Stepwise Variable Selection Research Articles

Identifying Undiagnosed Diabetes and Prediabetes in the Dental Setting in an Asian Population-A Clinical Risk Model.

To assess the glycaemic status of Asian patients in a tertiary care dental setting and develop a risk model for undiagnosed diabetes mellitus (DM). A total of 1074 participants completed a diabetes risk test questionnaire, full-mouth periodontal examination and a point-of-care HbA1c finger-prick blood test. Univariable logistic regression was performed to assess the effect of potential factors to predict DM, with confirmed diabetes as the outcome. Subsequently, multivariable logistic regression analysis with stepwise variable selection was employed to develop the final models for predicting DM. Sixty-five (6.1%) and 83 (7.7%) of the 1074 participants were medically confirmed with T2DM and prediabetes, respectively. The 'best' predictive risk model for DM included body mass index (BMI), family history of diabetes, smoking and a diagnosis of Stage III/IV or severe periodontitis with an area under the curve (AUC) of 0.717 (95% confidence interval, CI [0.689-0.744]) and 0.721 (95% CI [0.693-0.748]), respectively. Including the oral health measure marginally increased the AUC. Dental patients clinically diagnosed with advanced periodontitis in combination with high BMI, positive family history of DM and smoking are potentially at high risk for DM and should be screened for DM and referred for medical confirmation and management.

Overall and linked blood pressure variabilities in the first 24 hours and mortality after spontaneous intracerebral hemorrhage: a retrospective study of 1,036 patients.

This study aims to establish the individual contributions of blood pressure variability (BPV) indexes, categorized into overall and linked variability, to mortality following intracerebral hemorrhage (ICH) by examining the risk factors. Patients with spontaneous ICH (n = 1,036) were identified with valid blood pressures (BP) from the first 24-h systolic BP records in the Medical Information Mart for Intensive Care IV version 2.2 database (MIMIC IV). Information on the baseline characteristics, including age, sex, initial Glasgow Coma Scale (GCS) and National Institutes of Health Stroke Scale (NIHSS) scores, ICH location, Charlson comorbidity index score, and presence of diabetes with or without complications, were collected. Three indexes of BPV-range, standard deviation (SD), and generalized BPV (GBPV)-were calculated using the first 24-h systolic BPs. An automated stepwise variable-selection procedure was used to develop the final logistic model for predicting in-hospital mortality. Out of 1,036 patients, 802 (77.4%) survived and were discharged after spontaneous ICH. Factors associated with mortality included age; male sex; ICH in the brainstem, ventricle, or multiple locations; low GCS score (< 9); high NIHSS score (> 20); and diabetes with complications. Mean systolic BP, SD, and GBPV were also linked to mortality. Higher GBPV notably increased the risk of in-hospital death, with an odds ratio of 3.21 (95% confidence interval, 2.10 to 4.97) for every + 10 mmHg/h change in GBPV. This study underscores the additional impact of GBPV, herein linked to BPV, on mortality following ICH, providing further insights into the management of blood pressure in the early stages of ICH treatment.

Machine learning prediction for prognosis and long-term effectiveness for transcatheter ventricular septal defect closure: a 5-year single center experience

Abstract Background Transcatheter therapy, with minimal invasiveness and rapid recovery advantages, has emerged as the primary choice for treating perimembranous ventricular septal defect. However, patients receiving transcatheter occlusion may confront conduction abnormalities due to the occluder's proximity to the cardiac conduction bundle during implantation. Few have assessed the risks of postoperative conduction block (CB) via robust machine learning models. Purpose The purpose of this study was to evaluate our long-term follow up results of pmVSD occlusion and establish a prediction model to identify risk predictors of conduction abnormalities via robust machine learning methods. Methods Five methods including logistic regression with a stepwise selection of variables, lasso regularization; random forest; gradient descent boosting (GBM); and support vector machine, were used to train models for predicting risks of late-onset and persistent conduction block through 5 years of follow-up and were validated using 5-fold cross-validation. Receiver-operating characteristic curves and Brier scores were utilized to evaluate model discrimination and calibration, respectively. The top prediction variables were identified by using the best performing models, using the relative influence score of each variable in 5-fold cross-validation. Results The GBM model performed the best with an AUC of 0.82 (95% confidence interval [CI]: 0.75 to 0.89) for predicting late-onset CB (Brier score: 0.0204), and 0.70 (95% CI: 0.62 to 0.78) for persistent CB (Brier score: 0.0003) over entire study period. Conduction block on admission, advanced age and aortic regurgitation before discharge were strongly associated with late-onset CB, whereas decreased left ventricular ejection fraction, enlarged left ventricle end diastolic diameter at 1-month follow-up were the most significant predictors of persistent CB. Conclusions These models predict the risks of temporary and persistent postoperative conduction block in patients with pmVSD occlusion and shed light on preventing long-term complications.Study FlowchartCentral Illustration

