Potential Predictor Variables Research Articles

Factors Associated With Emergency Department Visits Among Patients Receiving Publicly-Funded Homecare Services: A Retrospective Chart Review From Southern Taiwan Regional Hospital.

The public health strategy of increasing access to comprehensive home or community-based healthcare services and emergency home visits is intent on reducing the overcrowding of emergency departments. However, scientific evidence regarding the association between home-based healthcare services and emergency department uses is surprisingly insufficient and controversial so far. The present retrospective study identified the risk factors for emergency department visits among patients receiving publicly-funded homecare services. The personal demographic and medical information, caregiver characteristics, and behaviours related to homecare services and emergency department visits from the medical records and structured questionnaires of 108 patients who were recipients of integrated homecare services in a regional hospital in southern Taiwan between January 1, 2020, and December 31, 2020, were collected. After screening the potential predictor variables using the preliminary univariate analyses, the multivariate logistic regression with best subset selection approach was conducted to identify best combination of determinants to predict unplanned emergency department utilizations. Best subset selection regression analysis showed Charlson Comorbidity Index (odds ratio (OR)=1.33, 95% CI=1.05 to 1.70), male caregiver (OR=0.18, 95% CI=0.05 to 0.66), duration of introducing homecare services (OR=0.97, 95% CI=0.95 to 1.00), working experience of dedicated nurses (OR=0.89, 95% CI=0.79 to 0.99) and number of emergency department utilizations within previous past year before enrollment (OR=1.54, 95% CI=1.14 to 2.10) as significant determinants for unplanned emergency department visits. The present evidence may help government agencies propose supportive policies to improve access to integrated homecare resources and promote appropriate care recommendations to reduce unplanned or nonurgent emergency department visits among patients receiving homecare services.

201 Prevalence of osteoporosis based on DXA criteria in large sample of older Irish adults attending GP or outpatient services

Abstract Background Osteoporosis has a markedly increased prevalence in older adults with an associated risk of fragility fractures that can significantly impact on morbidity, quality of life and mortality. Despite this, osteoporosis is both underdiagnosed and undertreated and its true prevalence in older Irish adults is not known. In this study, we evaluate the prevalence of osteoporosis in Irish adults aged ≥60 years and also examine its determinants. Methods Participants were from a large cross-sectional study of older Irish adults aged ≥60 years. We excluded those with a known diagnosis of osteoporosis and on pharmacological treatment for same. We also excluded participants with major risk factors for osteoporosis (history of any previous fracture, aromatase inhibitors, androgen deprivation therapy and current or long-term glucocorticoid therapy >3 months). We used logistic regression to assess the relationship between osteoporosis (defined by WHO criteria—DXA T-score < −2.5 at lumbar spine, total hip, or neck of femur) and potential predictor variables. Results There were 1,089 participants. Mean age was 70.0 ± 6.2 and 59.8% were female. 15% had osteoporosis and 47% osteopaenia. The only positive independent predictors of osteoporosis were body mass index (p < 0.0001) and 25-hydroxyvitamin D (p = 0.007) while negative predictors were female sex (p < 0.0001), older age (p = 0.037) and glomerular filtration rate (p = 0.028). Conclusion Almost one in six older adults with no history of fracture or major risk factors for bone loss had osteoporosis. However, half had osteopaenia and overall, two thirds had low bone density. The major predictors of osteoporosis were consistent with other studies. As the mean age in our sample was 70, the prevalence may be much greater with advancing age. The findings support international guidelines which recommend routine DXA screening for women aged ≥65 years and men ≥70 years or at a younger age if risk factors for low bone mass.

The integration of hierarchical levels of scale in tree species distribution models of silver fir (Abies alba Mill.) and European beech (Fagus sylvatica L.) in mountain forests

Taking into account and combining different levels of scale is essential in species distribution models when it comes to the investigation of the environmental niche of a species. The study focuses on two levels of spatial extent, on the one hand the continental level of Europe, and on the other hand the regional level of the federal state of Tyrol in Austria. Mountain forests in the European Alps cover the inner Alpine distribution margins of several tree species and therefore are particularly well-suited to reveal the predictive power of climate, soil, and topographic variables on the shaping of these margins. The potential occurrence of the two investigated tree species, Abies alba and Fagus sylvatica, is an important criterion for the delineation of elevational vegetation zones in forest site classification systems and was modelled with Deep Neural Networks. In the process, we observed a strong imbalance of absence and presence records at the continental level and evaluated different methods to address this issue. The potential predictor variables for species distribution modelling at the different spatial extents were grouped into climate, soil, and topographic information. The combination of the different hierarchical levels of extent and associated spatial resolution was implemented by using the outputs of the continental model as a predictor variable in the regional model. The binary classification of the 30% test dataset showed a True Skill Statistic of 0.73 to 0.76 for the regional level and 0.5 to 0.74 for the continental level, with slightly higher values for F. sylvatica than for A. alba. For both species and extent levels, the climate predictor group showed the greatest contribution (81 to 96%) to the models’ predictive power. At the regional level, climate was followed by soil and then topographic predictor groups. At the continental level however, topography showed stronger effects than soil information. In most cases, the consideration of soil information along climatic gradients led to an increase in the occurrence probability at the climatic distribution margins. There is evidence that soil conditions are more important in determining the inner Alpine distribution margins for F. sylvatica than for A. alba. To improve species distribution models at the regional level, e.g. in the Alpine area, a focus on soil information is proposed. In general, models which combine continental and regional data are preferable.

