Multiple Covariates Research Articles

Real-Time Use of SGLT2i Verified in Pre-dialysis: The RSVP Cross-sectional Study.

The use of sodium-glucose cotransporter 2 inhibitors (SGLT2i) in nephrology practice is increasingly becoming standard of care in patients with diabetes or those with proteinuria. The primary outcome was to identify the proportion of pre-dialysis patients with chronic kidney disease (CKD) G3a, G3b, or G4 prescribed an SGLT2i and describe their characteristics. This was a retrospective, multicentric, cross-sectional study of patients with CKD followed at 4 pre-dialysis clinics in the province of Quebec, Canada. We collected data of multiple covariates associated with prescribing SGLT2i in patients over 18 years of age with CKD G3a, G3b, or G4. We then performed a multivariate logistic regression to assess their associations. Of the 874 patients included, 22.7% were prescribed an SGLT2i. Factors most strongly associated included male sex (odds ratio [OR] = 4.88, 95% CI = 2.38-10.03), being prescribed metformin (OR = 4.30, 95% CI = 2.23-8.31), having type 2 diabetes (OR = 4.00, 95% CI = 1.86-8.62), or having an albumin-to-creatinine ratio greater than 300 mg/g (OR = 1.84, 95% CI = 1.08-3.14). The majority of patients (60.4%) had their SGLT2i initiated by the pre-dialysis clinic and the most frequent adverse event was an initial increase in serum creatinine 1 week after starting treatment (33.9%). An increasing number of patients with CKD are being prescribed SGLT2i. Nonetheless, significant disparities in sex, severity of disease, and comorbidities remain. We suggest that specific strategies be put in place to promote prescribing of SGLT2i in women and other at-risk populations, in particular among nephrology teams, to improve patient care.

Association of patient-reported cognitive impairment with quality of life and employment in multiple sclerosis

Background:Cognition is frequently affected in persons with multiple sclerosis (MS). Cognitive impairment (CI) is associated with decreased quality of life (QOL) and employment status. Yet, CI assessed using patient-reported outcome measures is not as well studied and is thought to be influenced by other symptoms. Health Utilities Index 3 (HUI3) is a multi-attribute health-status classification system that assesses 8 different single attributes, including cognition.Methods:The North American Consortium of Multiple Sclerosis (NARCOMS) Registry, a voluntary, self-report registry for persons with MS, Spring 2019 survey collected the HUI3 and self-reported assessment of health-related QOL (RAND-12), cognitive status, depression, fatigue, disability, employment, disease-modifying therapy use, and sociodemographic data. We assessed the relationship between patient-reported cognitive CI from the HUI3 (HUI-C), QOL, and employment while adjusting for factors previously associated with the outcomes. For employment outcomes, the cohort was limited to participants 65 years of age or younger.Results: Of the 6,227 respondents, 56.4 % reported cognitive difficulty with the HUI-C. After adjusting for multiple covariates, cognitive difficulty was associated with 1.2 point lower physical QOL for each 0.1 decrease in HUI-C (p < 0.0001). Mental QOL decreased by 2 points for each 0.1 decrease in HUI-C (p < 0.0001). Cognitive difficulty was associated with a 10 % decreased odds of employment in the multivariable model (p < 0.0001).Discussion: Patient-reported CI was associated with lower health-related and vocational outcomes for MS patients, even after accounting for age, income, depression, fatigue, and disability associated with cognition. The HUI-C is a single attribute score derived from the HUI3 that may facilitate the evaluation of CI in MS.

Propensity Score Analysis With Unreliable Covariates: A Comparison of Five Reliability-Adjustment Methods

Propensity score analysis (PSA) is a crucial tool for researchers in mitigating selection bias arising from multiple covariates in quasi-experimental studies. Nevertheless, the impact of low-reliability covariates on PSA necessitates careful consideration. This study employs Monte Carlo simulation to assess five methods to adjust propensity scores for unreliability of covariates. The findings reveal that the latent variable model incorporating inclusive factor scores (PSIF) results in the smallest relative bias of treatment effect estimates. Notably, only PSIF consistently provides unbiased treatment effect estimates across all conditions. Furthermore, the study underscores the potential for a misleading covariate balance evaluation when dealing with unreliable covariates, given that treatment effect estimates may be biased even when the covariate balance is perceived as adequate.

