Stepwise Selection Research Articles

A systematic review and meta-analysis of Nordic walking for chronic heart failure with low left ventricular ejection fraction.

Nordic walking (NW) has been reported to be beneficial in various chronic diseases. To determine whether NW improves function in patients with heart failure (HF) with low left ventricular ejection fraction more than conventional cardiac rehabilitation or usual care. Systematic literature searches in PubMed, ScienceDirect, and Web of Science were conducted. According to patient, intervention, comparison, and outcome (PICO) principles, a stepwise selection process was completed to identify eligible studies. Only randomized controlled trials (RCTs) were included in this systematic review and meta-analysis. Pooled effects were determined using a random effect analysis model for the meta-analysis. A total of 282 participants from four RCTs were included. The improvement of peak oxygen consumption (peak VO2) was greater in the NW group than in the control group (mean difference [MD] = 2.18 mL/kg/min; 95% confidence interval [CI] = 1.35-3.01; p < .01). The improvement of the 6-minute walk test (6MWT) distance was also greater in the NW group than in the control group (MD = 16.51 meters; 95% CI = 3.23-29.80; p = .01). This systematic review highlights the benefits of NW training in patients with chronic HF, particularly those with systolic dysfunction. Specifically, NW walking was associated with increased peak VO2 and 6MWT improvement compared to conventional cardiac rehabilitation or usual care. These results suggest that NW may be a useful component of cardiac rehabilitation. However, additional larger studies with a wider range of patients are needed.

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).

Forest above-ground biomass estimation based on strongly collinear variables derived from airborne laser scanning data

Airborne laser scanning technique (ALS) is the most appealing remote sensing technique for the precise estimation of forest above-ground biomass (AGB). Significantly strong correlations (collinearity) among the independent variables derived from ALS data decrease the accuracy of developed AGB models. To address this issue, we propose a novel variable selection algorithm-an improved sure independence screening (SPV), which integrates the Pearson correlation coefficient, threshold μ, and variance inflation factor. We further compared the performance of SPV-based, stepwise feature selection (SFS)-based, and least absolute shrinkage and selection operator (LASSO)-based AGB models developed with different regression approaches that were sensitive to strong collinearity. Field-measured data and corresponding ALS data, acquired from 1002 sample plots distributed across four distinct forest types within the Guangxi Zhuang Autonomous Region in Southern China, were used to evaluate variable selection techniques and develop AGB models. Results indicated that ALS variables selected by SPV exhibited weaker collinearity compared to those selected by SFS and LASSO. SPV-based AGB models outperformed SFS-based AGB models with higher leave-one-out cross-validation adjusted R2 (LOOCV Radj2) by 0.1% − 27.8%. SPV-based AGB models outperformed LASSO-based AGB models with higher LOOCV Radj2 by 0.4% − 16.3%. Hence, for variable selection in constructing AGB models (linear regression model, log–log regression model, and generalized additive model (GAM)) based on strongly collinear ALS variables, SPV is most preferred, followed by SFS and LASSO. The smooth curves from our GAMs developed using SPV-selected variables revealed that five canopy height variables (whp40, hmean, hp25, hp70, hskew), one canopy density variable (whp85), three density-related variables (nVegPts, bcc, and mcc), and one vertical structural variable (LADmean) were positively correlated with AGB. The canopy height variables (whp40, hmean, hp25, and hp70) were identified as the most important variables in estimating AGB for four forest types. The canopy density variable wdp85 showed a strong effect on AGB of the coniferous forests, whereas it had almost no effect on the AGB of broadleaved forests. Overall, this manuscript proposes a novel variable selection algorithm named SPV, aimed at addressing collinearity of variables derived from ALS data, which has significant implications for the application of ALS in forest inventory and forest modeling.

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.

A novel procedure for selection of molecular descriptors: QSAR model for mutagenicity of nitroaromatic compounds.