LASSO Regression with Multiple Imputations for the Selection of Key Variables Affecting the Fatty Acid Profile of Nannochloropsis oculata.

The marine microalga Nannochloropsis oculata has garnered significant interest as a potential source of lipids, both for biofuel and nutrition, containing significant amounts of C16:0, C16:1, and C20:5, n-3 (EPA) fatty acids (FA). Growth parameters such as temperature, pH, light intensity, and nutrient availability play a crucial role in the fatty acid profile of microalgae, with N. oculata being no exception. This study aims to identify key variables for the FA profile of N. oculata grown autotrophically. To that end, the most relevant literature data were gathered and combined with our previous work as well as with novel experimental data, with 121 observations in total. The examined variables were the percentages of C14:0, C16:0, C16:1, C18:1, C18:2, and C20:5, n-3 in total FAs, their respective ratios to C16:0, and the respective content of biomass in those fatty acids in terms of ash free dry weight. Many potential predictor variables were collected, while dummy variables were introduced to account for bias in the measured variables originating from different authors as well as for other parameters. The method of multiple imputations was chosen to handle missing data, with limits based on the literature and model-based estimation, such as using the software PHREEQC and residual modelling for the estimation of pH. To eliminate unimportant predictor variables, LASSO (Least Absolute Shrinkage and Selection Operator) regression analysis with a novel definition of optimal lambda was employed. LASSO regression identified the most relevant predictors while minimizing the risk of overfitting the model. Subsequently, stepwise linear regression with interaction terms was used to further study the effects of the selected predictors. After two rounds of regression, sparse refined models were acquired, and their coefficients were evaluated based on significance. Our analysis confirms well-known effects, such as that of temperature, and it uncovers novel unreported effects of aeration, calcium, magnesium, and manganese. Of special interest is the negative effect of aeration on polyunsaturated fatty acids (PUFAs), which is possibly related to the enzymatic kinetics of fatty acid desaturation under increased oxygen concentration. These findings contribute to the optimization of the fatty acid profile of N. oculata for different purposes, such as production of, high in PUFAs, food or feed, or production of, high in saturated and monounsaturated FA methyl esters (FAME), biofuels.

Improving the quality of coffee yield forecasting in Dak Lak Province, Vietnam, through the utilization of remote sensing data

This research aimed to identify sensitive areas for Robusta coffee trees in Dak Lak province, Vietnam, where frequent droughts caused fluctuations in productivity. To improve yield forecasting, a mask was developed to extract potential predictive variables from satellite-derived vegetation indices (VIs). Correlation coefficients between VIs and coffee yield were analyzed to determine sensitive areas, and grid cells with high multiple correlation coefficients and a variable over time were used to build the mask for extracting VIs as predictor variables. The study found that sensitive areas had more challenging farming conditions than long-term crops, and the Vegetation Health Index was the most appropriate index for predicting coffee yield. The forecast quality for 6-8 months in advance was relatively high, with a ‘Willmott’s index of agreement’ ranging from 0.85 to 0.97 and the Mean Absolute Percentage Error ranging from 4.9% to 7.5%. Compared to previous research, the forecast quality has significantly improved. This study provides valuable insights for predicting coffee yield in Dak Lak and highlights the importance of considering sensitive areas and VIs for accurate forecasting.