Associations of serum glucocorticoid levels on hypertension and blood pressure-related indicators: a nested case-control study in rural China.

The relationship between glucocorticoids and hypertension has shown inconsistent findings in previous studies. To address this, our study employed a nested case-control design in rural areas to further investigate the association between serum glucocorticoid levels and hypertension, and blood pressure-related indicators. This study employed a nested case-control design, involving 560 pairs of hypertensive cases and matched controls. The concentrations of serum cortisol (F), cortisone (E) and 11-deoxycortisol (S) were determined using liquid chromatography-tandem mass spectrometry. We employed various methods, including generalized linear model (GLM), conditional logistic regression model, restricted cubic spline regression, subgroup analysis, interaction, and joint effects, with adjustments for multiple covariates to analyze the relationships between glucocorticoids, hypertension, and blood pressure-related indicators. After multivariable adjustments, ln-F, ln-F/E, and ln-S were positively associated with SBP, DBP, pulse pressure (PP), and mean arterial pressure (MAP), while ln-E was negatively associated with DBP and MAP ( P < 0.05). Interestingly, ln-S showed no statistically significant association with hypertension prevalence ( P > 0.05), whereas ln-F and ln-F/E were positively associated with it ( P < 0.05). The adjusted odds ratios (ORs) and 95% confidence intervals (CIs) were 1.153 (1.011-1.315) for ln-F and 2.072 (1.622-2.645) for ln-F/E, respectively. In contrast, ln-E exhibited a negative association with hypertension prevalence (adjusted OR = 0.837, 95% CI 0.714-0.982). Moreover, a significant association was observed between the combined use of high-dose F/E and high-dose S with hypertension prevalence (adjusted OR = 3.273, 95% CI 2.013-5.321). Blood pressure indicators and hypertension prevalence significantly increased with elevated serum F and F/E concentrations ( P < 0.05). Interaction analysis further revealed that among women, the positive association between F/E and hypertension prevalence was more pronounced than in men ( P < 0.05), and S exhibited a more significant positive association with hypertension prevalence in the overweight population ( P < 0.05). Serum F/E and S levels demonstrated positive associations with hypertension and blood pressure-related indicators, and their combined influence exhibited a synergistic effect on hypertension. Notably, F, F/E, and S were associated with heightened hypertension risk, warranting particular attention in women and overweight populations.

Improved estimators in bell regression model with application

In this paper, we propose the application of shrinkage strategies to estimate coefficients in the Bell regression models when prior information about the coefficients is available. The Bell regression models are well-suited for modelling count data with multiple covariates. Furthermore, we provide a detailed explanation of the asymptotic properties of the proposed estimators, including asymptotic biases and mean squared errors. To assess the performance of the estimators, we conduct numerical studies using Monte Carlo simulations and evaluate their simulated relative efficiency. The results demonstrate that the suggested estimators outperform the unrestricted estimator when prior information is taken into account. Additionally, we present an empirical application to demonstrate the practical utility of the suggested estimators.

Shocks and Stability of Risk and Time Preferences among Poor Rural Households in Ethiopia

Abstract This paper examines the effects of shocks on temporal stability of risk and time preferences of rural households within a developing country context characterized by frequent droughts, chronic food insecurity, and low levels of education. Leveraging a three-wave large and diverse household panel data that elicits preferences every two years for more than 6,500 individuals from over 5,600 sample rural households in Ethiopia, we analyze the effects of droughts (self-reported) and rainfall shortfalls on household risk and time preferences. Our findings show that households become more risk-averse and impatient after experiencing drought and rainfall shocks; the change in risk preferences is more notable among households that experience recurrent droughts and multiple covariate shocks during the same period.

