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Patients with chronic kidney disease (CKD) are susceptible to SARS-CoV-2 infection and more prone to develop severe disease. It is important to know predictors of poor outcomes to optimize the strategies of care.93 patients with CKD and 93 age-sex matched patients without CKD were included in the study. Data on demographic, clinical features, hematological indices and outcomes were noted and compared between the groups. Neutrophile to lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), systemic immune inflammation index (SII) (platelet counts × neutrophil counts/lymphocyte counts) and lymphocyte-to-CRP ratio (LCR) were calculated on admission and the association of these markers with disease mortality in CKD patients was identified.CKD patients had higher risk of severe disease, and mortality compared to non-CKD patients (72% vs 50.5%,We report for the first time that SII is able to distinguish COVID-19 infected CKD patients of worse survival and it is as powerful as NLR in this regard. As SII is easily quantified from blood sample data, it may assist for early identification and timely management of CKD patients with worse survival.Los pacientes con enfermedad renal crónica (ERC) son susceptibles a la infección por SARS-CoV-2 y más propensos a desarrollar una enfermedad grave. Es importante conocer los predictores de los malos resultados para optimizar las estrategias de atención.Se incluyeron en el estudio 93 pacientes con ERC y 93 pacientes sin ERC, emparejados por edad y sexo. Los datos sobre las características demográficas, clínicas, índices hematológicos y resultados, se anotaron y compararon entre los grupos. La proporción de neutrófilos a linfocitos (NLR), la proporción de plaquetas a linfocitos (PLR), el índice de inflamación inmunitaria sistémica (SII) (recuentos de plaquetas × recuentos de neutrófilos/recuentos de linfocitos) y la proporción de linfocitos a PCR (LCR) se calcularon en el momento de la admisión y se identificó la asociación de estos marcadores con la mortalidad por enfermedad en pacientes con ERC.Los pacientes con ERC tuvieron un mayor riesgo de enfermedad grave y mortalidad en comparación con los pacientes sin ERC (72% vs 50,5%, p = 0,003, 36,6% vs 10,8%, p0,001, respectivamente) y tuvieron más probabilidades de tener valores más altos de índices inmuno inflamatorios (recuento de leucocitos, neutrófilos, NLR, SII y proteína C reactiva, etc.) y niveles más bajos de linfocitos y LCR. Además, se observaron niveles más altos de NLR, SII, PLR y un nivel más bajo de LCR en pacientes con ERC que murieron en comparación con los recuperados. En un análisis de la curva de características operativas del receptor, el área NLR, SII, PLR y LCR bajo la curva de mortalidad hospitalaria de pacientes con ERC fueron de 0,830, 0,811, 0,664 y 0,712, respectivamente. Entre todos los parámetros, NLR y SII se dió a conocer la mejor manera de distinguir a los pacientes con mayor riesgo de muerte. Con base en el valor de corte de 1180,5, se encontró que la sensibilidad y especificidad del SII, para predecir la mortalidad hospitalaria, fue del 67,5% y 79,6%, respectivamente. La sensibilidad y especificidad correspondientes del NLR fueron del 85,2% y 66,1%, respectivamente, en el valor de corte de 5,1.El análisis de regresión logística escalonada hacia adelante mostró que el NLR (≥5,1), SII (≥1180,5) y LCR (≤9) fueron predictores de mortalidad hospitalaria.Informamos, por primera vez, que el SII es capaz de distinguir pacientes con ERC infectados por COVID-19 de peor supervivencia y, en este sentido, es tan poderoso como el NLR. Como el SII se cuantifica fácilmente a partir de los datos de las muestras de sangre, puede ayudar a la identificación temprana y el manejo oportuno de los pacientes con ERC con peor supervivencia.

Patient-derived tumor xenografts (PDXs) are a powerful tool for drug discovery and screening in cancer. However, current studies have led to little understanding of genotype mismatches in PDXs, leading to massive economic losses. Here, we established PDX models from 53 lung cancer patients with a genotype matching rate of 79.2% (42/53). Furthermore, 17 clinicopathological features were examined and input in stepwise logistic regression (LR) models based on the lowest Akaike information criterion (AIC), least absolute shrinkage and selection operator (LASSO)-LR, support vector machine (SVM) recursive feature elimination (SVM-RFE), extreme gradient boosting (XGBoost), gradient boosting and categorical features (CatBoost), and the synthetic minority oversampling technique (SMOTE). Finally, the performance of all models was evaluated by the accuracy, area under the receiver operating characteristic curve (AUC), and F1 score in 100 testing groups. Two multivariable LR models revealed that age, number of driver gene mutations, epidermal growth factor receptor (EGFR) gene mutations, type of prior chemotherapy, prior tyrosine kinase inhibitor (TKI) therapy, and the source of the sample were powerful predictors. Moreover, CatBoost (mean accuracy = 0.960; mean AUC = 0.939; mean F1 score = 0.908) and the eight-feature SVM (mean accuracy = 0.950; mean AUC = 0.934; mean F1 score = 0.903) showed the best performance among the algorithms. Meanwhile, application of the SMOTE improved the predictive capability of most models, except CatBoost. Based on the SMOTE, the ensemble classifier of single models achieved the highest accuracy (mean = 0.975), AUC (mean = 0.949), and F1 score (mean = 0.938). In conclusion, we established an optimal predictive model to screen lung cancer patients for NOD/Shi-scid, interleukin-2 receptor (IL-2R) γnull (NOG)/PDX models and offer a general approach for building predictive models.

The model tree algorithm of M5 is integrated with the multiple linear regression technique called modified stepwise regression (MSR), resulting in a new method for modeling complex civil engineering problems. We purposefully chose artificial neural networks (ANN) for comparison against the proposed “M5+MSR” because they fall at the two ends of the model interpretability spectrum in machine learning. This research addresses the critical question of how to balance the trade-off between bias, variance and model complexity in machine learning through contrasting “M5+MSR” against other commonly applied methods. In two application cases (Case 1: concrete workability and Case 2: steel fabrication estimating), the proposed “M5+MSR” gave rise to explainable regression tree models featuring substantially reduced complexities against ANN and model prediction errors comparable to ANN. The resulting “M5+MSR” models consistently outperformed ANN in terms of model overfitting metrics by 19% in Case 1 and 21% in Case 2, thus boasting better learning performances. The proposed new method will potentially find applications in tackling a wide range of complicated engineering problems that entail fitting prediction models based on laboratory or field data.

Estimating individual treatment effects (ITEs) is crucial to personalized psychotherapy. It depends on identifying all covariates that interact with treatment, a challenging task considering the many patient characteristics hypothesized to influence treatment outcome. The goal of this study was to compare different covariate-selection strategies and their consequences on estimating ITEs. A Monte Carlo simulation was conducted to compare stepwise regression with and without cross-validation and shrinkage methods. The study was designed to mimic the setting of psychotherapy studies. No single covariate-selection strategy dominated all others across all factor-level combinations and on all performance measures. The least absolute shrinkage and selection operator showed the most accurate out-of-sample predictions, identified the highest number of true treatment-covariate interactions, and estimated ITEs with the highest precision across the most conditions. Domain backward stepwise regression and backward stepwise regression using Bayesian information criterion were least biased in estimating variance of ITEs across the most conditions.

Background: Coronary artery bypass grafting 30-day unplanned readmission is a focus for the CMS Hospital Readmissions Reduction Program. Awareness of the critical elements of the care delivery environment, including hospital infrastructure and patient clinical profiles that predispose toward readmission, is essential to proactively decrease readmissions. Methods: The Healthcare Cost and Utilization Project-State Inpatient Database, American Hospital Association Annual Health Survey Database, and Healthcare Information Management Systems Society data sets were merged to create a single data set of patient- and hospital-level data from 8 states. Isolated coronary artery bypass grafting procedures were queried for all-cause 30-day readmission, and backwards stepwise logistic regression was performed. Readmission rate was then used to categorize hospitals into quartiles, and analysis focused on the hospitals with the lowest (Q1) and highest (Q4) readmission rates. Univariate analysis was performed comparing Q1 and Q4 hospitals. Results: A total of 150,215 patients underwent isolated coronary artery bypass grafting with 23,244 (15.5%) readmitted patients among 903 hospitals. Model area under the curve was 0.709 (95% confidence interval, 0.702–0.716), with the top 3 readmission determinants related to discharge disposition. Compared to Q1, Q4 patients more often were female, were >70 years of age, and had Medicare as a primary payor (P < .001). Low readmission rate hospitals were characterized by higher costs; not-for-profit status; having Joint Commission accreditation; and higher total admissions, operative volume, hospital/ICU beds, full-time physicians, nurses, and ancillary personnel (P < .001). Conclusion: Readmission after coronary artery bypass grafting is strongly influenced by discharge disposition. However, hospital factors such as scale, personnel, and ownership structure are significant contributors to readmission. Focus beyond patient factors to include the entire continuum of care is required to enhance outcomes, of which readmission is one surrogate measure.