The effect of population density on outcomes of major trauma patients in Ireland

BackgroundTime-sensitive emergencies in areas of low population density have statistically poorer outcomes. This includes incidents of major trauma. This study assesses the effect that population density at a receiving hospital of a major trauma patient has on survival. MethodsPatients meeting Trauma Audit Research Network criteria for major trauma from 2016 to 2020 in Ireland were included in this retrospective observational study. Incident data were retrieved from the Major Trauma Audit, while data on population density were calculated from Irish state sources. The primary outcome measure of survival to discharge was compared to population density using logistic regression, adjusted for demographic and incident variables. Records were divided into population density tertiles to assess for between-group differences in potential predictor variables. ResultsPopulation density at a receiving hospital had no impact on mortality in Irish major trauma patients from our logistic regression model (OR = 1.01, 95% CI 0.98–1.05, p = 0.53). Factors that did have an impact were age, Charlson Comorbidity Index, Injury Severity Score, and the presence of an Orthopaedic Surgery service at the receiving hospital (all p < 0.001). Age and Charlson Comorbidity Index differed slightly by population density tertile; both were higher in areas of high population density (all p < 0.001). ConclusionsSurvival to discharge in Irish major trauma patients does not differ substantially based on population density. This is an important finding as Ireland moves to a new trauma system, with features based on population distribution. An Orthopaedic Surgery service is an important feature of a major trauma receiving hospital and its presence improves outcomes.

Trans-sacral interbody fixation in long fusions to the sacrum for adult spinal deformity: complications and fusion rates at minimum two years follow-up.

This study aims to investigate the fusion rate and complications associated with trans-sacral interbody fusion (TSIF) in long fusions to the sacrum for adult spinal deformity (ASD) over a twoyear follow-up period. Potential predictor variables associated with pseudarthrosis were also examined. A retrospective clinical review was conducted on a consecutive series of ASD patients who underwent long fusions to the sacrum, with TSIF performed as a same-day or staged procedure. Patient demographics, bone mineral density, operative details, perioperative and late complications, and fusion rates were reviewed. Univariate analysis was used to identify the risk factors associated with pseudarthrosis. The study included 43 patients with an average age of 55.3 ± 8.9years. The perioperative complication rate was 28%, with 12% of the complications directly related to TSIF. The late complication rate was 33%, with 16% related to TSIF. The most common complications were pseudarthrosis (14%) and postoperative ileus (7%). The overall radiographic fusion rate at twoyears was 86%. Univariate analysis revealed that revision surgery was significantly associated with pseudarthrosis (p = 0.027). Over the follow-up period, patients who underwent TSIF during long posterior fusions to the sacrum showed improvement in overall SRS scores, ODI scores, and SF-36 physical health and mental health (p < 0.05). TSIF is a relatively safe and minimally invasive method for achieving interbody fusion at the lumbosacral junction in the treatment of ASD, with acceptable fusion rates and a low complication rate. However, TSIF is not recommended for revision reconstruction in ASD.

Ongoing symptoms and functional impairment 12 weeks after testing positive for SARS-CoV-2 or influenza in Australia: an observational cohort study

ObjectiveIn a highly vaccinated Australian population, we aimed to compare ongoing symptoms and functional impairment 12 weeks after PCR-confirmed COVID-19 infection with PCR-confirmed influenza infection.Methods and analysisThe study commenced on...

Patterns, drivers, and a predictive model of dam removal cost in the United States

Given the burgeoning dam removal movement and the large number of dams approaching obsolescence in the United States, cost estimating data and tools are needed for dam removal prioritization, planning, and execution. We used the list of removed dams compiled by American Rivers to search for publicly available reported costs for dam removal projects. Total cost information could include component costs related to project planning, dam deconstruction, monitoring, and several categories of mitigation activities. We compiled reported costs from 455 unique sources for 668 dams removed in the United States from 1965 to 2020. The dam removals occurred within 571 unique projects involving 1–18 dams. When adjusted for inflation into 2020 USD, cost of these projects totaled $1.522 billion, with per-dam costs ranging from $1 thousand (k) to $268.8 million (M). The median cost for dam removals was $157k, $823k, and $6.2M for dams that were&amp;lt; 5 m, between 5–10 m, and &amp;gt; 10 m in height, respectively. Geographic differences in total costs showed that northern states in general, and the Pacific Northwest in particular, spent the most on dam removal. The Midwest and the Northeast spent proportionally more on removal of dams less than 5 m in height, whereas the Northwest and Southwest spent the most on larger dam removals &amp;gt; 10 m tall. We used stochastic gradient boosting with quantile regression to model dam removal cost against potential predictor variables including dam characteristics (dam height and material), hydrography (average annual discharge and drainage area), project complexity (inferred from construction and sediment management, mitigation, and post-removal cost drivers), and geographic region. Dam height, annual average discharge at the dam site, and project complexity were the predominant drivers of removal cost. The final model had an R2 of 57% and when applied to a test dataset model predictions had a root mean squared error of $5.09M and a mean absolute error of $1.45M, indicating its potential utility to predict estimated costs of dam removal. We developed a R shiny application for estimating dam removal costs using customized model inputs for exploratory analyses and potential dam removal planning.