Comparison of two modeling approaches for the identification of predictors of complications in children with cerebral palsy following spine surgery

BackgroundChildren with non-ambulatory cerebral palsy (CP) frequently develop progressive neuromuscular scoliosis and require surgical intervention. Due to their comorbidities, they are at high risk for developing peri- and post-operative complications. The objectives of this study were to compare stepwise and LASSO variable selection techniques for consistency in identifying predictors when modelling these post-operative complications and to identify potential predictors of respiratory complications and infections following spine surgery among children with CP.MethodsIn this retrospective cohort study, a large administrative claims database was queried to identify children who met the following criteria: 1) ≤ 25 years old, 2) diagnosis of CP, 3) underwent surgery during the study period, 4) had ≥ 12-months pre-operative, and 5) ≥ 3-months post-operative continuous health plan enrollment. Outcome measures included the development of a post-operative respiratory complication (e.g., pneumonia, aspiration pneumonia, atelectasis, pleural effusion, pneumothorax, pulmonary edema) or an infection (e.g., surgical site infection, urinary tract infection, meningitis, peritonitis, sepsis, or septicemia) within 3 months of surgery. Codes were used to identify CP, surgical procedures, medical comorbidities and the development of post-operative respiratory complications and infections. Two approaches to variable selection, stepwise and LASSO, were compared to determine which potential predictors of respiratory complications and infection development would be identified using each approach.ResultsThe sample included 220 children. During the 3-month follow-up, 21.8% (n = 48) developed a respiratory complication and 12.7% (n = 28) developed an infection. The prevalence of 11 variables including age, sex and 9 comorbidities were initially considered to be potential predictors based on the intended outcome of interest. Model discrimination utilizing LASSO for variable selection was slightly improved over the stepwise regression approach. LASSO resulted in retention of additional comorbidities that may have meaningful associations to consider for future studies, including gastrointestinal issues, bladder dysfunction, epilepsy, anemia and coagulation deficiency.ConclusionsPotential predictors of the development of post-operative complications were identified in this study and while identified predictors were similar using stepwise and LASSO regression approaches, model discrimination was slightly improved with LASSO. Findings will be used to inform future research processes determining which variables to consider for developing risk prediction models.

Evaluating Model-assisted Estimators: A Comparative Study in High-dimensional Survey Data

Model-assisted estimators have gained significant attention due to their ability to efficiently utilize auxiliary information during the estimation process. These estimators rely on a working model that links the survey variable to the auxiliary variables, which is then fitted to the sample data to generate predictions. These predictions are subsequently integrated into the estimation procedures. In this study, were explores various model-assisted estimators including Generalized Regression (GREG), Ridge regression, Lasso regression, CART (Classification and Regression Tree), Random Forest, Cubist and Principal Components Regression (PCR) estimator. The analysis involved 2,000 samples of size 50 (n/N ≈ 10%) and employed a stepwise variable selection method to determine the most significant auxiliary variables, incrementally adding them to the model. The performance of these estimators was assessed using relative bias (RB), relative root mean square error (RRMSE) and relative efficiency (RE). Our findings reveal that tree-based models like CART and Random Forest and penalized regression estimators such as Ridge and Lasso display robustness with increased number of auxiliary variables. Among all the estimators, Random Forest consistently yielded the lowest RRMSE, particularly with five auxiliary variables, demonstrating superior efficiency. Conversely, the GREG estimator exhibited poor performance as the number of auxiliary variables increased. This study underscores the importance of selecting suitable model-assisted estimation procedures tailored to the data characteristics and the relationship between survey and auxiliary variables within this high-dimensional dataset.

