Tag: pubmed

Health Care Manag Sci. 2022 Jun 23. doi: 10.1007/s10729-022-09604-5. Online ahead of print.

ABSTRACT

Many public health policymaking questions involve data subsets representing application-specific attributes and geographic location. We develop and evaluate standard and tailored techniques for clustering via unsupervised learning (UL) algorithms on such amalgamated (dual-domain) data sets. The aim of the associated algorithms is to identify geographically efficient clusters that also maximize the number of statistically significant differences in disease incidence and demographic variables across top clusters. Two standard UL approaches, k means with k++ initialization (k++) and the standard self-organizing map (SSOM), are considered along with a new, tailored version of the SOM (TSOM). The TSOM algorithm involves optimization of a customized objective function with terms promoting individual geographic cluster cohesion while also maximizing the number of differences across clusters, and two hyper-parameters controlling the relative weighting of geographic and attribute subspaces in a non-Euclidean distance measure within the clustering problem. The performance of these three techniques (k++, SSOM, TSOM) is compared and evaluated in the context of a data set for colorectal cancer incidence in the state of California, at the level of individual counties. Clusters are visualized via chloropleth maps and ordered graphs are also used to illustrate disparities in disease incidence among four identity groups. While all three approaches performed well, the TSOM identified the largest number of disease and demographic disparities while also yielding more geographically efficient top clusters. Techniques presented in this study are relevant to applications including the delivery of health care resources and identifying disparities among identity groups, and to questions involving coordination between county- and state-level policymakers.

PMID:35732967 | DOI:10.1007/s10729-022-09604-5

Eur J Clin Pharmacol. 2022 Jun 22. doi: 10.1007/s00228-022-03356-5. Online ahead of print.

ABSTRACT

PURPOSE: The coronavirus disease 2019 (COVID-19) pandemic has shown unprecedented impact world-wide since the eruption in late 2019. Importantly, emerging reports suggest an increased risk of thromboembolism development in patients with COVID-19. Meanwhile, it is found that aspirin reduced mortality in critically ill patients with non-COVID-19 acute respiratory distress syndrome. Therefore, a meta-analysis was performed to investigate the effects of aspirin on COVID-19 mortality.

METHODS: A systematic literature search was conducted in 10 electronic databases and 4 registries. Random effects models were used to calculate pooled relative risks (RRs) with 95% confidence intervals (Cis) to estimate the effect of aspirin on COVID-19 mortality. Relevant subgroup analyses and sensitivity analyses were also performed.

RESULTS: The results showed that aspirin use was associated with a reduction in COVID-19 mortality (adjusted RR 0.69; 95% CI 0.50-0.95; P < 0.001). Subgroup analysis found that the low-dose group was associated with a reduced COVID-19 mortality (adjusted RR 0.64; 95% CI 0.48-0.85; P < 0.01). Aspirin use was associated with reduced COVID-19 mortality in Europe and America (crude RR 0.71; 95% CI 0.52-0.98; P = 0.04), and results from cohort studies suggested that aspirin use was a protective factor for COVID-19 mortality (adjusted RR 0.73; 95% CI 0.52-0.99; P = 0.04). Meanwhile, aspirin use was not associated with bleeding risk (crude RR 1.22; 95% CI 0.80-1.87; P = 0.96).

CONCLUSIONS: This meta-analysis found that aspirin use was associated with a reduction in mortality in patients with COVID-19 and not with an increased risk of bleeding.

PMID:35732963 | DOI:10.1007/s00228-022-03356-5

Neurol Sci. 2022 Jun 22. doi: 10.1007/s10072-022-06204-7. Online ahead of print.

