Important Covariates Research Articles

Association between oral health and cognitive impairment in older adults: Insights from a Six-year prospective cohort study

ObjectiveThis study aims to investigate the relationships between four baseline oral conditions (periodontal status, dental caries, tooth wear, and dentition) and repeated global cognition or domain-specific cognition (memory, executive function, attention, and verbal fluency) in non-demented older adults over time. MethodsThis prospective cohort study (2011–2019) enrolled 516 non-demented community-dwelling older adults (age ≥ 65) to explore the association between oral health and cognitive function. Global and domain-specific cognition were assessed biennially (four repeats) using a battery of neuropsychological tests. The baseline oral health conditions were examined, including periodontal status, dental caries, tooth wear, and dentition. The association of these oral conditions with cognition was evaluated by generalized linear mixed models. Stratified analyses were performed by important covariates. ResultsOver time, dental caries was associated with poor memory in two different logical memory tests (β^= -0.06 and β^= -0.04). Incomplete dentition with less than 28 teeth was associated with poor performance in attention (β^= -0.05) and verbal fluency (β^= -0.03). These associations became more evident in those with an elevated inflammatory marker (IL-6, β^= -0.11 to -0.08). In contrast, tooth wear was associated with better memory in two different logical memory tests (β^= 0.33 and β^= 0.36) and better executive function (β^= 0.06) over time, and this association became more evident in those with the lowest inflammatory marker (IL-6, β^= 0.10). ConclusionsDental caries and incomplete dentition were associated with poor memory, attention, and verbal fluency performance. Conversely, tooth wear was associated with better memory performance and executive function. Clinical SignificanceFor early prevention of dementia, an evaluation of multiple dental and periodontal status in older adults helps predict the risk of dementia in the preclinical phase. Maintaining intact tooth structure without caries progression and eventually tooth loss may help prevent the worsening of memory, attention, and verbal fluency over time.

Decomposing Socioeconomic Effects on the Consumption of Calories and Macronutrients in Pakistan Between 2006 and 2016

BackgroundThis study examines the impact of socioeconomic factors on calorie intake and macronutrient composition at the household level in Pakistan from 2006 to 2016, using data from the Household Integrated Income and Consumption Survey (HIICS). By applying a copula-based decomposition method, it identifies key drivers such as urbanization, household size, paternal education, income, and cultivation, highlighting their roles in dietary changes and implications for public health. The findings are crucial for understanding nutritional shifts and addressing non-communicable diseases. ObjectivesThis study was conducted to assess the socioeconomic changes in total calorie intake per capita and calories obtained from macronutrients (fat, protein, and carbohydrates) at the household level in Pakistan. Methods: Cross-sectional data were taken from 2 national-level surveys published by the Pakistan Bureau of Statistics: the Household Integrated Economic Survey 2006 (14,948 households) and the Household Integrated Income and Consumption Survey 2016 (7842 households). Participants were from all 4 provinces of Pakistan. A copula-based decomposition method was applied to decompose the 10-y change in the distribution (mean, median, and quartiles) of the total calorie intake per capita and calories obtained from macronutrients. ResultsThe estimated results of decomposition revealed that total calorie intake per capita has increased on average and in the considered quartiles. The calories obtained from fat and carbohydrates have increased, whereas calories from protein have decreased, according to the distribution of the mean and quartile. The composition effect was negative for all outcome variables, and the main drivers of the composition effect were urbanization, household size, paternal education, income, and cultivation for all outcome variables. ConclusionsHousehold size and income are the most important covariates in an increase of total calories per capita and consumption of macronutrients, but urbanization, paternal education, and cultivation contribute negatively to the composition effect. Such findings are very important to inform researchers about nutritional change at the national level because the correlation between dietary change and risk factors for noncommunicable diseases such as heart disease and obesity is very strong.