The PROgnostic ModEl for chronic lung disease (PRO-MEL): development and temporal validation

BackgroundPatients with chronic lung diseases (CLDs), defined as progressive and life-limiting respiratory conditions, experience a heavy symptom burden as the conditions become more advanced, but palliative referral rates are low and late. Prognostic tools can help clinicians identify CLD patients at high risk of deterioration for needs assessments and referral to palliative care. As current prognostic tools may not generalize well across all CLD conditions, we aim to develop and validate a general model to predict one-year mortality in patients presenting with any CLD.MethodsA retrospective cohort study of patients with a CLD diagnosis at a public hospital from July 2016 to October 2017 was conducted. The outcome of interest was all-cause mortality within one-year of diagnosis. Potential prognostic factors were identified from reviews of prognostic studies in CLD, and data was extracted from electronic medical records. Missing data was imputed using multiple imputation by chained equations. Logistic regression models were developed using variable selection methods and validated in patients seen from January 2018 to December 2019. Discriminative ability, calibration and clinical usefulness of the model was assessed. Model coefficients and performance were pooled across all imputed datasets and reported.ResultsOf the 1000 patients, 122 (12.2%) died within one year. Patients had chronic obstructive pulmonary disease or emphysema (55%), bronchiectasis (38%), interstitial lung diseases (12%), or multiple diagnoses (6%). The model selected through forward stepwise variable selection had the highest AUC (0.77 (0.72–0.82)) and consisted of ten prognostic factors. The model AUC for the validation cohort was 0.75 (0.70, 0.81), and the calibration intercept and slope were − 0.14 (-0.54, 0.26) and 0.74 (0.53, 0.95) respectively. Classifying patients with a predicted risk of death exceeding 0.30 as high risk, the model would correctly identify 3 out 10 decedents and 9 of 10 survivors.ConclusionsWe developed and validated a prognostic model for one-year mortality in patients with CLD using routinely available administrative data. The model will support clinicians in identifying patients across various CLD etiologies who are at risk of deterioration for a basic palliative care assessment to identify unmet needs and trigger an early referral to palliative medicine.Trial registrationNot applicable (retrospective study).

Psychometric Evaluation of the Brief-COPE Inventory and Exploration of Factors Associated with Perceived Stress among Peruvian Nurses.

This study aimed to analyze the psychometric properties of the Brief-COPE Inventory and to determine its concurrent validity by examining its association with perceived stress among Peruvian nurses. A psychometric study was conducted with 434 Peruvian nurses to evaluate the psychometric properties of the Brief-COPE Inventory through confirmatory factor analysis. Three stepwise variable selection regression models were implemented. The three-factor model of the Brief-COPE Inventory demonstrated adequate fit indices (root mean square error of approximation = 0.052, standardized root mean square residual = 0.068, and both the comparative fit index and the Tucker-Lewis index = 0.95). Additionally, the factors were significantly correlated (p < 0.001), and the reliability was adequate (ω = 0.90). Nurses reported a medium level of perceived stress, with associated factors including having received stress management training, fear of COVID-19, and problem-focused coping strategies (p < 0.05). This study confirms that the Brief-COPE Inventory is a valid tool for measuring coping strategies among Peruvian nurses due to its good model fit, excellent reliability, and concurrent validity with perceived stress. However, further research is needed to assess its validity in the specific areas of performance perceived by nursing professionals.

Mandibular dose-volume predicts time-to-osteoradionecrosis in an actuarial normal-tissue complication probability (NTCP) model: External validation of right-censored clinico-dosimetric and competing risk application across international multi-institutional observational cohorts and online graphical user interface clinical support tool assessment.