Nitroaromatic compounds (NACs) stand out as pervasive organic pollutants, prompting an imperative need to investigate their hazardous effects. Computational chemistry methods play a crucial role in this exploration, offering a safer and more time-efficient approach, mandated by various legislations. In this study, our focus lay on the development of transparent, interpretable, reproducible, and publicly available methodologies aimed at deriving quantitative structure-activity relationship models and testing them by modelling the mutagenicity of NACs against the Salmonella typhimurium TA100 strain. Descriptors were selected from Mordred and RDKit molecular descriptors, along with several quantum chemistry descriptors. For that purpose, the genetic algorithm (GA), as the most widely used method in the literature, and three alternative algorithms (Boruta, Featurewiz, and ForwardSelector) combined with the forward stepwise selection technique were used. The construction of models utilized the multiple linear regression method, with subsequent scrutiny of fitting and predictive performance, reliability, and robustness through various statistical validation criteria. The models were ranked by the Multi-Criteria Decision Making procedure. Findings have revealed that the proposed methodology for descriptor selection outperforms GA, with Featurewiz showing a slight advantage over Boruta and ForwardSelector. These constructed models can serve as valuable tools for the quick and reliable prediction of NACs mutagenicity.

Factors associated with water consumption measured using the stable isotope techniques among Japanese adults: a cross-sectional study.

While some dietary guidelines have established daily water requirements for adults, those for older adults are not well defined. Factors associated with water turnover (WT), indicating water requirements and preformed water (PW), referring to fluids consumed from food and beverages, remain poorly understood. Therefore, we aimed to investigate these factors in Japanese adults. This cross-sectional study included 57 participants aged 54-85 years, living in Fukuoka City, Japan. The WT and PW were measured using the doubly labelled water (DLW) methods from August 2015 to September 2015. Dietary intake and physical activity were evaluated using a 3-day dietary record (DR) and a validated triaxial accelerometer, respectively. Fasting blood and urine samples, as well as environmental conditions, were assessed using a validated technique. We evaluated the variables for predicting water consumption by using a multivariate generalised linear model, with forward stepwise selection using these covariates. The median WT and PW were 2842 ml/day and 2227 ml/day, respectively. In the multivariate model, WT (R2 = 0.629) and PW (R2 = 0.621) were moderately predicted by variables such as sex, body weight, moderate-to-vigorous physical activity, water consumption estimated by DR, urinary aldosterone and osmolality, creatinine clearance and wet-bulb globe temperature. The PW estimates from DR were ~40% lower than those from DLW method, despite a significant correlation between the values. WT and PW are associated not only with lifestyle and environmental factors but also with urinary markers and kidney function. These findings provide useful insights into the differences in each individual's water requirement.

Deep vein thrombosis in patients with stroke or transient ischemic attack presenting with patent foramen ovale: a retrospective observational study

ObjectiveDeep vein thrombosis (DVT) is discussed as a source of embolism for cerebral ischemia in the presence of patent foramen ovale (PFO). However, previous studies reported varying rates of DVT in stroke patients, and recommendations for screening are lacking. This study aimed to characterize patients with stroke or transient ischemic attack (TIA) and concomitant PFO and explore the rate of DVT and associated parameters.MethodsMedical records were screened for patients with stroke or TIA and echocardiographic evidence of PFO. Concomitant DVT was identified according to compression ultrasonography of the lower limbs. A variety of demographic, clinical, and laboratory parameters, the RoPE and Wells scores were compared between patients with and without DVT.ResultsThree-hundred-thirty-nine patients (mean age 61.2 ± 15.4 years, 61.1% male) with stroke or TIA and PFO, treated between 01/2015 and 12/2020, were identified. Stroke and TIA patients did not differ for demographic and vascular risk factors. DVT was found in 17 cases out of 217 (7.8%) with compression ultrasonography. DVT was associated with a history of DVT, cancer, previous immobilization, calf compression pain, calf circumference difference, and a few laboratory abnormalities, e.g., increased D-dimer. A multivariate regression model with stepwise backward selection identified the Wells score (odds ratio 35.46, 95%-confidence interval 4.71–519.92) as a significant predictor for DVT.ConclusionDVT is present in a relevant proportion of patients with cerebral ischemia and PFO, which needs to be considered for the individual diagnostic workup. The Wells score seems suitable for guiding additional examinations, i.e., compression ultrasonography.