Background The Liver Imaging Reporting and Data System (LI-RADS) assigns a risk category for hepatocellular carcinoma (HCC) to imaging observations. Establishing the contributions of major features can inform the diagnostic algorithm. Purpose To perform a systematic review and individual patient data meta-analysis to establish the probability of HCC for each LI-RADS major feature using CT/MRI and contrast-enhanced US (CEUS) LI-RADS in patients at high risk for HCC. Materials and Methods Multiple databases (MEDLINE, Embase, Cochrane Central Register of Controlled Trials, and Scopus) were searched for studies from January 2014 to September 2019 that evaluated the accuracy of CT, MRI, and CEUS for HCC detection using LI-RADS (CT/MRI LI-RADS, versions 2014, 2017, and 2018; CEUS LI-RADS, versions 2016 and 2017). Data were centralized. Clustering was addressed at the study and patient levels using mixed models. Adjusted odds ratios (ORs) with 95% CIs were determined for each major feature using multivariable stepwise logistic regression. Risk of bias was assessed using Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2) (PROSPERO protocol: CRD42020164486). Results A total of 32 studies were included, with 1170 CT observations, 3341 MRI observations, and 853 CEUS observations. At multivariable analysis of CT/MRI LI-RADS, all major features were associated with HCC, except threshold growth (OR, 1.6; 95% CI: 0.7, 3.6; P = .07). Nonperipheral washout (OR, 13.2; 95% CI: 9.0, 19.2; P = .01) and nonrim arterial phase hyperenhancement (APHE) (OR, 10.3; 95% CI: 6.7, 15.6; P = .01) had stronger associations with HCC than enhancing capsule (OR, 2.4; 95% CI: 1.7, 3.5; P = .03). On CEUS images, APHE (OR, 7.3; 95% CI: 4.6, 11.5; P = .01), late and mild washout (OR, 4.1; 95% CI: 2.6, 6.6; P = .01), and size of at least 20 mm (OR, 1.6; 95% CI: 1.04, 2.5; P = .04) were associated with HCC. Twenty-five studies (78%) had high risk of bias due to reporting ambiguity or study design flaws. Conclusion Most Liver Imaging Reporting and Data System major features had different independent associations with hepatocellular carcinoma; for CT/MRI, arterial phase hyperenhancement and washout had the strongest associations, whereas threshold growth had no association. © RSNA, 2021 Online supplemental material is available for this article.

Purpose The likelihood of valproate (VPA) induced thrombocytopenia increases with higher VPA levels. In critically ill patients, the biological active free VPA level cannot be predicted from the total serum level. In this study, we evaluated the relationship between trough free VPA serum levels and concomitant platelet counts and assessed risk factors for the development of thrombocytopenia with the aim of generating a formula specifying the probabilities of developing thrombocytopenia based on trough free serum VPA levels. Methods Trough free VPA levels and concomitant platelet counts were collected from a large cohort of patients who participated in a prospective VPA monotherapy trial. Significant variables associated with thrombocytopenia in a univariate analysis were evaluated in a multivariate model. A receiver operator curve was performed to compute the trough free VPA levels with the greatest discriminating power in predicting thrombocytopenia. Results 844 trough free VPA levels and concomitant platelet counts obtained from 264 patients were analyzed. In a multivariate analysis, free trough VPA levels, gender, and baseline platelet counts were significantly associated with thrombocytopenia. Using stepwise regression and multivariate logistic regression analyses, we generated gender specific formulas for predicting platelet counts and probabilities of developing thrombocytopenia. The trough free VPA with the greatest discriminating power to predict platelet values ≤ 100,000/μL was 16.65 µg/mL. Conclusions The generated model was based on trough free VPA levels and achieved high sensitivity and specificity. Our results are therefore generalizable and can be applied to estimate the probability of developing thrombocytopenia in critically ill patients.

OBJECTIVE: Photoplethysmography (PPG) waveform analysis is being increasingly investigated for continuous, non-invasive, and cuff-less blood pressure (BP) measurement. However, the efficacy of this approach and the useful features and models remain largely unclear. The objectives were to develop easy-to-understand models relating PPG waveform features to BP changes (after a cuff calibration) and to determine their value in BP measurement accuracy. METHODS: The study data comprised finger, toe, and ear PPG waveforms, an ECG waveform, and reference manual cuff BP measurements from 32 human subjects (25% hypertensive) before and after slow breathing, mental arithmetic, cold pressor, and nitroglycerin administration. Stepwise linear regression was employed to create parsimonious models for predicting the intervention-induced BP changes from popular PPG waveform features, pulse arrival time (PAT, time delay between ECG R-wave and PPG foot), and subject demographics. Leave-one-subject-out cross validation was applied to compare the BP change prediction root-mean-squared-errors (RMSEs) of the resulting models to reference models in which PPG waveform features were excluded. RESULTS: Finger b-time (PPG foot to minimum second derivative time interval) and ear “STT” (PPG amplitude divided by maximum derivative), when combined with PAT, reduced the systolic BP change prediction RMSE of reference models by 6–7% (p

BACKGROUND Health authorities have expanded two strategies to diminish CRC-related influence: CR screening and improve diagnostic process in symptomatic patients. The aim of the current study is to design a predictive model to identify the most important risk factors that can efficiently predict patients who have high risk of colorectal neoplasia. METHOD A cross-sectional study was constructed to include all patients who had positive test for FIT or had one or more risk factors for colorectal cancer based on the guidelines of detecting high-risk groups for colorectal cancer in Iran. Multivariable binary logistic regression model was constructed for prediction of colorectal neoplasia. We used sensitivity, specificity, positive and negative predictive value, and positive and negative likelihood ratio to check the accuracy. The Hosmer-Lemeshow test, chi-square test, and p value were used to determine the precision of model. RESULT Following an AIC stepwise selection model, only nine potential variables, namely gender, watery diarrhea, IBD, abdominal pain, melena, body mass index, depression drug, anti-inflammatory drug, and age, were found to be a predictor of colorectal neoplasia. The best cut-point probability in the final model was 0.27 and results of sensitivity and specificity, based on maximizing these two criteria, were 66% and 62%, respectively. CONCLUSION Overall, our model prediction was comparable with other risk prediction models for colorectal cancer. It had a modest discriminatory power to distinguish an individual's neoplasia colorectal risk.

Introduction: High prevalence of work-related physical and psychological disorders among dentists has been reported. Only one study explored job satisfaction among Health Insurance Organization dentists, in Alexandria. However, job satisfaction among Ministry of Health and Population (MOHP) dentists has not been examined.Objectives: The objective of this study was to measure the level of job satisfaction among MOHP dentists, in Alexandria and to assess the association of work environment factors as well as personal and practice-related factors to job satisfaction.Material and Methods: This study was a cross sectional analytical survey conducted among dentists working in MOHP facilities, in Alexandria during a six month period from October 2019 to March 2020. A validated questionnaire modified from the Dentists Satisfaction Survey that was previously tested in Saudi Arabia was adopted. The first section was concerned with demographic and practice related data whereas the second section explored work environment factors. Multivariate stepwise linear regression analysis was performed to examine the association between total mean job satisfaction with personal/ practice related characteristics and work environment factors.RESULTS: The level of job satisfaction was neutral (2.91±0.41). Overall job satisfaction (Beta=0.437), monetary satisfaction (Beta=0.328) and personal time (Beta=0.265) reported the most significant associations with job satisfaction according to the multivariate linear regression analysis.CONCLUSIONS: This study revealed that overall job satisfaction, perception of income and personal time showed a significant impact on job satisfaction. The findings of this study would help policy makers to design plans to increase level of job satisfaction among dentists.