Trade-off between double cleavage-stage embryos transfer and single blastocyst-stage embryo transfer in patients with few good quality embryos in antagonist cycles: a retrospective study using a propensity score matching analysis

ObjectiveThis study aimed to compare the per OPU clinical outcomes for transfer of Day 3 double cleavage-stage embryos (DET) and Day 5 single blastocyst-stage (SBT) in patients with five or fewer good quality embryos on day 3 per occyte pick-up cycle (OPU) in antagonist cycles with consideration of blastocyst formation failure.MethodsThis was a retrospective, observational cohort study of 2,116 cases of OPU treated with antagonist protocol in the affiliated Chenggong Hospital of Xiamen University between January 2013 and December 2020. DET was performed in 1,811cycles and SBT was performed in 305 cycles. The DET group was matched to the SBT group by propensity score (PS) matching according to multiple maternal baseline covariates. After PS matching, there were 303 ET cycles in each group. The primary outcomes were the cumulative live birth rate (CLBR), cumulative multiple pregnancy rate(CMPR)per OPU and the number of ET to achieve live birth per OPU. Secondary outcomes were the percentage of clinical pregnancy(CPR), live birth rate(LBR), multiple pregnancy rate(MPR).ResultsFollowing PS mating, the CLBR was slightly higher (48.8% versus 40.3% ; P = 0.041) and the CMPR was significantly higher in the DET group compared to SBT group(44.2% versus 7.9%, P < 0.001). The CPR, LBR and MPR per fresh transfer were higher in DET group compared to SBT group(50.2% versus 28.7%; 41.3% versus 21.5%;29.6% versus 0%, P < 0.001). The number of ET to achieve live birth per OPU in SBT group was obiviously more than in DET group(1.48 ± 0.578 versus 1.22 ± 0.557 ,P < 0.001).ConclusionWith a marginal difference cumulative live birth rate, the lower live birth rate per fresh transfer and higher number of ET per OPU in the SBT group suggested that it might take longer time to achieve a live birth with single blastocyst strategy. A trade-off decision should be made between efficiency and safety.

Depression and dental caries in US adults, NHANES 2015–2018

PurposeThis study aimed to investigate the link between depression and untreated dental caries among adults in the United States.MethodsData were collected from the National Health and Nutrition Survey (2015–2018); respondents aged 20 years or older who completed a patient health questionnaire and underwent a comprehensive oral examination were included. Participants were categorized into three groups according to depressive symptoms as follows: those with no, mild, or moderate to severe depression. Data were weighted, and multiple potential covariates were included in the analysis to provide national estimates and account for the complex sample design. A multivariable weighted logistic regression model was performed to test the hypothesis that varying degrees of depression in American adults are associated with untreated dental caries. Subgroup analyses were performed based on age and gender after adjusting for potential covariates. A P value of <.05 was considered significant.ResultsAmong 8740 participants, the prevalence of untreated coronal and root caries was 20.50% and 12.92%, respectively. Moderate to severe depression was a significant risk factor (odds ratio, 1.25; 95% confidence interval, 1.09–1.66) for untreated root caries. The risk of untreated root caries increased by 87% in young adults (aged 20–44 years) and by 46% in women with moderate to severe depression. The suest analysis revealed that the impact of moderate to severe depressive disorder on untreated root caries was non-significantly different between the age subgroup (p=0.09) and sex subgroup (p=0.51). However, depression was non-significantly associated with untreated coronal caries (mild depression: OR, 1.07; 95% CI, 0.85–1.34; moderate to severe depression 1.06; 95% CI, 0.83–1.36; respectively).ConclusionThe results of this study suggested a significant association between moderate and severe depression and untreated root caries; however, the association with untreated coronal caries was non-significant. In the United States, moderate and severe depression in adults is associated with root caries.