Associations between habitual sleep characteristics and cardiometabolic disease risk in corporate executives

ObjectivesCorporate executive job demands may lead to poor sleep habits, increasing their risk for cardiometabolic disease. This study aimed to describe and explore associations between objectively measured habitual sleep characteristics and cardiometabolic disease risk of corporate executives, while accounting for occupational, psychological, and lifestyle factors. MethodsHabitual sleep was measured using wrist-worn actigraphy and a sleep diary over seven consecutive days in 61 (68.3% men) corporate executives aged 46.4 ± 8.7years. A composite cardiometabolic disease risk score was determined using body mass index, waist circumference, blood pressure and fasting glucose and lipid concentrations. Prediction models were built using a backward stepwise selection approach to explore associations between sleep characteristics and cardiometabolic disease risk factors adjusting for occupational, psychological, and lifestyle covariates. ResultsAverage total sleep time was 6.60 ± 0.75 hours, with 51.7% of participants reporting poor sleep quality and 26.2% extending their weekend sleep. Adjusted models showed that lower sleep efficiency (β = −0.25, 95%CI: −0.43; −0.08, P = .006), shorter weekday total sleep time (β = −1.37, 95% CI: −2.41, −0.32; P = .011) and catch-up sleep (β = 0.84, 95%CI: 0.08, 1.60, P = .002) were associated with higher cardiometabolic disease risk scores. Adjusted models also found that shorter average time-in-bed (ß=−2.00, 95%CI: −3.76; −0.18, P = .031), average total sleep time (ß=1.98, 95%CI: −3.70; −0.25, P = .025) and weekday total sleep time (β = −2.13, 95%CI: −3.56; −0.69, P = .025) as well as catch-up sleep (β = 1.67, 95% CI: 0.52; 2.83; P = .012) were all associated with a higher body mass index. ConclusionCorporate executives who compromise sleep duration during the working week may increase their risk for obesity and future cardiometabolic disease.

Restrictive Strategy vs Usual Care for Cholecystectomy in Patients With Abdominal Pain and Gallstones

The 1-year results of the SECURE trial, a randomized trial comparing a restrictive strategy vs usual care for select patients with symptomatic cholelithiasis for cholecystectomy, resulted in a significantly lower operation rate after restrictive strategy. However, a restrictive strategy did not result in more pain-free patients at 1 year. To gauge pain level and determine the proportion of pain-free patients, operation rate, and biliary and surgical complications at the 5-year follow-up. This randomized clinical trial was a multicenter, parallel-arm, noninferiority, prospective study. Between February 2014 and April 2017, patients from 24 hospitals with symptomatic, uncomplicated cholelithiasis were included. Uncomplicated cholelithiasis was defined as gallstone disease without signs of complicated cholelithiasis, ie, biliary pancreatitis, cholangitis, common bile duct stones, or cholecystitis. Follow-up data for this analysis were collected by telephone from July 11, 2019, to September 23, 2023. Patients were randomized (1:1) to receive usual care or a restrictive strategy with stepwise selection for cholecystectomy. The primary, noninferiority end point was proportion of patients who were pain free as evaluated by Izbicki pain score at the 5-year follow-up. A 5% noninferiority margin was chosen. The secondary end points included cholecystectomy rates, biliary and surgical complications, and patient satisfaction. Among 1067 patients, the median (IQR) age was 49.0 years (38.0-59.0 years); 786 (73.7%) were female, and 281 (26.3%) were male. At the 5-year follow-up, 228 of 363 patients (62.8%) were pain free in the usual care group, compared with 216 of 353 patients (61.2%) in restrictive strategy group (difference, 1.6%; 1-sided 95% lower confidence limit, -7.6%; noninferiority P = .18). After cholecystectomy, 187 of 294 patients (63.6%) in the usual care group and 160 of 254 patients (63.0%) in the restrictive strategy group were pain free, respectively (P = .88). The restrictive care strategy was associated with 387 of 529 cholecystectomies (73.2%) compared with 437 of 536 in the usual care group (81.5%; 8.3% difference; P = .001). No differences between groups were observed in biliary and surgical complications or in patient satisfaction. In the long-term, a restrictive strategy results in a significant but small reduction in operation rate compared with usual care and is not associated with increased biliary and surgical complications. However, regardless of the strategy, only two-third of patients were pain free. Further criteria for selecting patients with uncomplicated cholelithiasis for cholecystectomy and rethinking laparoscopic cholecystectomy as treatment is needed to improve patient-reported outcomes. CCMO Identifier: NTR4022.