The brown planthopper (BPH), Nilaparvata lugens (Stal) is a migratory rice pest that periodically erupts across China, and mainly come from Indochina Peninsula in spring. The climate change in Indochina Peninsula has caused obvious impacts on the immigration of BPH. To further understand the influence of climate change on BPH immigration, the light-trap data of BPH in southern China from 1980 to 2016 were collected, and the best performance data of each meteorological factor (temperature, humidity, precipitation and wind) in Indochina Peninsula by simulating eight Global Climate Models (GCMs) were chosen. The key predicting factors were screened out by identifying correlations between the number of BPH and the meteorological factors. The support vector machine (SVM), back propagation (BP) neural network, and stepwise regression were used to establish medium long-term prediction models of immigration amount of BPH, and their advantages and disadvantages were compared, and prediction of the occurrence of BPH in the future was carried out by using three models based on future climate data provided by GCMs (under RCP4.5 or RCP8.5 scenario). The results showed that: (1) BNU-CSM1–1 model was the best in temperature simulation; CESM1-CAM5 model was the best in humidity simulation; HadGEM2-AO model was the best in precipitation and meridional wind simulation; BCC-CSM1–1 model was the best in zonal wind simulation. (2) The accuracies of retrospective testing and forward forecasting of three models indicated the SVM model was more suitable for medium long-term predicting the occurrence of BPH than BP neural networkmodel and stepwise regression model. (3) The immigration of BPH in Guangxi, Jiangxi, Hunan and Hubei would be greater than other provinces in southern China in the next 7 years, and should be taken seriously.

In this study, the relationship between the environmental radiation dose rate and meteorological variables was investigated with multiple regression analysis and big data of those variables. The environmental radiation dose rate and 36 different meteorological variables were measured on Ulleung Island, Republic of Korea, from 2011 to 2015. Not all meteorological variables were used in the regression analysis because the different meteorological variables significantly affect the environmental radiation dose rate during different periods, and the degree of influence changes with time. By applying the Pearson correlation analysis and stepwise selection methods to the big dataset, the major meteorological variables influencing the environmental radiation dose rate were identified, which were then used as the independent variables for the regression model. Subsequently, multiple regression models for the monthly datasets and dataset of the entire period were developed.

Purposes: Adipokine alterations contribute to the development and remission of nonalcoholic fatty-liver disease (NAFLD). Adipsin is one of the most abundant adipokines and is almost exclusively produced by adipocytes. However, data on adipsin in human NAFLD are limited and controversial. We performed this study to investigate the association between adipsin and the remission of NAFLD in middle-aged and elderly Chinese adults. Methods: Whether adipsin is associated with the remission of NAFLD in a 3-year community-based prospective cohort study was investigated. Baseline levels of adipsin were measured in serum samples collected from 908 NAFLD participants. NAFLD was diagnosed using abdominal ultrasonography. Logistic regression analysis and a multiple stepwise logistic regression model including different variables were conducted to evaluate the association between serum adipsin levels and the remission of NAFLD. Results: During a mean follow-up of 3.14 ± 0.36 years, 247 (27.20%) participants with NAFLD at baseline were in remission. At baseline, serum adipsin concentration was positively correlated with body mass index (r: 0.39, p < 0.001), insulin (r: 0.31, p < 0.001), and homeostasis model assessment of insulin resistance (r: 0.31, p < 0.001) and was inversely associated with NAFLD remission with a fully adjusted odds ratio (OR) of 0.28 (0.16–0.48) (p trend < 0.001). In a multiple stepwise logistic regression model, circulating adipsin independently predicted NAFLD remission (OR: 0.284, 95% confidence interval [CI]: 0.172–0.471, p for trend p < 0.001) for the prediction model of NAFLD remission. Conclusions: We provide evidence for an association between serum adipsin levels and the remission of NAFLD in a community-based prospective cohort study. Serum adipsin can be a potential biomarker for predicting NAFLD remission.

Quantifying forest systems is of importance for ecological services and economic benefits in ecosystem models. This study aims to map the percent tree cover (PTC) of various forest stands in the Buyuk Menderes Basin, located in the western part of Turkey with different characteristics in the Mediterranean and Terrestrial transition regions Sentinel-2 data with 10-m spatial resolution. In recent years, some researches have been carried out in different fields to show the capabilities and potential of Sentinel-2 satellite sensors. However, the limited number of PTC researches conducted with Sentinel-2 images reveals the importance of this study. This study aimed to demonstrate reliable PTC data in landscape planning or ecosystem modeling by introducing an advanced approach with high spatial, spectral, and temporal resolution and more cost-effective. In this study, a regression tree algorithm, one of the popular machine learning techniques for ecological modeling, was used to estimate the tree cover's dependent variable based on high-resolution monthly metrics' spectral signatures. Six frames of TripleSat images were used as training data in the regression tree. Monthly Sentinel-2 bands and produced metrics including NDVI, LAI, fCOVER, MSAVI2, and MCARI were almost the first time used as predictor variables. Stepwise linear regression (SLR) was applied to select these predictor bands in the regression tree and a correlation coefficient of 0.83 was obtained. Result PTC maps were produced and the results were evaluated based on coniferous and broadleaf. The results were tested using high spatial resolution TripleSat images and higher model accuracy was determined in both forest types. The high correlation is due to the Sentinel 2 satellite's band characteristics and the metrics are directly related to the tree cover. As a result, the high-accuracy availability of the Sentinel2 satellite is seen to map the PTC on a regional scale, including complex forest types between the Mediterranean and terrestrial transition climates.

Background. Cerebrovascular disease has been the leading cause of death in China since 2017, and the control of medical expenses for these diseases is an urgent issue. Diagnosis-related groups (DRG) are increasingly being used to decrease the costs of healthcare worldwide. However, the classification variables and rules used vary from region to region. Of these variables, the question of whether the length of stay (LOS) should be used as a grouping variable is controversial. Aim. To identify the factors influencing inpatient medical expenditure in cerebrovascular disease patients. The performance of two sets of classification rules, and the effects of the extent of control of unreasonable medical treatment, were compared, to investigate whether the classification variables should include LOS. Methods. Data from 45,575 inpatients from a Healthcare Security Administration of a city in western China were used. Kruskal–Wallis H tests were used for single-factor analysis, and multiple linear stepwise regression was used to determine the main factors. A chi-squared automatic interaction detector (CHAID) algorithm was built as a decision tree model for grouping related data. The intensity of oversupply of service was controlled step by step from 10% to 100%, and the performance was calculated for each group. Results. The average hospitalization cost was 1,284 US dollars, and the total was 51.17 million US dollars. Of this, 43.42 million were paid by the government, and 7.75 million were paid by individuals. Factors including gender, age, type of insurance, level of hospital, LOS, surgery, therapeutic outcomes, main concomitant disease, and hypertension significantly influenced inpatient expenditure ( P < 0.05 ). Incorporating LOS, the patients were divided into seven DRG groups, while without LOS, the patients were divided into eight DRG groups. More clinical variables were needed to achieve good results without LOS. Of the two rule sets, smaller coefficient of variation (CV) and a lower upper limit for patient costs were found in the group including LOS. Using this type of economic control, 3.35 million US dollars could be saved in one year.

Background Endoscopic sinus surgery is a well-established treatment for chronic rhinosinusitis in patients with cystic fibrosis, though its benefits seem to be limited to improving sinonasal symptoms rather than affecting lung function. Objective This study aims to identify clinical and demographic factors that may influence sinonasal and pulmonary outcomes after surgery. Methods This is a six-year retrospective analysis of adult cystic fibrosis patients who underwent endoscopic sinus surgery at a tertiary care center. 22-Item Sino-Nasal Outcomes Test scores and mean forced expiratory volume data at baseline and three to six months after surgery were analyzed using t-test and stepwise regression with the following covariates: age, gender, lung transplant, revision surgery, and pseudomonas on sinus culture. Results 119 surgeries were performed on 88 patients, with 69% on patients with transplant. The overall mean (Standard Deviation) improvement in 22-Item Sino-Nasal Outcomes Test score was 9.42 (18.15) for the entire cohort ( P Conclusion Although surgery was associated with a clinically and statistically significant improvement in sinonasal scores in cystic fibrosis patients, patients with pseudomonas may experience less benefit. Revision surgery and older age may be associated with less favorable pulmonary outcomes. Awareness of such variables may help when deciding which cystic fibrosis patients should undergo surgery.

