MCI, n = 379), and AD dementia (n = 313). Baseline WB and GM volumes could accurately discriminate between clinical diagnostic groups and were significantly decreased with increasing cognitive impairment. MCI patients had a significantly larger change in WB, GM, and CGM volumes based on two MRIs (n = 95) compared to HC (FU>24months, p = 0.020). Linear regression models showed that baseline atrophy of WB, GM, CGM, and increased CSF volumes predicted cognitive impairment., Conclusion: WB and GM volumes extracted by MSmetrix could be used to define the clinical spectrum of AD accurately and along with CGM, they are able to predict cognitive impairment based on (decline in) MMSE scores. Therefore, MSmetrix can support clinicians in their diagnostic decisions, is able to detect clinical disease progression, and is of help to stratify populations for clinical trials.