Association between Heat Vulnerability Index and Stroke Severity.

Socioeconomically disadvantaged neighborhoods are particularly vulnerable to heat-related illnesses. We aim to investigate the association between the heat vulnerability index (HVI), an established neighborhood-level metric of heat-related mortality risk, and acute ischemic stroke (AIS) severity. We conducted a retrospective analysis of consecutive AIS admissions to a comprehensive stroke center between 2012 and 2021. Stroke severity was defined upon admission based on the National Institutes of Health Stroke Scale (NIHSS). Demographic, socioeconomic, and clinical characteristics were extracted from electronic health records. HVI status was assigned using residential ZIP codes. Multivariable logistic regression analyses were performed. Of 3429 AIS admissions, 1123 (32.8%) were from high-HVI (scores 4-5) neighborhoods and 868 (25.3%) had severe stroke (NIHSS score ≥ 10). In the multivariable regression model with stepwise selection, a high HVI was independently associated with severe stroke (adjusted odds ratio: 1.40 [95% confidence interval 1.16-1.69]). The association between a high HVI and severe stroke underscores the importance of targeting policy interventions to mitigate heat-related illness in socioeconomically disadvantaged neighborhoods.

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.

Risk factors associated with piglet pre-weaning mortality in a Midwestern U.S. swine production system from 2020 to 2022

Piglet pre-weaning mortality (PWM) is a significant issue in the U.S. swine industry, causing economic losses and raising sustainability and animal welfare concerns. This study conducted a multivariable analysis to identify factors associated with PWM in a Midwestern U.S. swine production system. Weekly data from 47 sow farms (7207 weaning weeks) were captured from January 2020 to December 2022. Initially, 29 variables regarding farm infrastructure, productivity parameters, health status, and interventions were selected for univariate analysis to assess their association with PWM. The initial multivariable analysis included the variables with P < 0.20 in the univariate analyses. A backward stepwise model selection was conducted by excluding variables with P > 0.05, and the final multivariable model consisted of 19 significant risk factors and 6 interaction terms. The overall average PWM for the study population was 14.02 %. Yearly variations in PWM were observed, with the highest recorded in 2020 (16.61 %) and the lowest in 2021 (15.78 %). Cohorts with a pond water source, lower farrowing rate (71.9 %), higher farrowing parity (5.1), shorter gestation length (116.2 days), and using oxytocin during farrowing had increased PWM. The higher productivity parameters such as mummies rate, stillborn rate, and average total born, the higher the PWM was. Additionally, health status and intervention-related factors were associated with PWM, where higher PWM rates were observed in herds facing porcine reproductive and respiratory syndrome virus (PRRSV) outbreaks, porcine epidemic diarrhea virus (PEDV) positive, the weeks before and during feed medication, and weeks without using Rotavirus vaccine or Rotavirus feedback. Altogether, these results corroborate that PWM is a multifactorial problem, and a better understanding of the risk factors is essential in developing strategies to improve survival rates. Therefore, this study identified the major risk factors associated with PWM for groups of pigs raised under field conditions, and the results underscore the significance of data analysis in comprehending the unique challenges and opportunities inherent to each system.