ASSOCIATION BETWEEN CHINESE VISCERAL ADIPOSITY INDEX AND RISK OF CARDIOMETABOLIC MULTIMORBIDITY IN MIDDLE-AGED AND ELDERLY CHINESE POPULATION: EVIDENCE FROM A NATIONAL COHORT STUDY

Objective: This cohort study was designed to investigate the associations between Chinese visceral adiposity index (CVAI) and Cardiometabolic multimorbidity (CMM) among middle-aged and elderly Chinese population. Design and method: A national cohort study that consisted of 6419 participants was conducted during a median follow-up time of 4 years. Three obesity- and lipid-related indices, including visceral adiposity index (VAI), Chinese visceral adiposity index (CVAI), lipid accumulation product (LAP) were recorded. Our study used multivariate adjusted Cox proportional hazard models, sensitivity analysis and restricted cubic spline analysis to investigate the associations of the Chinese visceral adiposity index with Cardiometabolic multimorbidity. Results were reported in log-relative hazard ratios and associated 95% CIs. Results: Of the 6686 participants included in the analysis, 6419 participants did not develop CMM and incident CMM occurred in 267 participants during the study period. Using multivariable-adjusted restricted cubic spline analyses, it was found that there was a significant nonlinear relationship between the levels of CVAI and the incidence of cardiometabolic multimorbidity (P&lt;0.0001). The risk of CMM gradually increased when the CVAI increased, and rapid upward after exceeding 91.2. After adjustment of multiple covariates, a U-shaped association between CVAI and CMM was evident in all participants. The HR of CVAI gradually increases and is positively correlated with the increase of CVAI. Conclusions: The CVAI are independent predictors of CMM in the middle-aged and elderly Chinese population. Higher CVAI is positively associated with CMM risk.

The Impact of a Sterinberg Theory-Based Training Program on Secondary School Students' Development of Critical and Creative Thinking Skills

This study aimed to test the impact of a training program based on Sternberg's theory of problem solving on the development of critical and creative thinking skills among secondary school students, using a semi-experimental approach. The sample consisted of 70 students from the first grade of literary secondary school at Al-Baraa bin Malik Secondary School in the Directorate of Education of Naour in Jordan and was divided equally into two groups: experimental and control. The researcher built a training program based on Sternberg's theory of problem solving, known as the 'thinking circle', which consists of seven dimensions: feeling the problem, identifying the problem, representing the problem, choosing or inventing a solution strategy, effectively allocating resources for the solution, monitoring the problem solution, and evaluating the problem solution. The program was implemented during 11 six-week training sessions in the second semester of 2021–2022. The California 2000 Critical Thinking Scale and the Torrens Creative Thinking Scale (A) were also used in both the pre- and post-group assessment processes. To answer the questions of the study, it was found that the average performance of the groups was control and experimental for the pre-measurement of the critical thinking scale (10.55) and (10.75), respectively, while the average performance of the groups was on the dimensional measurement (11.67) and (17.78), respectively. To examine the differences in each of the arithmetic averages, the researcher used the analysis of covariance (ANCOVA) for performance for the total score of the critical thinking scale. The average performance of the control and experimental groups in the pre-evaluation of the creative thinking test was (71.68) and (70.35) respectively, and the average performance of the control and experimental groups in the dimensional measurement was (72.12) and (129.65) respectively, and the common multiple covariance analysis (MANCOVA) was used to test the differences in the arithmetic mean of performance in the sub-dimensions of the creative thinking test. It was found that there were statistically significant differences in the arithmetic mean in the level of significance (α = 0.05), and the difference was in favor of the experimental group in critical and creative thinking skills, which confirms that the program has a significant impact on the development of creative and critical thinking skills.

Not just more, but more diverse: Green landscapes along urban roads may significantly reduce drivers' psychophysiological fatigue

The impact of roadside greenness on driving fatigue in real urban settings has been insufficiently investigated, presenting a critical knowledge gap for researchers, policymakers, professionals, and the public. In this onsite driving experiment, 34 urban residents completed seven driving tasks on different urban road routes in a randomized order with one-day intervals. A total of 238 tasks were conducted, each lasting an hour, assessing psychophysiological, visual, and muscular fatigue. A cardiovascular activity monitor (BioHarness) continuously measured the driver's heart rate, with lower rates indicating reduced psychophysiological fatigue. Visual and muscular fatigue were self-reported using a Visual Analog Scale questionnaire administered before, at the midpoint, and after completing the driving task. Deep transfer learning semantic segmentation analyzed road landscape characteristics and traffic conditions recorded from the drivers' view. Statistical analysis demonstrated that higher mean and variation in greenness significantly predicted lower psychophysiological fatigue after adjusting for multiple covariates. These results indicate that enhancing both the quantity and diversity of green landscapes along urban roads is vital for reducing driver's psychophysiological fatigue. This study reveals that roadside landscapes in urban settings are not trivial decorations, and they should be considered an essential component of transportation infrastructure.

