Discovery of associative patterns between workplace sound level and physiological wellbeing using wearable devices and empirical Bayes modeling.

We conducted a field study using multiple wearable devices on 231 federal office workers to assess the impact of the indoor environment on individual wellbeing. Past research has established that the workplace environment is closely tied to an individual's wellbeing. Since sound is the most-reported environmental factor causing stress and discomfort, we focus on quantifying its association with physiological wellbeing. Physiological wellbeing is represented as a latent variable in an empirical Bayes model with heart rate variability measures-SDNN and normalized-HF as the observed outcomes and with exogenous factors including sound level as inputs. We find that an individual's physiological wellbeing is optimal when sound level in the workplace is at 50 dBA. At lower (<50dBA) and higher (>50dBA) amplitude ranges, a 10 dBA increase in sound level is related to a 5.4% increase and 1.9% decrease in physiological wellbeing respectively. Age, body-mass-index, high blood pressure, anxiety, and computer use intensive work are person-level factors contributing to heterogeneity in the sound-wellbeing association.

Network analysis reveals dysregulated functional patterns in type II diabetic skin.

Skin disorders are one of the most common complications of type II diabetes (T2DM). Long-term effects of high blood glucose leave individuals with T2DM more susceptible to cutaneous diseases, but its underlying molecular mechanisms are unclear. Network-based methods consider the complex interactions between genes which can complement the analysis of single genes in previous research. Here, we use network analysis and topological properties to systematically investigate dysregulated gene co-expression patterns in type II diabetic skin with skin samples from the Genotype-Tissue Expression database. Our final network consisted of 8812 genes from 73 subjects with T2DM and 147 non-T2DM subjects matched for age, sex, and race. Two gene modules significantly related to T2DM were functionally enriched in the pathway lipid metabolism, activated by PPARA and SREBF (SREBP). Transcription factors KLF10, KLF4, SP1, and microRNA-21 were predicted to be important regulators of gene expression in these modules. Intramodular analysis and betweenness centrality identified NCOA6 as the hub gene while KHSRP and SIN3B are key coordinators that influence molecular activities differently between T2DM and non-T2DM populations. We built a TF-miRNA-mRNA regulatory network to reveal the novel mechanism (miR-21-PPARA-NCOA6) of dysregulated keratinocyte proliferation, differentiation, and migration in diabetic skin, which may provide new insights into the susceptibility of skin disorders in T2DM patients. Hub genes and key coordinators may serve as therapeutic targets to improve diabetic skincare.

Epidural blood patch for spontaneous intracranial hypotension (SIH): a report of two cases.

One of the latest diagnoses that need to be considered when evaluating patients with persistent headache is spontaneous (postural) intracranial hypotension (SIH). The diagnosis can be clinched by magnetic resonance imaging (MRI) findings of subdural collections, meningeal enhancement and tonsillar descent. Cerebrospinal fluid leak has been postulated as the cause, and both medical and surgical treatment options have been documented. The treatment of choice is, however, an epidural blood patch. Here we discuss two cases of SIH treated successfully with epidural blood patch.

A Novel Fracture Prediction Model Using Machine Learning in a Community-Based Cohort.