ABSTRACT

Using magnetic resonance (MR) images to evaluate changes in the shape of the hippocampus has been an active research topic. This paper presents a new shape analysis approach to quantify and visualize deformations of the hippocampus in epilepsy. The proposed method is based on Laplace-Beltrami (LB) eigenvalues and eigenfunctions as isometric invariant shape features, and thus, the procedure does not require any image registration. In addition to the LB-based shape features, total hippocampal volume and surface area are calculated using manually segmented images. Theses shape and volumetric descriptors are used to distinguish the patients with temporal lobe epilepsy (TLE) (N = 55) from healthy control subjects (N = 12, age = 32.2 ± 9.1, sex (M/F) = 6/6) and patients with right TLE (N = 26, age = 45.1 ± 11.0, sex (M/F) = 9/17) from left TLE (N = 29, age = 45.4 ± 11.9, sex (M/F) = 10/19). Experimental results illustrate the usefulness of the proposed approach for the diagnosis and lateralization of TLE with 93.0% and 86.4% of the cases, respectively. Moreover, the proposed method outperforms the volumetric analysis in terms of both sensitivity (94.9% vs. 88.1%) and specificity (83.3% vs. 50.0%) of the lateralization. The analysis of local hippocampal thickness variations suggests significant deformation in both ipsilateral and contralateral hippocampi of epileptic patients, while there were no differences between right and left hippocampi in controls. It is anticipated that the proposed method could be advantageous in the presurgical evaluation of patients with drug-resistant epilepsy; however, further validation of the method using a larger dataset is required.

PMID:35732961 | DOI:10.1007/s10072-022-06204-7

Strahlenther Onkol. 2022 Jun 22. doi: 10.1007/s00066-022-01964-9. Online ahead of print.

ABSTRACT

BACKGROUND: The gamma index and dose-volume histogram (DVH)-based patient-specific quality assurance (QA) measures commonly applied in radiotherapy planning are unable to simultaneously deliver detailed locations and magnitudes of discrepancy between isodoses of planned and delivered dose distributions. By exploiting statistical classification performance measures such as sensitivity or specificity, compliance between a planned and delivered isodose may be evaluated locally, both for organs-at-risk (OAR) and the planning target volume (PTV), at any specified isodose level. Thus, a patient-specific QA tool may be developed to supplement those presently available in clinical radiotherapy.

MATERIALS AND METHODS: A method was developed to locally establish and report dose delivery errors in three-dimensional (3D) isodoses of planned (reference) and delivered (evaluated) dose distributions simultaneously as a function the dose level and of spatial location. At any given isodose level, the total volume of delivered dose containing the reference and the evaluated isodoses is locally decomposed into four subregions: true positive-subregions within both reference and evaluated isodoses, true negative-outside of both of these isodoses, false positive-inside the evaluated isodose but not the reference isodose, and false negatives-inside the reference isodose but not the evaluated isodose. Such subregions may be established over the whole volume of delivered dose. This decomposition allows the construction of a confusion matrix and calculation of various indices to quantify the discrepancies between the selected planned and delivered isodose distributions, over the complete range of values of dose delivered. The 3D projection and visualization of the spatial distribution of these discrepancies facilitates the application of the developed method in clinical practice.

RESULTS: Several clinical photon radiotherapy plans were analyzed using the developed method. In some plans at certain isodose levels, dose delivery errors were found at anatomically significant locations. These errors were not otherwise highlighted-neither by gamma analysis nor by DVH-based QA measures. A specially developed 3D projection tool to visualize the spatial distribution of such errors against anatomical features of the patient aids in the proposed analysis of therapy plans.

CONCLUSIONS: The proposed method is able to spatially locate delivery errors at selected isodose levels and may supplement the presently applied gamma analysis and DVH-based QA measures in patient-specific radiotherapy planning.

PMID:35732919 | DOI:10.1007/s00066-022-01964-9

Environ Sci Pollut Res Int. 2022 Jun 22. doi: 10.1007/s11356-022-21522-1. Online ahead of print.