Existing studies on osteoradionecrosis of the jaw (ORNJ) have primarily used cross-sectional data, assessing risk factors at a single time point. Determining the time-to-event profile of ORNJ has important implications to monitor oral health in head and neck cancer (HNC) long-term survivors. Demographic, clinical and dosimetric data were retrospectively obtained for a clinical observational cohort of 1129 patients with HNC treated with radiotherapy (RT) at The University of Texas MD Anderson Cancer Center. ORNJ was diagnosed in 198 patients (18%). A multivariable logistic regression analysis with forward stepwise variable selection identified significant predictors for ORNJ. These predictors were then used to train a Weibull Accelerated Failure Time (AFT) model, which was externally validated using an independent cohort of 265 patients (92 ORNJ cases and 173 controls) treated at Guy's and St. Thomas' Hospitals. Our model identified that each unit increase in D25% is significantly associated with a 12% shorter time to ORNJ (Adjusted Time Ratio [ATR] 0·88, p<0·005); pre-RT dental extractions was associated to a 27% faster (ATR 0·73, p=0·13) onset of ORNJ; male patients experienced a 38% shorter time to ORNJ (ATR 0·62, p = 0·11). The model demonstrated strong internal calibration (integrated Brier score of 0·133, D-calibration p-value 0.998) and optimal discrimination at 72 months (Harrell's C-index of 0·72). The model also showed good generalization to the independent cohort, despite a slight drop in performance. This study is the first to demonstrate a direct relationship between radiation dose and the time to ORNJ onset, providing a novel characterization of the impact of delivered dose not only on the probability of a late effect (ORNJ), but the conditional risk during survivorship. This work was supported by various funding sources including NIH, NIDCR, NCI, NAPT, NASA, BCM, Affirmed Pharma, CRUK, KWF Dutch Cancer Society, NWO ZonMw, and the Apache Corporation.

Correlation and regression analysis of the influence of economic factors on wheat yield

The article presents the results of econometric modeling of wheat yield based on microdata from reports on the financial and economic status of agricultural producers. The relationship between wheat yield and economic factors is studied: indicators of the level of intensity (costs of mineral fertilizers, cost of fixed assets, direct labor costs for grain production, number of workers employed in agriculture per unit area) and specialization of agricultural production (proportion of revenue from sales of crop products in revenue from sales of agricultural products). Using the stepwise variable selection approach, statistically significant linear regression models were constructed that can be used for analysis and forecasting. The paired model included the cost of mineral fertilizers per 1 ha of sown area as a factor in wheat yield (coefficient of determination R2 = 0.503), and multiple regression was obtained by adding a second factor — the cost of fixed assets per 100 ha of agricultural land (multiple coefficient of determination R2 = 0.562). Decomposition of the multiple coefficient of determination into separate coefficients of determination allowed us to estimate the impact of each factor included in the model on yield: thus, the impact of mineral fertilizer costs was 42 %, and the impact of fixed assets was 15 %. According to the approach to decomposition of R2 based on β-coefficients, the net impact of mineral fertilizer costs on wheat yield was 35 %, the net impact of fixed assets was 5 %, while the impact of the systemic effect from the interaction of the two factors, which reflects the level of production intensification, was 17 %. Based on a multiple regression model, forecast values were constructed and estimated, and reserves for yield growth were determined.

Environmental factors and management practices associated with beef cattle carcass quality in the mid-west of Brazil.

The "Precoce MS" program, established by the Brazilian government in Mato Grosso do Sul in 2017, aims to encourage beef producers to harvest animals at younger ages to enhance carcass quality. About 40% of the beef produced in the state now comes from this program, which offers tax refunds ranging from 49% to 67% based on carcass classification and production system. Despite the program success, with participants delivering younger animals (with a maximum of 4 incisors), there remains significant variability in carcass quality. This paper investigates management practices and environmental factors affecting farm performance regarding carcass quality. Data from all animals harvested between the beginning of 2017 and the end of 2018 were analyzed, totaling 1,107 million animals from 1,470 farms. Farm performance was assessed based on the percentage of animals achieving grades "AAA" and "AA." Each batch of harvested cattle from each farm was categorized into two groups: high farm performance (HFP, with more than 50% of animals classified as "AAA" or "AA") and low farm performance (LFP, with less than 50% classified as such). A predictive logistic model was developed to forecast farm performance (FP) using 14 continuous and 15 discrete pre-selected variables. The most effective model, obtained through backward stepwise variable selection, had an R 2 of 0.18, accuracy of 71.5%, and AUC of 0.715. Key predictors included animal category, production system type, carcass weight, individual identification, traceability system, presence of a feed plant, location, and the Normalized Difference Vegetation Index (NDVI) from the 12-mo average before harvest. Developing predictive models of carcass quality by integrating data from commercial farms with other sources of information (animal, production system, and environment) can improve our understanding of production systems, optimize resource allocation, and advance sustainable animal production. Additionally, they offer valuable insights for designing and implementing better sectorial, social, and environmental policies by public administrations, not only in Brazil but also in other tropical and subtropical regions worldwide.