MSR3 A Review of Predictive Modelling in Hypertension Based on Using Risk Factors to Predict Cardiovascular Consequences: A Targeted Literature Review (TLR)

Hypertension is a chronic condition that if left unmanaged, will result in significant clinical, humanistic, social, and economic burdens for patients. These outcomes are closely associated with various socio-demographic variables, risk factors, comorbidities, and disease management/intervention strategies. Predicting these outcomes via predictive modelling can provide a critical support for hypertension management and downstream event prevention but requires a deep understanding of the progression pathway as well as the association of risk factors with outcomes. A targeted literature review (TLR) was thus conducted to study potential predictive variables and outcomes of interests as well as existing models to support the development of such a model based on patient characteristics.

Using machine learning to predict outcomes following carotid endarterectomy

ObjectivePrediction of outcomes following carotid endarterectomy (CEA) remains challenging, with a lack of standardized tools to guide perioperative management. We used machine learning (ML) to develop automated algorithms that predict outcomes following CEA. MethodsThe Vascular Quality Initiative (VQI) database was used to identify patients who underwent CEA between 2003 and 2022. We identified 71 potential predictor variables (features) from the index hospitalization (43 preoperative [demographic/clinical], 21 intraoperative [procedural], and 7 postoperative [in-hospital complications]). The primary outcome was stroke or death at 1 year following CEA. Our data were split into training (70%) and test (30%) sets. Using 10-fold cross-validation, we trained six ML models using preoperative features (Extreme Gradient Boosting [XGBoost], random forest, Naïve Bayes classifier, support vector machine, artificial neural network, and logistic regression). The primary model evaluation metric was area under the receiver operating characteristic curve (AUROC). After selecting the best performing algorithm, additional models were built using intra- and postoperative data. Model robustness was evaluated using calibration plots and Brier scores. Performance was assessed on subgroups based on age, sex, race, ethnicity, insurance status, symptom status, and urgency of surgery. ResultsOverall, 166,369 patients underwent CEA during the study period. In total, 7749 patients (4.7%) had the primary outcome of stroke or death at 1 year. Patients with an outcome were older with more comorbidities, had poorer functional status, and demonstrated higher risk anatomic features. They were also more likely to undergo intraoperative surgical re-exploration and have in-hospital complications. Our best performing prediction model at the preoperative stage was XGBoost, achieving an AUROC of 0.90 (95% confidence interval [CI], 0.89-0.91). In comparison, logistic regression had an AUROC of 0.65 (95% CI, 0.63-0.67), and existing tools in the literature demonstrate AUROCs ranging from 0.58 to 0.74. Our XGBoost models maintained excellent performance at the intra- and postoperative stages, with AUROCs of 0.90 (95% CI, 0.89-0.91) and 0.94 (95% CI, 0.93-0.95), respectively. Calibration plots showed good agreement between predicted and observed event probabilities with Brier scores of 0.15 (preoperative), 0.14 (intraoperative), and 0.11 (postoperative). Of the top 10 predictors, eight were preoperative features, including comorbidities, functional status, and previous procedures. Model performance remained robust on all subgroup analyses. ConclusionsWe developed ML models that accurately predict outcomes following CEA. Our algorithms perform better than logistic regression and existing tools, and therefore, have potential for important utility in guiding perioperative risk mitigation strategies to prevent adverse outcomes.

Assessing public interest, risk perceptions, and awareness of food safety in Saudi Arabia: A cross-sectional study

To the best of our knowledge, this is the first study conducted in Saudi Arabia to investigate public interest, risk perceptions, and awareness of food safety and its association with population characteristics among a large-scale, randomly selected sample. Of the 1000 participants, almost half (43.9%) indicated their reliance on food safety when making any food-related decisions. When assessing the relationship between food-related decisions and potential predictor variables, only age and educational level yielded significant results. The findings also showed that health physicians, followed by social media, were the channels most frequently used to obtain information about food-related risks. Moreover, the majority believed that food safety is better in current times and willing to pay more for notably approved and safe food products (67.3%). However, the results revealed an extensive lack of awareness of how the Saudi food safety system works, and the regression indicated that only age and disease presence yielded significant results. The present findings provide invaluable platform data for food-producing companies and regulatory authorities to recognise consumer interests and perceptions of food safety as an important determinant of the attitudes of people toward health. The findings also provide better insights into the extent to which the population is aware of the Saudi food safety system as well as the credibility of the sources that consumers use to find information about food safety.