Impella vs Veno-arterial extracorporeal membrane oxygenation in cardiogenic shock: a systematic review and meta-analysis of propensity score matched studies

Abstract Funding Acknowledgements Type of funding sources: Private company. Main funding source(s): Abiomed Singapore PTE. Ltd. Background/Introduction Cardiogenic shock is a life-threatening condition associated with multi-organ failure and death. Veno-arterial extracorporeal membrane oxygenation (VA-ECMO) is well-established in the management of cardiogenic shock; however, mortality rates have remained relatively consistent over the past two decades. Impella is a transluminal microaxial ventricular assist device that improves haemodynamics by unloading the left ventricle and represents an alternative to VA-ECMO. No randomised controlled trials exist to date comparing Impella versus VA-ECMO given difficulties in recruiting patients due to the acute nature of the condition. Whilst a plethora of non-randomised, comparative studies have been published, many are confounded by underlying differences in baseline disease characteristics. Propensity score matched / adjusted studies may mitigate confounding by accounting for differences in important covariates that impact outcomes. Purpose The objective of this study was to conduct a systematic review and meta-analysis of propensity score matched / adjusted studies to assess the clinical outcomes of Impella versus VA-ECMO in cardiogenic shock patients. Methods A systematic review was undertaken to identify all comparative studies of Impella and VA-ECMO in cardiogenic shock which performed propensity score matching / adjustment of outcomes to account for important covariates. The study limitations were assessed using the GRADE (Grading of Recommendations, Assessment, Development and Evaluations) approach. In-hospital/30-day survival and bleedings requiring transfusion were meta-analysed using the random effects method. Results Six propensity score matched / adjusted studies comparing Impella versus VA-ECMO in cardiogenic shock were included in the meta-analysis. A statistically significant difference in in-hospital/30-day mortality was detected between patients treated with Impella (39.6%) versus VA-ECMO (53.8%) (odds ratio [OR] 95% confidence interval [95% CI]: 0.57 [0.44, 0.74]; p<0.0001). Similarly, Impella (19.9%) was associated with statistically significantly fewer bleeding events requiring transfusion compared with VA-ECMO (28.8%) (OR=0.61 [0.46, 0.80]; p=0.0004). Conclusion(s) This systematic review identified no randomised controlled trials of Impella versus VA-ECMO. The meta-analysis of propensity matched observational trials, represents the highest level of evidence available to date, and suggests that Impella may be associated with increased survival rates and decreased bleeding. Randomised controlled trials are urgently needed to assess cardiac support modalities in cardiogenic shock.

First insights into the spatio-temporal ecology of sympatric large carnivores in Niokolo-Koba National Park, Senegal

Abstract Large carnivores play a crucial role in their native ecosystems, but their populations are rapidly declining across the African continent. West Africa is no exception, with large protected areas often forming the last strongholds for these species. Little is known about the population status and ecology of large carnivores in the region, hampering the design and implementation of effective conservation strategies. We conducted a camera-trap survey during the dry season in Niokolo-Koba National Park, the largest terrestrial protected area in Senegal and the second largest in West Africa, to investigate the spatio-temporal ecology of the four large carnivores inhabiting the Park: the spotted hyaena Crocuta crocuta, leopard Panthera pardus, West African lion Panthera leo leo and African wild dog Lycaon pictus. Spotted hyaenas and leopards had the widest spatial distribution and highest probability of site use. Spotted hyaena site use was positively associated with leopard relative abundance index and negatively associated with normalized difference vegetation index, whereas only distance to the nearest road influenced leopard site use. Distance to the Gambian River was the most important covariate positively affecting site use by lions. African wild dog site use was negatively associated with the relative abundance indices of lions and leopards. Lions, spotted hyaenas and leopards showed strong overlap in their activity patterns. By providing new information on the ecology of large carnivores in West Africa, including where they range and which habitats are critical for their survival, our study will facilitate conservation planning. Our findings lay the foundations for future research to conserve these threatened species in West Africa effectively and to guide ranger patrol efforts, which are key for their long-term survival.