A Novel Predictive Score Model for Successful Weaning From Mechanical Circulatory Support in Patients With Cardiogenic Shock

Clinical evidence regarding predictors of successful weaning from mechanical circulatory support (MCS) is lacking. This study aimed to create a simple risk score to predict successful weaning from MCS in patients with cardiogenic shock. This retrospective single-center cohort study included 114 consecutive patients with cardiogenic shock treated with venoarterial extracorporeal membrane oxygenation or IMPELLA between January 2013 and June 2023. Patients with out-of-hospital cardiac arrest were excluded. The primary end point was successful weaning from MCS, defined as successful decannulation without the need for MCS reimplantation and survival to discharge. Multivariable logistic regression with a stepwise variable selection was performed to generate the prediction model. We first developed a general weaning score model, and then created a simple version of the score model using the same variables. Fifty-five patients were weaned from MCS successfully. The following variables measured during weaning evaluation were selected as the components of the weaning score model: acute myocardial infarction (AMI), mean blood pressure, left ventricular ejection fraction (LVEF), lactate level, and QRS duration. According to the results, we conducted a novel weaning score model to predict successful weaning from MCS: 1.774 - 2.090 × (AMI) + 0.062 × [mean blood pressure (mm Hg)] + 0.139 × [LVEF (%)] - 0.322 × [Lactate (mg/dL)] - 0.066 × [QRS (ms)]. The following variables were selected as the components of the simple version of the weaning score model: AMI, mean blood pressure of ≥80 mm Hg, lactate of <10 mg/dL, QRS duration of ≤95 ms, and LVEF of >35%. We developed a simple model to predict successful weaning from MCS in patients with cardiogenic shock.

Using the kriging method based on cross-validation stepwise variable selection to optimize anaerobic wastewater treatment process

In this study, process parameters for wastewater treatment are optimised in order to improve the efficiency of energy recovery from wastewater treatment. In order to identify process parameters that have a significant impact on carbon recovery, we propose a stepwise forward variable selection method based on K-fold cross kriging to validate the loss ER.The results show that for complex wastewater treatment systems, the cross-validated kriging-based stepwise variable selection method can predict carbon recovery quickly and accurately. Finally, this paper combines the kriging prediction model with the PSO optimisation strategy to determine the optimal set points of process parameters with the objective of maximising carbon recovery.

Developing accident frequency prediction models for urban roads: A case study in São Paulo, Brazil

The growing number of vehicles and the evolving behaviour of road users present new and additional challenges to road safety. Study on the variables that influence the frequency of crash occurrences such as road geometry, junction, speed and land use are needed as they have proven effects on the number and severity of crashes. In this paper, we identify and assess the variables, namely road geometry, vehicle speed, traffic volume, land use and junction type, and develop accident frequency prediction models for a main urban transport corridor in São Paulo, Brazil. Crash data was provided by the traffic management company of the city, other datasets were obtained from a mix of primary and secondary sources including roadside cameras, Geographic Information Systems (GIS) and digital mapping tools. The studied road was segmented and the coefficients associated with variables in the segments were obtained using Poisson regression through a stepwise variable selection procedure. Two models with junctions density per type (access/km, T-junction unsignalised/km, T-junction signalised/km and crossroads/km) and junction density per merged type (signalised/km and unsignalised/km) along with land use per type (commercial and residential) are developed. The junction density and land use are found to be significant and positively correlated with crash frequency. The models were evaluated by statistical means for their accuracy of predicting the crashes, and validated with additional information obtained from field observation.