Predictors of Mortality in Early Neonatal Sepsis: A Single-Center Experience

Background and Objectives: Early neonatal sepsis is associated with a significant mortality rate despite modern treatment strategies. Our aim was to identify risk factors contributing to the occurrence of death in newborns with early neonatal sepsis. Materials and Methods: We conducted a retrospective cross-sectional study that included newborns with early sepsis who received care in the intensive and semi-intensive care units at the Institute of Neonatology, Belgrade, Serbia. Newborns with early neonatal sepsis who died comprised the case group, whereas those who survived made up the control group. The diagnostic and therapeutic approach to the septic condition was carried out independently of this study, according to valid hospital protocols and current good practice guidelines. The influence of a large number of variables on the examined dichotomous outcome, as well as the mutual interaction of potential predictor variables, was examined by binary logistic regression. Results: The study included 133 pregnant women and 136 newborns with early neonatal sepsis, of which 51 (37.5%) died, while the remaining 85 newborns (62.5%) survived. Newborns who died had a statistically significantly lower birth weight compared to those who survived (882.8 ± 372.2 g vs. 1660.9 ± 721.1 g, p = 0.000). Additionally, compared to newborns who survived, among the deceased neonates there was a significantly higher proportion of extremely preterm newborns (74.5% vs. 22.4%, p = 0.000). The following risk factors for the occurrence of death in early neonatal sepsis were identified: low birth weight, sepsis caused by gram-negative bacteria, and the use of double-inotropic therapy and erythrocyte transfusion during the first week. Conclusions: Pediatricians should pay special attention to infants with early neonatal sepsis in whom any of the identified risk factors are present in order to prevent a fatal outcome.

A182 PREDICTING HIGH DIRECT HEALTHCARE COSTS IN PEDIATRIC PATIENTS WITH INFLAMMATORY BOWEL DISEASE IN THE FIRST YEAR FOLLOWING DIAGNOSIS

Abstract Background The incidence of inflammatory bowel disease (IBD) continues to rise rapidly among Canadian children. The care of children results in higher direct healthcare costs than adults with IBD. It is imperative that we identify individuals who will become the highest-cost users of the health system in order to intervene early and decrease the individual- and system-level burden of IBD. Purpose To develop a predictive-model for high-cost health system users and (2) identify factors associated with high-cost healthcare use. Method Incident cases of IBD diagnosed ≤17y residing in Ontario and enrolled in the Canadian Children IBD Network (CIDsCaNN) between Dec 31 2013 and Jan 31 2019 were linked deterministically using health card number to health administrative data. Using a validated algorithm, direct healthcare costs accumulated between the 31st and 365th day after diagnosis were calculated using data from CIDsCaNN (medications) and health administrative data (health system encounters, including surgery). A predictive model was created to determine high-cost (≤25th percentile) and medium-cost (26th to 75th) users, compared to low-cost users (&amp;gt;75th) using ordinal logistic regression. Potential predictive variables were determined a priori based on clinical significance and magnitude of univariable association, based on sample size-informed degrees of freedom. Variables from CIDsCaNN data included: IBD type, age at diagnosis, sex, first line of therapy (steroids, aminosalicylates, exclusive enteral nutrition; yes or no, not mutually exclusive), disease activity (severe vs. moderate vs. none/mild based on PUCAI [UC] or wPCDAI [Crohn’s]). Predictive variables from the health administrative data included: rural/urban residence, hospitalization at diagnosis, intestinal resection or colectomy within 3 months of diagnosis, emergency department visit ±1 month of diagnosis, and a mental health encounter within the first year following diagnosis. Anti-TNF treatment was excluded from models due to the strong correlation with the outcome (direct costs). Overall model fit was estimated with a c-statistic. Result(s) Among the 487 (57% Crohn’s) children included in the study, the mean (sd) direct costs accumulated between the 31st and 365th days following IBD diagnosis was $14,451 (14,665). The mean cost among high-cost users was $33,533 (16,530); medium-cost users, $11,038 (5322); low-cost users, $2530 (831). The predictive model identified high-cost users of the health system with acceptable model fit (c-statistic 0.69). The relative contribution of individual variables, as measured by odds ratio (OR), is reported in the Table. Image Conclusion(s) The direct healthcare costs of pediatric IBD are substantial. Children with IBD who become high-cost users of the health system were identifiable using characteristics at diagnosis (e.g., need for mental health care, emergency visits, older age). Further research should assess whether interventions in patients at-risk for becoming high-cost users may help to reduce costs. Please acknowledge all funding agencies by checking the applicable boxes below Other Please indicate your source of funding; Ontario Academic Health Sciences Centres Alternate Funding Plan Innovation Fund Disclosure of Interest E. Kuenzig: None Declared, R. Duchen: None Declared, T. Walters Grant / Research support from: Janssen, Abbvie, Psfizer, Ferring, Amgen, Consultant of: Janssen, Abbvie, Psfizer, Ferring, Amgen, D. Mack: None Declared, A. Griffiths Grant / Research support from: Abbvie, Consultant of: Abbvie, Amgen, BristolMyersSquibb, Janssen, Lilly, Takeda, Speakers bureau of: Abbvie, Janssen, Takeda, C. Bernstein Grant / Research support from: Research grants from Abbvie Canada, Amgen Canada, Pfizer Canada, and Sandoz Canada and contract grants from Janssen, Abbvie and Pfizer, Consultant of: Abbvie Canada, Amgen Canada, Bristol Myers Squibb Canada, JAMP Pharmaceuticals, Janssen Canada, Pfizer Canada, Sandoz Canada, Takeda, Speakers bureau of: Abbvie Canada, Janssen Canada, Pfizer Canada and Takeda Canada, G. Kaplan Grant / Research support from: Ferring, Consultant of: AbbVie, Janssen, Pfizer, Amgen, Sandoz, Pendophram, and Takeda, Speakers bureau of: AbbVie, Janssen, Pfizer, Amgen, Sandoz, Pendophram, and Takeda, A. Otley Grant / Research support from: Research support: AbbVie Global. Research site: AbbVie, Pfizer, Eli-Lily, Janssen, Consultant of: AbbVie Canada, W. Yu: None Declared, X. Wang: None Declared, J. Guan: None Declared, S. Fung: None Declared, E. Benchimol Consultant of: McKesson Canada, Dairy Farmers of Ontario (unrelated to medications used to treat inflammatory bowel disease)