Health literacy and its association with the control of hypertension

Health literacy (HL) involves self-care skills of healthy people and those with chronic diseases. To assess the association between HL and blood pressure (BP) control in patients with hypertension. A cross-sectional survey was conducted from April to June 2022 in a family medicine unit from the Mexican Institute for Social Security (IMSS). This survey included 349 adults of 18 years of age or older with hypertension diagnosis and antihypertensive pharmacological treatment. HL was measured with the European Health Literacy Survey HLS-EU-Q12M, validated in Mexico. The characteristics of participants with and without BP control were compared with chi-square. Multiple Poisson regression was applied to assess the association between HL and BP control, and it was controlled based on the clinically important covariates. 60.2% of patients with hypertension had BP controlled; 23.8% had inadequate HL, 44.4% problematic HL, and 31.8% sufficient HL. Sufficient HL was associated with BP control only in patients with high school or higher education (adjusted prevalence ratio 2.18, 95% confidence interval 1.16-4.08). Only 60% of hypertensive patients achieve control of their BP. Sufficient HL is associated with greater likelihood of BP control in patients with high school or higher education.

Macroevolution of the plant-hummingbird pollination system.

Plant-hummingbird interactions are considered a classic example of coevolution, a process in which mutually dependent species influence each other's evolution. Plants depend on hummingbirds for pollination, whereas hummingbirds rely on nectar for food. As a step towards understanding coevolution, this review focuses on the macroevolutionary consequences of plant-hummingbird interactions, a relatively underexplored area in the current literature. We synthesize prior studies, illustrating the origins and dynamics of hummingbird pollination across different angiosperm clades previously pollinated by insects (mostly bees), bats, and passerine birds. In some cases, the crown age of hummingbirds pre-dates the plants they pollinate. In other cases, plant groups transitioned to hummingbird pollination early in the establishment of this bird group in the Americas, with the build-up of both diversities coinciding temporally, and hence suggesting co-diversification. Determining what triggers shifts to and away from hummingbird pollination remains a major open challenge. The impact of hummingbirds on plant diversification is complex, with many tropical plant lineages experiencing increased diversification after acquiring flowers that attract hummingbirds, and others experiencing no change or even a decrease in diversification rates. This mixed evidence suggests that other extrinsic or intrinsic factors, such as local climate and isolation, are important covariables driving the diversification of plants adapted to hummingbird pollination. To guide future studies, we discuss the mechanisms and contexts under which hummingbirds, as a clade and as individual species (e.g. traits, foraging behaviour, degree of specialization), could influence plant evolution. We conclude by commenting on how macroevolutionary signals of the mutualism could relate to coevolution, highlighting the unbalanced focus on the plant side of the interaction, and advocating for the use of species-level interaction data in macroevolutionary studies.

Elucidating vaccine efficacy using a correlate of protection, demographics, and logistic regression

BackgroundVaccine efficacy (VE) assessed in a randomized controlled clinical trial can be affected by demographic, clinical, and other subject-specific characteristics evaluated as baseline covariates. Understanding the effect of covariates on efficacy is key to decisions by vaccine developers and public health authorities.MethodsThis work evaluates the impact of including correlate of protection (CoP) data in logistic regression on its performance in identifying statistically and clinically significant covariates in settings typical for a vaccine phase 3 trial. The proposed approach uses CoP data and covariate data as predictors of clinical outcome (diseased versus non-diseased) and is compared to logistic regression (without CoP data) to relate vaccination status and covariate data to clinical outcome.ResultsClinical trial simulations, in which the true relationship between CoP data and clinical outcome probability is a sigmoid function, show that use of CoP data increases the positive predictive value for detection of a covariate effect. If the true relationship is characterized by a decreasing convex function, use of CoP data does not substantially change positive or negative predictive value. In either scenario, vaccine efficacy is estimated more precisely (i.e., confidence intervals are narrower) in covariate-defined subgroups if CoP data are used, implying that using CoP data increases the ability to determine clinical significance of baseline covariate effects on efficacy.ConclusionsThis study proposes and evaluates a novel approach for assessing baseline demographic covariates potentially affecting VE. Results show that the proposed approach can sensitively and specifically identify potentially important covariates and provides a method for evaluating their likely clinical significance in terms of predicted impact on vaccine efficacy. It shows further that inclusion of CoP data can enable more precise VE estimation, thus enhancing study power and/or efficiency and providing even better information to support health policy and development decisions.