Development and external validation of a model to predict multidrug-resistant bacterial infections in patients with cirrhosis.

We included patients with cirrhosis and bacterial infections from two prospective studies: a transcontinental study was used for model development and internal validation (n = 1302), and a study from Argentina and Uruguay was used for external validation (n = 472). All predictors were measured at the time of infection. Both culture-positive and culture-negative infections were included. The model was developed using logistic regression with backward stepwise predictor selection. We externally validated the optimism-adjusted model using calibration and discrimination statistics and evaluated its clinical utility. The prevalence of MDRO infections was 19% and 22% in the development and external validation datasets, respectively. The model's predictors were sex, prior antibiotic use, type and site of infection, MELD-Na, use of vasopressors, acute-on-chronic liver failure, and interaction terms. Upon external validation, the calibration slope was 77 (95% CI .48-1.05), and the area under the ROC curve was .68 (95% CI .61-.73). The application of the model significantly changed the post-test probability of having an MDRO infection, identifying patients with nosocomial infection at very low risk (8%) and patients with community-acquired infections at significant risk (36%). This model achieved adequate performance and could be used to improve the selection of empiric antibiotics, aligning with other antibiotic stewardship program strategies.

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.

Impact of acute therapeutic toxicities on anxiety during radiotherapy among Chinese patients with newly diagnosed nonmetastatic head and neck cancer: A cross-sectional study.

147 Background: Head and neck cancer (HNC) poses a significant threat to both psychological and physical health. This study aimed to depict the anxiety status of Chinese newly diagnosed nonmetastatic HNC patients during radiotherapy (RT) and to examine predictors of anxiety symptoms. Methods: Newly diagnosed nonmetastatic HNC patients who received RT at the Radiation Therapy Center of Sun Yat-sen University Cancer Center from August 23,2023 to August 24,2023 were recruited into this study. Using Zung Self-Rating Anxiety Scale (Zung SAS) to screen anxiety. Twenty-four common acute therapeutic toxicities (18 patient-reported and 6 clinician-rated) were assessed according to European Organization for Research and Treatment of Cancer Quality of Life Questionnaire Head and Neck 35 (EORTC QLQ-HN35) and Common Terminology Criteria for Adverse Events, fifth version (CTCAE V5.0). Chi-square ( χ2) test and multivariate binary logistic regression stepwise forward selection: likelihood ratio (LR) method were applied to identify factors associated with anxiety. Results: A total of 457 patients were included ultimately. Up to 29.10% of the patients showed varying degrees of anxiety and the average SAS standardized score was 45.66 ± 8.83. Chi-square test manifested that sex, annual household income, fatigue, anorexia, nausea, vomit, pain, dysphagia, sense problems (smell and taste), speech problems, trouble in social eating, trouble in social contact, sexual frustration, teeth problems, difficulty in opening mouth, xerostomia, sticky saliva, coughing, feeling ill, use of analgesics, use of nutritional supplements and weight loss were related to anxiety ( p &lt; 0.05). But multivariate logistic regression only demonstrated fatigue (OR = 3.696, 95% CI = 2.096-6.519), speech problems (OR = 3.247, 95% CI = 1.554-6.783), sexual frustration (OR = 2.029, 95% CI = 1.232-3.341), nausea (OR = 3.075, 95% CI = 1.807-5.234), feeling ill (OR = 2.283, 95% CI = 1.215-4.291), use of nutritional supplements (OR = 1.930, 95% CI = 1.136-3.280) and difficulty in opening mouth (OR = 3.007, 95% CI = 1.081-8.363) as independent predictive factors of anxiety. Conclusions: The Chinese newly diagnosed nonmetastatic HNC patients suffered greatly from anxiety during RT. This study identified the predictive contribution of sociodemographic and clinical characteristics and 24 common acute therapeutic toxicities to the vulnerability to anxiety, which can provide evidence and reference for medical staff to carry out psychological intervention.