Screening for gastric intestinal metaplasia (GIM) may lead to early gastric cancer detection. We developed and validated a pre-endoscopy risk prediction model for detection of GIM based on patient-level risk factors in a U.S. population. We used data from 423 GIM cases and 1796 controls from a cross-sectional study among primary care and endoscopy clinic patients at the Houston VA. We developed the model using backwards stepwise regression and assessed discrimination using area under the receiver operating characteristic (AUROC). The model was internally validated using cross-validation and bootstrapping. The final expanded model was compared to a model including H. pylori infection alone and a baseline model including remaining terms without H. pylori. Male sex, older age, non-white race/ethnicity, smoking status, and H. pylori were associated with GIM risk. The expanded model including these terms had AUROC 0.73 (95%CI 0.71–0.76) for predicting GIM and AUROC 0.82 (95%CI 0.79–0.86) for extensive GIM. This model discriminated better than a model including only H. pylori (AUROC 0.66; 95%CI 0.63–0.68) and the baseline model (AUROC 0.67; 95%CI 0.64–0.70). The expanded model performed similarly among primary care (AUROC 0.75) and endoscopy (AUROC 0.73) patients. The expanded model showed sufficient internal validity (cross-validation AUROC 0.72) with little evidence of over-fitting. We develop and validate a non-invasive risk model for GIM detection in a U.S. population that included terms for sex, age, race/ethnicity, smoking status, and H. pylori infection. Validated risk models would identify individuals with GIM who should be referred for endoscopic screening.

Background: Sex-based differences in neuromuscular characteristics relevant to anterior cruciate ligament (ACL) injury risk may arise as compensation for divergent strength development during puberty. Strength training during this period may prevent the development of these undesirable neuromuscular characteristics. Hypothesis: Strength-trained middle school girls will have improved jump-landing biomechanics compared with control participants. Study Design: Cohort study. Level of Evidence: Level 3. Methods: Maximum voluntary isometric contraction in hip extension and abduction and knee extension and flexion as well as Landing Error Scoring System (LESS) scores were collected for healthy female middle school students of grades 6 to 8. Strength-training participants (STR: N = 30; height, 1.63 ± 0.07 m; mass, 48.1 ± 7.6 kg; age, 12.5 ± 1.0 y) were matched with control participants (CON: N = 30; height, 1.60 ± 0.09 m; mass, 47.2 ± 8.9 kg; age, 12.6 ± 0.9 y). The training consisted of a 6-month strength-training program administered through a gym class curriculum that targeted the lower extremity. A repeated-measures mixed-model analysis of variance was used for comparisons between groups and across time (α = 0.05). Stepwise linear regression was used to examine the relationship between strength change and LESS score change. Results: Strength values (N·m/kg) increased across time and to a greater degree in STR for hip extension (baseline 3.98 ± 1.15 vs follow-up 4.77 ± 1.80), hip abduction (4.22 ± 1.09 vs 5.13 ± 2.55), and knee flexion (3.27 ± 0.62 vs 3.64 ± 1.40) compared with CON. LESS grades significantly decreased across time in STR (5.58 ± 1.21 vs 4.86 ± 1.44) and were significantly lower than CON (5.98 ± 1.42) at follow-up ( P < 0.001). The change in hip extension and knee extension strength explained 67% of the variance ( P < 0.001) in the LESS change score in the STR group. Conclusion: A school-based strength-training program that focused on hip and knee musculature significantly improved jump-landing biomechanics (as determined by LESS) relevant to ACL injury risk. Further investigation using different strength-training approaches in this age group is warranted. Clinical Relevance: Strength training during adolescence holds promise as an injury prevention program. The use of a school-based approach is novel and may represent a robust opportunity for injury prevention programs, as physical education class is often mandatory in this age group.

Based on former empirical studies and theoretical considerations the present study investigated, whether stress load and stress coping are influencing severity of depression over time in young girls. Stepwise multiple regression with different specific predictors was used to obtain the best predictive power. 148 girls with a mean age of 15 years where studied. 74 fulfilled DSM-IV criteria for major depression. Stress symptoms and stress coping was measured with SSKJ 3-8 and SVFKJ. The best predictor after a time interval of six months was severity of depression at first assessment with an explained variance of 62 %. Psychological stress load and destructive stress coping also added significantly to explained variance in depression severity. The results were interpreted with respect to treatment and prevention of depression in adolescents.

Postbariatric hypoglycemia (PBH) is a rare but growing complication of bariatric surgery. Many aspects have yet to be established, including the blood glucose threshold which represents clinically important hypoglycemia in affected patients.To confirm the glucose threshold below which neuroglycopenic (NG) symptoms arise in patients with PBH during provoked and real-world hypoglycemia as an indicator of clinically important hypoglycemia.Stanford University School of Medicine.Forty patients with PBH were enrolled. Thirty-two patients underwent hypoglycemia provocation in the clinical research unit (CRU) during which symptoms and blood glucose concentrations were assessed. A sensitivity analysis and stepwise linear regression were conducted evaluating relationships between symptoms and glucose levels. To validate CRU findings in the real-world setting, 8 sex-, age-, body mass index (BMI)-, and disease severity-matched patients underwent 20 days of at-home continuous glucose monitoring (CGM), self-monitoring of blood glucose (SMBG), and symptom assessment by electronic diary (eDiary).In response to hypoglycemia provocation 19%, 59%, and 22% of patients developed a postprandial glucose nadir70-54 mg/dL ,54-40 mg/dL, and40 mg/dL, respectively. Number of NG symptoms was highest when glucose was in the54-40 mg/dL range, although 23% of those with NG symptoms in this range, and 37% with NG symptoms below this range lacked autonomic symptoms, indicating substantial hypoglycemia unawareness. Sensitivity of symptoms to detect hypoglycemia was poor other than for drowsiness, while specificity was high for all NG symptoms. Confusion, sweating, drowsiness, and incoordination were significant independent predictors of hypoglycemia. Events captured during real-world monitoring mirrored CRU data, showing a spike in NG symptoms in the54-40 mg/dL range. CGM captured up to 10-fold more events than were patient-perceived and captured by SMBG/eDiary.Due to the peak in NG symptoms at glucose54-40 mg/dL during provoked and real-world hypoglycemia, the low sensitivity/high specificity of NG symptoms to detect hypoglycemia, and high prevalence of hypoglycemia unawareness at glucose values54 mg/dL, we propose that blood glucose54 mg/dL should be taken to signify clinically important hypoglycemia in patients with established PBH.

Identification of the factors controlling the migration of hexavalent chromium (Cr (VI)) in soil can provide valuable information for pollution control and risk assessment. In this study, we assessed the processes of transportation and migration of Cr (VI) in soil by batch and column experiments. Breakthrough curves were used to examine the mobility of Cr (VI) in soil columns. Most of them could be described by a typical S-shaped curve. The Yoon–Nelson model and Thomas model were applied to predict the behavior of Cr (VI) in soil and obtained parameters to characterize the rate and capacity of adsorption. It was observed that all obtained parameters changed along with soil properties. Moreover, stepwise multiple regression and grey correlation analysis were used to determine the main control factors by evaluating the relationship between soil properties and dynamic or static parameters. The results showed Fe oxide content was the key factor no matter in static or dynamic adsorption while soil texture had a great influence on the transportation of Cr (VI) in soil columns. Also, in the actual situation, the external input condition should not be ignored. Our research provides meaningful information for authorities to develop better soil management and remediation strategies.

Background Older adults with Hodgkin Lymphoma (HL) have poorer outcomes than younger patients. There are little data about which baseline patient and disease factors inform prognosis among older patients. We sought to create a prediction model for 1-year mortality among older patients with new HL who received dose-intense chemotherapy. Methods We included adults ≥65 years old with a new diagnosis of classical HL between 2000–2013 from the Surveillance, Epidemiology, and End Results (SEER)-Medicare dataset who received full-regimen chemotherapy. Through a non-random 2:1 split, we created development and validation cohorts. Multiple imputation was used for missing data. Using stepwise selection and logistic regression, we identified predictive variables for 1-year mortality. The model was applied to the validation cohort. A final model was then fit in the full cohort. Results We included 1315 patients. In the development cohort (n = 813), we identified significant predictors of 1-year mortality including age, Charlson comorbidity index (CCI), B symptoms at diagnosis, and advanced stage at diagnosis. The c-statistic was 0.70. When this model was applied to the validation cohort (n = 502), the c-statistic was 0.65. Predictors of 1-year mortality in the final model were CCI (OR = 1.41), B symptoms (OR = 1.54), advanced stage (OR = 1.44), and older age at diagnosis (OR = 1.33). Conclusion We present a prediction model for use among older adults with HL who receive intensive chemotherapy. We identify risk factors for death within 1 year of diagnosis. Future work will build upon prognostication and shared decision-making after diagnosis for this population.