Digital soil mapping for soil types using machine learning approaches at the landscape scale in the arid regions of Iran

The aims of this research are (i) to compare random forest (RF), boosted regression tree (BRT), and multinomial logistic regression (MnLR) models to prepare the prediction maps of soil great group and subgroup levels, (ii) determination of the most important environmental covariates influencing the production of digital soil mapping (DSM) in an arid climate, (iii) to evaluate the efficiency of spectra indices extracted from Sentinel-2A digital images and data capability of ALOS-PALSAR radar data, and (iv) investigating the effect of sub-surface genetic horizons in the modeling of different types of soil map classes distribution. The principal component analysis method was employed to select the best set from the pool of environmental covariates (n = 46) such as geomorphometric parameters (GPs), RS indices, and diagnostic soil properties (DSP). The relative importance results indicate that Gypsic (GYP) subsurface horizon, standardized height (StH), slope length (SL), and normalized different vegetation index (NDVI) had an important role in the prediction of soil classes compared to the other selected covariates. DSM methodology was used in this research by incorporating of RF model and representative soil-forming factors that can be used for preparing the maps of soil classes in low-relief areas with a similar soil-landscape relationship. Totally, in this study places a spotlight on the profound impact of sub-surface genetic horizons, shedding light on their pivotal role in accurately modeling soil class distributions. These findings not only advance our comprehension of soil variability in arid regions but also hold immense implications for the burgeoning field of pedometrics.

Time-Varying Clearance in Milrinone Pharmacokinetics from Premature Neonates to Adolescents.

Milrinone is an inotrope and vasodilator used for prophylaxis or treatment of low cardiac output syndrome after weaning from cardiopulmonary bypass (CPB). It is renally eliminated and has an acceptable therapeutic range of 100-300μg/L, but weight-based dosing alone is associated with poor target attainment. We aimed to develop a population pharmacokinetic model for milrinone from premature neonates to adolescents, and to evaluate how age, renal function and recovery from CPB may impact dose selection. Fifty paediatric patients (aged 4 days to 16 years) were studied after undergoing cardiac surgery supported by CPB. Data from 29 premature neonates (23-28 weeks' postmenstrual age) treated for prophylaxis of low systemic blood flow were available for a pooled pharmacokinetic analysis. Population parameters were estimated using non-linear mixed effects modelling (NONMEM 7.5.1). There were 369 milrinone measurements available for analysis. A one-compartment model with zero-order input and first-order elimination was used to describe milrinone disposition. Population parameters were clearance 17.8L/70kg [95% CI 15.8-19.9] and volume 20.4L/h/70kg [95% CI 17.8-22.1]. Covariates included size, postmenstrual age and renal function for clearance, and size and postnatal age for volume. Milrinone clearance is reduced by 39.5% [95% CI 24.0-53.7] immediately after bypass, and recovers to baseline clearance with a half-time of 12.0h [95% CI 9.7-15.2]. Milrinone volume was 2.07 [95% CI 1.87-2.27] times greater at birth than the population standard and decreased over the first days of life with a half-time of 0.977days [95% CI 0.833-1.12]. Milrinone is predominately renally eliminated and so renal function is an important covariate describing variability in clearance. Increasing clearance over time likely reflects increasing cardiac output and renal perfusion due to milrinone and return to baseline following CPB.

Using explainable AI to investigate electrocardiogram changes during healthy aging-From expert features to raw signals.

