Glucocorticoids Increase Fracture Risk and Fracture Prevalence Independently from Bone Mineral Density and Clinical Risk Factors: Results from the Gliwice Osteoporosis (GO) Study

The aim of the study was to establish the influence of glucocorticoids (GC) on fracture risk, probability, and prevalence. A set of 1548 postmenopausal women were divided into study group – treated with GC (n=114, age 66.48±7.6 years) and controls (n=1434, age 66.46±6.83 years). Data on clinical risk factors for osteoporosis and fractures were collected. Hip bone densitometry was performed using a device Prodigy (GE, USA). Fracture probability was established by FRAX, and fracture risk by Garvan algorithm and POL-RISK. Fracture risk and fracture probability were significantly greater for GC-treated women in comparison to controls. In the study group, there were 24, 3, 24, and 6 fractures noted at spine, hip, forearm, and arm, respectively. The respective numbers of fractures reported in controls at those skeletal sites were: 186, 23, 240, and 25. The use of GCs increased significantly prevalence of all major, spine and arm fractures. Also the number of all fractures was affected by GC use. Following factors significantly increased fracture probability: age (OR 1.04 per each year; 95% CI: 1.03–1.06), GC use (OR 1.54; 95% CI: 1.03–2.31), falls (OR 2.09; 95% CI: 1.60–2.73), and FN T-score (OR 0.62 per each unit; 95% CI: 0.54–0.71). In conclusion, in patients treated with GCs the fracture risk, probability, and prevalence were increased. This effect was evident regardless of whether GC therapy is included in the algorithm as a risk factor (FRAX, POL-RISK) or not taken into consideration (Garvan nomogram).

Extrapulmonary tuberculosis: risk factors

Objective. To identify the main demographic and clinical risk factors for the development of extrapulmonary tuberculosis (EPTB).Materials and methods. A retrospective study of tuberculosis cases registered from 2016 to 2020 in the Gomel region was conducted (330 patients with EPTB and 2,505 patients with pulmonary tuberculosis). The odds ratios were calculated to assess the risk factors for the development of EPTB.Results. The prevalence of EPTB was studied over the course of five years. The most significant risk factors for the development of tuberculosis in extrapulmonary localizations have been identified.Conclusion. The risk factors for the development of EPTB are age (EPTB often develops in children and older persons), females, and in those who have had an episode of tuberculosis previously. Awareness of the predisposing factors may help physicians maintain a high index of suspicion regarding the development of EPTB.

A Deep Learning Nomogram for the Prediction of Early Recurrence in Hepatocellular Carcinoma After Curative Surgery

Purpose: The accurate prediction of post-hepatectomy early recurrence in patients with hepatocellular carcinoma (HCC) is crucial for decision-making regarding postoperative adjuvant treatment and monitoring. We aimed to develop and validate a deep-learning (DL) nomogram based on MRI for predicting early recurrence in HCC after curative resection. Methods: We retrospectively included 285 HCC patients who underwent Gadolinium-ethoxybenzyl-diethylenetriamine pentaacetic acid (Gd-EOB-DTPA)-enhanced MRI within one month before curative resection. Deep features were extracted from images of the arterial phase (AP), portal venous phase (PVP), and hepatobiliary phase (HBP) using VGGNet-19. Pearson’s correlation was firstly used to exclude redundant features. Three feature selection methods and five classification methods were combined to construct the DL signature. Univariate and multivariate logistic regression analyses were used to identify the independent risk factors for the early recurrence, which were incorporated into the DL nomogram. Results: Microvascular invasion (P = 0.039), tumor number (P = 0.001), and three-phase-based DL signature (P<0.0001) were independent risk factors for early recurrence. The DL nomogram integrating the DL signature and clinical risk factors outperformed the clinical nomogram which combined clinical risk factors, in the training set (AUC: 0.949 vs. 0.751; P<0.0001) and validation set (AUC: 0.908 vs. 0.712; P = 0.002). Excellent calibration was achieved for the DL nomogram in both training and validation sets. Decision curve analysis confirmed the clinical usefulness of the DL nomogram. Conclusions: The proposed DL nomogram was superior to the traditional clinical nomogram in predicting early recurrence for HCC patients after curative resection.