Malnutrition is relatively common in stroke survivors and it also affects weight loss and muscle strength. Various nutritional assessment tools have been used to monitor changes in nutritional status. Among such tools, the Controlling Nutritional Status (CONUT) score is a convenient and cost-effective index calculated from serum albumin level, total peripheral lymphocyte count, and total cholesterol level. This study investigated the prognostic role of malnutrition, as assessed by the CONUT scoring system. We hypothesized that malnutrition negatively affects outcomes as expressed by Functional Independence Measure (FIM) motor or Berg Balance Scale (BBS) change in stroke patients. This was a retrospective cohort study involving 117 individuals including first-time subacute stroke inpatients from March 2017 to February 2020. All participants were evaluated with BBS and FIM. We used multiple linear regression analysis with backward stepwise selection to examine the association between CONUT and changes during rehabilitation. After adjusting for independent predictors, we found the CONUT score to be associated with FIM motor (B = -1.848 ± 5.811, P

Introduction: Localised bone mineral density (BMD) adaptation of the lumbar spine, particularly on the contralateral side to the bowling arm, has been observed in elite male cricket fast bowlers. No study has investigated this in adolescents, or the role of fast bowling technique on lumbar BMD adaptation. This study aims to investigate lumbar BMD adaptation in adolescent cricket fast bowlers, and its relationship with fast bowling technique. \ud \ud Methods: 39 adolescent fast bowlers underwent antero-posterior DXA scan of their lumbar spine. Hip, lumbopelvic and thoracolumbar joint kinematics, and vertical ground reaction kinetics were determined using 3D motion capture and force plates. Significant partial (covariate: fat free mass) and bivariate correlations of the technique parameters with whole lumbar (L1-L4) BMD and BMD asymmetry (L3 and L4) were advanced as candidate variables for multiple stepwise linear regression.\ud \ud Results: Adolescent fast bowlers demonstrated high lumbar Z-Scores (+1.0; 95%CI: 0.7 – 1.4) and significantly greater BMD on the contralateral side of L3 (9.0%; 95%CI: 5.8 – 12.1%) and L4 (8.2%; 95%CI: 4.9 – 11.5%). Maximum contralateral thoracolumbar rotation and maximum ipsilateral lumbopelvic rotation in the period between back foot contact (BFC) and ball release (BR), as well as contralateral pelvic drop at front foot contact (FFC), were identified as predictors of L1-L4 BMD, explaining 65% of the variation. Maximum ipsilateral lumbopelvic rotation between BFC and BR, as well as ipsilateral lumbopelvic rotation and contralateral thoracolumbar side flexion at BR, were predictors of lumbar asymmetry within L3 and L4. \ud \ud Conclusion: Thoracolumbar and lumbopelvic motion are implicated in the aetiology of the unique lumbar bone adaptation observed in fast bowlers whereas vertical ground reaction force, independent of body mass, was not. This may further implicate the osteogenic potential of torsional rather than impact loading in exercise-induced adaptation.

Background Interplay between vaginal microbiome and human papillomavirus (HPV) remains unclear, partly due to heterogeneity of microbiota. Methods We used data from 546 women enrolled in a cross-sectional study in 5 Brazil. We genotyped vaginal samples for HPV and sequenced V3–V4 region of 16S rRNA gene for vaginal microbiome analysis. We used stepwise logistic regression to construct 2 linear scores to predict high-risk HPV (hrHPV) positivity: one based exclusively on presence of individual bacterial taxa (microbiome-based [MB] score) and the other exclusively on participants’ sociodemographic, behavioral, and clinical (SBC) characteristics. MB score combined coefficients of 30 (of 116) species. SBC score retained 6 of 25 candidate variables. We constructed receiver operating characteristic curves for scores as hrHPV correlates and compared areas under the curve (AUC) and 95% confidence intervals (CI). Results Overall, prevalence of hrHPV was 15.8%, and 26.2% had a Lactobacillus-depleted microbiome. AUCs were 0.8022 (95% CI, .7517–.8527) for MB score and 0.7027 (95% CI, .6419–.7636) for SBC score (P = .0163). Conclusions The proposed MB score is strongly correlated with hrHPV positivity—exceeding the predictive value of behavioral variables—suggesting its potential as an indicator of infection and possible value for clinical risk stratification.

This retrospective study examined the perception and predictors of health locus of control (LOC) in 71 individuals with a spinal cord injury (SCI) at discharge from inpatient rehabilitation (average age 39 years, 77% male, 54% black, 51% cervical SCI, 51% incomplete SCI and average time post-SCI 20 days). We also determined if health LOC beliefs and predictors change over the 1st year after SCI in a representative subsample of 36 individuals. The participants completed surveys regarding the health LOC, self-esteem and depression whereas demographic and SCI information were retrieved from medical records. At inpatient discharge, 55% of SCI individuals endorsed the doctor LOC category compared to internal (14%), other people (6%), chance (3%) or multiple LOC categories (22%). A similar pattern was found at 1-year postinjury (doctor LOC 44% and non-doctor LOC combined 56%). A backward stepwise regression revealed that white race (P = 0.093), >12 years of education (P = 0.001) and cervical level of SCI (P = 0.033) were significant predictors of the doctor LOC category at inpatient discharge (overall classification accuracy 76%). Similarly, >12 years of education (P = 0.055), cervical level of SCI at inpatient discharge (P = 0.033) and higher self-esteem at 1-year post-SCI (P = 0.113) were significant predictors of the doctor LOC category at 1-year post-SCI (overall classification accuracy 78%). We conclude that health LOC remains stable over the 1st year after SCI with the majority of individuals believing that the control over outcomes of their health is in the hands of doctors. These findings have implications for the provision of rehabilitation services after SCI.

One of the ways to implement practical plans in planning and protecting them is to be aware of the ecological potential of these resources and to investigate the factors affecting their degradation. In this Hyrcanian forest study in northern Iran, a study was conducted to determine the ecological capability of the forest based on three methods: AHP, fuzzy gamma and regression based on GIS and weighting of factors with AHP model. Then, using field data and stepwise regression model, a region decline map was constructed. The validation results of the three methods using the ROC model showed that the fuzzy gamma estimates the region capability with the highest accuracy and only 27% of the region has high and very high capability. Livestock grazing, climate change, land use change and diseases have played the most important role in the decline. The decline map developed along with the capability map can be helpful to any management of oak forests in the area to reduce decline factors and prioritize areas for conservation and support programs.

The aim of this study is to determine the predictive strength of the students’ characteristics and their teaching-learning process on the physics course learning level. The variables were also examined according to the academic achievement levels. The study is a descriptive study in survey model. Purposive sampling method was used in the formation of the study group, and 621 ninth grade students were selected for the study group. Cognitive Entry Behaviors (CEB) test, Academic Self-Concept (ASC) scale, Perception of Teaching Service Adequacy (PTSA) scale, Time Allotted to Learning (TAL) form and Learning Level (LL) test were used to collect the data. One-way variance, simple linear regression, and stepwise regression technique were used in the data analysis. The results of the study showed that the predictive strength of the variables examined about the student and the teaching-learning process was significant. The variability in the learning level is 59.9% in CEB, 18.1% in ASC, and 17.1% in PTSA. The relationship between the individual time allocated to learning and the learning level is negative, and has the power to explain the variability in learning level by 2.9%. All variables have the power to explain the variability in learning level by 62.7% together. One of the most important results of this study is that the magnitude of the predictive power of cognitive entry behaviors on the learning level has been revealed. It is thought that the results of the study will contribute to the literature aimed at increasing the learning level of the physics course.

OBJECTIVE Although several reports have described adjacent-segment disease (ASD) after posterior lumbar interbody fusion (PLIF), there have been only a few reports focusing on early-onset ASD occurring within 3 years after primary PLIF. The purpose of this study was to investigate the prevalence and postoperative pathologies of early-onset ASD and its relation with radiological parameters such as segmental lordosis (SL). METHODS The authors reviewed a total of 256 patients who underwent single-segment PLIF at L4–5 for degenerative lumbar spondylolisthesis (DLS) and were followed up for at least 5 years. The definition of ASD was a symptomatic condition requiring an additional operation at the adjacent fusion segment in patients who had undergone PLIF. ASD occurring within 3 years after primary PLIF was categorized as early-onset ASD. As a control group, 54 age- and sex-matched patients who had not suffered from ASD for more than 10 years were selected from this series. RESULTS There were 42 patients with ASD at the final follow-up. ASD prevalence rates at 3, 5, and 10 years postoperatively and at the final follow-up were 5.0%, 8.2%, 14.1%, and 16.4%, respectively. With respect to ASD pathologies, lumbar disc herniation (LDH) was significantly more common in early-onset ASD, while lumbar spinal stenosis and DLS occurred more frequently in late-onset ASD. Significant differences were detected in the overall postoperative range of motion (ROM) and in the changes in ROM (ΔROM) at L3–4 (the cranial adjacent fusion segment) and changes in SL (ΔSL) at L4–5 (the fused segment), while there were no significant differences in other pre- and postoperative parameters. In stepwise logistic regression analysis, ΔSL was identified as an independent variable (p = 0.008) that demonstrated significant differences, especially in early-onset ASD (control 1.1° vs overall ASD −2.4°, p = 0.002; control 1.1° vs early-onset ASD −6.6°, p = 0.00004). CONCLUSIONS The study results indicated that LDH was significantly more common as a pathology in early-onset ASD and that ΔSL was a major risk factor for ASD, especially early-onset ASD.