ABSTRACT

Spatial distribution and pollution assessment of selected heavy metals such as barium (Ba), chromium (Cr), nickel (Ni), lead (Pb), vanadium (V), arsenic (As), zinc (Zn), and copper (Cu) in sediments of the Brahmaputra River watershed in Bangladesh was investigated. The mean abundances (ppm) of heavy metals in sediment samples were in decreasing order Ba (375.60) > V (67.60) > Cr (54.10) > Zn (48.20) > Ni (22.28) > Pb (20.25) > Cu (7.59) > As (4.21). Concentrations of Pb and As in the sediments are enriched relative to the average upper continental crust composition, while Ba, V, Cr, Zn, Ni, and Cu decrease considerably. A higher concentration of Pb and Ni indicates that Brahmaputra River watershed samples receive a significant contribution from anthropogenic sources of heavy metals. Chromium displays marked positive correlation with V (r = 0.91, p = < 0.01), inferring a similar source materials input into the watershed. The geo-accumulation index (I_geo) values suggest that the sediments were uncontaminated to moderately contaminated by Ni, Zn, Pb, V, and Cr, whereas moderate to heavily contaminated by As and Cu. The contamination factor (CF) confirmed that sediments in the watershed were moderate to highly contaminated by As, Cu, and Cr. The pollution load index (PLI) values for most of the samples were over one (> 1), indicating an advanced decline in the watershed sediment quality. The overall results of a multivariate statistical analysis suggest that Ba, V, Cr, and Zn contents were all-natural sources, and Pb, Ni, As, and Cu were derived from both natural and anthropogenic sources.

PMID:35732893 | DOI:10.1007/s11356-022-21522-1

Doc Ophthalmol. 2022 Jun 22. doi: 10.1007/s10633-022-09879-7. Online ahead of print.

ABSTRACT

PURPOSE: Hydroxychloroquine (HCQ) is an anti-inflammatory drug in widespread use for the treatment of systemic auto-immune diseases. Vision loss caused by retinal toxicity is a significant risk associated with long term HCQ therapy. Identifying patients at risk of developing retinal toxicity can help prevent vision loss and improve the quality of life for patients. This paper presents updated reference thresholds and examines the diagnostic accuracy of a machine learning approach for identifying retinal toxicity using the multifocal Electroretinogram (mfERG).

METHODS: A retrospective study of patients referred for mfERG testing to detect HCQ retinopathy. A consecutive series of all patients referred to Kensington Vision and Research Centre between August 2017 and July 2020 were considered eligible. Eyes suspect for other ocular pathology including widespread retinal disease and advanced macular pathology unrelated to HCQ or with poor quality mfERG recordings were excluded. All patients received mfERG testing and Ocular Coherence Tomography (OCT) imaging. Presence of HCQ retinopathy was based on ring ratio analysis using clinical reference thresholds established at KVRC coupled with structural features observed on OCT, the clinical reference standard. A Support Vector Machine (SVM) using selected features of the mfERG was trained. Accuracy, sensitivity and specificity are reported.

RESULTS: 1463 eyes of 748 patients were included in the study. SVM model performance was assessed on 293 eyes from 265 patients. 55 eyes from 54 patients were identified as demonstrating HCQ retinopathy based on the clinical reference standard, 50 eyes from 49 patients were identified by the SVM. Our SVM achieves an accuracy of 85.3% with a sensitivity of 90.9% and specificity of 84.0%.

CONCLUSIONS: Machine learning approaches can be applied to mfERG analysis to identify patients at risk of retinopathy caused by HCQ therapy.

PMID:35732856 | DOI:10.1007/s10633-022-09879-7

Drug Test Anal. 2022 Jun 22. doi: 10.1002/dta.3329. Online ahead of print.

ABSTRACT

The determinants of success in Olympic Games competition are specific to the athletic demands of the sporting event. A global evaluation to quantify the athletic demands across the spectrum of the Olympic Games sport events has not previously been conducted. Thus far, the interpretation and the comparison of sport physiological characteristics within anti-doping organisations (ADOs) risk assessments remains subjective without a standardised framework. Despite its subjective assessment, this information is a key component of any anti-doping programme. Sport characteristics inevitably influence the type of substances and/or methods used for doping purpose and should be captured through a comprehensive analysis. Seven applied sport scientists independently conducted an assessment to quantify the athletic demands across six preselected athletic variables. A Principal Component Analysis was performed on the results of the panel’s quantitative assessment for 160 Olympic Sport events. Sport events were clustered using the Hierarchical Density Based Spatial Clustering of Applications with Noise (HDBSCAN) algorithm. The HDBSCAN identified 19 independent cluster groups, 36 sport events remained statistically unassigned to a cluster group representing unique and eventspecific athletic demands. This investigation provides guidance to the anti-doping community to assist in the development of the sport specific physiology component of the risk assessment for Olympic Games disciplines. The dominant athletic characteristics to excel in each of these individual events will highlight areas of how athletes may strive to gain a competitive advantage through doping strategies, and inform the development of an effective and proportionate allocation of testing resources.