Repeated Sieving for Prediction Model Building with High-Dimensional Data.

Background: The prediction of patients' outcomes is a key component in personalized medicine. Oftentimes, a prediction model is developed using a large number of candidate predictors, called high-dimensional data, including genomic data, lab tests, electronic health records, etc. Variable selection, also called dimension reduction, is a critical step in developing a prediction model using high-dimensional data. Methods: In this paper, we compare the variable selection and prediction performance of popular machine learning (ML) methods with our proposed method. LASSO is a popular ML method that selects variables by imposing an L1-norm penalty to the likelihood. By this approach, LASSO selects features based on the size of regression estimates, rather than their statistical significance. As a result, LASSO can miss significant features while it is known to over-select features. Elastic net (EN), another popular ML method, tends to select even more features than LASSO since it uses a combination of L1- and L2-norm penalties that is less strict than an L1-norm penalty. Insignificant features included in a fitted prediction model act like white noises, so that the fitted model will lose prediction accuracy. Furthermore, for the future use of a fitted prediction model, we have to collect the data of all the features included in the model, which will cost a lot and possibly lower the accuracy of the data if the number of features is too many. Therefore, we propose an ML method, called repeated sieving, extending the standard regression methods with stepwise variable selection. By selecting features based on their statistical significance, it resolves the over-selection issue with high-dimensional data. Results: Through extensive numerical studies and real data examples, our results show that the repeated sieving method selects far fewer features than LASSO and EN, but has higher prediction accuracy than the existing ML methods. Conclusions: We conclude that our repeated sieving method performs well in both variable selection and prediction, and it saves the cost of future investigation on the selected factors.

Bystander defibrillation for out-of-hospital cardiac arrest in Ireland

AimsTo describe and explore predictors of bystander defibrillation in Ireland during the period 2012 to 2020. To examine the relationship between bystander defibrillation and health system developments. MethodsNational level Out of Hospital Cardiac Arrest (OHCA) registry data were interrogated, focusing on patients who had defibrillation performed. Bystander defibrillation (as compared to EMS initiated defibrillation) was the key outcome of concern. Logistic regression models were built and refined by fitting predictors, performing stepwise variable selection and by adding pairwise interactions that improved fit. ResultsThe data included 5,751 cases of OHCA where defibrillation was performed. Increasing year over time (OR 1.17, 95% CI 1.13, 1.21) was associated with increased adjusted odds of bystander defibrillation. Non-cardiac aetiology was associated with reduced adjusted odds of bystander defibrillation (OR 0.30, 95% CI 0.21, 0.42), as were increasing age in years (OR 0.99, 95% CI 0.987, 0.996) and night-time occurrence of OHCA (OR 0.67, 95% CI 0.53, 0.83). Six further variables in the final model (sex, call response interval, incident location (home or other), who witnessed collapse (bystander or not witnessed), urban or rural location, and the COVID period) were involved in significant interactions. Bystander defibrillation was in general less likely in urban settings and at home locations. Whilst women were less likely to receive bystander defibrillation overall, in witnessed OHCAs, occurring outside the home, in urban areas and outside of the COVID-19 period women were more likely, to receive bystander defibrillation. ConclusionsDefibrillation by bystanders has increased incrementally over time in Ireland. Interventions to address sex and age-based disparities, alongside interventions to increase bystander defibrillation at night, in urban settings and at home locations are required.