BACKGROUND: Several groups have proposed features to identify low-risk patients who may benefit from endoscopic kidney-sparing surgery in upper tract urothelial carcinoma (UTUC). OBJECTIVE: To evaluate standard risk stratification features, develop an optimal model to identify ≥pT2/N+ stage at radical nephroureterectomy (RNU), and compare it with the existing unvalidated models. DESIGN, SETTING, AND PARTICIPANTS: This was a collaborative retrospective study that included 1214 patients who underwent ureterorenoscopy with biopsy followed by RNU for nonmetastatic UTUC between 2000 and 2017. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: We performed multiple imputation of chained equations for missing data and multivariable logistic regression analysis with a stepwise selection algorithm to create the optimal predictive model. The area under the curve and a decision curve analysis were used to compare the models. RESULTS AND LIMITATIONS: Overall, 659 (54.3%) and 555 (45.7%) patients had ≤pT1N0/Nx and ≥pT2/N+ disease, respectively. In the multivariable logistic regression analysis of our model, age (odds ratio [OR] 1.02, 95% confidence interval [CI] 1.0-1.03, p = 0.013), high-grade biopsy (OR 1.81, 95% CI 1.37-2.40, p

Body weight (BW) and linear body measurements of 120 Nigerian Nsukka ecotype local hens in terms of body length (BL), chest girth (CG), thigh circumference (TC), thigh length (TL), and shank length (SL) were taken and subjected to Pearson correlation analysis to obtain the relationship between these growth traits. Multiple Linear Stepwise Regression procedure was used to find the best linear combination of the linear body measurements that can best predict BW. The findings showed that linear body measurement traits of Nigerian heavy ecotype local hens were strongly (p < 0.01) and positively correlated (0.5 < r) except thigh length which recorded a moderate value (0.3 < r > 0.5). The regression equations of linear body measurement traits with BW were positive with coefficient of determination ranging from 60.5-74.6%. The stepwise analyses showed shank length as best single measurement with R2 = 0.605, while inclusion of our five predictors in an equation resulted in R2 = 0.746. Thus, the following equation was generated: BW = – 1485.70 + 82.06 SL + 58.34 BL + 38.70 TC + 8.39 CG + 3.85 TL. This showed that they accounted for 74.6% of the variations in the BW of Nigerian heavy ecotype local hens. The prediction equations generated from linear body measurement traits are positive and strong, therefore can accurately be employed to predict the relative BW of Nigerian heavy ecotype local hens during selection programs as well as in the market with the use of measuring tape.

Aims The aim of the study was to develop a simple and objective score using clinical variables and quantified perfusion measures to identify embolic stroke with large vessel occlusions. Methods Eligible patients from five centers participating in the International Stroke Perfusion Imaging Registry were included in this study. Patients were split into a derivation cohort (n = 213) and a validation cohort (n = 116). A score was developed according to the coefficients of independent predictors of embolic stroke from stepwise logistic regression model in the derivation cohort. The performance of the score was validated by assessing its discrimination and calibration. Results The independent predictors of embolic stroke made up the Chinese Embolic Stroke Score (CHESS). There were: history of atrial fibrillation (3 points), non-hypertension history (2 points), and delay time>6 s volume/delay time>3 s volume on perfusion imaging ≥0.23 (2 points). The AUC of CHESS in the derivation cohort and validation cohort were 0.87 and 0.79, respectively. Patients with a CHESS of 0 could be identified as low-risk of embolic stroke, with a CHESS of 2-4 could be identified as medium-risk and with a CHESS of 5-7 could be regarded as high-risk. The observed rate of embolic stroke of each risk group was well-calibrated with the predicted rate. Conclusion CHESS could reliably and independently identify embolic stroke as the cause of large vessel occlusion.

This study aimed to identify characteristics and movement-based tests that predict upper quadrant musculoskeletal injury (UQI) in military personnel over a 12-month follow-up.A prospective observational cohort study of military members (n = 494; 91.9% male) was conducted. Baseline predictors associated with UQI were gathered through surveys and movement-based tests. Survey data included demographic information, injury history, and biosocial factors. Movement-based tests include the following: Y Balance Tests (YBT), Functional Movement Screen, Selective Functional Movement Assessment lumbar multisegmental mobility, modified-modified Schober, side bridge, ankle mobility, modified Sorensen, and passive lumbar extension. Self-reported UQI was collected through monthly online surveys, and 87% completed the follow-up. Univariate associations were determined between potential predictors and UQI. A forward, stepwise logistic regression model was used to identify the best combination of predictors for UQI.Twenty-seven had UQI. Univariate associations existed with three demographic (smoking,1 previous UQI, baseline upper quadrant function ≤90%), three pain-related (Selective Functional Movement Assessment rotation, side bridge, hurdle step), and six movement-based variables (YBT upper quarter (UQ) superolateral worst score ≤57.75 cm, YBT-UQ composite worst score ≤81.1%, failed shoulder clearance, Sorenson72.14 s, in-line lunge total score15, and in-line lunge asymmetry1). Smoking, baseline upper quadrant function ≤90%, and YBT-UQ composite score ≤81.1% predicted UQI in the logistic regression while controlling for age and sex. Presenting two or more predictors resulted in good specificity (85.6%; odds ratio, 4.8; 95% confidence interval, 2.2-10.8), and at least one predictor resulted in 81.5% sensitivity (odds ratio, 3.2; 95% confidence interval, 1.2-8.7).A modifiable movement-based test (YBT-UQ), perceived upper limb function, and smoking predicted UQI. A specific (two or more) and sensitive (at least one predictor) model could identify persons at higher risk.

This study gives insight into the spatiotemporal variability of respirable PM concentrations around typically highly mechanized opencast coal mines in India and its influence on ambient air quality, which at present is scant in the literature. The results suggest that at a distance of 500 m from the pit boundary, the respirable PM concentrations were found higher than the background concentration (1.52, 1.80 and 1.89 times for PM10, PM2.5 and PM1 at Mine 1 and 1.4, 1.35 and 1.35 times for PM10, PM2.5 and PM1 at Mine 2) which suggests that residents up to and beyond 500 m from the mine are exposed to the PM emitted from mining activities. For PM2.5 and PM1 concentrations, RH was the most important determinant (PM2.5: 24.8%; PM1: 30.1%). Pit boundary PM concentration was the weakest determinant (7%) for the PM2.5 concentration in mine surroundings, and wind speed was found as the weakest determinant (6.6%) for PM1 concentration. Conversely, distance (20.8%) was the important determinant of PM10 concentrations. All the four predictors (RH, wind speed, distance and Pit boundary PM concentration) could explain 46–54% variability in the PM concentrations. Inclusion of pit boundary PM concentration as a predictor increased the prediction capability of the models (all the developed models have R2 > 0.40 at significance level p

Aims: This study aims to develop the first race‐specific and sex‐specific risk prediction models for heart failure with preserved (HFpEF) and reduced ejection fraction (HFrEF). Methods and results: We created a cohort of 1.8 million individuals who had an outpatient clinic visit between 2002 and 2007 within the Veterans Affairs (VA) Healthcare System and obtained information on HFpEF, HFrEF, and several risk factors from electronic health records (EHR). Variables were selected for the risk prediction models in a ‘derivation cohort’ that consisted of individuals with baseline date in 2002, 2003, or 2004 using a forward stepwise selection based on a change in C‐index threshold. Discrimination and calibration were assessed in the remaining participants (internal ‘validation cohort’). A total of 66 831 individuals developed HFpEF, and 92 233 developed HFrEF (52 679 and 71 463 in the derivation cohort) over a median of 11.1 years of follow‐up. The HFpEF risk prediction model included age, diabetes, BMI, COPD, previous MI, antihypertensive treatment, SBP, smoking status, atrial fibrillation, and estimated glomerular filtration rate (eGFR), while the HFrEF model additionally included previous CAD. For the HFpEF model, C‐indices were 0.74 (SE = 0.002) for white men, 0.76 (0.005) for black men, 0.79 (0.015) for white women, and 0.77 (0.026) for black women, compared with 0.72 (0.002), 0.72 (0.004), 0.77 (0.017), and 0.75 (0.028), respectively, for the HFrEF model. These risk prediction models were generally well calibrated in each race‐specific and sex‐specific stratum of the validation cohort. Conclusions: Our race‐specific and sex‐specific risk prediction models, which used easily obtainable clinical variables, can be a useful tool to implement preventive strategies or subtype‐specific prevention trials in the nine million users of the VA healthcare system and the general population after external validation.