PMID:35732071 | DOI:10.1002/dta.3329

Adv Mind Body Med. 2022 Spring;36(2):8-13.

ABSTRACT

CONTEXT: Higher Brain Living (HBL) is a light-touch therapy, which practitioners claim can increase well-being. Although studies have suggested that its component elements-light touch, focused breathing, and positive self-talk-can increase well-being for specific populations in specific contexts, no empirical research has occurred on HBL’s efficacy.

OBJECTIVE: The study intended to measure the effects of HBL therapy on the well-being of individuals who have received it.

DESIGN: The research team designed a quasi-experimental controlled trial that used a survey to gather self-reported data related to well-being.

SETTING: The study took place in individual HBL practitioners’ locations throughout the USA.

PARTICIPANTS: Participants were adults who had attended an introductory presentation about HBL.

INTERVENTION: Participants were assigned to one of three groups: (1) the intervention group, who had responded to the baseline and postintervention surveys and had participated in HBL sessions (n = 14); (2) the control group, who had responded to the baseline and postintervention surveys and had not participated in HBL sessions (n = 9); and (3) the noncompleter group who had responded to the baseline surveys and had not completed the postintervention survey (n = 54).

OUTCOME MEASURES: Well-being was assessed using five measures that evaluated constructs associated with well-being: (1) happiness using the Subjective Happiness Scale (SHS), (2) anxiety using the Anxiety Index (AI), (3) depression using Depression Index (DI) (4) mastery using the Pearlin Mastery Scale (PM), and (5) flourishing using the Flourishing Scale (FS).

RESULTS: The study included baseline data from 77 respondents; 23 participants completed the surveys at baseline and postintervention, 14 in the intervention group and 9 in the control group. A statistically significant, greater improvement occurred for the intervention group in the measures for flourishing, mastery, and happiness compared to the control group.

CONCLUSIONS: The current study provides a foundation of empirical evidence suggesting the effectiveness of HBL as a potential treatment for improving well-being, upon which further investigation can be based.

PMID:35732064

J Occup Environ Med. 2022 Jun 16. doi: 10.1097/JOM.0000000000002550. Online ahead of print.

ABSTRACT

Investigate the associations between drug abuse and the preva- lence of the engagement and burnout dichotomy in law professionals. Methods: Eligible participants completed a questionnaire where odds ratios of drug abuse and other confounding variables and their association to engagement or burnout were calculated using multiple logistic regression. Results: When looking at all law professionals, burnout is a statistically significant predictor for drug abuse (P ¼ 0.04, not shown). Law professionals whose burnout scores fell in the highest bin have 4.71 (95% CI [1.38 – 16.08]) times higher odds of having a problem with drug abuse than those whose burnout scores fell in the second bin. Conclusion: Study findings showed a possible way to affect the prevalence of drug abuse in law professionals by affecting the engagement and burnout dichotomy.

PMID:35732047 | DOI:10.1097/JOM.0000000000002550

J Occup Environ Med. 2022 Jun 16. doi: 10.1097/JOM.0000000000002567. Online ahead of print.

ABSTRACT

OBJECTIVE: To investigate the relationship between daily activities (paid work, childcare, caregiving, voluntary work, sports and social contact), occupational balance, and depressive symptoms during the COVID-19 pandemic.

METHODS: We analyzed data from the Austrian Corona Panel Project (four timepoints, 6-month period) using regression models with logarithmically transformed data and non-parametric repeated-measures tests (N = 871).

RESULTS: Results showed higher depressive symptoms among women. Family caregivers (either parents or those caring for other relatives) were at highest risk for occupational imbalance and depressive symptoms. Sports and social contact were initially associated with better outcomes, but the effects waned. There was a main effect for time point driven by the last wave (amidst the second lockdown) but no significant interaction effects between predictors and time point were found.

CONCLUSION: The results provide a nuanced depiction of the relationship between different daily activities and health-related outcomes during the pandemic, highlighting groups at risk.

PMID:35732038 | DOI:10.1097/JOM.0000000000002567