54 MicroRNA Signatures in Clear Cell Renal Cell Carcinoma: Exploring Potential Implications for Prognosis

Abstract Background Renal cell carcinoma (RCC), the most common malignancy of the renal epithelium, accounts for over 90% of kidney cancer cases, with clear cell RCC (ccRCC) representing about 75% of these diagnoses. MicroRNAs (miRNAs), short non-coding RNAs of 21 to 23 nucleotides, play significant roles in RNA silencing and post-transcriptional regulation of gene expression. They are also crucial in tumorigenesis. We aimed to identify differentially expressed miRNA between ccRCC tumor and normal-adjacent samples, aiming to find miRNA signatures for ccRCC diagnostics and prognosis. Methods We collected tumor and normal-adjacent kidney samples from the Dartmouth Renal Tumor Biobank spanning 1994 to 2009. Samples were mechanically and enzymatically disassociated and preserved at -80 Celsius until processing. The single-stranded sequencing data was processed using the smrnaseq (v2.2.4) Nextflow analysis pipeline to map miRNAs to the reference database. The preprocessed counts data was imported into R (v4.3.3). We employed strict quality control measures excluding samples with low counts with a mean log-transcripts per million of less than -2, a read length below 25% within the range of 20-25 nucleotides, a microRNA fraction less than 20%, non-primate reads exceeding 70%, or a mapping rate greater than 1.5 times the interquartile range. Differential expression analysis was conducted using the Bioconductor DESeq2 package (v1.42.1), comparing tumor and normal samples as well as across different survival time categories. miRNAs with a false discovery rate (FDR) &amp;lt;0.05 and log2 fold change &amp;gt;2 were considered differentially expressed (DE) after adjustment for batch effects, tumor grade, sex, and tumor stage. An overrepresentation enrichment analysis was performed for the DE microRNAs using the miRNA Enrichment Analysis and AnnotationTool (miEAA). Survival analysis, utilizing stepwise selection in both directions, was conducted using identified DE miRNAs while adjusting for the covariates above plus age at diagnosis. Mature and hairpin miRNAs were analyzed separately for DE and survival. Results Among 213 samples with mature microRNA data, 198 samples (excluding one control) passed the quality control. These comprised 153 tumor samples and 45 normal samples, originating from a cohort of 159 patients. Among these patients, 13.8% have survived longer than 5 years, while 51.6% survived between 1 to 5 years, and 34.6% less than 1 year. 35 mature microRNAs were found to be differentially expressed between tumor and normal samples. Three mature miRNAs were under-expressed among samples with longer survival compared to those with short to medium survival. The DE mature miRNAs were overrepresented in vascular diseases (FDR=4.44 E-4), carcinoma (FDR=1.87 E-3) and clear cell renal cell carcinoma (FDR=5.65 E-3). miR-187-3p, miR-224-5p, miR-155-5p, miR-514a-3p, miR-490-5p were significantly associated with survival after adjustment (p &amp;lt;0.05). Out of the 219 samples analyzed for hairpin microRNA, 200 samples (excluding one control) passed quality control (155 tumor samples and 45 normal samples from 160 patients). Among these patients, 13.7% survived longer than 5 years, 51.9% survived between 1 to 5 years, and 34.4% survived less than 1 year. Differential expression analysis identified 28 hairpin microRNAs showing significant differences between tumor and normal samples. Notably, the differentially expressed hairpin microRNAs were overrepresented in renal cell carcinoma (FDR=5.38e-4) and renal clear cell carcinoma (FDR=0.01), as well as pathways associated with transcription factor TGFB1 (FDR=7.8×e−3), while being underrepresented in pathways associated with transcription factor KDM5B (FDR=9.5×e−3). Furthermore, hsa-mir-21, hsa-mir-155, hsa-mir-1291, and hsa-mir-506 were found to be significantly associated with survival after adjustment (p &amp;lt; 0.05). Conclusions This exploratory analysis identified distinct miRNA differential expression between ccRCC tumors and normal tissue, suggesting a potential miRNA signature for ccRCC. Our findings will be further explored using miRNA clustering analysis, functional analysis, network analysis, and differential transcript usage analysis. DOD CDMRP Funding: yes