We examined the relationship between patient-centered communication and cancer risk information avoidance and estimated the mediating role of self-efficacy in this relationship.Using nationally representative cross-sectional data from the U.S. Health Information National Trends Survey (N = 2033), this study aims to provide a comprehensive understanding of the relationship between patient-centered communication and cancer risk information avoidance via correlation analysis, stepwise regression models, and mediation analysis.Patient-centered communication was significantly negatively associated with cancer risk information avoidance (β= -0.09, p0.01) after controlling for gender, income, education, and cancer risk perception. Self-efficacy fully mediated the relationship of patient-centered communication with cancer risk information avoidance.Patient-centered communication can improve patients' self-efficacy, thereby preventing them from avoiding cancer risk information.The negative relationship between patient-centered communication and cancer risk information avoidance substantiates that improving patient-centered communication is a promising approach to support caregivers in their activities, reduce patients' subjective cancer burden, and even improve their health. To address cancer-related issues, policymakers can consider interventions from the external environment and internal personal cognition perspectives.

Zhouming Ren, Xinzheng Fu Department of Neurology, Haining People’s Hospital, Haining, Zhejiang, People’s Republic of ChinaCorrespondence: Xinzheng FuDepartment of Neurology, Haining People’s Hospital, No. 2 Qianjiang West Road, Haining, Zhejiang, People’s Republic of ChinaTel +86-15325739220Email fxzncfxzncfxznc@126.comPurpose: This research intended to identify significant risk factors of stroke among the elderly population in the United States using the k-means clustering method.Patients and Methods: In this cross-sectional study, we analyzed data of 4346 subjects aged ≥ 60 years using the National Health and Nutrition Examination Survey (NHANES) 2013– 2018 datasets. Questionnaire data, dietary data, and laboratory data were accessed to acquire measurements of the potential risk factors. A pre-defined classification method was used based on the Medical Condition Questionnaire to define the stroke group. K-means clustering analysis used all potential risk factors for differentiating both groups. A stepwise logistic regression analysis examined the association between significant risk factors and the odds of stroke.Results: Age (OR:1.053, 95% CI:1.029– 1.077), diabetes (OR: 28.019, 95% CI: 19.139– 41.020), glycohemoglobin (OR: 2.309, 95% CI: 1.818– 2.934), plasma fasting glucose (OR: 1.017, 95% CI: 1.010– 1.024), hypertension (OR: 2.343, 95% CI: 1.602– 3.426), dietary fiber consumption (OR:0.980, 95% CI:0.964– 0.995), and education level (OR:0.541, 95% CI: 0.411– 0.713) were identified as significant risk factor for stroke among the elderly population in the k-means clustering method. In the pre-defined grouping method, age (OR:1.093, 95% CI:1.054– 1.132), diabetes (OR:2.228, 95% CI: 1.432– 3.466), hypertension (OR:2.295, 95% CI:1.338– 3.938), and dietary fiber consumption (OR: 0.966, 95CI%:0.947– 0.985) were found to influence to the risk of stroke.Conclusion: Age, hypertension, dietary fiber consumption, and education level are the significant risk factors of stroke among elders aged > 60 years. Among all the risk factors, diabetes is the strongest predictor of stroke. Glycohemoglobin and plasma fasting glucose are also associated with stroke risks, implying that glycemic control is particularly crucial in stroke prevention and management among older adults.Keywords: diabetes, hyperglycemia, k-means clustering method

BACKGROUND Adequate total and meal-specific protein intake is considered an important prerequisite to preserve appendicular lean mass (ALM) in older adults and to prevent sarcopenia. OBJECTIVES We analyzed the meal-specific protein intake across the main meals between participants with normal vs. low ALM to BMI ratio (ALMBMI). METHODS 782 participants [59.6% men; median 69 (IQR: 65, 71) y] of the Berlin Aging Study II have been included in this analysis. ALM was assessed by dual X-ray absorptiometry. Low lean mass was defined as ALMBMI using recommended sex-specific cut-offs. A 5-day nutritional protocol was used to assess total and meal-specific protein intake. RESULTS Median total protein intake was 0.89 (IQR: 0.74, 1.05) g/kg/d body weight (BW) in participants with low ALMBMI and 1.02 (IQR: 0.86, 1.21) g/kg BW in participants with normal ALMBMI (P

Purpose: To present a nomogram for prediction of overall survival (OS) in patients with locally advanced cervical cancer (LACC) undergoing definitive radiochemotherapy including image-guided adaptive brachytherapy (IGABT). Methods and Materials: Seven hundred twenty patients with LACC treated with radiochemotherapy including IGABT in 12 institutions (median follow-up 56 months) were analyzed; 248 deaths occurred. Thirteen candidate predictors for OS were a priori chosen on the basis of the literature and expert knowledge. Missing data (7.2%) were imputed using multiple imputation and predictive mean matching. Univariate analysis with a multivariable Cox regression model for OS stratified by center was performed. Stepwise selection of predictive factors with the Akaike Information Criterion was used to obtain a predictive model and construct a nomogram for OS predictions 60 months from diagnosis; this was internally validated by concordance probability as a measure of discrimination and a calibration plot. Results: Thirteen potential predictive factors were evaluated; 10 factors reached statistical significance in univariate analysis (age, Hemoglobin, FIGO Stage2009, tumor width, corpus involvement, lymph node involvement, concurrent chemotherapy, dose to 90% of the high-risk clinical target volume, volume of CTV at the first brachytherapy [CTVHRVolumeBT], overall treatment time [OTT]). Four factors were confirmed significant within the multivariable Cox regression model (FIGO Stage2009, lymph node involvement, concurrent chemotherapy, CTVHRVolumeBT). The predictive model and corresponding nomogram were based on 7 Akaike Information Criterion–selected factors (age, corpus involvement, FIGO Stage2009, lymph node involvement, concurrent chemotherapy, CTVHRVolumeBT, OTT) and showed promising calibration and discrimination (cross-validated concordance probability c = 0.73). Conclusions: This is the first nomogram to predict OS in patients with LACC treated with IGABT. In addition to previously reported factors (age, FIGO2009 stage, corpus involvement, chemotherapy delivery, OTT, lymph node involvement), status of primary tumor at the time of brachytherapy seems to be an essential outcome predictor. These results can facilitate individualized tailoring of treatment and patient counseling during the treatment.

BP neural network (BPNN) is widely used due to its good generalization and robustness, but the model has the defect that it cannot automatically optimize the input variables. In response to this problem, this study uses the grey relational analysis method to rank the importance of input variables, obtains the key variables and the best BPNN model structure through multiple training and learning for the BPNN models, and proposes a variable optimization selection algorithm combining grey relational analysis and BP neural network. The predicted values from the metabolic GM (1, 1) model for key variables was used as input to the best BPNN model for prediction modeling, and a grey BP neural network model prediction model (GR-BPNN) was proposed. The long short-term memory neural network (LSTM), convolutional neural network (CNN), traditional BP neural network (BP), GM (1, N) model, and stepwise regression (SR) are also implemented as benchmark models to prove the superiority and applicability of the new model. Finally, the GR-BPNN forecasting model was applied to the grain yield forecast of the whole province and subregions for Henan Province. The forecasting results found that the growth rate of grain production in Henan Province slowed down and the center of gravity for grain production shifted northwards.

Aim In the present study, the prevalence and predictors of symptomatic and full remission were investigated in patients with first-episode psychosis (FEP) at the 12-month follow-up. Methods A total of 308 participants aged 18-45 years fulfilled the study inclusion criteria and 214 completed the 12-month follow-up. Results At the 12-month follow-up, 67.3% (142) and 25.9% (55) of the FEP patients met the criteria for symptomatic and full remission, respectively. Stepwise logistic regression analysis showed a shorter duration of untreated psychosis (DUP), no family history, lower Positive and Negative Syndrome Scale (PANSS) negative symptom scores at baseline and higher familial support predicted symptomatic remission at the 12-month follow-up. A higher educational level, shorter DUP, lower PANSS general symptoms scores at baseline and higher subjective well-being under neuroleptics emotional regulation scores predicted full remission. Conclusions Our findings regarding the rates of symptomatic and full remission are consistent with previous studies. The results indicate a large discrepancy between symptomatic versus full remission rates at a 12-month follow-up in patients with FEP. Effective psychosocial interventions are necessary to improve the outcomes of FEP patients.