Development of historic monthly land use regression models of SO2, NOx and suspended particulate matter for birth cohort ELSPAC

Vulnerable windows in child development in utero and after birth are critical time points for uncovering the links between environment and health. Particular attention is paid to the first 1000 days of life from conception to the second year of life.The ELSPAC (European Longitudinal Study of Pregnancy and Childhood) birth cohort, launched in the early 1990s, is a rich source of longitudinal data about health and life events, based mainly in Brno, Czechia. There are currently no air quality concentration maps that can be used to assess exposure to air pollutants for this period of the 1990s in Central Europe. Simply transferring current models to the 1990's is burdened with the error introduced by the temporal change in emission sources and land use of the area. Therefore, Czech air quality monitoring data were used to develop monthly land use regression (LUR) models, which combine collected spatial variables with monitoring data to predict the variation in exposures to pollutants. Monthly pollutant concentrations were regressed against the GIS-based potential predictor variables to develop LUR models, following a supervised forward linear regression, with several predefined constraints.We constructed 180 LUR monthly models for sulphur dioxide (SO2), nitrogen oxides (NOx) and suspended particulate matter (SPM) for 1990–1994, that completely cover the first 1000 days for all ELSPAC study participants. The final models showed, on average reasonably good performance (adjusted R2 = 0.59 with hold-out validation (HOV) R2 = 0.40 for SO2; adjusted R2 = 0.75 with HOV R2 = 0.35 for NOx; and adjusted R2 = 0.61 with HOV R2 = 0.31 for SPM; with a mean number of stations of 74, 38 and 41, respectively). For these models, roads and greenness were predominantly selected as the best predictors.The modelled exposures will serve in many subsequent ELSPAC epidemiological studies, but our models may be also used in other Czech and possibly other Central European cities in that period.

A novel nomogram based on routine clinical indicators for screening for Wilson's disease

Background and aimsThere is currently no single model for predicting Wilson's disease (WD). We aimed to create a nomogram using daily clinical parameters to improve the accuracy of WD diagnosis in patients with abnormal liver function. MethodsBetween July 2016 and December 2020, we identified 90 WD patients with abnormal liver function who had homozygous or compound heterozygous mutations in the ATP7B gene. The control group included 128 patients with similar liver function but no WD during the same time period. To create a nomogram, we screened potential predictive variables using the least absolute shrinkage and selection operator model and multivariate logistic regression. ResultsWe developed a nomogram for screening for WD based on six predictive factors: serum copper, direct bilirubin, uric acid, cholinesterase, prealbumin, and reticulocyte percentage. In the training cohort, the area under curve (AUC) of the nomogram reached 0.967 (95% confidence interval (CI) 0.946–0.988), while the area under the precision-recall curve was 0.961. Based on the optimal cutpoint of 213.55, our nomogram performed well, with a sensitivity of 96% and a specificity of 87%. In the validation cohort, the AUC of the nomogram was as high as 0.991 (95% CI 0.970–1.000). ConclusionsWe developed a nomogram that can predict the risk of WD prior to the detection of serum ceruloplasmin or urinary copper, greatly increasing screening efficiency for patients with abnormal liver function.

Does the Severity of Vocal Fold Paresis on Laryngeal Electromyography Correlate With Radiographic Findings on Cross Sectional Imaging?