HiMIC-Monthly: A 1 km high-resolution atmospheric moisture index collection over China, 2003–2020

Near-surface atmospheric moisture is a key environmental and hydro-climatic variable that has significant implications for the natural and human systems. However, high-resolution moisture data are severely lacking for fine-scale studies. Here, we develop the first 1 km high spatial resolution dataset of monthly moisture index collection in China (HiMIC-Monthly) over a long period of 2003~2020. HiMIC-Monthly is generated by the light gradient boosting machine algorithm (LightGBM) based on observations at 2,419 weather stations and multiple covariates, including land surface temperature, vapor pressure, land cover, impervious surface proportion, population density, and topography. This collection includes six commonly used moisture indices, enabling fine-scale assessment of moisture conditions from different perspectives. Results show that the HiMIC-Monthly dataset has a good performance, with R2 values for all six moisture indices exceeding 0.96 and root mean square error and mean absolute error values within a reasonable range. The dataset exhibits high consistency with in situ observations over various spatial and temporal regimes, demonstrating broad applicability and strong reliability.

Fatty acid supplementation during warming improves pregnancy outcomes after frozen blastocyst transfers: a propensity score-matched study

This study aimed to examine the viability of human blastocysts after warming with fatty acids (FAs) using an in vitro outgrowth model and to assess pregnancy outcomes after a single vitrified-warmed blastocyst transfer (SVBT). For the experimental study, we used 446 discarded vitrified human blastocysts donated for research purposes by consenting couples. The blastocysts were warmed using FA‐supplemented (FA group) or non-FA-supplemented (control group) solutions. The outgrowth area was significantly larger in the FA group (P = 0.0428), despite comparable blastocyst adhesion rates between the groups. Furthermore, the incidence of outgrowth degeneration was significantly lower in the FA group than in the control group (P = 0.0158). For the clinical study, we retrospectively analyzed the treatment records of women who underwent SVBT in natural cycles between January and August 2022. Multiple covariates that affected the outcomes were used for propensity score matching as follows: 1342 patients in the FA group were matched to 2316 patients in the control group. Pregnancy outcomes were compared between the groups. The rates of implantation, clinical pregnancy, and ongoing pregnancy significantly increased in the FA group after SVBTs (P = 0.0091–0.0266). These results indicate that warming solutions supplemented with FAs improve blastocyst outgrowth and pregnancy outcomes after SVBTs.

Collider bias correction for multiple covariates in GWAS using robust multivariable Mendelian randomization.

Genome-wide association studies (GWAS) have identified many genetic loci associated with complex traits and diseases in the past 20 years. Multiple heritable covariates may be added into GWAS regression models to estimate direct effects of genetic variants on a focal trait, or to improve the power by accounting for environmental effects and other sources of trait variations. When one or more covariates are causally affected by both genetic variants and hidden confounders, adjusting for them in GWAS will produce biased estimation of SNP effects, known as collider bias. Several approaches have been developed to correct collider bias through estimating the bias by Mendelian randomization (MR). However, these methods work for only one covariate, some of which utilize MR methods with relatively strong assumptions, both of which may not hold in practice. In this paper, we extend the bias-correction approaches in two aspects: first we derive an analytical expression for the collider bias in the presence of multiple covariates, then we propose estimating the bias using a robust multivariable MR (MVMR) method based on constrained maximum likelihood (called MVMR-cML), allowing the presence of invalid instrumental variables (IVs) and correlated pleiotropy. We also established the estimation consistency and asymptotic normality of the new bias-corrected estimator. We conducted simulations to show that all methods mitigated collider bias under various scenarios. In real data analyses, we applied the methods to two GWAS examples, the first a GWAS of waist-hip ratio with adjustment for only one covariate, body-mass index (BMI), and the second a GWAS of BMI adjusting metabolomic principle components as multiple covariates, illustrating the effectiveness of bias correction.