Radiomics Analysis for Predicting Epilepsy in Patients With Unruptured Brain Arteriovenous Malformations

Objectives: To investigate the association between radiomics features and epilepsy in patients with unruptured brain arteriovenous malformations (bAVMs) and to develop a prediction model based on radiomics features and clinical characteristics for bAVM-related epilepsy.Methods: This retrospective study enrolled 176 patients with unruptured bAVMs. After manual lesion segmentation, a total of 858 radiomics features were extracted from time-of-flight magnetic resonance angiography (TOF-MRA). A radiomics model was constructed, and a radiomics score was calculated. Meanwhile, the demographic and angioarchitectural characteristics of patients were assessed to build a clinical model. Incorporating the radiomics score and independent clinical risk factors, a combined model was constructed. The performance of the models was assessed with respect to discrimination, calibration, and clinical usefulness.Results: The clinical model incorporating 3 clinical features had an area under the curve (AUC) of 0.71. Fifteen radiomics features were used to build the radiomics model, which had a higher AUC of 0.78. Incorporating the radiomics score and clinical risk factors, the combined model showed a favorable discrimination ability and calibration, with an AUC of 0.82. Decision curve analysis (DCA) demonstrated that the combined model outperformed the clinical model and radiomics model in terms of clinical usefulness.Conclusions: The radiomics features extracted from TOF-MRA were associated with epilepsy in patients with unruptured bAVMs. The radiomics-clinical nomogram, which was constructed based on the model incorporating the radiomics score and clinical features, showed favorable predictive efficacy for bAVM-related epilepsy.

Clinical risk factors for mortality in an analysis of 1375 patients admitted for COVID treatment

The goal of the present work was to examine clinical risk factors for mortality in 1375 COVID + patients admitted to a hospital in Suffolk County, NY. Data were collated by the hospital epidemiological service for patients admitted from 3/7/2020 to 9/1/2020. Time until final discharge or death was the outcome. Cox proportional hazards models were used to estimate time until death among admitted patients. In total, all cases had resolved leading to 207 deaths. Length of stay was significantly longer in those who died as compared to those who did not (p = 0.007). Of patients who had been discharged, 54 were readmitted and nine subsequently died. Multivariable-adjusted Cox proportional hazards regression revealed that in addition to older age, male sex, and a history of chronic heart failure, chronic obstructive pulmonary disease, and diabetes, that a history of premorbid depression was a risk factors for COVID-19 mortality (aHR = 2.42 [1.38–4.23] P = 0.002), and that this association remained after adjusting for age and for neuropsychiatric conditions as well as medical comorbidities including cardiovascular disease and pulmonary conditions. Sex-stratified analyses revealed that associations between mortality and depression was strongest in males (aHR = 4.45 [2.04–9.72], P < 0.001), and that the association between heart failure and mortality was strongest in participants aged < 65 years old (aHR = 30.50 [9.17–101.48], P < 0.001). While an increasing number of studies have identified several comorbid medical conditions including chronic heart failure and age of patient as risk factors for mortality in COVID + patients, this study confirmed several prior reports and also noted that a history of depression is an independent risk factor for COVID-19 mortality.

Multiple polygenic scores improve bone mineral density prediction in an independent sample of Caucasian women

Purpose of the studyTo determine if multiple Genetic Risk Scores (GRSs) improve bone mineral density (BMD) prediction over single GRS in an independent sample of Caucasian women.Study designBased on summary statistics of four genome-wide association studies related to two osteoporosis-associated traits, namely BMD and heel quantitative ultrasound derived estimated BMD (eBMD), four GRSs were derived for 1205 individuals in the Genome-Wide Scan for Female Osteoporosis Gene Study. The effect of each GRS on BMD variation was assessed using multivariable linear regression, with conventional risk factors adjusted for. Next, the eBMD-related GRS that explained the most variance in BMD was selected to be entered into a multi-score model, along with the BMD-related GRS. Elastic net regularised regression was used to develop the multiscore model, which estimated the joint effect of two GRSs (GRS_BMD and GRS_eBMD) on BMD variation, after being adjusted for conventional risk factors.ResultsWith the same clinical risk factors having been adjusted for, the model that included GRS_BMD performed best by explaining 32.53% of the variance in BMD; the single-score model that included GRS_eBMD explained 34.03% of BMD variance. The model that includes both GRS_BMD and GRS_ eBMD, as well as the clinical risk factors, aggregately explained 35.05% in BMD variation. Compared with the single GRS models, the multiscore model explained significantly more variance in BMD.ConclusionsThe multipolygenic score model explained a considerable amount of BMD variation. Compared with single score models, multipolygenic score model provided significant improvement in explaining BMD variation.