Unilateral vocal fold paresis or paralysis (UVFP) is a condition for which radiographic evaluation and laryngeal electromyography (LEMG) are valuable to evaluate severity of injury and direct treatment plans. Despite extensive research involving findings suggestive of UVFP with radiographic imaging, no study has attempted to determine which findings suggestive of UVFP on imaging are clinically significant and suggest a need for intervention. The purpose of this study was to evaluate whether the severity of vocal fold paresis/paralysis affects the likelihood of encountering radiographic findings suggestive of UVFP. We also aimed to determine which findings suggestive of UVFP on imaging were clinically significant and were associated with surgical intervention. A retrospective chart review was conducted of patients who had been diagnosed with unilateral vocal fold paresis or paralysis and had been evaluated by CT scan and/or magnetic resonance imaging and laryngeal electromyography (EMG) between the dates of January 1, 2017 and January 9, 2018. Fisher's exact testing with Monte Carlo Simulation was utilized to determine statistical significance of identified relationships.Univariate analysis was conducted to assess for individual relationships between imaging results and the potential predictor variables. Chi square analysis was conducted with the various categorical variables to assess for any potential relationships to imaging results. Statistical significance was determined utilizing chi square analysis. After data collection, 130 patients were included in the study population. Of the 112 patients with documented MRI results, 17% had a reported imaging abnormality suggestive of true vocal fold paresis or paralysis (VFP). Of the 71 patients with documented CT Neck results, 15.4% had an abnormality potentially concerning for true VFP. The average decrease in recruitment of the right and left SLN was 23.8% and 26.1%, respectively. The average decrease in recruitment of the right and left RLN was 37.3% and 57.78%, respectively. Seventy four percent of patients who exhibited abnormal MRI were found to have isolated SLN weakness, and 21% of patients were found to have a combined SLN and ipsilateral RLN weakness. In patients with abnormal CT scans 45% were found to have isolated SLN weakness, and 35% were found to have a combined SLN and RLN weakness. MRI imaging again failed to display any significant degree of paresis. However, abnormal CT results displayed severe CN X paresis in 36.84% vs 1.96% in normal scans. The chance of an abnormal MRI and CT result was 2.78 and 5.55 times greater, respectively, for each increase in the degree of severity of CN X paresis. When looking at the ability of imaging to predict the chance of a patient undergoing surgery, 34.8% of patients with an abnormal MRI underwent surgery compared to just 14.61% of those with normal scans. For CT scans, 35% of patients with an abnormal scan underwent surgery, compared with only 15.69% with normal imaging. When pooled, over 33% of patients with any abnormal imaging underwent a laryngeal procedure compared to 13% of patients with normal imaging. There is a relationship between severity of vocal fold paresis found on laryngeal EMG and likelihood of detection on imaging. While CT was more likely to find characteristics of UVFP than MRI, patients who had an abnormal finding on either modality were more likely to undergo surgical intervention. These findings highlight the importance of early referral of patients with abnormal laryngeal imaging to an otolaryngologist for evaluation and possible intervention.

Understanding Ohio X-Waivered Advanced Practice Registered Nurses' Rate of Naltrexone Prescription for Alcohol Use Disordered Patients.

Alcohol use disorder (AUD) is overrepresented within the United States. Naltrexone, a recommended treatment for AUD, is underutilized. However, the prescribing behaviors of advanced practice registered nurses (APRNs) regarding naltrexone for AUD patients have not been studied. The purpose of our study was to explore the prescriptive practices of a sample of APRNs. To describe and analyze survey responses of a sample of Ohio APRNs with training in medication for addiction treatment (MAT) for substance use disorders (as evidenced by DEA x-waiver receipt) regarding prescribing practices of naltrexone for AUD patients. Public information collected from the first author's Nursing Board (list of APRNs in Ohio) was checked against the public information of x-waivered providers nationally from the Substance Abuse and Mental Health Services Administration's (SAMHSA) website. This generated a potential sample size of 824 APRNs, all of whom were sent email solicitations to complete a Qualtrics survey. After 3 weeks, 55 surveys were completed, and the data were analyzed. Descriptive statistics were generated as well as a logistic regression with five potential predictor variables against the outcome variable (defined as use of naltrexone for AUD patients). Years practicing as an APRN was found to negatively predict naltrexone prescribing behavior for AUD patients. Practice setting and work experience with an addiction specialist physician were not found to predict naltrexone prescribing behavior for AUD patients. Implications for further study were discussed, with emphasis on regulatory variance between states.