Cardiovascular diseases remain the leading global cause of mortality. Age is an important covariate whose effect is most easily investigated in a healthy cohort to properly distinguish the former from disease-related changes. Traditionally, most of such insights have been drawn from the analysis of electrocardiogram (ECG) feature changes in individuals as they age. However, these features, while informative, may potentially obscure underlying data relationships. In this paper we present the following contributions: (1) We employ a deep-learning model and a tree-based model to analyze ECG data from a robust dataset of healthy individuals across varying ages in both raw signals and ECG feature format. (2) We use explainable AI methods to identify the most discriminative ECG features across age groups.(3) Our analysis with tree-based classifiers reveals age-related declines in inferred breathing rates and identifies notably high SDANN values as indicative of elderly individuals, distinguishing them from younger adults. (4) Furthermore, the deep-learning model underscores the pivotal role of the P-wave in age predictions across all age groups, suggesting potential changes in the distribution of different P-wave types with age. These findings shed new light on age-related ECG changes, offering insights that transcend traditional feature-based approaches.

Association of Arterial Stiffness With Mid- to Long-Term Home Blood Pressure Variability in the Electronic Framingham Heart Study: Cohort Study.

Short-term blood pressure variability (BPV) is associated with arterial stiffness in patients with hypertension. Few studies have examined associations between arterial stiffness and digital home BPV over a mid- to long-term time span, irrespective of underlying hypertension. This study aims to investigate if arterial stiffness traits were associated with subsequent mid- to long-term home BPV in the electronic Framingham Heart Study (eFHS). We hypothesized that higher arterial stiffness was associated with higher home BPV over up to 1-year follow-up. At a Framingham Heart Study research examination (2016-2019), participants underwent arterial tonometry to acquire measures of arterial stiffness (carotid-femoral pulse wave velocity [CFPWV]; forward pressure wave amplitude [FWA]) and wave reflection (reflection coefficient [RC]). Participants who agreed to enroll in eFHS were provided with a digital blood pressure (BP) cuff to measure home BP weekly over up to 1-year follow-up. Participants with less than 3 weeks of BP readings were excluded. Linear regression models were used to examine associations of arterial measures with average real variability (ARV) of week-to-week home systolic (SBP) and diastolic (DBP) BP adjusting for important covariates. We obtained ARV as an average of the absolute differences of consecutive home BP measurements. ARV considers not only the dispersion of the BP readings around the mean but also the order of BP readings. In addition, ARV is more sensitive to measurement-to-measurement BPV compared with traditional BPV measures. Among 857 eFHS participants (mean age 54, SD 9 years; 508/857, 59% women; mean SBP/DBP 119/76 mm Hg; 405/857, 47% hypertension), 1 SD increment in FWA was associated with 0.16 (95% CI 0.09-0.23) SD increments in ARV of home SBP and 0.08 (95% CI 0.01-0.15) SD increments in ARV of home DBP; 1 SD increment in RC was associated with 0.14 (95% CI 0.07-0.22) SD increments in ARV of home SBP and 0.11 (95% CI 0.04-0.19) SD increments in ARV of home DBP. After adjusting for important covariates, there was no significant association between CFPWV and ARV of home SBP, and similarly, no significant association existed between CFPWV and ARV of home DBP (P>.05). In eFHS, higher FWA and RC were associated with higher mid- to long-term ARV of week-to-week home SBP and DBP over 1-year follow-up in individuals across the BP spectrum. Our findings suggest that higher aortic stiffness and wave reflection are associated with higher week-to-week variation of BP in a home-based setting over a mid- to long-term time span.