The prediction of fracture risk in osteoporotic patients has been a topic of interest for decades, and models have been developed for the accurate prediction of fracture, including the fracture risk assessment tool (FRAX). As machine-learning methodologies have recently emerged as a potential model for medical prediction tools, we aimed to develop a novel fracture prediction model using machine-learning methods in a prospective community-based cohort. In this study, 2227 participants (1257 females) with a baseline bone mineral density (BMD) and trabecular bone score were enrolled from the Ansung cohort. The primary endpoint was the fragility fractures reported by patients or confirmed by X-rays. We used 3 different models: CatBoost, support vector machine (SVM), and logistic regression. During a mean 7.5-year follow-up (range, 2.5 to 10 years), fragility fractures occurred in 537 (25.6%) of participants. In predicting total fragility fractures, the area under the curve (AUC) values of the CatBoost, SVM, and logistic regression models were 0.688, 0.500, and 0.614, respectively. The AUC value of CatBoost was significantly better than that of FRAX (0.663; p < 0.001), whereas the the SVM and logistic regression models were not. Compared with the conventional models such as SVM and logistic regression, the CatBoost model had the best performance in predicting total fragility fractures (p < 0.001). According to feature importance in the CatBoost model, the top predicting factors (listed in order) were total hip, lumbar spine, and femur neck BMD, subjective arthralgia score, serum creatinine, and homocysteine. The latter three factors were listed higher than conventional predictors such as age or previous fracture history. In summary, we hereby report the development of a prediction model for fragility fractures using a machine-learning method, CatBoost, which outperforms the FRAX model as well as two conventional machine-learning models. The model was also able to propose novel high-ranking predictors. © 2020 The Authors. JBMR Plus published by Wiley Periodicals, Inc. on behalf of American Society for Bone and Mineral Research.

Wellbuilt for wellbeing: Controlling relative humidity in the workplace matters for our health.

This study offers a new perspective on the role of relative humidity in strategies to improve the health and wellbeing of office workers. A lack of studies of sufficient participant size and diversity relating relative humidity (RH) to measured health outcomes has been a driving factor in relaxing thermal comfort standards for RH and removing a lower limit for dry air. We examined the association between RH and objectively measured stress responses, physical activity (PA), and sleep quality. A diverse group of office workers (n = 134) from four well-functioning federal buildings wore chest-mounted heart rate variability monitors for three consecutive days, while at the same time, RH and temperature (T) were measured in their workplaces. Those who spent the majority of their time at the office in conditions of 30%-60% RH experienced 25% less stress at the office than those who spent the majority of their time in drier conditions. Further, a correlational study of our stress response suggests optimal values for RH may exist within an even narrower range around 45%. Finally, we found an indirect effect of objectively measured poorer sleep quality, mediated by stress responses, for those outside this range.

Giant Cervicodorsal Schwannoma.

OBJECTIVE: Schwannomas are benign slow growing tumors that arise from myelin producing Schwann cells. Schwannomas developing in cervical-dorsal region are rare benign neoplasms which are emerges leisurely remains asymptomatic some times and functionally inactive tumours. Giant Schwannomas extending over two or more vertebral levels have been documented and attempts have been made to classify these in available literature, however inadequate. Advancement in imaging modalities and microsurgery has bettered management of these tumors. A rare case of intradural extramedullary cervicodorsal schwannoma extending along seven vertebral levels to thoracic levels is reported in a 55 year old male patient with progressive weakness and numbness of over one and a half years. MRI of cervical spine showed a heterogeneously lesion with cord oedema till D7 level on T1contrast saggital view. Histological examination revealed encapsulated spindle cell lesion with hypocellular and hypercellular areas with verucay bodies, occasional bizzare nuclei, hyalinized blood vessels, with no evidence of necrosis/atypical mitosis, suggestive of schwannoma. In the prone position, C4 to D7 enbloc laminotomy was done and total excision of intradural extramedullary lesion was done. Post-operative CT scans revealed normal spinal canal dimensions with implants in situ. At quarterly follow up upto one year post-operative, the patient had near normal power in all four limbs and normal bladder function.

Effects of office workstation type on physical activity and stress.

OBJECTIVE: Office environments have been causally linked to workplace-related illnesses and stress, yet little is known about how office workstation type is linked to objective metrics of physical activity and stress. We aimed to explore these associations among office workers in US federal office buildings. METHODS: We conducted a wearable, sensor-based, observational study of 231 workers in four office buildings. Outcome variables included workers' physiological stress response, physical activity and perceived stress. Relationships between office workstation type and these variables were assessed using structural equation modelling. RESULTS: Workers in open bench seating were more active at the office than those in private offices and cubicles (open bench seating vs private office=225.52 mG (31.83% higher on average) (95% CI 136.57 to 314.46); open bench seating vs cubicle=185.13 mG (20.16% higher on average) (95% CI 66.53 to 303.72)). Furthermore, workers in open bench seating experienced lower perceived stress at the office than those in cubicles (-0.27 (9.10% lower on average) (95% CI -0.54 to -0.02)). Finally, higher physical activity at the office was related to lower physiological stress (higher heart rate variability in the time domain) outside the office (-26.12 ms/mG (14.18% higher on average) (95% CI -40.48 to -4.16)). CONCLUSIONS: Office workstation type was related to enhanced physical activity and reduced physiological and perceived stress. This research highlights how office design, driven by office workstation type, could be a health-promoting factor.