Predicting the financial performance of microfinance institutions with machine learning techniques

Purpose Given the growing emphasis among scholars, practitioners and policymakers on financial sustainability, this study aims to explore the applicability of machine learning techniques in predicting the financial performance of microfinance institutions (MFIs). Design/methodology/approach This study gathered 9,059 firm-year observations spanning from 2003 to 2018 from the World Bank's Mix Market database. To predict the financial performance of MFIs, the authors applied a range of machine learning regression approaches to both training and testing data sets. These included linear regression, partial least squares, linear regression with stepwise selection, elastic net, random forest, quantile random forest, Bayesian ridge regression, K-Nearest Neighbors and support vector regression. All models were implemented using Python. Findings The findings revealed the random forest model as the most suitable choice, outperforming the other models considered. The effectiveness of the random forest model varied depending on specific scenarios, particularly the balance between training and testing data set proportions. More importantly, the results identified operational self-sufficiency as the most critical factor influencing the financial performance of MFIs. Research limitations/implications This study leveraged machine learning on a well-defined data set to identify the factors predicting the financial performance of MFIs. These insights offer valuable guidance for MFIs aiming to predict their long-term financial sustainability. Investors and donors can also use these findings to make informed decisions when selecting their potential recipients. Furthermore, practitioners and policymakers can use these findings to identify potential financial performance vulnerabilities. Originality/value This study stands out by using a global data set to investigate the best model for predicting the financial performance of MFIs, a relatively scarce subject in the existing microfinance literature. Moreover, it uses advanced machine learning techniques to gain a deeper understanding of the factors affecting the financial performance of MFIs.

Integrated machine learning screened glutamine metabolism-associated biomarker SLC1A5 to predict immunotherapy response in hepatocellular carcinoma

Hepatocellular carcinoma (HCC) stands as one of the most prevalent malignancies. While PD-1 immune checkpoint inhibitors have demonstrated promising therapeutic efficacy in HCC, not all patients exhibit a favorable response to these treatments. Glutamine is a crucial immune cell regulatory factor, and tumor cells exhibit glutamine dependence. In this study, HCC patients were divided into two subtypes (C1 and C2) based on glutamine metabolism-related genes via consensus clustering. The C1 pattern, in contrast to C2, was associated with a lower survival probability among HCC patients. Additionally, the C1 pattern exhibited higher proportions of patients with advanced tumor stages. The activity of C1 in glutamine metabolism and transport is significantly enhanced, while its oxidative phosphorylation activity is reduced. And, C1 was mainly involved in the progression-related pathway of HCC. Furthermore, C1 exhibited high levels of immunosuppressive cells, cytokine-receptor interactions and immune checkpoint genes, suggesting C1 as an immunosuppressive subtype. After stepwise selection based on integrated four machine learning methods, SLC1A5 was finally identified as the pivotal gene that distinguishes the subtypes. The expression of SLC1A5 was significantly positively correlated with immunosuppressive status. SLC1A5 showed the most significant correlation with macrophage infiltration, and this correlation was confirmed through the RNA-seq data of CLCA project and our cohort. Low-SLC1A5-expression samples had better immunogenicity and responsiveness to immunotherapy. As expected, SubMap and survival analysis indicated that individuals with low SLC1A5 expression were more responsive to anti-PD1 therapy. Collectively, this study categorized HCC patients based on glutamine metabolism-related genes and proposed two subclasses with different clinical traits, biological behavior, and immune status. Machine learning was utilized to identify the hub gene SLC1A5 for HCC classification, which also could predict immunotherapy response.

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.