Estimation and selection for spatial zero‐inflated count models

AbstractThe count data arise in many scientific areas. Our concerns here focus on spatial count responses with an excessive number of zeros and a set of available covariates. Estimating model parameters and selecting important covariates for spatial zero‐inflated count models are both essential. Importantly, to alleviate deviations from model assumptions, we propose a spatial zero‐inflated Poisson‐like methodology to model this type of data, which relies only on assumptions for the first two moments of spatial count responses. We then design an effective iterative estimation procedure between the generalized estimating equation and the weighted least squares method to respectively estimate the regression coefficients and the variogram of the data model. Moreover, the stabilization of estimators is evaluated via a block jackknife technique. Furthermore, a distribution‐free model selection criterion based on an estimate of the mean squared error of the estimated mean structure is proposed to select the best subset of covariates. The effectiveness of the proposed methodology is demonstrated by simulation studies under various scenarios, and a real dataset regarding the number of maternal deaths in Mozambique is analyzed for illustration.

Modeling Geospatial Distribution of Peat Layer Thickness Using Machine Learning and Aerial Laser Scanning Data

Organic horizons including peat deposits are important terrestrial carbon pools, and various chemical, biological, and water exchange processes take place within them. Accurate information on the spatial distribution of organic soils and their properties is important for decision-making and land management. In this study, we present a machine learning approach for mapping the distribution of organic soils and determining the thickness of the peat layer using more than 24,000 peat layer thickness measurements obtained from field data, airborne laser scanning (ALS) data and various indices obtained from therein, as well as other cartographic materials. Our objectives encompassed two primary aims. Firstly, we endeavored to develop updated cartographic materials depicting the spatial distribution of peat layers. Secondly, we aimed to predict the depth of peat layers, thereby enhancing our understanding of soil organic carbon content. Continentality, a wet area map, latitude, a depth to water map with catchment area of 10 ha, and a digital elevation model were the most important covariates for the machine learning model. As a result, we obtained a map with three peat layer thickness classes, an overall classification accuracy of 0.88, and a kappa value of 0.74. This research contributes to a better understanding of organic soil dynamics and facilitates improved assessments of soil organic carbon stocks.

Brain volumes, behavioral inhibition, and anxiety disorders in children: results from the adolescent brain cognitive development study

BackgroundMagnetic resonance imaging (MRI) studies have identified brain changes associated with anxiety disorders (ADs), but the results remain mixed, particularly at a younger age. One key predictor of ADs is behavioral inhibition (BI), a childhood tendency for high avoidance of novel stimuli. This study aimed to evaluate the relationships between candidate brain regions, BI, and ADs among children using baseline data from the Adolescent Brain Cognitive Development (ABCD) study.MethodsWe analyzed global and regional brain volumes of 9,353 children (9–10 years old) in relation to BI and current ADs, using linear mixed models accounting for family clustering and important demographic and socioeconomic covariates. We further investigated whether and how past anxiety was related to brain volumes.ResultsAmong included participants, 249 (2.66%) had a current AD. Larger total white matter (Beta = -0.152; 95% CI [-0.281, -0.023]), thalamus (Beta = -0.168; 95% CI [-0.291, -0.044]), and smaller hippocampus volumes (Beta = 0.094; 95% CI [-0.008, 0.196]) were associated with lower BI scores. Amygdala volume was not related to BI. Larger total cortical (OR = 0.751; 95% CI [0.580;0.970]), amygdala (OR = 0.798; 95%CI [0.666;0.956]), and precentral gyrus (OR = 0.802; 95% CI [0.661;0.973]) volumes were associated with lower odds of currently having ADs. Children with past ADs had smaller total white matter and amygdala volumes.ConclusionsThe results show associations between brain volumes and both BI and ADs at an early age. Importantly, results suggest that ADs and BI have different neurobiological correlates and that earlier occurrences of ADs may influence brain structures related to BI and ADs, motivating research that can better delineate the similarities and divergence in the neurobiological underpinnings and building blocks of BI and ADs across their development in early life.

Precarious employment in young adulthood and later alcohol-related morbidity: a register-based cohort study

ObjectivesThe prevalence of precarious employment is increasing, particularly among young adults where less is known about the long-term health consequences. The present study aims to test if being precariously employed...