The basic approach of this research is to use an estimated series of effective reproduction number Rt and multiple series of index from Oxford COVID-19 Government Response Tracker (OxCGRT) to measure the effect of Japanese government’s response on COVID-19 epidemic by running a time-varying regression with flexible least squares method. Then, we use estimated series of time-varying coefficients obtained from the previous step as proxy variables for the government response’s effect and run stepwise regressions with policy indicators of OxCGRT to identify which specific policy can mitigate the spreading of the COVID-19 epidemic in Japan. The main finding is that the response of Japanese government on COVID-19 epidemic is basically effective. However, the effect of Japanese government’ policy is gradually weakening. Under our identification scheme, we find that policies of quarantine and movement restrictions are still most effective, but policies of public health system do not show much effectiveness in the regression analysis. Another important empirical finding is that policies of economic support are effective in reducing the spread of COVID-19. Within the framework of empirical strategy proposed in this paper, the conclusion should be explained in the context of the socio-political and health situation in Japan, but the methodology is assumed to be applicable to other countries and regions in the analysis of government performance of response to COVID-19.

This study aims to explore the influence of the training sample selection method and the presence and absence sample ratio on the study of landslide susceptibility by the logistic stepwise regression (LSR) model. Eight-kilometer range of the main road in Wenchuan County is taken as the research area, and the neighboring Mao County is considered to test the robustness of LSR models. The performance of the models obtained by randomly selecting (RS) 70% of the data as the training sample from the sample set and selecting the data as the training sample by tenfold cross-validation (CV) is compared. At the same time, the difference in model performance when the number of absence samples in the training samples is 1, 5, 7, and 10 times that of the presence samples is also studied. The results show that (1) the CV-LSR model is not only better than the RS-LSR model in terms of model prediction accuracy, but is also more stable and effective in factor selection; (2) when the number of absence samples in the training sample is too large, the model will underestimate the landslide susceptibility, and when the number of presence and absence samples is the same, the prediction effect of the model is the best.

Objectives: To examine the relationship between cerebrospinal fluid (CSF) biomarkers of Alzheimer’s disease (AD) and tap test response to elucidate the effects of comorbidity of AD in idiopathic normal-pressure hydrocephalus (iNPH). Design: Case–control study. Setting: Osaka University Hospital. Participants: Patients with possible iNPH underwent a CSF tap test. Measurements: Concentrations of amyloid beta (Aβ) 1–40, 1–42, and total tau in CSF were measured. The response of tap test was judged using Timed Up and Go test (TUG), 10-m reciprocation walking test (10MWT), Mini-Mental State Examination (MMSE), and iNPH grading scale. The ratio of Aβ1–42 to Aβ1–40 (Aβ42/40 ratio) and total tau concentration was compared between tap test-negative (iNPH-nTT) and -positive (iNPH-pTT) patients. Results: We identified 27 patients as iNPH-nTT and 81 as iNPH-pTT. Aβ42/40 ratio was significantly lower (mean [SD] = 0.063 [0.026] vs. 0.083 [0.036], p = 0.008), and total tau in CSF was significantly higher (mean [SD] = 385.6 [237.2] vs. 293.6 [165.0], p = 0.028) in iNPH-nTT than in iNPH-pTT. Stepwise logistic regression analysis revealed that low Aβ42/40 ratio was significantly associated with the negativity of the tap test. The response of cognition was significantly related to Aβ42/40 ratio. The association between Aβ42/40 ratio and tap test response, especially in cognition, remained after adjusting for disease duration and severity at baseline. Conclusions: A low CSF Aβ42/40 ratio is associated with a poorer cognitive response, but not gait and urinary response, to a tap test in iNPH. Even if CSF biomarkers suggest AD comorbidity, treatment with iNPH may be effective for gait and urinary dysfunction.

BACKGROUND The identification of factors associated with perioperative red blood cell (RBC) transfusion provides an opportunity to optimize the patient and surgical plan, and to guide perioperative crossmatch and RBC orders. We examined the association among potential bleeding risk factors and RBC requirements to develop a novel predictive model for RBC transfusion in patients undergoing brain tumor surgery. METHODS This retrospective study included 696 adults who underwent brain tumor surgery between 2008 and 2018. Multivariable logistic regression with backward stepwise selection for predictor selection was used during modeling. Model performance was evaluated using area under the receiver operating characteristic curve, and calibration was evaluated with Hosmer-Lemeshow goodness-of-fit χ2-estimate. RESULTS Preoperative hemoglobin level was inversely associated with the probability of RBC transfusion (odds ratio [OR]: 0.50; 95% confidence interval [CI]: 0.39-0.63; P

INTRODUCTION Fatigue is important, but ignored symptom among COPD patients. At present, there is very limited data are available for the prevalence of fatigue and its risk factors among COPD patients in China. OBJECTIVE The purpose of this study is to determine the prevalence of fatigue and to investigate the factors associated with fatigue among clinically stable patients with COPD in China. METHODS This is a cross-sectional study using a questionnaire to collect data on sociodemographic, related to COPD disease, and exercise habits. Multidimensional fatigue inventory (MFI-20) was used to assess the prevalence of fatigue. Independent samples t test, bivariate correlation, one-way ANOVA test, and test for several independent samples were used to compare the sociodemographic factors with MFI-20 scores of COPD patients. Multiple stepwise linear regression was performed to estimate influencing factors related to the MFI-20 of COPD patients. RESULTS Among the participants, the prevalence of fatigue was 88.62%. Negative correlations were found between FEV1% and multidimensional fatigue (r = -0.40, p

This research focuses on big experimental data analysis and the replenishment operation in a crane & shuttle based storage and retrieval system (C&SBS/RS). It is proposed that the order structure is expressed by three indicators: the order strength, the order size, and the order density. This aims to develop a regression function for the replenishment strategy to reveal the relationship between the order structure and replenishment parameters. The simulation model of the system is developed using FlexSim 7.1. A total of 13,440 combinations of order structures are provided as the simulation input. The statistical analyses are completed using the popular statistical software MINITAB. In this study, we use two different methods, best subset regression, and stepwise regression, to fit the regression function and find the best regression. According to the regression function, operation managers can develop better operation strategies.

Accurate liver necroinflammatory activity diagnosis could guide the clinical decision-making in chronic hepatitis B (CHB) patients.This study aimed to build a non-invasive diagnostic model for liver necroinflammatory activity by incorporating deep learning features and clinico-biochemical characteristics in CHB patients. A total of 239 CHB patients who underwent liver biopsy were recruited and randomly divided into a training cohort (n = 179) and an independent validation cohort (n = 60). Bidirectional stepwise selection identified independent clinico-biochemical characteristics. Multivariate logistic regression analysis was used to establish the final combined model by incorporating clinico-biochemical and deep learning features. Predictive performance was evaluated by discrimination and clinical usefulness. Immunoglobulin M, platelets, laminin, type IV collagen, gamma-glutamyl transferase, alanine aminotransferase, aspartate transaminase, alkaline phosphatase, direct bilirubin, and total bilirubin were identified as independent factors. The combined model exhibited better performance than models based on clinico-biochemical characteristics alone, with an AUC of 0.942 (95% confidence interval [CI], 0.912-0.969) for necroinflammatory activity ≥G2 and 0.885 (95% CI, 0.829-0.934) for ≥G3 in the training cohort, and 0.938 (95% CI, 0.867-0.993) and 0.854 (95% CI, 0.764-0.934) in the validation cohort, respectively. The decision curve confirmed its clinical usefulness. The combined model provided an accurate non-invasive prediction of liver necroinflammatory activity, which might contribute to clinical decision-making in CHB patients.

Semantic processing is important in language comprehension and production, and context can facilitate understanding and accelerate processing speed by pre-activating semantically related words. There are many studies suggesting that patients with schizophrenia have inferior language ability. This study was aimed to examine the differences between patients with schizophrenia and healthy people in semantic processing with Chinese classifier-noun pairs rating tasks. Participants were required to finish rating tasks to judge acceptability of classifier-noun pairs. Also, the Positive and Negative Syndrome Scale (PANSS) was conducted in the schizophrenia group. According to results of variance analysis, schizophrenic patients' accuracy of judgment on the acceptability of classifier-noun pairs differed from the control group (F = 4.13, p