Machine learning procedures for predictor variable selection for schoolwork-related anxiety: evidence from PISA 2015 mathematics, reading, and science assessments

BackgroundLarge-scale international studies offer researchers a rich source of data to examine the relationship among variables. Machine learning embodies a range of flexible statistical procedures to identify key indicators of a response variable among a collection of hundreds or even thousands of potential predictor variables. Among these, penalized regression approaches, including least absolute selection and shrinkage operator (LASSO) and elastic net (Enet), have been advanced as useful tools capable of handling large number of predictors for variable selection for model generation. While the utility of penalized regression within educational research is emerging, less application of these machine learning methods, including random forest, to predictor variable selection in large-scale international data appears in the literature. In response, this study compared LASSO, Enet, and random forest for predictor variable selection, including the traditional forward stepwise (FS) regression approach, for students’ test anxiety or, more specifically, schoolwork-related anxiety based on PISA 2015 data.MethodsPrediction of the three machine learning methods were compared for variable selection of 188 indicators of schoolwork-related anxiety. Data were based on US students (N = 5593) who participated in PISA 2015. With the exception of FS, LASSO, Enet, and random forest were iterated 100 times to consider the bias resulting from data-splitting to determine the selection or non-selection of each predictor. This resulted in the reporting of number of selected variables into the following five count categories: 1 or more, 25 or more, 50 or more, 75 or more, and all 100 iterations.ResultsLASSO and Enet both outperformed random forest but did not differ from one another in terms of prediction performance in 100 iterations of modeling. Correspondingly, LASSO was compared to FS in which, of the 188 predictors, 27 were identified as key indicators of schoolwork-related anxiety across 100 iterations, and 26 variables were also statistically significant with FS regression. Aligned with previous research, key indicators included personal, situational, and mathematics and reading achievement. Further, LASSO identified 28 variables (14.89%) statistically unrelated to schoolwork-related anxiety, which included indicators aligned to students’ academic- and non-academic behaviors.ConclusionsLASSO and Enet outperformed random forest and yielded comparable results in which determinants of schoolwork-related anxiety included personal and environmental factors, including achievement goals, sense of belonging, and confidence to explain scientific phenomenon. LASSO and FS also identified similar predictor variables related, as well as unrelated, to schoolwork-related anxiety. Aligned with previous research, females reported higher schoolwork-related anxiety than males. Mathematics achievement was negatively related to anxiety, whereas reading performance was positively associated with anxiety. This study also bears significance as one of the first penalized regression studies to incorporate sampling weights and reflect the complex sampling schemes of large-scale educational assessment data.

Diversity and distribution patterns of Ecuador’s dung beetles (Coleoptera: Scarabaeinae)

IntroductionEcuador harbors an astounding number of ecosystems and species. However, anthropogenic land-use changes are the primary drivers of biodiversity loss in major taxonomic groups, especially insects. Among them, the Scarabaeinae subfamily containing dung beetles, is an excellent taxon for studying taxonomic and functional diversity, as they are relatively stable taxonomically and have a wide variety of ecological services. Their distribution is mainly influenced by biogeography and climate as their main ecological and environmental factors will allow us to quantify what aspects of diversity are being impacted under different circumstances and at different scales.MethodsTo understand the main of dung beetle distribution drivers, we analyzed a museum database from the National Institute of Biodiversity, Ecuador (INABIO) of over 5000 dung beetle specimens with 122 species collected throughout the country, we addressed the following questions: i) How does tribe distribution vary across climatic and elevational gradients? and ii) How does functional and taxonomic beta diversity vary across spatial scales? To address them, we focused on three main tribes: Canthonini, Coprini, and Phanaeini. We constructed GLM’s and niche-based models to estimate Ecuador’s distributions based on climate variables to explore potential predictor variables, using tree classification models, along with taxonomic and functional beta diversity across scales.ResultsThe main variables influencing dung beetle distribution were elevation, and precipitation. The Phanaeini niche model is significantly better at predicting dung beetle presence throughout Ecuador than Canthonini and Coprini. We found high turnover in functional groups at larger scales, suggesting that dung beetles show high levels of habitat specialization, which associates to our findings where taxonomic beta diversity was higher in the Amazon basin compared to the coastal region. This may be due to the higher rate of dung production in Amazonia. Our findings also suggest that dung beetles are not found in areas above 2000m, mainly because dung beetles are well adapted to warmer and moist climatic regions. Precipitation and elevation are consistently essential variables for predicting Canthonini and Coprini presence, while temperature explains Phanaeini presence. Low levels of species turnover at the regional scale may be because the total species richness in Ecuador is different, where divergence in taxonomic beta diversity between the two regions is an artifact of such differences in richness in Amazonia versus the coast, the distinction is also due to nonrandomly low taxonomic beta diversity levels in the coastal region.ConclusionsOur results provide an essential framework for evaluating potential dung beetle habitat and diversity at different scales; therefore, by identifying dung beetles’ diversity, combined with considerations of habitat fragmentation, human land-use alteration, and climate change, will be an important next step to inform better and prioritize dung beetle conservation efforts in other countries.