Bayesian relative composite quantile regression approach of ordinal latent regression model with L1/2 regularization

AbstractOrdinal data frequently occur in various fields such as knowledge level assessment, credit rating, clinical disease diagnosis, and psychological evaluation. The classic models including cumulative logistic regression or probit regression are often used to model such ordinal data. But these modeling approaches conditionally depict the mean characteristic of response variable on a cluster of predictive variables, which often results in non‐robust estimation results. As a considerable alternative, composite quantile regression (CQR) approach is usually employed to gain more robust and relatively efficient results. In this paper, we propose a Bayesian CQR modeling approach for ordinal latent regression model. In order to overcome the recognizability problem of the considered model and obtain more robust estimation results, we advocate to using the Bayesian relative CQR approach to estimate regression parameters. Additionally, in regression modeling, it is a highly desirable task to obtain a parsimonious model that retains only important covariates. We incorporate the Bayesian penalty into the ordinal latent CQR regression model to simultaneously conduct parameter estimation and variable selection. Finally, the proposed Bayesian relative CQR approach is illustrated by Monte Carlo simulations and a real data application. Simulation results and real data examples show that the suggested Bayesian relative CQR approach has good performance for the ordinal regression models.

Positive fluid balance and poor outcomes after initial intensive care unit admission in sepsis resuscitation: a retrospective study.

Fluid resuscitation of patients with sepsis is crucial. This study explored the role of fluid balance in the early resuscitation of sepsis patients in the intensive care unit (ICU). A retrospective study of patients with sepsis using the Peking Union Medical College Hospital Intensive Care Medical Information System and Database from January 2014 to June 2020 was performed. Based on the survival status on day 28, the training cohort was divided into an alive group (n = 1,803) and a deceased group (n = 429). Univariate and multivariate analyses were used to identify risk factors, and the integrated learning XGBoost algorithm was used to construct a model for predicting outcomes. ROC and Kaplan-Meier survival curves were used to evaluate the effectiveness of the model. A verification cohort (n = 433) was used to verify the model. Univariate analysis showed that fluid balance is an important covariate. Based on the scatterplot distribution, a significant difference in mortality was determined between groups stratified with a balance of 1000 ml. There were associations in the multivariate analysis between poor outcomes and sex, PO2/FiO2, serum creatinine, FiO2, platelets, respiratory rate, SPO2, temperature, and total fluid volume (1000 ml). Among these variables, total fluid balance (1000 ml) had an OR of 1.98 (CI: 1.41-2.77, p < 0.001). Therefore, the model was built with these nine factors using XGBoost. Cross validation was used to verify generalizability. This model performed better than the SOFA and APACHE II models. The result was well verified in the verification cohort. A causal forest model suggested that patients with hypoxemia may suffer from positive fluid balance. Sepsis fluid resuscitation in the ICU should be a targeted and goal-oriented treatment. A new prognostic prediction model was constructed and indicated that a 6-hour positive fluid balance after ICU initial admission is a risk factor for poor outcomes in sepsis patients. A 6-hour fluid balance above 1000 ml should be performed with caution.

Variable selection in Bayesian multiple instance regression using shotgun stochastic search

In multiple instance learning (MIL), a bag represents a sample that has a set of instances, each of which is described by a vector of explanatory variables, but the entire bag only has one label/response. Though many methods for MIL have been developed to date, few have paid attention to interpretability of models and results. The proposed Bayesian regression model stands on two levels of hierarchy, which transparently show how explanatory variables explain and instances contribute to bag responses. Moreover, two selection problems are simultaneously addressed; the instance selection to find out the instances in each bag responsible for the bag response, and the variable selection to search for the important covariates. To explore a joint discrete space of indicator variables created for selection of both explanatory variables and instances, the shotgun stochastic search algorithm is modified to fit in the MIL context. Also, the proposed model offers a natural and rigorous way to quantify uncertainty in coefficient estimation and outcome prediction, which many modern MIL applications call for. The simulation study shows the proposed regression model can select variables and instances with high performance (AUC greater than 0.86), thus predicting responses well. The proposed method is applied to the musk data for prediction of binding strengths (labels) between molecules (bags) with different conformations (instances) and target receptors. It outperforms all existing methods, and can identify variables relevant in modeling responses.