Predicting High-Cost Patients at Point of Admission Using Network Science.

Data mining models for high-cost patient encounter prediction at the point-of-admission (HPEPP) in inpatient wards are scarce in the literature. This is due to the lack of availability of relevant features at such an early stage of treatment. In this study, we create a disease co-occurrence network (DCN) using a subset of the state inpatient database of Arizona. We explore this network for community formation and structural properties to create new input features for HPEPP models. Tree-based data mining models are trained using input feature sets including these new network features, and distinct disease communities in the DCN are identified. We propose community membership and high-cost propensity scores as two network-based features for HPEPP modeling. We compare the performance of models with different input feature sets and find that the new features significantly improve the accuracy sensitivity and specificity of prediction models. This model has the potential to improve targeted care management and reduce health care expenditure.

Consensus Statement on Electronic Health Predictive Analytics: A Guiding Framework to Address Challenges.

CONTEXT: The recent explosion in available electronic health record (EHR) data is motivating a rapid expansion of electronic health care predictive analytic (e-HPA) applications, defined as the use of electronic algorithms that forecast clinical events in real time with the intent to improve patient outcomes and reduce costs. There is an urgent need for a systematic framework to guide the development and application of e-HPA to ensure that the field develops in a scientifically sound, ethical, and efficient manner. OBJECTIVES: Building upon earlier frameworks of model development and utilization, we identify the emerging opportunities and challenges of e-HPA, propose a framework that enables us to realize these opportunities, address these challenges, and motivate e-HPA stakeholders to both adopt and continuously refine the framework as the applications of e-HPA emerge. METHODS: To achieve these objectives, 17 experts with diverse expertise including methodology, ethics, legal, regulation, and health care delivery systems were assembled to identify emerging opportunities and challenges of e-HPA and to propose a framework to guide the development and application of e-HPA. FINDINGS: The framework proposed by the panel includes three key domains where e-HPA differs qualitatively from earlier generations of models and algorithms (Data Barriers, Transparency, and ETHICS) and areas where current frameworks are insufficient to address the emerging opportunities and challenges of e-HPA (Regulation and Certification; and Education and Training). The following list of recommendations summarizes the key points of the framework: Data Barriers: Establish mechanisms within the scientific community to support data sharing for predictive model development and testing.Transparency: Set standards around e-HPA validation based on principles of scientific transparency and reproducibility. ETHICS: Develop both individual-centered and society-centered risk-benefit approaches to evaluate e-HPA.Regulation and Certification: Construct a self-regulation and certification framework within e-HPA.Education and Training: Make significant changes to medical, nursing, and paraprofessional curricula by including training for understanding, evaluating, and utilizing predictive models.

Predicting asthma-related emergency department visits using big data.

Asthma is one of the most prevalent and costly chronic conditions in the United States, which cannot be cured. However, accurate and timely surveillance data could allow for timely and targeted interventions at the community or individual level. Current national asthma disease surveillance systems can have data availability lags of up to two weeks. Rapid progress has been made in gathering nontraditional, digital information to perform disease surveillance. We introduce a novel method of using multiple data sources for predicting the number of asthma-related emergency department (ED) visits in a specific area. Twitter data, Google search interests, and environmental sensor data were collected for this purpose. Our preliminary findings show that our model can predict the number of asthma ED visits based on near-real-time environmental and social media data with approximately 70% precision. The results can be helpful for public health surveillance, ED preparedness, and targeted patient interventions.