Health and wellbeing of staff working at higher education institutions globally during the post-COVID-19 pandemic period: evidence from a cross-sectional study

BackgroundThe ongoing global crisis of Higher Education (HE) institutions during the post-COVID-19 pandemic period has increased the likelihood of enduring psychological stressors for staff. This study aimed to identify factors associated with job insecurity, burnout, psychological distress and coping amongst staff working at HE institutions globally.MethodsAn anonymous cross-sectional study was conducted in 2023 with staff at HE institutions across 16 countries. Job insecurity was measured using the Job Insecurity Scale (JIS), burnout using the Perceived Burnout measure question, psychological distress using the Kessler Psychological Distress Scale (K10), and coping using the Brief Resilient Coping Scale. Multivariable logistic regression with a stepwise variable selection method was used to identify associations.ResultsA total of 2,353 staff participated; the mean age (± SD) was 43(± 10) years and 61% were females. Most staff (85%) did not feel job insecurity, one-third (29%) perceived burnout in their jobs, more than two-thirds (73%) experienced moderate to very high levels of psychological distress, and more than half (58%) exhibited medium to high resilient coping. Perceived job insecurity was associated with staff working part-time [Adjusted Odds Ratio 1.53 (95% Confidence Intervals 1.15–2.02)], having an academic appointment [2.45 (1.78–3.27)], having multiple co-morbidities [1.86 (1.41–2.48)], perceived burnout [1.99 (1.54–2.56)] and moderate to very high level of psychological distress [1.68 (1.18–2.39)]. Perceived burnout was associated with being female [1.35 (1.12–1.63)], having multiple co-morbidities [1.53 (1.20–1.97)], perceived job insecurity [1.99 (1.55–2.57)], and moderate to very high levels of psychological distress [3.23 (2.42–4.30)]. Staff with multiple co-morbidities [1.46 (1.11–1.92)], mental health issues [2.73 (1.79–4.15)], perceived job insecurity [1.61 (1.13–2.30)], and perceived burnout [3.22 (2.41–4.31)] were associated with moderate to very high levels of psychological distress. Staff who perceived their mental health as good to excellent [3.36 (2.69–4.19)] were more likely to have medium to high resilient coping.ConclusionsFactors identified in this study should be considered in reviewing and updating current support strategies for staff at HE institutions across all countries to reduce stress and burnout and improve wellbeing.

Dupilumab responder types and predicting factors in patients with type 2 severe asthma: A real-world cohort study

BackgroundSevere asthma (SA) presents a considerable healthcare challenge despite optimal standard treatment. Dupilumab, which is effective in type 2 (T2) SA patients, demonstrates variable responses, categorizing patients as non-responders, partial responders, or those achieving clinical remission. However, real-world response rates remain underexplored. Additionally, understanding the characteristics of patients achieving clinical remission is crucial for predicting favourable responses to dupilumab. ObjectiveTo investigate responder types and identify predictors of clinical remission and non-response induced by dupilumab in a real-world cohort of SA patients. MethodsWe analyzed retrospective data from SA patients undergoing dupilumab treatment in a study conducted at Franciscus Gasthuis & Vlietland hospital. Data were collected at baseline and at a 12 to 24-months follow-up (T = 12). Response rates were evaluated at T = 12. Predictors of non-response and clinical remission were investigated using multivariate logistic regression analysis with a stepwise forward variable selection approach. ResultsAmong the 175 patients screened, 136 met the inclusion criteria. At T = 12, 31.6 % achieved clinical remission, 47.1 % were partial responders and 21.3 % were non-responders. Predictors associated with clinical remission included high baseline blood eosinophil counts (BEC) and male sex. Conversely, younger age at baseline, low baseline total immunoglobin E (IgE) and low baseline fractional exhaled nitric oxide (FeNO) levels were identified as predictors of non-response. ConclusionsDupilumab results in clinical disease remission in one-third of the treated patients. Clinical remission is predicted by high BEC and male sex, whereas low total IgE, low FeNO and younger age indicate a lower likelihood of response.

Fundamental factors and the level of underpricing

Underpricing has been the subject of substantial research at a global scale and has been thoroughly described in the literature, but it is still unclear what factors influence its level in the Polish capital market, particularly in the context of fundamental factors. Since investors have very limited information of the companies going public, the companies must convey or signal information that can be used for valuing their shares. The aim of the article is to identify the impact of a company’s fundamental factors on the level of underpricing of an initial public offering. The research is based on the sample of 227 companies debuting in the regulated market of the Warsaw Stock Exchange between 2005 and 2022. The empirical analysis and regression results estimation were based on the data coming from prospectuses of the companies going public. To prepare the econometric linear regression model, the stepwise forward variable selection procedure was carried out. The evidence indicates that underpricing is significantly affected by a few financial variables, such as assets, ROA, BVS, and EPS.