Test anxiety fluctuations during low-stakes secondary school assessments: The role of the needs for autonomy and competence over and above the number of tests

Test anxiety poses a fundamental educational challenge as it is associated with lower academic performance and well-being. Grounded in the Self-Determination Theory, this study will focus on test anxiety fluctuations in relation to low-stakes assessments and investigates whether fluctuations in students’ experiences of autonomy and competence satisfaction and frustration relate to their test anxiety. For this purpose, 253 secondary school students completed a survey at three different times throughout the second semester. Students' feelings of autonomy and competence in the classroom were administered as well as their test anxiety. Each student completed the same two test anxiety scales at each measurement occasion, with one scale consistently administered to all students and the other two scales randomly assigned between classes. Multilevel analyses revealed that students showed higher test anxiety in weeks in which their need for competence was more frustrated and when they had to take more low-stakes tests. This association was robust across the three test anxiety instruments and after considering important test anxiety covariates (e.g., gender and prior achievement). These findings imply that competence frustration is an important underlying mechanism of test anxiety that should be taken into account when designing anxiety-reducing interventions.

Abstract 3413: Physical activity and sedentary behavior of cancer survivors compared to individuals without cancer in NHANES

Abstract INTRODUCTION: Physical activity may improve quality of life and reduce mortality among cancer survivors. As cancer death rates continue to decrease, the population of cancer survivors is expected to grow, therefore making it important to study their health behaviors. Research has been conducted on prevalence of physical activity and sedentary behaviors of survivors of certain cancers, however, little research has been conducted comparing cancer survivors to individuals without cancer. The purpose of this study was to update previously published results from earlier National Health and Nutrition Examination Survey (NHANES) cycles (2007-2010) with more recent data comparing physical activity and sedentary behavior among cancer survivors and individuals without cancer. METHODS: Using the NHANES 2011-2018, we identified 1320 cancer survivors and 19,109 participants without cancer who provided data on recreational, work-related, and transportation-related physical activity and sedentary behavior. The included participants were over 20 years of age, not pregnant at the time of the survey, and were more than three years from their cancer diagnosis, if applicable. Comparisons between cancer survivors and individuals without cancer were conducted for duration of total physical activity (min/day) and sedentary time per day. To account for the complex sampling design, weighted multivariable analysis was used to calculate odds ratios (ORs) and 95% confidence intervals (CIs). RESULTS: Compared to individuals without cancer, cancer survivors reported less average ± standard error physical activity minutes per day (190.06 ± 5.24 and 147.29 ± 13.14, respectively), and slightly more sedentary hours per day (6.95 ± 0.09 and 7.56 ± 0.36, respectively). However, after adjusting for age, race and ethnicity, sex, educational status, body mass index, and smoking status, there was no association between physical activity duration or sedentary time and cancer survivor status. The OR (95%CI) for more than 60 minutes per day of physical activity was 1.00 (0.83, 1.20) compared to no physical activity, and for more than 8 hours of sedentary time per day was 1.00 (0.82, 1.21) compared to less than 5 hours per day. DISCUSSION: In a previous publication of earlier cycles of NHANES (2007-2010), cancer survivors reported higher physical activity but also higher sedentary time per day compared to individuals without cancer. However, these results were not supported in this updated analysis using more recent NHANES cycles (2011-2018). After adjustment for important covariates, we did not find differences between cancer survivors and individuals without cancer in reported physical activity or sedentary time. Further research in other populations is needed to corroborate these findings and document whether they reflect a growing awareness of the negative impact of sedentary behavior in cancer survivorship. Citation Format: Anna Marie Pavy, Susan E. Steck. Physical activity and sedentary behavior of cancer survivors compared to individuals without cancer in NHANES [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 3413.