Categorization of continuous covariates and complex regression models—friends or foes in intersectionality research

ObjectivesTo reduce health inequities, it is important to identify intersections in characteristics of individuals subject to privilege or disadvantage. Different proposals for that have recently been published. One approach (1) considers models specified with first- and all second-order effects and another (2) the stratification based on multiple covariates; both categorize continuous covariates. A simulation study was conducted in order to review both methods with regard to identification of intersections showing true differences, rate of false-positive results, and generalizability to independent data compared to an established approach (3) of backward variable elimination according to Bayesian information criterion (BE-BIC) combined with splines. Study Design and SettingR software has been used to simulate the covariates age, sex, body mass index, education, and diabetes to examine their association with a continuous frailty score for osteoporosis using multiple linear regression. In setting 1, none of the covariates was associated with the frailty score, that is, only noise is present in the data. In setting 2, the covariates age, sex, and their interaction were associated with the frailty score, such that only females above 55 years formed an intersection associated with an increased frailty score. All approaches were compared under varying sample sizes (N = 200–3000) and signal-to-noise ratios (SNRs, 0.5–4) in 1000 replications. For model evaluation, bootstrap resampling was used. The models were fitted in internal learning data and then used to predict outcomes in the internal validation data. The mean squared error (MSE) was used for comparison and the frequency of false-positive findings calculated. ResultsIn setting 1, approaches 1 and 2 generated spurious effects in more than 90% of simulations across all sample sizes. In a smaller sample size, approach 3 (BE-BIC) selected 36.5% of the correct model, in larger sample size in 89.8% and always had a lower number of spurious effects. MSE in independent data was generally higher for approaches 1 and 2 when compared to 3. In setting 2, approach 1 selected most frequently the correct interaction but frequently showed spurious effects (>75%). Across all sample sizes and SNR, approach 3 generated least often spurious results and had lowest MSE in independent data. ConclusionCategorization of continuous covariates is detrimental to studies on intersectionality. Due to high and unrestricted model complexity, such approaches are prone to spurious effects and often lack interpretability. Approach 3 (BE-BIC) is considerably more robust against spurious findings, showed better generalizability to independent data, and can be used with most statistical software. For intersectionality research, we consider it most important to describe relevant differences between intersections and to avoid nonreproducible and spurious findings.

Education and Democracy in Sub-Saharan Africa

Abstract The relationship between education and democracy has been empirically researched since the late 1950s, with most evidence stemming from global datasets. Initial results showed no relationship between education and democracy after looking at the within-country estimation. But, as more advanced statistical methods emerged, addressing endogeneity problems of the within-country estimation, a statistically significant relationship between education, measured through average years of total schooling, and democracy appeared. However, all studies have looked at the association broadly, and only a handful have applied this nexus to the context of Sub-Saharan Africa. By creating a new dataset incorporating thirty countries, this study shows that education is statistically significantly associated with democracy in Sub-Saharan Africa. Results are robust to multiple other covariates and mirror the ones reported in the academic literature. This study echoes the continued focus on education, especially in light of recent developments and multiple organisations announcing that democracy declined worldwide.

Online monitoring of profiles via RVC model with an application to industrial busbar

ABSTRACT Profile monitoring is used to check the stability of the functional relationship of the process response on several predictors. In most of the existing literature, the monitored functional relationship usually involves only one covariate and the coefficient of the covariate is assumed to be constant. In some applications, the process response depends on multiple covariates, and the functional relationship is not constant but dynamic over time, which is different from those studied in the literature. To implement profile monitoring in these applications, a semiparametric random time-varying coefficient model is employed to characterize this dynamic relationship over time. Based on this model, a profile monitoring scheme integrated with dynamic probability control limits is proposed, which can adapt to the case of the within-profile autocorrelation and arbitrary design points. In Phase I, this paper uses a backfitting iterative procedure with the REML-Based EM-Algorithm to estimate the model. In Phase II, an exponentially weighted moving average scheme on residuals with dynamic probability control limits is developed. The simulation results show that the proposed scheme performs better in many scenarios. Finally, an application to industrial busbar running process monitoring is given to illustrate the scheme’s implementation.