Investigation of Precepting-Related Burnout in Dietetics Preceptors

BackgroundDietetics preceptors are at risk of abandoning their role because of potential burnout. Identifying factors associated with greater and lesser burnout can help key stakeholders identify and implement strategies to prevent or mitigate burnout in dietetics preceptors. ObjectiveThe purpose of this study was to identify factors associated with the burnout preceptors attribute specifically to the work of precepting. DesignThis study used a cross-sectional study design. A survey instrument including burnout measures, precepting measures, and items querying personal and work characteristics was distributed via e-mail to a random sample of 10 000 credentialed nutrition and dietetics practitioners. Participants settingRespondents who lived in the United States, were currently practicing, and had precepted during the past year were included in the sample (n = 310). The survey was conducted in October and November of 2022. Main outcome measuresPrecepting-related burnout was measured using scores on the Copenhagen Burnout Inventory; scores ranged from 0 to 100 and higher numbers indicated greater burnout. Statistical analyses performedFactors associated with precepting-related burnout were identified using an analysis of covariance. Pairwise comparisons with Tukey adjustments were done to determine differences across categories for the categorical variable in the model (ie, percent of interns requiring additional coaching). A stepwise variable selection process was performed to determine the best analytic models. ResultsFactors associated with lower precepting-related burnout in dietetics preceptors included higher Commitment to the Preceptor Role scores, lower percentages of interns requiring additional coaching, feeling appreciated by the dietetic interns, and higher Preceptors’ Perceptions of Support scores. A factor associated with higher precepting-related burnout was precepting a greater number of dietetic interns in the past year. For every 1-point change in each variable, the degree of change in precepting-related burnout was –7.9 for Commitment to the Preceptor Role, –3.2 for feeling appreciated by dietetic interns, –4.5 for Preceptors’ Perceptions of Support, and +1.2 for number of dietetic interns per year. The mean ± SE difference in precepting-related burnout scores between preceptors who reported < 10% of dietetic interns requiring additional coaching and those who reported > 50% of dietetic interns requiring additional coaching was –13.7 ± 3.7. ConclusionsThe factors found to be associated with precepting-related burnout scores in dietetics preceptors are potentially modifiable, suggesting this burnout might be prevented or mitigated to some degree.

A Machine Learning Approach for Prediction of CDAI Remission with TNF Inhibitors: A Concept of Precision Medicine from the FIRST Registry.

This study aimed to develop low-cost models using machine learning approaches predicting the achievement of Clinical Disease Activity Index (CDAI) remission 6months after initiation of tumor necrosis factor inhibitors (TNFi) as primary biologic/targeted synthetic disease-modifying antirheumatic drugs (b/tsDMARDs) for rheumatoid arthritis (RA). Data of patients with RA initiating TNFi as first b/tsDMARD after unsuccessful methotrexate treatment were collected from the FIRST registry (August 2003 to October 2022). Baseline characteristics and 6-month CDAI were collected. The analysis used various machine learning approaches including logistic regression with stepwise variable selection, decision tree, support vector machine, and lasso logistic regression (Lasso), with 48 factors accessible in routine clinical practice for the prediction model. Robustness was ensured by k-fold cross validation. Among the approaches tested, Lasso showed the advantages in predicting CDAI remission: with a mean area under the curve 0.704, sensitivity 61.7%, and specificity 69.9%. Predicted TNFi responders achieved CDAI remission at an average rate of 53.2%, while only 26.4% of predicted TNFi non-responders achieved remission. Encouragingly, the models generated relied solely on patient-reported outcomes and quantitative parameters, excluding subjective physician input. While external cohort validation is warranted for broader applicability, this study highlights the potential for a low-cost predictive model to predict CDAI remission following TNFi treatment. The approach of the study using only baseline data and 6-month CDAI measures, suggests the feasibility of establishing regional cohorts to generate low-cost models tailored to specific regions or institutions. This may facilitate the application of regional/in-house precision medicine strategies in RA management.