Partial-linear single-index transformation models with censored data.

In studies with time-to-event outcomes, multiple, inter-correlated, and time-varying covariates are commonly observed. It is of great interest to model their joint effects by allowing a flexible functional form and to delineate their relative contributions to survival risk. A class of semiparametric transformation (ST) models offers flexible specifications of the intensity function and can be a general framework to accommodate nonlinear covariate effects. In this paper, we propose a partial-linear single-index (PLSI) transformation model that reduces the dimensionality of multiple covariates into a single index and provides interpretable estimates of the covariate effects. We develop an iterative algorithm using the regression spline technique to model the nonparametric single-index function for possibly nonlinear joint effects, followed by nonparametric maximum likelihood estimation. We also propose a nonparametric testing procedure to formally examine the linearity of covariate effects. We conduct Monte Carlo simulation studies to compare the PLSI transformation model with the standard ST model and apply it to NYU Langone Health de-identified electronic health record data on COVID-19 hospitalized patients' mortality and a Veteran's Administration lung cancer trial.

Prognostic value of serum immunoglobulin M levels in patients with acute coronary syndrome

Background and aimsThe immuno-inflammatory response is a crucial early step in the development of acute coronary syndrome (ACS). In this study, we investigated whether immunoglobulin M (IgM) in the body's initial immune response can predict the prognosis of patients with ACS. MethodsThis prospective cohort study enrolled 1556 ACS patients at Beijing Hospital between March 2017 and October 2020. All patients underwent coronary angiography (CAG). The serum IgM concentration and biochemical indicators were evaluated prior to CAG. The primary endpoint was the composite endpoint of major adverse cardiovascular and cerebrovascular events (MACCEs). Multivariate Cox proportional hazards models was used to explore the association between IgM levels and the endpoint. ResultsThe average serum IgM levels of the population was 61.3 (42.6–88.4) mg/dL. During the median follow-up period of 55 months, 150 MACCEs occurred. Kaplan-Meier analysis showed that low serum IgM levels were associated with occurrence of MACCEs (log-rank p = 0.009). Univariate Cox proportional hazards models showed that low serum IgM (≤78.05 mg/dL) was associated with MACCEs (hazard ratio (HR) 1.648, 95 % confidence interval (CI): 1.129–2.406, p = 0.010). In patients with IgM ≤78.05 mg/dL, the HR for partially adjusted MACCEs events was 1.576 (95 % CI: 1.075–2.310) and 1.930 (95 % CI: 1.080–3.449) after adjusting for multiple covariates. The subgroup analysis showed that for patients in ≤24 BMI, never smoking and non-dyslipidemia subgroup, the lower serum IgM levels was significantly associated with the risk of MACCEs (pinteraction < 0.001, pinteraction = 0.037, pinteraction = 0.024, respectively). ConclusionsLow serum IgM levels was independently associated with MACCEs in ACS patients, especially for patients without obesity, smoking and dyslipidemia.

A Framework for Detecting Both Main Effect and Interactive DIF in Multidimensional Forced-Choice Assessments

In recent decades, multidimensional forced-choice (MFC) tests have gained widespread popularity in organizational settings due to their effectiveness in reducing response biases. Detecting differential item functioning (DIF) is crucial in developing MFC tests, as it relates to test fairness and validity. However, existing methods appear insufficient for detecting DIF induced by the interaction between multiple covariates. Furthermore, for multi-category, ordered or continuous covariates, existing approaches often dichotomize them using a-priori cutoffs, commonly using the median of the covariates. This may lead to information loss and reduced power in detecting MFC DIF. To address these limitations, we propose a method to identify both main effect DIF and interactive DIF. This method can automatically search for the optimal cutoffs for ordered or continuous covariates without pre-defined cutoffs. We introduce the rationale behind the proposed method and evaluate its performance through three Monte Carlo simulation studies. Results demonstrate that the proposed method effectively identifies various DIF forms in MFC tests, thereby increasing detection power. Finally, we provide an empirical application to illustrate the practical applicability of the proposed method.